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The Medium-Resolution Spectral Imager-II (MERSI-II) instrument aboard China's Fengyun-3D satellite shares similarities with NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) sensor, enabling the retrieval of global aerosol optical depth (AOD). However, no officially released operational MERSI-II aerosol products currently exist over the ocean. This study focuses on adapting the MODIS dark target (DT) ocean algorithm to the MERSI-II sensor. A retrieval test is conducted on the 2019 MERSI-II data over the global ocean, and the retrieved AODs are validated against ground-based measurements from the automatic Aerosol Robotic Network (AERONET) and the shipborne Maritime Aerosol Network (MAN). The operational MODIS DT aerosol products are also used for comparison purposes. The results show that MERSI-II AOD granule retrievals are in good agreement with MODIS products, boasting high correlation coefficients (R) of up to 0.96 and consistent spatial distribution trends. Furthermore, the MERSI-II retrievals perform well in comparison to AERONET and MAN measurements, with high R-values (>0.86). However, the low-value retrievals from MERSI-II tend to be slightly overestimated compared to MODIS, despite both AODs displaying a positive bias. Notably, the monthly gridded AODs over the high latitudes of the northern and southern hemispheres suggest that MERSI-II exhibits greater stability in space and time, effectively reducing unrealistically high-value noise in the MODIS products. These results illustrate that the MERSI-II retrievals meet specific accuracy requirements by maintaining the algorithmic framework and most of the algorithmic assumptions, providing a crucial data supplement for aerosol studies and climate change. [ABSTRACT FROM AUTHOR]

Climate change has a substantial influence on the end of the growing season (EOS). The time-lag and cumulative effects are non-negligible phenomena when studying the interactions between climate and vegetation. However, quantification of the temporal effects of climatic factors on the EOS in the context of changing hydrothermal patterns remains scarce. Based on the Moderate Resolution Imaging Spectroradiometer (MODIS) fraction of absorbed photosynthetically active radiation (FPAR), this study first inverted the EOS of typical steppe vegetation in a semi-arid region of China and then quantified the time-lag and cumulative effects of monthly total precipitation (PRE) and monthly average temperature (TEM) on the EOS during 2003–2022. The results showed that a turning point occurred in 2011, when the EOS displayed an advancing trend until 2011, followed by a delayed trend. Accordingly, the climatic background has changed from warming and drying conditions during 2003–2011 to warming and wetting conditions during 2011–2022. The time-lag scales of PRE and TEM on the EOS decreased from 2- and 4-month scales during 2003–2011, respectively, to 1- and 2-month scales during 2011–2022, respectively. The time-lag degree of the hydrothermal factors on the EOS weakened with increased precipitation. The cumulative time scales of the EOS response to PRE and TEM were mainly concentrated within 1-month during different time periods, but the EOS was more sensitive to short-term precipitation. The time lag and cumulative partial correlation coefficient of PRE to EOS changed from mainly negative regulation during 2003–2011 (39.2% and 50.0%, respectively) to mainly positive regulation during 2011–2022 (67.8% and 93.7%, respectively). The time-lag and cumulative effects of TEM on the EOS were positive with the precipitation and temperature gradient under a warming and wetting climate, which indicated that increased precipitation was a prerequisite for temperature to induce a delayed EOS in the semi-arid study region. This study emphasizes the important role of precipitation in regulating the EOS response to hydrothermal factors in semi-arid regions. [ABSTRACT FROM AUTHOR]

Aerosols are microscopic liquid or solid particles that enter the atmosphere through natural and man-made processes. Time series trends and correlations of aerosol optical depth and cloud parameters over Addis Ababa have become the subject of scientific research because of the importance of clouds in controlling climate. In this study, we have examined the temporal variations (time series analysis) in aerosol particles over Addis Ababa and the impact of these variations on various optical properties of clouds using Moderate Resolution Imaging Spectroradiometer (MODIS) data from January 2003 to December 2018. The maximum aerosol optical depth (AOD) was found during the summer period, with minimum values in winter. During summer, the air temperature is high, which leads to abundant atmospheric water vapor, facilitating the hygroscopic growth of aerosols. We then analyzed the relationships between AOD and other cloud parameters, namely, water vapor (WV), cloud fraction (CF), cloud top temperature (CTT), and cloud top pressure (CTP), to provide a better understanding of aerosol–cloud interactions. The correlation between AOD and CF and WV was positive for Addis Ababa, while there was a negative correlation with CTT and CTP. [ABSTRACT FROM AUTHOR]

