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Snowpack in mountainous areas often provides water storage for summer and fall, especially in the Western United States. In situ observations of snow properties in mountainous terrain are limited by cost and effort, impacting both temporal and spatial sampling, while remote sensing estimates provide more complete spacetime coverage. Spatial estimates of fractional snow covered area (fSCA) at 30m are available every 16 days from the series of multispectral scanning instruments on Landsat platforms. Daily estimates at 463m spatial resolution are also available from the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument on the Terra satellite. Fusing Landsat and MODIS fSCA images creates high resolution daily spatial estimates of fSCA that are needed for various uses: to support scientists and managers interested in energy and water budgets for water resources and to understand the movement of animals in a changing climate. Here, we propose a new machine learning approach conditioned on MODIS fSCA, as well as a set of physiographic features, and fit to Landsat fSCA over a portion of the Sierra Nevada USA. The predictions are daily 30m fSCA. The approach relies on two stages of spatially‐varying models. The first classifies fSCA into three categories and the second yields estimates within (0, 100) percent fSCA. Separate models are applied and fitted within sub‐regions of the study domain. Compared with a recently‐published machine learning model (Rittger, Krock, et al., 2021), this approach uses spatially local (rather than global) random forests, and improves the classification error of fSCA by 16%, and fractionally‐covered pixel estimates by 18%. Key Points: Fusion of multiple remote sensing estimates is a promising approach to generate high resolution daily estimates of snow coverA new machine learning approach outperforms a state‐of‐the‐art competitorSpatially‐varying fitted random forests allow for geographically‐distributed feature importance [ABSTRACT FROM AUTHOR]

A lack of high‐density rootzone soil moisture (θRZ) observations limits the estimation of continental‐scale, space‐time contiguous θRZ dynamics. We derive a proxy of daily θRZ dynamics — active rootzone degree of saturation (SRZ) — by recursive low‐pass (LP) filtering of surface soil moisture (θS) within a terrestrial water‐energy coupling (WEC) framework. We estimate the LP filter parameters and WEC thresholds for the piecewise‐linear coupling between SRZ and evaporative fraction (EF) at remote sensing and field scale over the Contiguous U.S. We use θS from the Soil Moisture Active‐Passive (SMAP) satellite and 218 in‐situ stations, with EF from the Moderate Resolution Imaging Spectroradiometer. The estimated SRZ compares well against SMAP Level‐4 estimates and in‐situ θRZ, at the corresponding scale. The instantaneous hydrologic state (SRZ) vis‐à‐vis the WEC thresholds is proposed as a rootzone soil moisture stress index (SMSRZ) for near‐real‐time operational agricultural drought monitoring and agrees well with established drought metrics. Plain Language Summary: Rootzone soil moisture plays a vital role in agricultural, hydrological, and ecosystem processes. The available spaceborne satellites for monitoring soil moisture can only capture variability in a shallow soil layer at the surface, typically limited to the top 5 cm. Hence, spatiotemporally continuous estimation of rootzone soil moisture dynamics typically relies on soil moisture estimates from land‐surface models, which are subject to errors in the surface meteorological forcing data, process formulations, and model parameters. Some studies suggest that the rootzone soil moisture dynamics can be estimated by filtering the high‐frequency variability in the surface soil moisture. However, such "filters" require observed rootzone data (often unavailable at high spatial density) for calibration. This study uses the relationship between surface soil moisture and evaporative fraction derived using spaceborne observations from the Soil Moisture Active Passive mission and the Moderate Resolution Imaging Spectroradiometer to estimate rootzone soil moisture dynamics for the Contiguous U.S. at 9 km grid resolution. We further demonstrate that this approach can be extended into a near‐real‐time agricultural drought monitor to assess drought impacts on vegetation using surface soil moisture observations. Key Points: Terrestrial water‐energy coupling is used to parameterize low‐pass filter to estimate rootzone dynamics from surface soil moistureRootzone degree of saturation and water‐energy coupling thresholds are estimated using evaporative fraction and surface soil moistureSMAP‐based rootzone degree of saturation can used for operational, near‐real‐time agricultural drought monitoring over Contiguous U.S [ABSTRACT FROM AUTHOR]

Estimating foliar dust (FD) is essential in understanding the complex interaction between FD, vegetation, and the environment. The elevated FD has a significant impacts on vegetation physiological processes. The present study aims to explore the potential of multi‐sensor optical satellite data sets (e.g., Landsat‐8, 9; Sentinel‐2B, and PlanetScope) in conjunction with in situ data sets for FD estimation over the Jharsuguda coal mining region in Eastern India. The efficacy of different spectral bands and various radiometric indices (RIs) was tested using linear regression models for FD estimation. Furthermore, the study attempts to quantify the impacts of FD on vegetation's physiological processes (e.g., carbon uptake, transpiration, water use efficiency, leaf temperature) through proxy data sets. The key findings of the study uncovered sensor‐specific and common trends in vegetation spectral profiles under varying FD concentrations. A saturation threshold was observed around 50 g/m2 of FD concentration, beyond which additional FD concentration exhibited limited impact on spectral reflectance. On the other hand, the assessment of FD estimation models revealed distinct performances and shared trends across various satellite sensors. Notably, near‐infrared and shortwave infrared‐1 bands, along with certain RIs, such as the Global Environmental Monitoring Index and the Non‐Linear Index, emerged as pivotal for accurate FD estimation. Besides, the study results revealed that vegetation‐associated carbon uptake experienced a ∼2 to 3 gC reduction for every additional gram of FD per square meter. Moreover, the vegetation transpiration reduction per unit of FD ranged from approximately 0.0005 to 0.0006 mm/m2/day, highlighting a moderate impact on transpiration levels. These findings aid a significant evidence base to our understanding of FD's impact on vegetation physiological processes. Plain Language Summary: Estimating foliar dust (FD) cover is essential to understand its impact on vegetation and the environment. Elevated FD levels can reduce photosynthesis, hinder carbon sequestration, and affect transpiration, thereby altering plant health and ecosystem functions. Accurate FD estimation helps in assessing these ecological impacts and developing strategies to mitigate adverse effects. Furthermore, FD can affect remote sensing‐based analysis by altering the spectral reflectance properties of vegetation, potentially leading to inaccuracies in vegetation‐related studies (e.g., health assessment, productivity estimation, species identification, etc.). By understanding these effects, the study shall aid in refining remote sensing models for more precise vegetation analysis. So, the present study utilizes multi‐sensor optical satellite data sets (Landsat‐8, Landsat‐9, Sentinel‐2B, and PlanetScope) along with in situ data to estimate FD in the Jharsuguda coal mining region in Eastern India. Vegetation's spectral profiles under dusty and non‐dusty conditions are also studied. This study also quantifies the impacts of FD on vegetation physiological processes, providing critical insights into the ecological consequences of dust accumulation on plant surfaces. Key Points: Demonstrated an approach to predict foliar dust concentration from satellite dataEvaluated the efficacy of spectral bands of 4 satellite sensors and 26 radiometric indicesQuantified the effect of foliar dust on vegetation physiological processes [ABSTRACT FROM AUTHOR]

Highly fragile small island states experience disproportionate climate impacts given their limited capacity to implement cost‐effective tools for detecting emerging signals of drying conditions and monitoring systems for sensitive sectors such as agriculture, especially for uncertain, 'creeping' events such as droughts. Despite the existence of open‐source Google Earth Engine datasets, untapped potential remains for their full deployment in disaster management infrastructure. Given this gap, this paper explores the utility of the Enhanced Vegetation Index (EVI) for detecting spatio‐temporal variations of Mid‐Summer Drought (MSD) impacts on vegetation in the small island of Jamaica, with emphasis on major historical drought events. Geospatial analyses of EVI datasets from the Terra Moderate‐Resolution Imaging Spectroradiometer (MODIS) between 2000−2015 archived by the United States Geological Survey (USGS), were computed, and validated by station‐based precipitation and production data for selected parishes for historical case study MSD events. Results revealed highly asymmetrical drought impacts, with Jamaica's agriculturally intense Southern coastline displaying the most stressed vegetation (EVI < 0.5). North‐Western and North‐Eastern regions had the healthiest vegetation during the MSD (EVI > 0.6). A 'fair' to 'moderate' concurrent correlation was found between EVI and precipitation (R > 0.6), with lower correlations vis‐a‐vis agricultural production (R = 0.2–0.4). The results provide evidence of EVI's utility as a drought monitoring tool in a small island context. [ABSTRACT FROM AUTHOR]

Studies of lake ice phenology have historically relied on limited in situ data. Relatively few observations exist for ice out and fewer still for ice in, both of which are necessary to determine the temporal extent of ice cover. Satellite data provide an opportunity to better document patterns of ice phenology across landscapes and relate them to the climatological drivers behind changing ice phenology. We developed a model, the Cumulative Sum Method (CSM), that uses daytime and nighttime surface temperature observations from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor on board the Earth‐observing Aqua satellite to approximate ice in (the onset of ice cover) and ice out from training datasets of 13 and 58 Maine lakes, respectively, during the 2002/2003 through 2017/2018 ice seasons. Ice in was signaled by reaching a threshold of cumulative negative degrees following the first day of the season below 0°C. Ice out was signaled by reaching a threshold of cumulative positive degrees following the first day of the year above 0°C. The comparison of observed and remotely sensed ice‐in dates showed relative agreement with a correlation coefficient of 0.71 and a mean absolute error (MAE) of 9.8 days. Ice‐out approximations had a correlation coefficient of 0.67 and an MAE of 8.8 days. Lakes smaller in surface area and nearer the Atlantic coast had the greatest error in approximation. Application of the CSM to 20 additional lakes in Maine produced a comparable ice‐out MAE of 8.9 days. Ice‐out model performance was weaker for the warmest years; there was a larger MAE of 12.0 days when the model was applied to the years 2019–2023 for the original 58 lakes. The development of this model, which utilizes daily satellite data, demonstrates the promise of remote sensing for quantifying ice phenology over short, temporal scales, and wider geographic regions than can be observed in situ, and allows exploration of the influence of surface temperature patterns on the process and timing of ice in and ice out. [ABSTRACT FROM AUTHOR]

As a potential carbon sink, mangroves play an important role in climate mitigation. India houses several major global mangrove patches, which remain vulnerable to climate change. The ecosystem‐atmosphere CO2 exchange is most accurately measured by the eddy covariance method, whereas satellites provide the biophysical parameters for a wider area. In the present study, the Sentinel‐2 satellite data is used to map the land cover types in the Pichavaram mangrove forest and identify two major dominant species (Rhizophora spp. and Avicennia marina), which indicated more than 95% classification accuracy. We used 2 years (2017 and 2018) of in situ gross primary productivity (GPP) and leaf area index (LAI) measurements and rectified the Moderate Resolution Imaging Spectroradiometer (MODIS) GPP and LAI products from 2010 to 2018. The modified MODIS GPP and LAI products were used to develop machine learning models, that is, Random Forest (RF) and Extreme Gradient Boosting (XGBoost) to study the climate influence on mangrove productivity. The RF model (R2 = 0.85 and root mean square error (RMSE) = 0.2) outperformed the XGBoost model (R2 = 0.75 and RMSE = 0.26) and was used to project the impact of climate change on the mangrove GPP for two extreme climate change scenarios, namely SSP1‐1.26 and SSP5‐8.5. The GPP increases and decreases in future during wet and dry periods, respectively. Overall, the projected GPP indicated a reduction of 3.73%–20.3% from 2050 to 2060 and of 4.82%–28.15% from 2090 to 2100, compared to its current average (from 2010 to 2018). Plain Language Summary: Mangroves are natural ecosystems found at the confluence of land and water. Their productivity is regulated by the complex environmental conditions prevailing at the interface of terrestrial, marine, and freshwater realms. Geographically, they mostly occur between 25°N and 25°S with a significant presence in the Indian subcontinent. The climate and ecosystem models used for predicting the changes in land and aquatic ecosystems have limited predictive capability for mangroves. Machine learning (ML) models are capable of capturing the coupled non‐linear relationships among various processes and thus can improve such predictions. The present study uses in situ and satellite data to develop long‐term productivity and canopy growth in the Pichavaram mangrove in India, which are subsequently used to develop ML models. These ML models are applied to identify the dominant mangrove species in this ecosystem and predict the changes in its photosynthetic carbon uptake in the middle‐term (2050–2060) and long‐term (2090–2100) climate change. The future carbon uptake increases (decreases) during wet (dry) periods. Overall, the projected decrease in annual photosynthesis ranges from 3.73% to 28.15%, compared to its current average. The seasonal variation in air temperature plays a key role in regulating the mangrove carbon uptake. [ABSTRACT FROM AUTHOR]