As the main component of terrestrial ecosystem, vegetation plays a very important role in regional ecosystem environmental change, global carbon cycle and climate regulation. The Lower Mekong region (LMR) is at the core of Southeast Asia, its vegetation changes will affect the regional ecosystem and climate. Five countries of LMR were selected as the study area, based on MODIS (Moderate-Resolution Imaging Spectroradiometer) NDVI(Normal Difference Vegetation Index) data from 2000 to 2022, using the Sen's slope estimator, Mann–Kendall trend test and geographic detector to study the spatial and temporal variation trends and driving forces of vegetation coverage. The results showed that:(1) From 2000 to 2022,the vegetation coverage in the LMR showed an overall fluctuating upward trend, the annual average Fractional Vegetation Cover(FVC) value was 0.70, mainly with high vegetation coverage and relatively high vegetation coverage. Vegetation distribution had obviously spatial heterogeneity, and the vegetation of Myanmar, Laos and Vietnam was significantly larger than Thailand and Cambodia.(2) The variation trend analysis of Sen_MK showed that the proportion of improved and degraded vegetation coverage areas in the LMR were 56.33% and 37.55% respectively. The vegetation improvement area was much larger than the vegetation degradation area during 2000–2022. According to the variation trend analysis of different countries, the vegetation coverage improvement area in Vietnam, Myanmar and Thailand were larger than the degraded , the overall vegetation coverage variation trend were good. However, in Laos and Cambodia, the degraded areas were larger than the improved, the overall variation trends of coverage were not good.(3) The results of geographic detector showed that the Land Use and Land Cover(LULC) had the greatest influence on vegetation coverage in the study area.The influencing factors of vegetation coverage were different in the LMR. For Vietnam, Thailand and Laos,elevation and slope factors were second only to LULC, for Myanmar and Cambodia, the influence of precipitation factor was second only to LULC. The results provide scientific data support for understanding the ecological environment status and future changes in the research area. [ABSTRACT FROM AUTHOR]

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Vegetation is one of the important components of ecosystems that usually changes seasonally. An accurate parameterization of vegetation cover dynamics by developing time series models can strengthen our understanding of vegetation change. This research aims to investigate and model the temporal changes of net primary production (NPP) and normalized difference vegetation index (NDVI) across bioclimatic regions of Iran, including the Khazari, Baluchi, semi-desert, steppe, semi-steppe, and arid forests. We used Moderate Resolution Imaging Spectroradiometer (MODIS) sensor products for NPP and NDVI time series (MOD17A2 and MOD13Q1, respectively). The SARIMA (Seasonal Autoregressive Integrated Moving Average) time series model is developed for NPP and NDVI time series. The investigation of autocorrelation functions (ACF) showed a strong seasonality in NPP and NDVI at the 12-month lag time. Comparing the lag times from 1 to 24 month for different regions shows that the NPP variable has a stronger seasonality. The evaluation of error criteria which showed NPP time series models based on RMSE, R2, MRE, and CE criteria was better, while based on the ME criteria, the models perform better for NDVI time series (for example, in Khazari region for NPP and NDVI time series, respectively, ME = 3.67, 0.05, RMSE = 0.12, 0.18, R2 = 0.87, 0.63, MRE = 0.02, 0.12, and CE = 0.84, 0.12). The selected models provided a short-term forecasting of the NPP and NDVI index for study regions at 24-month time, which is useful for the planning and management to reduce vegetation degradation and preserve ecosystem and biodiversity. [ABSTRACT FROM AUTHOR]

The generation characteristics of internal solitary waves (ISWs) in the northern Andaman Sea are studied using remote sensing data and numerical simulations. The dataset comprises 230 images taken by the Moderate Resolution Imaging Spectroradiometer (MODIS) from 2020 to 2023, which reveal two distinct propagation directions of ISWs: southeastward (type-SE) and southwestward (type-SW). Generation hotspots are identified at the southern sill of the Preparis South Channel for type-SE ISWs and over the eastern shelf break for type-SW ISWs. Here, ISWs are intermittently generated, resulting in wave-free spatial gaps. The observed gaps motivate the hypothesis that ISWs are phase-locked with semidiurnal tides, resulting in specific generation time windows around slack tides. This study also reveals that the generation of ISWs is closely related to tidal ranges, with larger tidal ranges leading to more frequent ISWs. Specifically, type-SE and type-SW ISWs are detected only when the tidal range exceeds 1.3 m and 1.4 m, respectively. Numerical simulations with the MIT general circulation model further show that both types of ISWs are generated by nonlinear steepening of internal tides. The influence of background currents on ISW generation is also examined, which supports the proposed generation time window hypothesis. These findings highlight spatial gaps are attributable to the synergistic effects of MODIS observation times and intermittent generation of ISWs. [ABSTRACT FROM AUTHOR]