Albedo is one of the key parameters in climatic studies. Investigating its temporal and spatial behavior can be a tool for understanding environmental changes. The MODIS sensor continuously produces the land surface albedo on a global scale and with the appropriate spatial resolution and makes it available to researchers. In this study, to analyze Iran's surface albedo trend, first, the daily albedo data of the MODIS on Iran in the period from January 1, 2001 to December 30, 2021 with a spatial resolution of 500 m were prepared from the NASA website. After the necessary pre‐processing, the long‐term seasonal and annual trend of Iran's albedo was calculated at the 90% confidence level using the non‐parametric Mann–Kendall test. The findings showed that the albedo trend is positive in the lowland interior areas of Iran and negative in the highland areas. Since the decreasing trend of albedo in highland areas indicates the reduction of snow cover in these areas, this issue can challenge the life and water resources of these areas that rely on the accumulation of snow. [ABSTRACT FROM AUTHOR]

Although afforestation is a potential strategy to mitigate climate change by sequestering carbon, its potential biophysical effects on climate, such as regulating surface albedo, evapotranspiration, and energy balance, have not been fully incorporated into climate change mitigation strategies. This is partly due to the challenges associated with modeling the complex bidirectional interactions between vegetation and climate. In this study, we assess the impact of afforestation on low cloud cover using a regional climate model (RCM) and Earth observation data, applying a space‐for‐time approach to overcome limitations that may arise from comparing satellite and RCM results, such as different background climate conditions or different extents of land cover change. Our results show a consistent increase in low cloud cover in Europe due to afforestation in both datasets (3.71% and 3.56% on average, respectively), but the magnitude and direction of this effect depend on various factors, including location, seasonality, and forest type. These results suggest that afforestation can have important feedbacks on the climate system, and that its biophysical effects must be considered in climate change mitigation strategies. Furthermore, we emphasize the role of the modeling community in developing accurate and reliable approaches to assess the biophysical effects of land cover change on climate. Plain Language Summary: Although forests can help combat climate change by capturing carbon dioxide, we haven't fully considered the potential impact they can have on the climate in terms of biophysical factors such as surface reflectivity, evaporation, and energy balance. This is because it's challenging to accurately model the complex interactions between forests and climate. In this study, we used a regional climate model and Earth observation data to examine how forests affect the amount of low‐lying clouds. We used a method that compares satellite and model data to overcome limitations arising from different climate conditions and changes in land cover. Our findings consistently show that forests increase the coverage of low clouds in Europe. However, the specific effects depend on factors such as the location, season, and forest type. These results indicate that forests can have significant impacts on the climate system, and it is important to consider these effects when developing climate change strategies. We also emphasize the need for accurate modeling to better understand how changes in forest cover influence the climate. Key Points: Forests increase the amount of low clouds in EuropeForests impact climate beyond carbon sequestration [ABSTRACT FROM AUTHOR]

The Amazon River is a large source of terrigenous dissolved organic carbon (tDOC) to the Atlantic Ocean. The fate of this tDOC in the ocean remains unclear despite its importance to the global carbon cycle. Here, we used two decades of satellite ocean color to describe variability in tDOC in the Amazon River plume. Our analyses showed that tDOC distribution has a distinct seasonal pattern, reaching northwest toward the Caribbean during high discharge periods, and moving eastward entrained in the North Brazil Current retroflection during low discharge periods. Elevated tDOC content extended beyond the shelfbreak in all months of the year, suggesting that cross‐shelf carbon transport occurs year‐round. Maximum variability was found at the plume core, where seasonality accounted for 40% of the total variance, while interannual variability accounted for 15% of the variance. Our results revealed a seasonal pattern in tDOC removal over the shelf with increased consumption in May when river discharge is high. Anomalies in tDOC removal over the shelf with respect to the seasonal cycle were significantly correlated with anomalies in tDOC concentration offshore of the shelfbreak with a lag of 30–40 days, so that anomalously high inshore tDOC removal was associated with anomalously low tDOC content offshore. This suggests that variability in the offshore transport of tDOC in the Amazon River plume is modulated by interannual changes in tDOC removal over the shelf. Plain Language Summary: The Amazon River is an important source of dissolved organic carbon (DOC) of terrigenous origin to the Atlantic Ocean. It is often difficult to characterize the distribution and variability of this material in the ocean due to the lack of high resolution in situ observations spanning long periods of time. Here, we used satellite observations of terrigenous DOC (tDOC) to describe the behavior of the plume at seasonal and interannual scales. Our analyses showed that enhanced tDOC content is observed offshore of the continental margin during all months of the year, extending toward the Caribbean during high river discharge conditions and eastward during low discharge conditions associated with variability in the large‐scale circulation. Our results also revealed that tDOC removal over the shelf varies seasonally, being enhanced earlier in the year when river discharge is high. Anomalies in tDOC removal over the shelf are significantly correlated with anomalies in tDOC content offshore with a lag of 30–40 days, suggesting that the offshore transport of carbon is modulated by tDOC consumption over the shelf. Key Points: Satellite ocean color was used to describe variability in terrigenous dissolved organic carbon (tDOC) in the Amazon River plumetDOC degradation and/or flocculation over the shelf increase early in the year when river discharge also increasesInterannual variability in tDOC content off the continental margin is related to variability in tDOC removal over the shelf [ABSTRACT FROM AUTHOR]

In situ and spaceborne studies reveal the prevalence of thin clouds in the major Stratocumulus‐to‐Cumulus Transition (SCT) regions. Using instantaneous satellite and reanalysis data, this study investigates the properties of thin clouds in the Southeast Pacific Ocean and their impact on the cloud radiative effect (CRE). Our findings demonstrate that thin clouds are intrinsic to the SCT. The overcast stratocumulus‐dominated regime exhibits a minimal presence of thin clouds, which become notably prominent after the clouds breakup into the cumulus‐dominated regime. The regime dependence of the occurrence of thin clouds is also observed in terms of the marine cold‐air outbreak parameter and the sea surface temperature. Thin clouds at a given cloud cover significantly modulate the shortwave (SW) and longwave (LW) components of CRE. SW CRE decreases by 46 %–65 % with increasing thin cloud cover. They account for a larger variance in cloud albedo than the combined influence of the liquid water path and effective radius. Furthermore, LW CRE decreases by about 12 %–52 % with thin cloud cover. An increase in the fraction of thin clouds also leads to a larger fraction of negative SW CRE offset by positive LW CRE at a given cloud cover. This LW compensation ranges from approximately 8 % at overcast cloud cover to as much as 19 % at about 50 % cloud cover. These findings elucidate the crucial role of thin clouds, and thus cloud morphology, in modulating CRE and underscore the necessity of their accurate representation in climate models. Plain Language Summary: The eastern regions of major global oceans are primarily covered by low‐altitude clouds. These bright clouds overlay the dark ocean surface, reflecting incoming solar radiation and cooling the Earth. Concurrently, they act as a barrier to the energy emitted by the ocean surface, preventing it from escaping the Earth and thereby contributing to the Earth's warming. Satellite observations reveal the prevalence of thin clouds in these oceanic regions. In contrast to their thicker counterparts, thin clouds are less effective in reflecting solar radiation and blocking surface emissions. This study investigates the impact of thin clouds on radiation using data from various sources. We show that the presence of thin clouds significantly reduces cloud brightness by approximately 46 %–65 %, even when the cloud cover remains constant. Thin clouds have a more substantial effect on a cloud's ability to reflect solar radiation compared to the combined influence of cloud water and cloud droplet size. Furthermore, since thin clouds allow some surface emissions to pass through, their presence at a constant cloud cover reduces their warming effect by up to 52 %. These findings emphasize the crucial role of thin clouds in shaping the contribution of clouds to Earth's energy balance. Key Points: Thin clouds dim cloud brightness by up to 65 percent at a fixed cloud coverThin clouds exert a greater influence on cloud albedo than the combined effects of cloud water and droplet size under constant cloud coverThin clouds reduce longwave warming by up to 52 percent and modulate the balance between longwave warming and shortwave cooling [ABSTRACT FROM AUTHOR]

Gross primary productivity (GPP) is an important component of the terrestrial carbon cycle in climate change research. The global GPP product derived using Moderate Resolution Imaging Spectroradiometer (MODIS) data is perhaps the most widely used. Unfortunately, many studies have indicated evident error patterns in the MODIS GPP product. One of the main reasons for this is that the applied big‐leaf (BL) MOD17 model cannot properly handle the variable relative contribution of sunlit and shaded leaves to the total canopy‐level GPP. In this study, we developed a model for correcting the errors in the MODIS GPP product by bridging BL and two‐leaf (TL) light use efficiency (LUE) models (CTL‐MOD17). With the available MODIS GPP product, which considers environmental stress factors, the CTL‐MOD17 model only needs to reuse the two inputs of the leaf area index (LAI) and incoming radiation. The CTL‐MOD17 model was calibrated and validated at 153 global FLUXNET eddy covariance (EC) sites. The results indicate that the modeled GPP obtained with the correction model matches better with the EC GPP than the original MODIS GPP product at different time scales, with an improvement of 0.07 in R2 and a reduction in root‐mean‐square error (RMSE) of 117.08 g C m−2 year−1. The improvements are more significant in the green season when the contribution of shaded leaves is larger. In terms of the global spatial pattern, the obvious underestimation in the regions with high LAI and the overestimation in the low LAI regions of the MODIS GPP product is effectively corrected by the CTL‐MOD17 model. This paper not only bridges the BL and TL LUE models, but also provides a new and simple method to obtain accurate GPP through reusing two inputs used in producing the MODIS GPP product. Plain Language Summary: Gross primary productivity (GPP) is crucial for terrestrial carbon cycle study. The Moderate Resolution Imaging Spectroradiometer (MODIS) GPP data is perhaps the most widely used product, but many studies have indicated evident error patterns in it. These errors can be mainly explained by the applied big‐leaf (BL) MOD17 model cannot properly handle the variable relative contribution of sunlit and shaded leaves to the total canopy‐level GPP. As a result, this paper developed a novel and simple model (CTL‐MOD17) to obtain accurate GPP by reusing two inputs data from the MODIS GPP product, based on a two‐leaf (TL) theory. The CTL‐MOD17 model is evaluated at 153 global FLUXNET eddy covariance (EC) sites, and compared with other TL GPP products and models. The results indicate that CTL‐MOD17 can significantly improve the accuracy of MODIS GPP products at different time scales, to be similar to other TL models but with fewer data inputs. The obvious underestimation/overestimation of MODIS GPP in the high/low leaf area index (LAI) regions are all effectively corrected. This study proves the possibility of bridging the BL and TL models for the first time, which can be migrated to other BL products and models. Key Points: A novel product‐based model (CTL‐MOD17) corrects Moderate Resolution Imaging Spectroradiometer (MODIS) gross primary productivity (GPP) product bias, achieving results similar to two‐leaf models with fewer inputsIt is the first time to prove the possibility of bridging the big‐leaf and two‐leaf models, which can be migrated to other big‐leaf GPP products and modelsThe obvious underestimation/overestimation of MODIS GPP in high/low LAI regions are all effectively corrected by the CTL‐MOD17 model [ABSTRACT FROM AUTHOR]

The surface temperature‐mediated change in cloud properties, referred to as the cloud feedback, continues to dominate the uncertainty in climate projections. A larger number of contemporary global climate models (GCMs) project a higher degree of warming than the previous generation of GCMs. This greater projected warming has been attributed to a less negative cloud feedback in the Southern Ocean. Here, we apply a novel "double decomposition method" that employs the "cloud radiative kernel" and "cloud regime" concepts, to two data sets of satellite observations to decompose the interannual cloud feedback into contributions arising from changes within and shifts between cloud morphologies. Our results show that contributions from the latter to the cloud feedback are large for certain regimes. We then focus on interpreting how both changes within and between cloud morphologies impact the shortwave cloud optical depth feedback over the Southern Ocean in light of additional observations. Results from the former cloud morphological changes reveal the importance of the wind response to warming increases low‐ and mid‐level cloud optical thickness in the same region. Results from the latter cloud morphological changes reveal that a general shift from thick storm‐track clouds to thinner oceanic low‐level clouds contributes to a positive feedback over the Southern Ocean that is offset by shifts from thinner broken clouds to thicker mid‐ and low‐level clouds. Our novel analysis can be applied to evaluate GCMs and potentially diagnose shortcomings pertaining to their physical parameterizations of particular cloud morphologies. Plain Language Summary: Climate models project that rising levels of greenhouse gas emissions will raise Earth's global mean surface air temperature. However, the amount of warming to be expected remains highly uncertain. A main underlying cause for this uncertainty is the representation of atmospheric clouds in these models. In particular, the response of clouds to global warming, known as the "cloud feedback" over the Southern Ocean has been linked to higher levels of projected warming. To address the uncertainty, we apply a novel method to two satellite data sets. This method teases apart contributions from changing cloud properties such as their horizontal spatial coverage and their ability to reflect sunlight, to the cloud feedback, and then further dissects each of these contributions into those arising from changes occurring both within groups of cloud types and changes occurring between these groups. Results suggest that a general shift from thick storm‐track clouds to thinner oceanic low‐level clouds exacerbates warming in the Southern Ocean but are partially counterbalanced by cloud thickening in part due to stronger near‐surface winds in the same region. Our novel methods and results can potentially be used to evaluate climate models and aid in tracing their shortcomings to poorly represented physical processes. Key Points: MODIS satellite observations indicate that in the Southern Ocean the interannual cloud optical depth feedback is slightly positiveMore frequently occurring thin low‐level clouds and fewer storm‐track clouds contribute to a positive feedback in the Southern OceanStrengthened near‐surface winds contribute to a negative low‐level cloud feedback in the Southern Ocean [ABSTRACT FROM AUTHOR]

There has been an acceleration of groundwater loss in the Great Basin (GB) of the western U.S. as determined from total water storage (TWS) measurements from the GRACE/FO satellite missions. From 2002 to 2023, there was a loss of TWS in the GB of ∼68.7 km3 which is more than six times the current volume of the Lake Mead Reservoir. In this arid/semi‐arid region, groundwater is the primary factor contributing to the decade‐scale decline in TWS. Stronger declining trends are found in the western versus the eastern GB. Snow loading is the major cause of seasonal fluctuations of TWS in the GB. Despite annual replenishment of groundwater by snow, the downward trend persists even in notable snow years. Likely causes include declining snow mass, upstream water diversions and increased evaporation/sublimation due to increasing temperatures. Dire consequences for humans and wildlife are associated with this large loss of groundwater. Plain Language Summary: The 21st Century megadrought in the southwestern U.S. caused a dramatic acceleration of groundwater loss in the Great Basin (GB) of the western U.S. as determined from changes in the Earth's gravity measured by the GRACE satellites. Groundwater is a major component of total water storage (TWS) in the GB, but snowfall and snowmelt are the major causes of seasonal fluctuations of TWS. As a snowpack accumulates or melts, water is redistributed causing a rapid regional change in gravity that can be measured from space. From 2002 to 2023, there is a substantial loss of groundwater in the GB of ∼68.7 km3 which is more than six times the current volume of water in the Lake Mead Reservoir in Arizona/Nevada. Stronger declining trends of groundwater loss are found in the western part of the GB while weaker declining trends are found in the eastern part. Despite annual replenishment by snowfall, even in notable snow years like 2010–2011, 2016–2017, 2018–2019, and 2022–2023, the downward trend of groundwater depletion persists. Likely causes for this decline include declining snow mass, upstream water diversions and increased evaporation/sublimation due to increasing air and surface temperatures. Groundwater depletion is associated with dire consequences for humans and wildlife. Key Points: Snow accumulation in the Great Basin (GB) triggers an increase in total water storage (TWS) while snow ablation triggers a drop in TWSThere is an 68.7 km3 loss of groundwater in the GB from 2002 to 2023 which is more than six times greater than the current volume of Lake MeadThe 2002–2023 TWS decline in the GB is more pronounced in the western GB than in the eastern GB [ABSTRACT FROM AUTHOR]

NASA's Deep Blue aerosol project has developed global aerosol data records using consistent retrieval algorithms applied to various satellite sensors. The primary components of these data records are derived from the series of Visible Infrared Imaging Radiometer Suite (VIIRS) aboard the Suomi National Polar‐orbiting Partnership (SNPP) and the National Oceanic and Atmospheric Administration or NOAA‐20+ satellites as well as the Moderate Resolution Imaging Spectroradiometer (MODIS), among others. These instruments provide over 23 years of measurements with similar radiometric characteristics for aerosol retrievals. The algorithms used for the initial Version 1 SNPP VIIRS data set were based on the MODIS Collection 6.1 Deep Blue algorithm over land and Satellite Ocean Aerosol Retrieval (SOAR) algorithm over water. For VIIRS Version 2 data reprocessing, major updates have been made to the algorithm suite, including better accounting for effects of surface pressure, improved determination of surface reflectance, and the inclusion of fine‐mode aerosol optical models to better represent anthropogenic aerosols over land. Cross‐calibration gain factors are derived for the NOAA‐20 VIIRS measurements to be consistent with the SNPP VIIRS, which allows the use of a unified algorithm package for both instruments. Comparisons against AERONET observations indicate that the Version 2 AOD data from SNPP VIIRS are significantly better than the Version 1 counterpart over land and slightly degraded over water in exchange for better spatial coverage. The AOD data from SNPP and NOAA‐20 VIIRS are comparable, indicating that cross‐calibration enables the creation of consistent aerosol data records using the series of VIIRS sensors. Plain Language Summary: Aerosols are tiny particles suspended in the air that can come from natural sources like dust and sea salt, as well as human activities such as burning fossil fuels and industrial processes. Understanding these particles are important because they play a crucial role in the Earth's system through interactions with sunlight, clouds, and precipitation. These interactions are in turn closely related to global warming and cooling effects. Additionally, certain types of aerosols can harm human health when inhaled, leading to respiratory problems. Because of these important aspects, scientists have developed various tools to study aerosols. Satellite remote sensing is one such tool and have provided valuable data on global aerosol properties. This study describes updates made to a satellite remote sensing technique to enhance data quality. The new data sets are significantly improved compared to previous versions, indicating increased utility of the data sets. Key Points: Introducing NASA's VIIRS Version 2 Deep Blue aerosol productsMajor updates in Deep Blue algorithm resulting in significant improvements in global aerosol optical depth retrievalsConsistent data records between SNPP and NOAA‐20+ VIIRS ensuring long‐term data continuity [ABSTRACT FROM AUTHOR]

Aim: Humans have altered ecosystem productivity and biodiversity worldwide by changing land‐cover types and management. High local species richness is commonly found in geographic areas and ecosystems with high net primary productivity (NPP), but the long‐term effects of modification on productivity and biodiversity change, and particularly on the relationship between the two, are poorly understood. Here we evaluate whether human modification tends to increase biodiversity in low‐productivity ecosystems (where human management is likely to increase productivity) and decrease biodiversity in ecosystems that were originally high‐productive. Location: Global. Time Period: 2001–2013. Major Taxa Studied: Plants, mammals, birds, hexapods, arachnids, other terrestrial invertebrates, amphibians, reptiles and fungi. Methods: We assembled a large worldwide dataset of NPP and associated species richness from MODIS land cover and NPP products and the PREDICTS biodiversity database, involving 11,849 sites. This enabled comparisons of species richness and NPP differences between samples of relatively natural and human‐modified vegetation within the same geographic regions, considering 102 types of land‐cover transitions. Results: (1) Modification from non‐forest vegetation to forests on average increased NPP by 7.76%, and vice versa. (2) Human modification of less productive areas tended to increase NPP and vice versa, with stronger effects of major modification. (3) However, site‐level species richness decreases were associated with nearly all land‐cover transitions from relatively natural to modified vegetation. (4) Despite expectations, we found no significant relationship between species richness differences (between relatively natural and modified vegetation) and productivity differences, when considering conversions across land‐cover types and excluding human‐appropriated productivity in croplands. Main Conclusions: Human modification tends to increase NPP in low‐productivity ecosystems, but species richness declines are associated with most major human‐induced land‐cover changes. Therefore, increasing or decreasing NPP did not generate corresponding increases or decreases in species richness, contrary to expectations of the general species richness‐productivity relationship. [ABSTRACT FROM AUTHOR]

Global and regional trends of the Aerosol Optical Depth (AOD) from Coupled Model Intercomparison Project (CMIP) Phase 6 simulations for the study period 1971–2014 were compared against the satellite retrievals and the intermodel variations were analysed. The AOD from multimodel mean (MMM) of eight general circulation models (GCMs) has been evaluated against the Moderate Resolution Imaging Spectroradiometer (MODIS) and Multi‐angle Imaging Spectro Radiometer (MISR) AOD for the 2001–2014 period. Angstrom exponents (AE and its first derivative) that represent the size distribution of aerosols are estimated globally from the perturbed initial condition ensemble of MRI‐ESM2‐0 and MPI‐ESM‐1‐2‐HAM models to report the aerosol variations through their size distribution. We found that the global AOD obtained from the MMM8 showed an insignificant decreasing trend, while this trend is significantly positive over the northern tropical region. The MMM8 has overestimated the MODIS AOD over North Africa, India, China, and Australia while this overestimation is confined to North Africa and eastern China when compared against MISR AOD. The absolute percent bias of MMM8 is 28.1% and 24.1% over the globe when compared against MODIS and MISR AOD, respectively. The spatial pattern of AE showed the dominance of fine‐ and coarse‐mode particles during the boreal/austral winter and summer seasons, respectively, that replicate the seasonality of aerosols. The AE derived from MPI‐ESM‐1‐2‐HR demonstrated better agreement with AATSR SU's (Advanced Along Track Scanning Radiometer instrument series, with the algorithm developed by Swansea University) AE (550–870 nm). On the other hand, MRI‐ESM2‐0 consistently underestimated AE across different regions and wavelength ranges, suggesting an over representation of larger aerosol particles in the model's portrayal of aerosol size distribution compared to satellite observations. [ABSTRACT FROM AUTHOR]

Passive microwave (PM) observations have been used to monitor ice retreat in the Arctic. However, various PM sea ice concentration (SIC) algorithms are prone to underestimate ice fraction during summer. We evaluated the accuracy of 2002–2019 low SICs in the Central Arctic Ocean of four PM products from the University of Bremen, the National Snow and Ice Data Center (NSIDC), and the Ocean and Sea Ice Satellite Application Facility (OSI SAF), and two reanalysis data sets from the fifth generation of European ReAnalysis (ERA5) and the Modern‐Era Retrospective analysis for Research and Applications, Version 2 (MERRA‐2). Three reference fields were used: (a) Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS) true‐color composites, (b) MODIS sea ice extent, and (c) multi‐product ensemble (MPE‐SIC) comprising the median of collocated SIC estimates. Our results indicate SICs derived from the Advanced Microwave Scanning Radiometer ‐ Earth Observing System (AMSR‐E) and the Advanced Microwave Scanning Radiometer 2 (AMSR2) high frequency channels have the best accuracy. Reanalysis SICs indicate almost identical patterns as their remote sensing inputs. The assessment shows that the Bremen (+1.06%) and NSIDC (+0.99%) SICs are higher than the median field, while the OSI‐401 (−6.65%) and OSI‐408 (−4.64%) have negative mean deviations. The mean error of MODIS‐derived SIC (−0.80%) is smaller than PM SICs. These small mean values belie wide distributions of values. The correlation coefficients of pairs of time series of Low sea‐Ice Concentration Index range from 0.37 to 0.96. Key Points: We compared Arctic sea ice concentration (SIC) using passive microwave (PM), optical, and reanalysis data sets for the summers of 2002–2019The correlation coefficients of pairs of time series of Low sea‐Ice Concentration Index range from 0.37 to 0.96The average differences between multi‐product ensemble (MPE‐SIC) and PM SIC range from −6.65% to +1.09% [ABSTRACT FROM AUTHOR]