Surface albedo is an important parameter in radiative-transfer simulations of the Earth's system as it is fundamental for correctly calculating the energy budget of the planet. The Moderate Resolution Imaging Spectroradiometer (MODIS) instruments on NASA's Terra and Aqua satellites continuously monitor daily and yearly changes in reflection at the planetary surface. The MODIS Surface Reflectance Black-Sky Albedo dataset (version 6.1 of MCD43D) provides detailed albedo maps for seven spectral bands in the visible and near-infrared range. These albedo maps allow us to classify different Lambertian surface types and their seasonal and yearly variability and change, albeit only into seven spectral bands. However, a complete set of albedo maps covering the entire wavelength range is required to simulate radiance spectra and correctly retrieve atmospheric and cloud properties from remote sensing observations of the Earth. We use a principal component analysis (PCA) regression algorithm to generate hyperspectral albedo maps of the Earth. By combining different datasets containing laboratory measurements of hyperspectral reflectance for various dry soils, vegetation surfaces, and mixtures of both, we reconstruct albedo maps across the entire wavelength range from 400 to 2500 nm. The PCA method is trained with a 10-year average of MODIS data for each day of the year. We obtain hyperspectral albedo maps with a spatial resolution of 0.05° in latitude and longitude, a spectral resolution of 10 nm, and a temporal resolution of 1 d (day). Using the hyperspectral albedo maps, we estimate the spectral profiles of different land surfaces, such as forests, deserts, cities, and icy surfaces, and study their seasonal variability. These albedo maps will enable us to refine calculations of the Earth's energy budget and its seasonal variability and improve climate simulations. [ABSTRACT FROM AUTHOR]

Remote sensing satellite data have been a crucial tool in understanding urban climates. The variety of sensors with different spatiotemporal characteristics and retrieval methodologies gave rise to a multitude of approaches when analyzing the surface urban heat island effect (SUHI). Although there are considerable advantages that arise from these different characteristics (spatiotemporal resolution, time of observation, etc.), it also means that there is a need for understanding the ability of sensors in capturing spatial and temporal SUHI patterns. For this, several land surface temperature products are compared for the cities of Madrid and Paris, retrieved from five sensors: the Spinning Enhanced Visible and InfraRed Imager onboard Meteosat Second Generation, the Advanced Very-High-Resolution Radiometer onboard Metop, the Moderate-resolution Imaging Spectroradiometer onboard both Aqua and Terra, and the Thermal Infrared Sensor onboard Landsat 8 and 9. These products span a wide range of LST algorithms, including split-window, single-channel, and temperature–emissivity separation methods. Results show that the diurnal amplitude of SUHI may not be well represented when considering daytime and nighttime polar orbiting platforms. Also, significant differences arise in SUHI intensity and spatial and temporal variability due to the different methods implemented for LST retrieval. [ABSTRACT FROM AUTHOR]