The multi‐spectral satellite sensors such as MODIS have a large swath, high spatial resolution, and well onboard calibration, enabling aerosol retrievals with daily global coverage. Despite numerous available bands, MODIS aerosol algorithms over land typically only utilize measurements from 2 to 3 spectral wavelengths to retrieve Aerosol Optical Depth (AOD) based on prescribed aerosol models. To make full use of multi‐spectral measurements and prior information, we developed an aerosol algorithm based on physics‐informed deep learning (PDL) approach. With physical constraint from radiative transfer simulation, PDL can construct model functions between the whole spectral measurements and each retrieved aerosol parameter separately. AERONET validations in eastern China show that MODIS PDL algorithm can accurately retrieve AOD and fine AOD (R = 0.936) at 1 km resolution and has reliable performance in coarse AOD as well as notable sensitivity to aerosol absorption. The flexible and efficient PDL method provides a generalized algorithm for common multi‐spectral satellite measurements. Plain Language Summary: Atmospheric aerosols play a crucial role in the Earth's radiative energy balance, hydrological cycle, and air quality as well as biogeochemical cycles. Owing to short lifetime and diverse emission sources, aerosol amount and properties vary largely over space and time, making a great challenge in quantifying its climate and environmental effects. With the recognition of aerosols' important role, dedicated satellite instruments have been launched to obtain global aerosol observations since late 1990s. Despite a lack of angular and polarized information, current widely used satellite aerosol products are mainly derived from multi‐spectral measurement due to their daily global coverage and long‐term well‐calibrated observation. However, common algorithms only utilize 2–3 spectral measurements and retrieve one quantitative Aerosol Optical Depth with prescribed aerosol models. By combining the physical constraints from Radiative Transfer simulation and modeling ability of Deep Learning, we developed a generalized physics‐informed DL method that can make full use of multi‐spectral measurements and prior information for aerosol retrievals. Key Points: A generalized aerosol algorithm is developed based on physics‐informed deep learning (PDL) method for multi‐spectral satellite measurementMODIS PDL retrieval has not only high accuracy in AOD and fine AOD, but also marked sensitivity to coarse AOD and aerosol absorptionPDL method can make full use of the multi‐spectral satellite measurement and prior information with high computational efficiency [ABSTRACT FROM AUTHOR]

Cloud descriptions and parameterizations are major sources of uncertainty in weather and climate modelling studies. In this study, we investigated the cloud properties over the Yunnan–Guizhou Plateau (YGP) by using the Moderate Resolution Imaging Spectroradiometer (MODIS) and Himawari‐8 (H8_Japan and H8_Zhuge) cloud datasets. Corresponding to their distinct terrain features, the cloud climate patterns on the western and eastern sides of the YGP are different. Our results show that the spatial patterns of the cloud parameters in all three datasets are very similar over the YGP, especially the cloud cover and cloud effective radius. The high cloud frequency region is located to the east of Yunnan–Guizhou quasi‐stationary front in cold season, and the largest gradient is along front. On frontal days, the regional differences in the cloud properties over the YGP are much more obvious, with the cloud cover across the front increasing from ~20%–35% on its western side to more than 95% on its eastern side. Furthermore, higher thin clouds with larger cloud effective radius typically exist on the western side of the front, while lower thick clouds with smaller particles appear on its eastern side. This study enhances our understanding how cloud characteristics impact by the frontal system and provide a valuable insight for the design and improvement of cloud microphysics parameters in weather forecast models over the YGP region. [ABSTRACT FROM AUTHOR]

This study assesses the physical and optical properties and estimated the radiative forcing of aerosol at Agra over the Indo‐Gangetic Basin (IGB) during July 2016–December 2019 using black carbon (BC) mass concentration (AE‐33 aethalometer), data sets from satellite and model simulations. The optical properties of aerosol and radiative forcing have been measured by the Optical and Physical Properties of Aerosols and Clouds (OPAC) and Santa Barbara Discrete Ordinate Radiative Transfer Atmospheric Radiative Transfer (SBDART) model. The high BC mass concentration has been observed in November and lowest in August. An adverse meteorological condition due to a combination of temperature and low wind speed results in poor dispersion in the wintertime is a common factor for high concentration level pollutants over Agra. The diurnal and temporal cycle of BC mass concentration exhibits a high concentration at nighttime due to the lower atmospheric boundary layer. The seasonal variation of absorption coefficient (βabs) and Absorption Angstrom Exponent (AAE) is found to be higher during post‐monsoon and lowest in monsoon season. This suggests that black carbon concentration over Agra is mainly generated from crop burning, waste burning, automobile exhaust and long‐range transport from Punjab and Haryana as the present site is downwind. OPAC‐derived aerosol optical depth (AOD), single‐scattering albedo (SSA), Angstrom Exponent (AE) and asymmetry parameter (AsyP) were estimated to be 0.57 ± 0.07, 0.78 ± 0.16, 0.99 ± 0.21 and 0.81 ± 0.15, respectively. AOD and AE from the OPAC and the moderate resolution imaging spectroradiometer (MODIS) have shown the consistent relationship. The mean radiative forcing is 18.3 ± 2.1 W m−2 at the top of the atmosphere while, at the surface, net radiative forcing is −42.4 ± 7.2 and 59.1 ± 6.5 W m−2 at the atmosphere during the study period. Vertical profiles were estimated using the observations from Cloud‐Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) satellite and the change in heating rate from the SBDART model over Agra. [ABSTRACT FROM AUTHOR]

Terrestrial ecosystems play a central role in the global carbon cycle and climate mitigation due to their offering of a large carbon sink. More than one‐fifth of the geographical area of India, one of the largest nations on the Earth, is forested, which is highly diverse in vegetation and climate types, offers huge potential for carbon sequestration, but remains vulnerable to climate change. Hence, it is imperative to know the future changes in the terrestrial carbon budget over this region. Gross primary productivity (GPP) represents the carbon uptake by terrestrial ecosystems. The multimodel ensembles of GPP simulated by the sixth phase of the Coupled Model Intercomparison Project (CMIP6) provide a useful means in this regard. In this work, we study the strength and variability of GPP over India in the near‐past and future using these simulations. In future, all the models show an increasing trend in GPP, however, with widely varying trends. The preferred month of carbon uptake differs among the models. A comparison with a satellite biophysical record shows the models underestimated the GPP during the near‐past over India. The carbon uptake in the Eastern Himalaya dominates the Western Himalaya and central Indian regions. Specifically, till 2100, the growth rate of GPP varies from 4.9 to 16.69 gC m−2 y−2, from 2.47 to 18.91 gC m−2 y−2, and from 0.32 to 21.95 gC m−2 y−2 over these three regions, respectively. Plain Language Summary: Climate change is an imminent problem driven by greenhouse gases, with adverse impacts on the Earth system. Carbon dioxide is a major greenhouse gas. Plants offer a natural strategy to mitigate climate change by their photosynthetic carbon uptake. For climate change impact assessment, projections of climate models are widely used, developed from our understanding of the Earth system, as measured by the observations. India is a large and populous country that remains highly vulnerable to climate change; it also plays a key role in global climate change and its mitigation. However, to devise a suitable mitigation strategy the future carbon sequestration by the natural ecosystems in this region needs to be quantified. The World Climate Research Program developed the Coupled Model Intercomparison Project, a multi‐model ensemble study providing future projections of climate variables under different climate change scenarios. In this work, we use these ensembles to study the future photosynthetic carbon uptake over India and its three key forested regions. We find the photosynthetic carbon uptake to increase manifold over India, with significant inter‐model uncertainties. We also find the Eastern Himalaya to have stronger biospheric carbon removal from the atmosphere than the Western Himalaya and Central Indian regions. Key Points: Photosynthetic carbon uptake increases over India in future with the annual growth rate varying from 3 to 15 gC m−2 y−2The photosynthetic carbon uptake is larger in Eastern Himalaya compared to Western Himalaya and Central IndiaThere is a large discord on the strength, trend and variability of Gross primary productivity over India among CMIP6 models and Moderate Resolution Imaging Spectroradiometer [ABSTRACT FROM AUTHOR]

The Arctic has experienced the frequent and severe fires in recent years. Due to the unique geography and vegetation type in Arctic region, the fire trend and its driving mechanism are different from other regions and still unclear. This study investigated the fire trends across Arctic region based on Moderate‐resolution Imaging Spectroradiometer (MODIS) burned area product MCD64A1 during 2001–2020. In addition, the burned area change was attributed at spatial and temporal scales based on 15 natural and anthropogenic factors. Results show that the average burned area in Arctic region during 2001–2020 is 31.2 × 103 km2, with a maximum of 51.9 × 103 km2 in 2019. The change of burned area in Arctic region is spatially heterogeneous, with a significant increasing trend (Z > 1.96) in central Russia and a significant decreasing trend (Z < −1.96) in western Russia. There is a clear seasonality in the occurrence of fires in the Arctic, with a peak in the summer months and almost no fires in the winter. Relative humidity has the largest explanatory power for the spatial variation of burned area (q = 0.487), followed by vapor pressure deficit (q = 0.475) and land surface temperature (q = 0.367). The combined effect of precipitation and land surface temperature is the most powerful interaction (q = 0.587) to explain the spatial variation of burned area. The significant increase in time‐series burned area is primarily driven by the decrease of relative humidity. The results of this study are helpful to understand the fire trends and the contribution of climate change to Arctic fires. Plain Language Summary: In recent years, the Arctic has experienced frequent and severe fires. Due to the unique geographical location and vegetation type of the Arctic, it is necessary to understand its fire trends and driving mechanisms. This paper explores the spatial and temporal trends of fires in the Arctic from 2001 to 2020 based on the MODIS satellite‐based burned area product MCD64A1, and considers 15 natural and human influences to explore the driving mechanisms. The results show that the Arctic region burned an average of 31.2 × 103 km2 in 2001–2020, with the highest burned area of 51.9 × 103 km2 in 2019, and that burning events were mostly concentrated in summer. The burned area increases significantly in central Russia and decreases significantly in the west. Relative humidity is the most influential factor in the spatial and temporal variability of Arctic fires, and precipitation and land surface temperature have the greatest combined effect on Arctic fires. The results of this study contribute to the understanding of the process of Arctic fire change and the impact of climate change on Arctic fires. Key Points: The burned area significantly increased (decreased) in central (western) RussiaThe interaction between precipitation and land surface temperature has the greatest effect on Arctic firesThe change in burned area is mainly driven by the change in relative humidity [ABSTRACT FROM AUTHOR]

Geobotanical subdivision of landcover is a baseline for many studies. The High–Low Arctic boundary is considered to be of fundamental natural importance. The wide application of different delimitation schemes in various ecological studies and climatic scenarios raises the following questions: (i) What are the common criteria to define the High and Low Arctic? (ii) Could human impact significantly change the distribution of the delimitation criteria? (iii) Is the widely accepted temperature criterion still relevant given ongoing climate change? and (iv) Could we locate the High–Low Arctic boundary by mapping these criteria derived from modern open remote sensing and climatic data? Researchers rely on common criteria for geobotanical delimitation of the Arctic. Unified circumpolar criteria are based on the structure of vegetation cover and climate, while regional specifics are reflected in the floral composition. However, the published delimitation schemes vary greatly. The disagreement in the location of geobotanical boundaries across the studies manifests in poorly comparable results. While maintaining the common principles of geobotanical subdivision, we derived the boundary between the High and Low Arctic using the most up‐to‐date field data and modern techniques: species distribution modeling, radar, thermal and optical satellite imagery processing, and climatic data analysis. The position of the High–Low Arctic boundary in Western Siberia was clarified and mapped. The new boundary is located 50–100 km further north compared to all the previously presented ones. Long‐term anthropogenic press contributes to a change in the vegetation structure but does not noticeably affect key species ranges. A previously specified climatic criterion for the High–Low Arctic boundary accepted in scientific literature has not coincided with the boundary in Western Siberia for over 70 years. The High–Low Arctic boundary is distinctly reflected in biodiversity distribution. The presented approach is appropriate for accurate mapping of the High–Low Arctic boundary in the circumpolar extent. [ABSTRACT FROM AUTHOR]

Many lakes in China suffer from algal bloom problems. However, the spatial and temporal distribution of lacustrine algal blooms at the national scale has not been well characterized. Here, we developed an automated algal bloom surface scums (hereafter referred to as algal bloom) detection algorithm for Moderate‐Resolution Imaging Spectroradiometer (MODIS) images based on a normalized floating algal index and the Commission on Illumination colorimetry system. This algorithm was then applied to 35,556 daily MODIS images acquired between 2003 and 2020 to hindcast the spatial and temporal dynamics of lacustrine algal blooms in 171 lakes in China. The results show that 103 (60.2%) of the examined lakes have been affected by algal blooms over the past two decades, and the bloom occurrence in 95 lakes showed an increasing trend. The prevailing increasing trends of algal blooms in Chinese lakes were also manifested by an earlier onset time and prolonged potential occurrence period. We found that approximately 80% of the historical algal blooms occurred under calm water surfaces (wind speed <3 m/s) and high temperatures (>16°C), and we revealed positive correlations between bloom occurrence and fertilizer use. We further demonstrated that the increasing trends in algal blooms were highly linked to recent increases in air temperature. The results here not only highlight the severe lacustrine algal bloom problems in China but also provide important baseline information for lake management and restoration efforts for the government. Key Points: Spatiotemporal dynamics of algal blooms in 171 lakes in China were analyzed by an automated algal bloom detection algorithm for Moderate‐Resolution Imaging Spectroradiometer images60.2% of the lakes have been affected by algal blooms, and 95 lakes showed increasing trendsThe increasing trends were highly linked to recent increases in air temperature [ABSTRACT FROM AUTHOR]

Large‐scale classical dust cycle models, developed more than two decades ago, assume for simplicity that the Earth's land surface is devoid of vegetation, reduce dust emission estimates using a vegetation cover complement, and calibrate estimates to observed atmospheric dust optical depth (DOD). Consequently, these models are expected to be valid for use with dust‐climate projections in Earth System Models. We reveal little spatial relation between DOD frequency and satellite observed dust emission from point sources (DPS) and a difference of up to 2 orders of magnitude. We compared DPS data to an exemplar traditional dust emission model (TEM) and the albedo‐based dust emission model (AEM) which represents aerodynamic roughness over space and time. Both models overestimated dust emission probability but showed strong spatial relations to DPS, suitable for calibration. Relative to the AEM calibrated to the DPS, the TEM overestimated large dust emission over vast vegetated areas and produced considerable false change in dust emission. It is difficult to avoid the conclusion that calibrating dust cycle models to DOD has hidden for more than two decades, these TEM modeling weaknesses. The AEM overcomes these weaknesses without using masks or vegetation cover data. Considerable potential therefore exists for ESMs driven by prognostic albedo, to reveal new insights of aerosol effects on, and responses to, contemporary and environmental change projections. Plain Language Summary: Mineral dust influences Earth's systems, and understanding its impacts relies on numerical models which include large uncertainties. We compared measurements of dust optical depth (DOD) frequency of occurrence (probability) and satellite observed dust emission frequency from point sources (DPS) across North America. We found up to 2 orders of magnitude difference between DOD probability and DPS probability. Compared with DPS probability, we found an exemplar traditional dust emission model (TEM) and the albedo‐based dust emission model (AEM) both overestimated dust emission probability by up to 1 order of magnitude with statistically significant relations, suitable for calibration. Relative to the AEM calibrated to DPS, the exemplar TEM overestimated large dust emission over vast vegetated areas and produced considerable false change in dust emission. Tuning dust cycle models to DOD has very likely hidden, for more than two decades, these TEM weaknesses with implications for our understanding of Earth's systems. Considerable potential exists for new insights of dust‐climate in Earth System Models by using AEM with prognostic albedo. Key Points: Tuning dust models to dust optical depth (DOD) hides dust emission model weaknesses including overestimates and false change in vegetated areasNew shadow‐shelter model calibrated to observed dust emission circumvents unrealistic model assumptionsTwo orders of magnitude difference between DOD and observed dust emission without significant relation [ABSTRACT FROM AUTHOR]

Recent studies have identified several statistically significant trends in cloud genera frequency over Poland. Notably, there has been an increase in high and convective clouds, along with a decrease in Stratus, Altostratus and Nimbostratus. These earlier studies were, however, only based on traditional, surface data. As the ability to observe mid and high‐level clouds from the ground is limited, due to overlapping, we address the question of whether these trends are confirmed by other sources of cloud data, such as satellite records. In this paper, we analyse cloud data from two fully independent sources to characterize the cloud type climatology over Poland for the period 2003–2021: cloud optical thickness (COT) and cloud top pressure (CTP) parameters retrieved from Moderate Resolution Imaging Spectroradiometer (MODIS), and surface (SYNOP) observations of cloud genera from the country's 27 ground‐based stations. The International Satellite Cloud Climatology Project (ISCCP) COT–CTP classification was used to define MODIS cloud types. We explore the hypothesis that MODIS data, classified using the ISCCP philosophy, confirms the changes in cloud type frequencies found in SYNOP observations. Our analysis is based on the application of the Spearman rank correlation test to mean monthly SYNOP and MODIS cloud type frequencies, and we compare per decade trends for two time series. Our hypothesis was confirmed, but only for Cirrus, Altostratus + Nimbostratus and Cumulus. We therefore conclude that the two analysed data sources are not fully consistent, and cannot substitute for each other in cloud type climatologies. Hence, at least for Poland, they should be treated as independent rather than complementary. We also demonstrate that the increase in high‐level cloud frequency over Poland, which has been observed by other authors who based their research on synoptic data, is not due to a decrease in the frequency of low‐ and mid‐level clouds. Our findings are confirmed by satellite records. [ABSTRACT FROM AUTHOR]

The influence of climate change on forest condition is noticeable. Forest ecosystem stress caused by climate change has already been manifested in several parts of Europe, including Poland. Thus, the main objective of this paper is to investigate for the entire area of Poland a long‐term trend and variability of forest greenness expressed as the Normalized Difference Vegetation Index (NDVI), derived from two decades (2002–2021) of remote sensing Moderate Resolution Imaging Spectroradiometer (MODIS) data. In the next step, selected meteorological elements – temperature (T), precipitation (P) and evapotranspiration (ETo), derived from ERA5‐Land reanalysis—were used to determine the influence of climatic conditions on the variability of NDVI in forests. The study documents the general greening of forests in Poland in 2002–2021. The greening is mostly visible in central‐eastern Poland, where the annual mean NDVI increased by 0.030 in 20 years, while it is weaker in the Baltic coast and in the southern edges of Poland (increase by 0.009 in 20 years). Overall, the positive, statistically significant trends in annual NDVI prevail over the negative, statistically significant trends and account for 32.5% of forest area, whereas the negative trends account for 3.9%. The study indicates an overall moderate impact of meteorological elements on variability of NDVI in forests in Poland. The most important factors affecting forest condition are P and ETo. The strongest correlations between NDVI and P and ETo reach 0.55 and are located in central Poland, in the form of a belt from western to eastern borders. [ABSTRACT FROM AUTHOR]

Enhanced transport of warm subsurface Atlantic Water (AW) into Greenland fjords has driven glacier mass loss, but the mechanisms transporting AW to the fjords remain poorly characterized. Here, we provide the first direct satellite‐based observations of rapid (∼0.2 m/s) AW intrusion toward Sermilik Fjord abutting Helheim Glacier, one of Greenland's largest glaciers. The intrusions arise when coastal upwelling—through interactions with Sermilik's bathymetric trough on the continental shelf—triggers enhanced AW upwelling and inflow that can travel tens of kilometers along the trough within hours. A weakening or reversal of northeasterly alongshore winds stimulates the intrusions and is often associated with the passing of cyclones and subsequent sea surface lowering. Mooring data show that these intrusions produce subsurface ocean warming both at Sermilik Fjord mouth and within the fjord and that the warming signal in the fjord does not diminish during subsequent coastal downwelling events. Satellite imagery captures near‐synchronous AW intrusions at multiple troughs rimming southeast Greenland suggesting that these wind‐driven processes may play a substantial role in ocean heat transport toward the Greenland Ice Sheet. Plain Language Summary: Greater transport of the warm subtropical Atlantic Waters into Greenland fjords has driven glacier mass loss, but the mechanisms transporting the subtropical waters to glacier fronts remain poorly characterized. Here, we provide the first satellite‐based observations of rapid flow of subtropical water toward Sermilik Fjord abutting Helheim Glacier, one of Greenland's largest glaciers. Often associated with the passing of cyclones, strong alongshore wind events stimulate ocean circulation that brings subtropical waters from offshore onto the continental shelf along an underwater trough that leads to Helheim. Our measurements show that when these events produce ocean warming nearshore, they tend to transport more heat toward Helheim Glacier's front where it may increase ice melting. A higher number of such wind events in a season has the potential to impact glacier calving, thinning, and retreat. Satellite imagery shows that these events can occur simultaneously along other bathymetric troughs leading toward other Greenland glaciers in the southeast and potentially elsewhere. Therefore, these ocean events may be important for predicting future Greenland Ice Sheet ice loss. Key Points: Alongshore wind‐driven coastal upwelling drives Atlantic Water intrusions onto the continental shelf along Sermilik TroughThis work represents the first remote‐sensing evidence of intrusion events along a bathymetric depressionRapid intrusions often transport warm subsurface water to the inner shelf and fjord in less than 1 day [ABSTRACT FROM AUTHOR]

The use of standard aerosol models for atmospheric correction over inland and nearshore coastal waters generates large uncertainties due to their inability to appropriately characterize aerosols in those regions. To address this problem, we developed new aerosol models using data acquired from 1,475 Aerosol Robotic Network (AERONET) sites distributed in inland and coastal regions throughout the globe. We established spatially gridded (5° × 5°) aerosol models on a climatological monthly scale, with a total of 3,207 new aerosol models developed for 310 grid cells worldwide. The monthly aerosol model for each grid was derived using mean monthly values of microphysical aerosol properties from AERONET data. The optical properties (i.e., single scattering albedo and Ångström coefficient) of our global gridded aerosol models demonstrated considerable spatial and seasonal dynamics and deviated substantially from the aerosol models currently utilized by the National Aeronautics and Space Administration (NASA). We built associated lookup tables for all aerosol models and incorporated them into two atmospheric correction algorithms: the combined near‐infrared and shortwave infrared algorithm (NIR‐SWIR) and the Atmospheric Correction algorithm for inLand and Nearshore Coastal waters (ACLANC). For both algorithms, the accuracies of atmospherically corrected satellite remote sensing reflectance (Rrs) improved markedly when the new aerosol models were used, and improvements were noted across all spectral bands and across all six inland and nearshore water bodies used in this study. Our new aerosol models can be easily applied to all ocean color satellite missions to obtain accurate Rrs products for inland and coastal waters worldwide. Plain Language Summary: The use of standard aerosol models for atmospheric correction over inland and nearshore coastal waters generates large uncertainties due to their inability to characterize aerosols in those regions appropriately. This issue also represents the most challenging task of quantitative remote sensing of the inland and coastal environments. To address this problem, we developed new aerosol models using data acquired from 1,475 Aerosol Robotic Network sites distributed in inland and coastal regions throughout the globe. We established spatially gridded (5° × 5°) aerosol models on a climatological monthly scale, with a total of 3,207 new aerosol models developed for 310 grid cells worldwide. Key Points: Develop new aerosol models using data from 1,475 global Aerosol Robotic Network sitesEstablish spatially gridded aerosol models on a climatological monthly scaleAccuracies of the Rrs product improved markedly using new aerosol models [ABSTRACT FROM AUTHOR]

Grassland ecosystems cover up to 40% of the global land area and provide many ecosystem services directly supporting the livelihoods of over 1 billion people. Monitoring long-term changes in grasslands is crucial for food security, biodiversity conservation, achieving Land Degradation Neutrality goals, and modeling the global carbon budget. Although long-term grassland monitoring using remote sensing is extensive, it is typically based on a single vegetation index and does not account for temporal and spatial autocorrelation, which means that some trends are falsely identified while others are missed. Our goal was to analyze trends in grasslands in Eurasia, the largest continuous grassland ecosystems on Earth. To do so, we calculated Cumulative Endmember Fractions (annual sums of monthly ground cover fractions) derived from MODIS 2002-2020 time series, and applied a new statistical approach PARTS that explicitly accounts for temporal and spatial autocorrelation in trends. We examined trends in green vegetation, non-photosynthetic vegetation, and soil ground cover fractions considering their independent change trajectories and relations among fractions over time. We derived temporally uncorrelated pixel-based trend maps and statistically tested whether observed trends could be explained by elevation, land cover, SPEI3, climate, country, and their combinations, all while accounting for spatial autocorrelation. We found no statistical evidence for a decrease in vegetation cover in grasslands in Eurasia. Instead, there was a significant map-level increase in non-photosynthetic vegetation across the region and local increases in green vegetation with a concomitant decrease in soil fraction. Independent environmental variables affected trends significantly, but effects varied by region. Overall, our analyses show in a statistically robust manner that Eurasian grasslands have changed considerably over the past two decades. Our approach enhances remote sensing-based monitoring of trends in grasslands so that underlying processes can be discerned. [ABSTRACT FROM AUTHOR]