The magnitude of integrated water vapor (IWV) varies considerably in the spatial‐temporal domain, which demonstrates the significance of high‐spatiotemporal‐resolution IWV observations for atmospheric water vapor distribution monitoring, both locally and globally. Unlike previously published algorithms based on data fusion, an empirical retrieval algorithm is for the first time proposed to directly retrieve high‐temporal‐resolution IWV estimates from near‐infrared radiance observations of the non‐geostationary Ocean and Land Color Instrument (OLCI). The retrieval algorithm is developed based on an observed regression relationship between satellite‐based OLCI‐estimated transmittance and ground‐based Global Navigation Satellite System (GNSS)‐estimated IWV in the temporal domain. The results show that all newly retrieved IWV estimates have an overall good consistency with ground‐based IWV from additional GNSS‐sensed measurements, indicating the feasibility of the retrieval approach. The performance of the retrieval algorithm is acceptable and satisfactory when compared with that of IWV retrievals listed in previous studies. Plain Language Summary: Integrated water vapor (IWV) is the largest natural greenhouse component, which plays a crucially important role in weather, climate, and other related fields. Remote sensing of IWV from satellite‐based instruments provides a unique technique for monitoring atmospheric water vapor distribution at proper spatial and temporal resolutions in both local and global areas. However, non‐geostationary satellite‐retrieved IWV observations have much lower temporal resolutions compared to geostationary satellite‐sensed IWV measurements. The previously published improvements in the temporal resolution of non‐geostationary satellite‐retrieved IWV estimates are primarily performed based on data fusion approaches using reanalysis‐based high‐temporal‐resolution IWV data. We propose a feasible IWV retrieval algorithm for directly retrieving high‐temporal‐resolution IWV data from non‐geostationary Ocean and Land Color Instrument (OLCI)‐sensed near‐infrared radiance observations. For the first time, this study provides implications for the direct retrieval of high‐temporal‐resolution IWV estimates from non‐geostationary satellite measurements. The retrieval algorithm has significant potential to be applicable to other non‐geostationary OLCI‐like instruments, such as Medium Resolution Imaging Spectrometer (MERIS), Medium Resolution Spectral Imager (MERSI), and Moderate Resolution Imaging Spectroradiometer (MODIS). Key Points: For the first time, a relationship between Ocean and Land Color Instrument and Global Navigation Satellite System is observed and definedAn empirical retrieval model is proposed to directly derive high‐temporal‐resolution water vapor from Ocean and Land Color Instrument dataThe newly retrieved water vapor has satisfactory performance when compared with additional Global Navigation Satellite System measurements [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]

With a large footprint size, multiple scattering measurements of clouds from spaceborne lidar provide useful information about cloud physical properties, such as cloud optical depths and cloud droplet size, both during daytime and nighttime. A neural network algorithm, with a subset of cloud backscatter profiles of dualpolarization and dual-wavelength Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) lidar measurements during daytime as input variables and cloud physical properties derived from collocated Moderate Resolution Imaging Spectroradiometer (MODIS) multi-spectral measurements as output, is developed and evaluated with an independent subset of the collocated CALIPSO and MODIS measurements. The study suggests that with a receiver footprint size of 110 m, CALIPSO lidar measurements are sensitive to liquid-phase cloud optical depth variations from0 to 25. A larger footprint size, thus more multiple scattering, is required for lidar to have sensitivities to all liquid-phase clouds. The technique can be applied to all 17 years of CALIPSO daytime and nighttime measurements and, thus, provides useful information about global distributions of cloud physical properties both during day and night. [ABSTRACT FROM AUTHOR]

Despite its significance, net primary productivity is rarely used as an indicator of climate change. The purpose of this study was to assess the spatiotemporal dynamics of vegetation net primary productivity and its response to climate variability in the Welmel watershed, southeastern Ethiopia. The investigation utilized data from the Moderate Resolution Imaging Spectroradiometer, and the Climate Hazards Group Infrared Precipitation with Stations accessed through the Google Earth Engine cloud computing platform. To evaluate the trend and response of net primary productivity to climatic factors, the Mann-Kendall and Theil Sen's tests, the Pearson correlation coefficient, and stability analysis through the coefficient of variation of net primary productivity were utilized. The analysis of the temporal trend of net primary productivity revealed that the entire watershed, forest, wood, and cultivated land areas exhibited decreasing trends with net primary productivity values of -0.681, -4.361, -1.41, and − 3.25 g C m− 2/year, respectively. On the other hand, shrubs and grazing land displayed increasing trends of 1.15 and 2.04 g C m− 2/year, respectively. The wood and shrubland trends were statistically significant (p < 0.05). In the spatial analysis, an area of 64.6% showed a decreasing trend, whereas 33.78% displayed an increasing trend in net primary productivity values. The areas with stable and unstable variations in net primary productivity accounted for 26.47% and 8.02%, respectively. The net primary productivity of the land cover types and entire watershed were positively correlated with rainfall, however, only forests and woodlands were statistically significantly correlated (p < 0.05). The net primary productivity and land surface temperature were negatively correlated, except for forests and cultivated land, which were positively correlated. The results of this study provide essential information for managing ecosystems, supporting agricultural practices, and enhancing community resilience in a changing climate. [ABSTRACT FROM AUTHOR]