The eruption of Hunga Tonga‐Hunga Ha'apai (HTHH) volcano on 15 January 2022 injected a great amount of H2O and a moderate amount of SO2 into the stratosphere, producing a pronounced and persistent sulfate aerosol layer centered around the mid‐stratosphere, mostly confined to Southern Hemisphere (SH) tropics. These aerosols affect the Moderate Resolution Imaging Spectroradiometer (MODIS) retrievals of sea surface temperature (SST) where negative biases reached −0.3 K and an annual mean of −0.1 K north of 40°S in the SH. The spatial and temporal evolutions of MODIS SST anomalies are presented. Radiative transfer simulations demonstrate the aerosol effect on MODIS SST retrievals by causing an additional brightness temperature (BT) deficit at 11 μm and a reduction in BT differences since the characteristic of spectral attenuation between 11 and 12 μm is opposite to that of H2O. A correction for HTHH aerosol effects in the retrieval algorithm is therefore desirable. Plain Language Summary: The limit on the accuracy of sea surface temperature (SST), an Essential Climate Variable, derived from measurements of infrared radiometers on satellites is determined by the correction for the effects of the intervening atmosphere. Anomalous atmospheric conditions cause larger errors. The eruption of the submarine volcano Hunga Tonga‐Hunga Ha'apai in January 2022 injected much water vapor and other material into the atmosphere. By comparing satellite‐derived SSTs with those measured from drifting buoys in the latitude zone significantly influenced by the volcano's eruption, we quantify the negative SST errors introduced and monitor the evolution of the volcano's influence for the following year. It will be important to generate corrections to satisfy the high accuracy requirements for SST in scientific studies, for example, as input to climate models. Key Points: Stratospheric sulfate aerosols in Tonga eruption plume affect sea‐surface temperature from Moderate Resolution Imaging SpectroradiometerNegative biases of sea‐surface temperature were pronounced in Southern Hemispheric tropics initially and spread to mid‐latitude after MayA negative offset of 11–12 μm brightness temperature difference is due to more absorption by aerosol at 11 μm, opposite to H2O absorption [ABSTRACT FROM AUTHOR]

BACKGROUND: Accurate and timely access to large‐scale crop damage information provides an essential reference for responding to agricultural disaster prevention and mitigation needs and ensuring food production security. The present study aimed to reveal the new characteristics of low‐temperature cold damage to maize in the context of climate warming. Heilongjiang, one of the provinces with the highest latitude, the most significant climate change and the largest maize production in China, was taken as the study area. We combined meteorological stations and MODIS remote sensing data to spatially identify the occurrence and intensity of cold damage to maize based on the growing season temperature distance level index, as well as to assess the extent of cold damage. RESULTS: The main findings are: (i) The frequency and intensity range of cold damage in the growing season (May to September) in Heilongjiang Province from 1991 to 2020 against climate warming showed a decreasing trend. The average temperature from 1991 to 2000 was 17.777 °C, with seven occurrences of maize cold damage years, of which 5 years comprised widespread cold damage and 2 years comprised regional cold damage. The average temperature from 2000 to 2010 was 18.137 °C, with cold damage three times, of which 2 years comprised regional cold damage and 1 year comprised widespread cold damage. The average temperature from 2010 to 2020 was 18.130 °C, with one maize cold damage year occurring, which comprised regional cold damage. The frequency of maize chilling injury decreased significantly from 1991 to 2020, from 0.23 in 1991–2000 to 0.1 in 2000–2010 and, finally, to 0.03 in 2010–2020. (ii) The good consistency between MODIS_LST data and temperature data from meteorological stations suggests that MODIS_LST data can be used to build a temperature remote sensing estimation model for spatially extensive cold damage monitoring and intensity discrimination. (iii) Taking 2009 as an example of a large‐scale cold damage year, the spatial discrimination of maize cold damage intensity shows that the spatial distribution of chilling injury intensity has no obvious geographical features. The intensity of cold damage was mainly mild cold damage. According to administrative regions, the scope of chilling injury was the largest in Mudanjiang City, Heihe City, and Jixi City, accounting for 91.56%, 86.25%, and 84.91%, respectively. The areas with the most extensive range of severe chilling injuries were the Great Khingan Mountains region, Heihe City, Mudanjiang City, Yichun City, and Jixi City. CONCLUSION: In the context of climate warming, the frequency and intensity range of maize cold damage showed a decreasing trend from 1991 to 2020 in Heilongjiang Province. The results of cold damage identification based on MODIS_LST data are accurate and can improve the spatial accuracy. The results of the present study provide a reference and guidance for dealing with the occurrence and defence of spatially refined cold damage. © 2023 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

Evapotranspiration (ET) is a critical process that regulates the transfer of heat and water between land and the atmosphere. While satellite‐based ET algorithms can provide area‐wide daily ET estimates, few are independent of local meteorological measurements. The Variant of the Moderate Resolution Imaging Spectroradiometer Standard Evapotranspiration Algorithm (VISEA) can potentially run without ground‐based observations. However, the surface energy budgets used in VISEA generally overpredict daily net radiation and related ET. To improve the accuracy of net radiation and ET, we incorporated Brutsaert's atmosphere emissivity model and corrected the routine to calculate the downward long‐wave radiation on cloudy days (which we called VISEA2023 model). The VISEA2023 model predicted net radiation and ET that agreed well with measurements at seven ChinaFlux sites, with an R2 of 0.8 and an Root Mean Square Error (RMSE) of 30 W m−2 for net radiation, and an R2 of 0.6 and an RMSE of 1 mm day−1 for ET, respectively. Additionally, the VISEA2023 model is more robust in comparison to the Variable Infiltration Capacity hydrologic model output in 10 major river basins and 80 sub‐river basins, with an R2 of 0.91 (0.78) and an RMSE of 85 (127.4) mm year−1, than MOD16, which has an R2 of 0.81 (0.82) and an RMSE of 134.8 (157.7) mm year−1, and Advanced Very High Resolution Radiometer ET, which has an R2 of 0.92 (0.62) and an RMSE of 90.1 (136.1) mm year−1. The improved VISEA2023 model can offer near‐real‐time ET on a larger scale, improving our understanding of water cycles and their relation to climate change. Plain Language Summary: Evapotranspiration, also known as ET, is an essential process that plays a key role in the Earth's water cycle. Large‐scale ET can be calculated based on satellite observations, but current algorithms are not always accurate. The Variant of the Moderate Resolution Imaging Spectroradiometer Standard Evapotranspiration Algorithm (VISEA) is an innovative algorithm that can calculate ET without ground‐based data. However, VISEA overpredicts net radiation and the subsequent ET. We developed an improved algorithm by introducing Brutsaert's atmosphere emissivity model and quantifying the longwave radiation component as a function of cloudiness. The developed ET compares well with flux tower measurements and other ET products over China. This improved VISEA model has the potential to provide near‐real time ET on a larger scale, leading to a better understanding of water cycles and their relation to climate changes. Key Points: Am improved downward long‐wave radiation procedure is introduced in a satellite‐based evapotranspiration (ET) model on cloudy daysThis revised procedure can improve the accuracy of the daily net radiation and ET compared with in‐situ measurementsThe multi‐year mean value of the developed ET is more robust than other satellite‐based ET products in river basin comparison over China [ABSTRACT FROM AUTHOR]

Our longest, stable record of cloud‐top pressure (CTP) and cloud‐top height (CTH) are derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Multi‐Angle Imaging Spectroradiometer (MISR) on Terra. Because of single cloud‐layer assumptions in their standard algorithms, they provide only single CTP/CTH retrievals in multi‐layered situations. In the predominant multi‐layered regime of thin cirrus over low clouds, MODIS significantly overestimates cirrus CTP and emissivity, while MISR accurately retrieves low‐cloud CTH. Utilizing these complementary capabilities, we develop a retrieval algorithm for accurately determining both‐layer CTP and cirrus emissivity for such 2‐layered clouds, by applying the MISR low‐cloud CTH as a boundary condition to a modified MODIS CO2‐slicing retrieval. We evaluate our 2‐layered retrievals against collocated Cloud‐Aerosol Transport System (CATS) lidar observations. Relative to CATS, the mean bias of the upper cloud CTP and emissivity are reduced by ∼90% and ∼75% respectively in the new technique, compared to standard MODIS products. We develop an error model for the 2‐layered retrieval accounting for systematic and random errors. We find up to 87% of all residuals lie within modeled 95% confidence intervals, indicating a near‐closure of error budget. This reduction in error leads to a reduction in modeled atmospheric longwave radiative flux biases ranging between 5 and 40 W m−2, depending on the position and optical properties of the layers. Given this large radiative impact, we recommend that the pixel‐level 2‐layered MODIS + MISR fusion algorithm be applied over the entire MISR swath for the 22‐year Terra record, leading to a first‐of‐its‐kind 2‐layered cloud climatology from Terra's morning orbit. Plain Language Summary: Our longest climate‐quality record of global cloud‐top heights (CTH) comes from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Multi‐Angle Imaging Spectroradiometer (MISR) on the Terra satellite. These sensors assume a single cloud‐layer in retrieving CTH, even though ∼30% of global cloud cover is multi‐layered. Multi‐layered clouds predominantly consist of thin ice clouds over low clouds. Under such conditions, MISR accurately retrieves low‐cloud CTH, while MODIS systematically underestimates upper‐cloud‐layer CTH. Here, we have developed a 2‐layered MODIS + MISR fusion CTH retrieval by using MISR's accurate low‐cloud CTH as an input to a modified MODIS algorithm. This algorithm combines the complementary capabilities of MISR and MODIS in distinguishing higher and lower clouds and estimates both‐layer cloud heights and high‐cloud emissivity. Through comparisons against coincident Cloud‐Aerosol Transport System lidar observations, we find that the new algorithm improves the accuracies in retrieved CTH and cloud emissivities by ∼75% over standard MODIS products. We further demonstrate significant improvements in estimates of simulated atmospheric longwave radiation from our implementation. Owing to its large radiative impact, we suggest that the pixel‐level fusion algorithm be applied to all 22 years of Terra record to facilitate public dissemination of the first 2‐layered cloud record from its morning orbit. Key Points: Accurate, high‐precision Multi‐Angle Imaging Spectroradiometer low cloud heights are employed in a physics‐based correction to Moderate Resolution Imaging Spectroradiometer CO2‐slicing in multi‐layered scenesCloud‐top pressure (CTP) bias drops from 65 to 5 hPa, resulting in a quartering of cloud‐height and emissivity bias for cirrus over low cloudUp to 87% of CTP retrieval errors are bound by theoretical estimates, resulting in near‐closure of CO2‐slicing error budget [ABSTRACT FROM AUTHOR]

Accurate estimation of near‐surface air temperature (TsurfAir) at fine spatiotemporal resolution is crucial for environmental applications, data assimilation into land surface models, and climate change studies. The Atmospheric Infrared Sounder (AIRS) on board the Aqua satellite measures TsurfAir twice a day at the global scale. In this study, the AIRS TsurfAir is validated using ground‐based weather stations in North America. Owing to the high density of weather stations, spatial heterogeneity (<1°) of TsurfAir is modeled using semi‐variograms and further used to calculate the validation reference via ordinary kriging at AIRS retrieval footprints. This analysis intends to reveal error characteristics in cold and hot seasons by selecting six months of data in January and July of three years, along with a case study on two extreme events. Furthermore, MODIS‐enabled land surface temperature and land cover are used to attribute the observed error in TsurfAir. Results show an overall warm bias of 0.11 K from AIRS TsurfAir and conditional bias over‐ and underestimating cold and hot temperatures, respectively. Spatial heterogeneity of TsurfAir is found to influence the variance of AIRS TsurfAir error. As a possible source of TsurfAir error, land surface temperatures (Tskin) from AIRS and MODIS are compared. Close match between the two Tskin products indicates a lesser influence of the Tskin difference on TsurfAir error. Overall, the ground validation of AIRS TsurfAir along with the error attribution can facilitate post‐processing of AIRS products and uncertainty analysis for the wide range of applications of AIRS TsurfAir. Key Points: Near‐surface air temperature retrievals from Atmospheric Infrared Sounder are validated using ground truth from a dense network of weather stations in North AmericaHigh density of ground truth reveals fine‐scale heterogeneity and representativeness error of TsurfAir via ordinary KrigingMODIS‐based land surface temperatures and land cover types are used to attribute errors in TsurfAir [ABSTRACT FROM AUTHOR]