Our main aim was to estimate and compare the effects of six environmental variables (air temperature, soil temperature, rainfall, runoff, soil moisture, and the enhanced vegetation index) on excess cases of cutaneous leishmaniasis in Colombia. We used epidemiological data from the Colombian Public Health Surveillance System (January 2007 to December 2019). Environmental data were obtained from remote sensing sources including the National Oceanic and Atmospheric Administration, the Global Land Data Assimilation System (GLDAS), and the Moderate Resolution Imaging Spectroradiometer. Data on population were obtained from the TerriData dataset. We implemented a causal inference approach using a machine learning algorithm to estimate the causal association of the environmental variables on the monthly occurrence of excess cases of cutaneous leishmaniasis. The results showed that the largest causal association corresponded to soil moisture with a lag of 3 months, with an average increase of 8.0% (95% confidence interval [CI] 7.7–8.3%) in the occurrence of excess cases. The temperature-related variables (air temperature and soil temperature) had a positive causal effect on the excess cases of cutaneous leishmaniasis. It is noteworthy that rainfall did not have a statistically significant causal effect. This information could potentially help to monitor and control cutaneous leishmaniasis in Colombia, providing estimates of causal effects using remote sensor variables. [ABSTRACT FROM AUTHOR]

The Standardized Precipitation Evapotranspiration Index (SPEI) is a widely recognized and effective tool for monitoring meteorological droughts. However, existing SPEI datasets suffer from spatial discontinuity or coarse spatial resolution problems, which limits their applications at the local level for drought monitoring research. Therefore, we calculated the SPEI index at meteorological stations, combined with the Global Precipitation Measurement (GPM) Precipitation (Pre), Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature (LST), ERA5‐Land Shortwave Radiation (SR), Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM) datasets and Random Forest Regression (RFR) model, developed a high spatial resolution (1 km) SPEI (HSPEI) datasets with multiple time scales in mainland China from 2001 to 2022. Compared to other SPEI datasets, the HSPEI datasets have higher spatial resolution and can effectively identify the detailed characteristics of drought in mainland China from 2001 to 2022. Overall, the HSPEI datasets can be effectively applied to the research of different droughts in China from 2001 to 2022. [ABSTRACT FROM AUTHOR]

Phenology reflects the life cycle of vegetation, crucial for monitoring global vegetation diversity, ecosystem stability, and agricultural security. However, there is currently no dataset related to phenology. The 24 solar terms (24STs), based on the Sun's annual motion, reflect the changing seasons, temperature fluctuations, and phenological phenomena. They serve as a vital means to characterize vegetation phenology. This study generate a global Normalized Difference Vegetation Index (NDVI) product based on 24STs using Moderate Resolution Imaging Spectroradiometer (MODIS) on the Google Earth Engine (GEE). The 24STs NDVI dataset adopted the maximum value compositing (MVC) to process the NDVI values between two adjacent 24STs. The product has a spatial resolution of 250 m, covering the period from 2001 to 2022. Comparing with the MOD13Q1, good spatiotemporal consistency between the two datasets was observed, confirming the reliability of the 24STs product. However, the 24STs product holds distinct phenological meanings. This product introduces, for the first time, a vegetation index dataset based on the 24STs, enriching the vegetation index dataset and facilitating further research on phenology. [ABSTRACT FROM AUTHOR]

This research studies the characteristics of snow-covered area (SCA) from two vastly different sensors: optical (Moderate-Resolution Imaging Spectroradiometer, or MODIS, equipped on board the Terra satellite) and radar (Synthetic Aperture Radar (SAR) on-board Sentinel-1 satellites). The focus are the five mountain ranges of the Iberian Peninsula (Cantabrian System, Central System, Iberian Range, Pyrenees, and Sierra Nevada). The MODIS product was selected to identify SCA dynamics in these ranges using the Probability of Snow Cover Presence Index (PSCPI). In addition, we evaluate the potential advantage of the use of SAR remote sensing to complete optical SCA under cloudy conditions. For this purpose, we utilize the Copernicus High-Resolution Snow and Ice SAR Wet Snow (HRS&I SWS) product. The Pyrenees and the Sierra Nevada showed longer-lasting SCA duration and a higher PSCPI throughout the average year. Moreover, we demonstrate that the latitude gradient has a significant influence on the snowline elevation in the Iberian mountains (R2 ≥ 0.84). In the Iberian mountains, a general negative SCA trend is observed due to the recent climate change impacts, with a particularly pronounced decline in the winter months (December and January). Finally, in the Pyrenees, we found that wet snow detection has high potential for the spatial gap-filling of MODIS SCA in spring, contributing above 27% to the total SCA. Notably, the additional SCA provided in winter is also significant. Based on the results obtained in the Pyrenees, we can conclude that implementing techniques that combine SAR and optical satellite sensors for SCA detection may provide valuable additional SCA data for the other Iberian mountains, in which the radar product is not available. [ABSTRACT FROM AUTHOR]