Australia is a region of high sensitivity to the El Niño–Southern Oscillation events in association with atmospheric water vapour, which is an essential atmospheric parameter in hydrological cycle, energy transport and climate monitoring. In this article, we thoroughly validated the quality of multisource precipitable water vapour (PWV) products sourced from ERA5 reanalysis, advanced Medium Resolution Spectral Imager (MERSI‐II) sensor on the FY‐3D satellite, Ocean and Land Colour Instrument (OLCI) sensor on the Sentinel‐3A and Sentinel‐3B satellites, and Moderate Resolution Imaging Spectroradiometer (MODIS) sensor on the Aqua and Terra satellites. The PWV estimates measured by 453 in situ Global Positioning System (GPS) sites from 1 June 2019 to 31 May 2020 over Australia were employed as the common reference. The validation results show that all the reanalysis‐based and satellite‐based PWV products had a good agreement with reference GPS‐based PWV data. ERA5 reanalysis had the highest PWV accuracy with a root‐mean‐square error (RMSE) of 2.016 mm and a relative RMSE (RRMSE) of 12.038%, while the MODIS/Terra instrument had the lowest PWV accuracy (RMSE = 4.903 mm and RRMSE = 34.187%). In contrast to the MERSI‐II/FY‐3D instrument that underestimated PWV, the PWV data from ERA5, OLCI/Sentinel‐3A, OLCI/Sentinel‐3B, MODIS/Aqua and MODIS/Terra overestimated the water vapour values. The quality of reanalysis‐based and satellite‐based PWV measurements showed significant dependencies on several variables – location, PWV, solar zenith angle, season, elevation, land surface cover and latitude. It is expected that this research can help enhance the understanding of the quality of reanalysis and satellite water vapour products in Australia, that is, ERA5, OLCI/Sentinel‐3A, OLCI/Sentinel‐3B, MODIS/Aqua and MODIS/Terra. This work could provide an insight into improving the accuracy of reanalysis‐based and satellite‐based PWV data products after exploring the dependency factors that affect PWV performance as presented in our work. [ABSTRACT FROM AUTHOR]

Satellite‐based post‐tornado assessments have been widely used for the detection of tornado tracks, which heavily relies on the identification of vegetation changes through observations at visible and near‐infrared channels. During the deadly 10–11 December 2021 tornado outbreak, a series of violent tornadoes first touched down over northeastern Arkansas, an area dominated by cropland with rare vegetation coverage in winter. Through the examination of Moderate Resolution Imaging Spectroradiometer multi‐spectral observations, this study reveals significant scars on shortwave infrared channels over this region, but none are captured by visible and near‐infrared channels. The dominant soil type is aquert (one of vertisols), whose high clay content well preserves the severe changes in soil structure during the tornado passage, when the topmost soil layer was removed and underlying soil with higher moisture content was exposed to the air. This study suggests a quick post‐tornado assessment method over less vegetated area by using shortwave infrared channels. Plain Language Summary: This study showcases an application of using Moderate Resolution Imaging Spectroradiometer (MODIS) data to detect tornado damage tracks over northeastern Arkansas after the deadly 10–11 December 2021 tornado outbreak. The special soil type with rich clay content well retains the tornado scars, whose moisture content is higher than the surrounding area, causing significant signals on MODIS shortwave infrared channels. Although coarse in spatial resolution, MODIS's rapid revisit cycle and minimal latency in data availability make it an ideal platform for the post‐tornado assessment over the winter farmland, when the vegetation coverage is low, thus the conventional vegetation‐based tornado track detection does not work well. Key Points: The 2021 winter tornado outbreak left significant linear scars on Arkansas aquert farmland observed from Moderate Resolution Imaging Spectroradiometer (MODIS) shortwave infrared (SWIR) channelsThe scars are unrecognizable on MODIS visible and near‐infrared channels due to the coarse resolution and rare vegetation cover in winterSWIR provides a fast assessment of tornado track as topmost layer is removed by suction vortices and wetter underlying soil is exposed [ABSTRACT FROM AUTHOR]

The global community faces challenges of agroecosystem sustainability in the face of changing climate and intensified agriculture, particularly in Africa. In this study, annual and seasonal cropland Normalized Vegetation Difference Index (NDVI) trends and phonological variations, and associated key drivers in North‐East Africa were generated using Moderate Resolution Imaging Spectral‐radiometer NDVI of 8‐day satellite images and climate data between 2001 and 2020, which were further analyzed by a modified dynamic threshold method. The results showed that the annual cropland NDVI increased at an average rate of 0.0106 per year. The seasonal cropland NDVI was positive for the most significant changes observed in the boreal autumn season across the region. The start of the season and end of the season cropland NDVI in the boreal autumn season increased at an average rate of 0.015 (13.4% significant, p < 0.05), and 0.0151 × (12.8% significant, p < 0.05), respectively. The findings indicated that the boreal autumn season was getting longer, whereas the boreal summer season was becoming shorter during the last two decades. The most rapid cropland NDVI changes and maximum farmland NDVI variability were observed in the sub‐tropic warm arid and tropic warm semiarid agro‐ecological zones. The land surface temperature was found to be the main driving factor for the cropland NDVI and associated agroecosystem changes. This study provides essential information on the cropland productivity and shifting of greening patterns, which will support climate change mitigation measures at national and regional scales, contributing to sustainable agriculture. Plain Language Summary: Climate change and land use change impose various impacts on agricultural productivity and agroecosystem services in Africa. In this research, we analyzed annual and seasonal cropland changes using time series Normalized Vegetation Difference Index (NDVI) as monthly image composites from 2001 to 2020. We assessed key transition stages in the vegetation and crop phenology trends including annual cropland NDVI, the start of the growing season (SOS), and the end of the growing season (EOS). At an annual time‐step, there were more greening patterns across large parts of North‐East Africa with the highest greening trends in the croplands of Egypt followed by Uganda and South Sudan, while more browning patterns were in Djibouti, Eritrea, and Somalia. The most greening and significant SOS trends were shown in the boreal autumn season, while negative EOS trends were common in the boreal summer season. The boreal summer was getting shorter as the boreal autumn experienced longer periods. The sub‐tropic warm arid and tropic warm semiarid agro‐ecological zones showed the highest farmland productivity changes. The cropland NDVI changes was strongly associated with land surface temperature. The study provides important phonological information, which is helpful to understand the challenges of food security at a regional scale. Key Points: There were rapid cropland Normalized Vegetation Difference Index (NDVI) changes in sub‐tropic warm arid and tropic warm semiaridThe boreal autumn cropland productivity is getting longer in North‐East AfricaThe land surface temperature was the main deriving factor for cropland NDVI changes [ABSTRACT FROM AUTHOR]

We present a novel technique for developing a probabilistic cloud mask using sensors on a geostationary platform. The clear‐sky probability of a pixel is estimated using the past 45 days' observations by making use of the Gaussian mixture model, spectral clustering, and Bayes' theorem. The proposed cloud‐masking algorithm is applied on INSAT‐3D imager observations for the whole years 2017 and 2018. The INSAT‐3D imager cloud mask generated using the proposed method is compared against the Moderate‐resolution Imaging Spectroradiometer (MODIS) MYD35 cloud‐mask product. The skill of the algorithm, against the MODIS cloud‐mask product, is evaluated for day and night as well as for land and ocean separately. Overall, the Kuipers skill score (KSS) is found to be 0.82. The best performance of the algorithm is obtained during daytime over the land with KSS=0.84$$ \mathrm{KSS}=0.84 $$. [ABSTRACT FROM AUTHOR]

Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua daily land surface temperature (LST) products are important data sets for global warming monitoring. To reveal the consistency and differences between MODIS Terra and Aqua daily LST products for monitoring LST trends, this study used these two products to extract the mean annual surface temperature (MAST) of the Tibetan Plateau from 2003 to 2020. Then, nine MAST combinations were constructed for daytime, nighttime, and daily average conditions separately with Terra and Aqua MAST series, including Terra only, Aqua only, and the integrated one. Finally, the Mann‐Kendall trend test was applied to these series and obtained the spatiotemporal variability. The results indicated that there was high spatial consistency of the multi‐year averaged MAST of different combinations at daytime/nighttime/daily average scales. Meanwhile, there are quite similar temporal trends in terms of spatial distribution and change rate. The MAST changes are dominated by warming trends, and the areas and change rates of the significant changing pixels at different elevations, vegetation cover ranges, and land cover types presented similar distribution patterns. Although there are some discrepancies in the detailed location for pixels with significant trends among different combinations, the proportion of the pixels with the same trends of daytime/nighttime/daily‐average groups is as high as 68.88%, 66.75%, and 71.76%, respectively. These findings reveal that different MODIS LST products have similar performance in monitoring LST trends, indicating that the detection can well reflect the variation of surface thermal environment regardless of which MODIS LST product is used. Key Points: Different land surface temperature (LST) combinations were generated with Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua daily daytime and nighttime LST productsThe daytime, nighttime, and daily averaged mean annual surface temperature from different combinations show similar spatial distributions over the Tibetan PlateauThe detection well reflects the variation of surface thermal environment regardless of which MODIS LST product is used [ABSTRACT FROM AUTHOR]

Terminal lakes in the Great Basin (GB) of the western US host critical wildlife habitat and food for migrating birds and can be associated with serious human health and economic consequences when they desiccate. Water levels have declined dramatically in the last 100+ years due to diversion of inflows, drought and climate change. Satellite‐derived environmental science data records (ESDRs) from the MODerate‐resolution Imaging Spectroradiometer (MODIS) (snow cover, evapotranspiration (ET) and land surface temperature (LST)), enable a unique approach to evaluate the effects of aridification on terminal lakes and to study their individual vulnerabilities. Surface and air temperatures in the GB are rising dramatically, with a sharp rise in the rate of increase observed beginning around 2011, while the number of days of snow cover is declining especially in the western mountainous part of the GB as exemplified in Mono Basin, California. Rising temperatures coincide with fewer days of snow cover, a decrease of inflow to the lakes and greater evaporation of water from the lakes. MODIS ESDRs show strong and statistically significant increasing surface temperature (LST) in the GB, a reduction in the number of days of snow cover, and mixed results in ET. ET declined slightly in the more arid parts of the GB due to greater moisture restrictions to evaporation from extended drought, while ET increased in the more‐vegetated, wetter, mountainous northeastern parts as temperatures have risen. Severe and costly ecological, human health and economic consequences are expected if the lakes continue to decline as predicted. Plain Language Summary: Terminal lakes in the Great Basin (GB) of the western US host critical wildlife habitat and food sources for migrating birds and can be associated with costly human health and economic consequences when they desiccate. Toxic minerals in the expanding lakebeds may become airborne during windstorms, contributing to air pollution. Satellite data products (snow cover, evapotranspiration and surface temperature) have enabled a unique understanding of the dynamics of aridification in the GB and associated effects on terminal lakes which are usually saline. Surface temperature is rising dramatically, with a sharp rise in the rate of increase observed beginning around 2011, while snow cover is declining especially in the western mountainous part of the GB as exemplified in Mono Basin, California. Evapotranspiration has declined slightly in lower elevation parts of the GB likely due to a decrease in vegetation there, while it has increased in the wetter, mountainous eastern part as temperatures have risen. Though we recognize that 21 years is not adequate for assessing trends, it is clear that increasing temperatures, greater evaporation of lake water and decreasing number of days of snow cover are contributing to desiccation of terminal lakes, with severe environmental and human health consequences expected. Key Points: MODerate‐resolution Imaging Spectroradiometer data products provide a unique way to assess individual vulnerabilities of terminal lakes as temperatures rise in the US WestSurface and air temperatures in the Great Basin (GB) are rising dramatically, with a sharp rise in the rate of increase observed beginning ∼2011ET is generally lower in the GB, exacerbated by drought restrictions on surface evaporation, likely reinforcing regional warming [ABSTRACT FROM AUTHOR]

Glacier surface albedo is a key parameter of the glacier mass and energy balance process. Many scholars have used the albedo method to study mass balance for an individual glacier, but less attention has been given to large‐area glaciers. Based on Moderate Resolution Imaging Spectroradiometer (MODIS) retrieved glacier surface albedo and in situ mass balance, the correlations between albedo and mass balance are reported. The minimum/mean method (the whole glacier albedo is calculated by averaging the minimum/mean albedo of each grid cell), the MODIS snow albedo products (MxD10A1) and the MCD43A3 shortwave albedo were tested during the melt season (June–August) for four glaciers in the High Mountain Asia (HMA). Results reveal that albedo is well correlated with mass balance, the correlation (r) ranging from 0.41 to 0.90. Overall, the minimum method is better than the mean method, and MxD10A1 is better than MCD43A3. The albedo changes were calculated based on the MxD10A1 and the mean method, and a linear relationship is established between albedo changes and digital elevation models (DEMs) calculated mass balance from 2000 to 2016. A strong linear correlation (r =.9) is found between albedo changes and mass balance while excluding some areas that have insignificant seasonal variation in albedo, and the reconstructed albedo–mass balance relationship can be used to retrieve mass balance in large‐scale glaciers. Furthermore, the average loss of mass from 2000 to 2020 is calculated as −0.225 ± 0.040 m w.e.a−1. The greatest glacier mass loss is concentrated in the southeastern part of HMA, the mass loss in the north and west is less than that in the southeast, and the glacier mass is in a state of aggravated loss in recent years. Our study also offers a new method for estimating the large‐scale glacier mass balance. [ABSTRACT FROM AUTHOR]

Investigating the spatial and temporal changes in snow cover over mountain areas is significant for understanding the impact of regional climate variability. In this study, using cloud‐removed snow cover data, which are generated based on Moderate Resolution Imaging Spectroradiometer (MODIS) daily snow cover products, the spatiotemporal changes of snow cover fraction (SCF) and its relationship with temperature and precipitation changes from 2002 to 2020 were examined over Altai Mountains, China. The results demonstrate that the distribution and changes of SCF are highly spatiotemporally heterogeneous. Within a year, the maximum SCF occurs in January at 98.6%, and the minimum appears in July at 8.7%. The annual‐mean SCF shows an increasing trend at 0.09%·annum−1, owing to the significantly increasing SCF in the snow accumulation period at 0.5%·annum−1 and the decreasing SCF in the snow melting period at −0.2%·annum−1. The SCF distribution, as well as its interannual change, is greatly influenced by elevation. During the snow cover period, a positive linear correlation between SCF and elevation is found at 0.02%·m−1 (p <.01). The annual‐mean SCF decreases in the area below 1,200 m, whereas it increases in the area above 1,200 m. Accordingly, the elevation‐dependent SCF results in various SCF distributions on different slopes and watersheds. The SCF shows an apparent pattern in different aspects, with similar SCFs between the north and east aspects and between the west and south aspects but a difference between the northeast aspects and the westsouth aspects. The SCF is negatively correlated with air temperature (r = −0.74, p < 0.01) and positively correlated with precipitation (r = 0.74, p < 0.01). In addition, temperature shows a significant and larger correlation with SCF in both the snow accumulation and melting periods, indicating the major factor of temperature for the changes in SCF. [ABSTRACT FROM AUTHOR]

Epidemiological studies investigating the relationship between air temperature or heat and health, still, by and large, rely on either information from the nearest weather station or on coarse gridded temperature predictions, thereby ignoring small‐scale intra‐urban variations. Recent methodological advances show promise in achieving high spatiotemporal temperature predictions, thus improving the characterization of spatial variations in temperature and decreasing bias in health studies. Here, we applied a two‐stage approach using random forest to (a) impute missing moderate resolution imaging spectroradiometer (MODIS) land surface temperature at a 1 × 1 km resolution and (b) to use the gap‐filled MODIS data to explain spatiotemporal variation in the measured ground‐based air temperature data at a 100 × 100 m resolution across Switzerland using a range of predictor variables, including meteorological parameters, normalized difference vegetation index, impervious surface and altitude. Models presented here managed to capture temporal and spatial variations in air temperature in Switzerland from 2003 to 2018 at a fine spatial resolution of 100 × 100 m. Stage 1 models achieved an overall R2 of 0.98 and a root mean squared error (RMSE) of 1.49°C (independent validation), and the stage 2 model performed well for all years with R2 and RMSE ranging from 0.94 to 0.99 and 1.05 to 1.86°C, respectively. We were also able to capture the urban heat island effect and some typical weather phenomena caused by Switzerland's complex topography, like the foehn effect and inversion conditions. The resulting daily temperature surfaces for 2003–2018 will facilitate ongoing epidemiological research investigating the health effects of heat. [ABSTRACT FROM AUTHOR]

This study aimed to investigate urban forms susceptible to heightened heat intensities in the Tokyo Prefecture in Japan. Adopting Landsat 8 data at a pixel resolution of 100 m, local climate zones (LCZ) were identified. LCZs contain urban forms which are primarily defined by building compactness and height. Daytime spatial distribution of land surface temperatures (LST) was provided by MODIS 100 m resolution data from 2013 to 2021. Median LSTs for compact and super high‐rise, high‐rise, mid‐rise, and low‐rise LCZs were 34.4, 35.5, 37.3, and 38.1°C, respectively. Additionally, LSTs for open and super high‐rise, high‐rise, and mid‐rise LCZs were 37.4, 37.5, and 37.1°C, respectively. Therefore, this suggests lower‐rise and open LCZs are prone to increased urban heat intensities and higher‐rise and compact LCZs are an urban heat mitigation strategy. Open mid‐rise also offers heat reduction capabilities. Compact low‐rise and open mid‐rise spatial analysis also confirmed this trend with vegetation indices validating urban configuration as significantly influencing LSTs. Furthermore, due to LSTs constituting heat health risks, 11 municipalities comprised of predominantly compact low‐rise LCZs were identified as a priority for urban heat mitigation. Among these, Nakano, Nerima, and Suginami posed the greatest heat risks. [ABSTRACT FROM AUTHOR]

Global river systems are experiencing rapid changes in sediment transport under growing anthropogenic and climatic stresses. However, the response of sediment discharge to the coupled influence of anthropogenic and natural factors and the associated impacts on the fluvial geomorphology in the Yangtze and Mekong rivers are not comprehensively assessed. Here, we recalibrated a seamless retrieval algorithm of the total suspended sediment (TSS) concentrations using in situ data and concurrent satellite data sets to analyze spatiotemporal patterns of the TSS concentrations in the lower Yangtze and Mekong rivers. Combined with soil erosion rates estimated by the Revised Universal Soil Loss Equation for the past 20 years, we examined the contributions of different factors to TSS trends. The results show that TSS concentrations in the Yangtze River decreased from 0.47 g L−1 in 2000 to 0.23 g L−1 in 2018 due to the construction of the Three Gorges Dam (TGD), especially in the Jingjiang reach, with a declining magnitude of 0.3 g L−1 (∼56%) since the TGD began operating. The Mekong River experienced increasing TSS concentration trends upstream and decreasing trends downstream from 2000 to 2018, possibly attributed to increased upstream soil erosion and decreased downstream water discharge. Declining TSS concentrations in both rivers have driven varying degrees of river channel erosion over the past two decades. This study investigated long‐term changes in the TSS concentrations and soil erosion in the Yangtze and Mekong rivers, and the results provide baseline information for the sustainable development of river sediment delivery. Plain Language Summary: Growing anthropogenic and climatic stresses have driven rapid changes in sediment transport for river systems globally. However, poorly available studies about the response of sediment discharge to coupled human and natural forces over the Yangtze and Mekong rivers limit the assessments of their impacts on river channel morphology. In this study, we estimated the spatiotemporal patterns of total suspended sediment (TSS) concentration in river channels of these two rivers and examined the contributions of driving factors to its temporal trends for the past 20 years. Three Gorges Dams caused TSS decrease of over 50% from 2000 to 2018 in the Yangtze River. In the Mekong River, soil erosion drove TSS increase in the upstream, while downstream TSS concentration decreased under declining water discharge during 2000–2018. Declining TSS concentrations enhanced river channel erosion in the Yangtze and Mekong rivers. We anticipate that the data sets here could serve as important information to assess the trade‐offs of future dam strategies and sustainable developments of river systems under human and climatic impacts. Key Points: Developed a seamless total suspended sediment (TSS) algorithm to determine the spatiotemporal patterns of the TSS concentration in channels of the Yangtze and Mekong riversExamined the contributions of dams and soil erosion to the TSS trendsExplored the impacts of declining TSS concentrations on river width dynamics [ABSTRACT FROM AUTHOR]

Although the potential of river discharge to support ocean productivity and marine ecosystems is known, the specifics of this relationship are poorly understood in many regions of the world. Global estimates of river flow indicate that river discharge is decreasing due to the increasing fragmentation, extraction and regulation of rivers. This likely means that the contribution of river flow to coastal productivity and water quality is changing, potentially leading to fewer and smaller magnitude ocean fertilisation events. We developed a simple analysis method, based on Earth observation data, to investigate where coastal ocean chlorophyll‐a is most strongly influenced by river discharge. The per‐pixel spatiotemporal correlation technique (implemented using Python) correlates chlorophyll‐a concentration (a proxy for phytoplankton biomass and indicator of primary productivity) from MODIS ocean colour data with river discharge data. The method was tested globally on 11 different rivers discharging into coastal ocean regions. Our findings suggest some of the world's largest river systems, such as the Amazon River, have zones of elevated coastal chl‐a that extend hundreds to thousands of km from the river mouth. These findings suggest the influence of river discharge may have been underestimated in many coastal regions of the world. The method appears more effective for larger river systems discharging to ocean waters with less complex nutrient dynamics and weaker seasonal productivity patterns, most notably in temperate regions. Increasing our understanding of the specific areas influenced by river discharge, and the degree of influence over space and time, is an important step towards the improved river and coastal management. This method will increase the capacity of researchers to monitor how, when and where coastal waters are affected as river discharge continues to change into the future. [ABSTRACT FROM AUTHOR]

This study presents the results of permafrost mapping in Mongolia based on the TTOP (temperature‐on‐top‐of‐permafrost) approach, which were validated against in situ measurements at various locations. In situ measurements indicated that the mean annual ground temperature (MAGT) ranged from 0.6 to 2.2°C interannually, showing the greatest variability when furthest from 0°C. The differences between the modeled and measured MAGTs exceeded ±1°C in locations where permafrost was in a nonequilibrium state and was controlled predominantly by local factors. It was estimated that permafrost occupies one‐third of Mongolia. We divided the extent of the permafrost into five zones: continuous, discontinuous, sporadic, isolated, and seasonally frozen ground. In total, the permafrost zones cover ~462.8 × 103 km2, accounting for 29.3% of Mongolia. Of this total area, continuous permafrost accounted for 118.3 × 103 km2 (7.5%), discontinuous permafrost 127.7 × 103 km2 (8.1%), sporadic permafrost 112.4 × 103 km2 (7.1%), and isolated permafrost 104.4 × 103 km2 (6.6%). [ABSTRACT FROM AUTHOR]

Aim: Spatial patterns in resource supply drive variability in vegetation structure and function, yet quantification of this variability for tropical dry forests (TDFs) remains rudimentary. Several climate‐driven indices have been developed to classify and delineate TDFs globally, but there has not been a climo‐edaphic synthesis of these indices to assess and delineate the extent of TDFs. A statistical climo‐edaphic synthesis of these indices is therefore required. Location: Pantropical. Time period: Modern. Major taxa studied: Vascular plants. Methods: We assembled most known prior descriptions of TDFs into a single data layer and assessed statistically how the TDF biome, which we call tropical dry landscapes (TDLs) composed of forest and non‐forest vegetation, varied with respect to the normalized difference vegetation index (NDVI) sensed by MODIS (250 m pixel resolution). We examined how the NDVI varied with respect to mean annual temperature (MAT) and rainfall (MAR), precipitation regime, evapotranspiration and the physical, chemical and biological properties of TDL soils. Results: Overall, the NDVI varied widely across TDLs, and we were able to identify five principal NDVI categories. A regression tree model captured 90% of NDVI variation across TDLs, with 14 climate and soil metrics as predictors. The model was then pruned to use only the three strongest metrics. These included the Lang aridity index, total evapotranspiration (ET) and MAT, which aligned with identified NDVI thresholds and accounted for 70% of the variation in NDVI. We found that across a global TDL distribution, ET was the strongest positive predictor and MAT the strongest negative predictor of the NDVI. Main conclusions: The remote sensing‐based approach described here provides a comprehensive and quantitative biogeographical characterization of global TDL occurrence and the climatic and edaphic drivers of these landscapes. [ABSTRACT FROM AUTHOR]

Soil nitrogen (N) is an important driver of plant productivity and ecosystem functioning; consequently, it is critical to understand its spatial variability from local‐to‐global scales. Here, we provide a quantitative assessment of the three‐dimensional spatial distribution of soil N across the United States (CONUS) using a digital soil mapping approach. We used a random forest‐regression kriging algorithm to predict soil N concentrations and associated uncertainty across six soil depths (0–5, 5–15, 15–30, 30–60, 60–100, and 100–200 cm) at 5‐km spatial grids. Across CONUS, there is a strong spatial dependence of soil N, where soil N concentrations decrease but uncertainty increases with soil depth. Soil N was higher in Pacific Northwest, Northeast, and Great Lakes National Ecological Observatory Network (NEON) ecoclimatic domains. Model uncertainty was higher in Atlantic Neotropical, Southern Rockies/Colorado Plateau, and Southeast NEON domains. We also compared our soil N predictions with satellite‐derived gross primary production and forest biomass from the National Biomass and Carbon Dataset. Finally, we used uncertainty information to propose optimized locations for designing future soil surveys and found that the Atlantic Neotropical, Pacific Northwest, Pacific Southwest, and Appalachian/Cumberland Plateau NEON domains may require larger survey efforts. We highlight the need to increase knowledge of biophysical factors regulating soil processes at deeper depths to better characterize the three‐dimensional space of soils. Our results provide a national benchmark regarding the spatial variability and uncertainty of soil N and reveal areas in need of a better representation. [ABSTRACT FROM AUTHOR]