This study explores machine learning for estimating soil moisture at multiple depths (0–5 cm, 0–10 cm, 0–20 cm, 0–50 cm, and 0–100 cm) across the coterminous United States. A framework is developed that integrates soil moisture from Soil Moisture Active Passive (SMAP), precipitation from the Global Precipitation Measurement (GPM), evapotranspiration from the Ecosystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS), vegetation data from the Moderate Resolution Imaging Spectroradiometer (MODIS), soil properties from gridded National Soil Survey Geographic (gNATSGO), and land cover information from the National Land Cover Database (NLCD). Five machine learning algorithms are evaluated including the feed-forward artificial neural network, random forest, extreme gradient boosting (XGBoost), Categorical Boosting, and Light Gradient Boosting Machine. The methods are tested by comparing to in situ soil moisture observations from several national and regional networks. XGBoost exhibits the best performance for estimating soil moisture, achieving higher correlation coefficients (ranging from 0.76 at 0–5 cm depth to 0.86 at 0–100 cm depth), lower root mean squared errors (from 0.024 cm3/cm3 at 0–100 cm depth to 0.039 cm3/cm3 at 0–5 cm depth), higher Nash–Sutcliffe Efficiencies (from 0.551 at 0–5 cm depth to 0.694 at 0–100 cm depth), and higher Kling–Gupta Efficiencies (0.511 at 0–5 cm depth to 0.696 at 0–100 cm depth). Additionally, XGBoost outperforms the SMAP Level 4 product in representing the time series of soil moisture for the networks. Key factors influencing the soil moisture estimation are elevation, clay content, aridity index, and antecedent soil moisture derived from SMAP. [ABSTRACT FROM AUTHOR]

Accurate land surface temperature (LST) retrieval depends on precise knowledge of the land surface emissivity (LSE). Neglecting or inaccurately estimating the emissivity introduces substantial errors and uncertainty in LST measurements. The emissivity, which varies across different surfaces and land uses, reflects material composition and surface roughness. Satellite data offer a robust means to determine LSE at large scales. This study utilises the Normalised Difference Vegetation Index Threshold Method (NDVITHM) to produce a novel emissivity dataset spanning the last 40 years, specifically tailored for the Fennoscandian region, including Norway, Sweden, and Finland. Leveraging the long and continuous data series from the Advanced Very High Resolution Radiometer (AVHRR) sensors aboard the NOAA and MetOp satellites, an emissivity dataset is generated for 1981–2022. This dataset incorporates snow-cover information, enabling the creation of annual emissivity time series that account for winter conditions. LSE time series were extracted for six 15 × 15 km study sites and compared against the Moderate Resolution Imaging Spectroradiometer (MODIS) MOD11A2 LSE product. The intercomparison reveals that, while both datasets generally align, significant seasonal differences exist. These disparities are attributable to differences in spectral response functions and temporal resolutions, as well as the method considering fixed values employed to calculate the emissivity. This study presents, for the first time, a 40-year time series of the emissivity for AVHRR channels 4 and 5 in Fennoscandia, highlighting the seasonal variability, land-cover influences, and wavelength-dependent emissivity differences. This dataset provides a valuable resource for future research on long-term land surface temperature and emissivity (LST&E) trends, as well as land-cover changes in the region, particularly with the use of Sentinel-3 data and upcoming missions such as EUMETSAT's MetOp Second Generation, scheduled for launch in 2025. [ABSTRACT FROM AUTHOR]

Sea ice monitoring is key to analyzing the Earth's climate system. Long-term sea ice extent (SIE) has been continuously monitored using various spaceborne passive microwave radiometers (PMRs) since November 1978. As the lifetime of a satellite is usually approximately 5 years, bias caused by differences in PMRs should be eliminated to obtain objective SIE trends. Most sea ice products have been analyzed for long-term trends with a bias adjustment based on the coarse resolution special sensor microwave imager (SSM/I) in operation for the longest period. However, since 2002, Japanese microwave radiometers of the Advanced Microwave Scanning Radiometer (AMSR) series, which have the highest spatial resolution in PMR, have been available. In this study, we developed standardization techniques for processing SIE including calibration of the brightness temperature (TB), tuning the sea ice concentration (SIC) algorithm, and adjusting the SIC threshold to retrieve a consistent SIE trend based on the AMSR for the Earth Observing System (AMSR-E, one of the AMSR that operated from May 2002 to October 2011). Analysis results showed that the root-mean-square error between AMSR-E SICs and those of moderate resolution imaging spectroradiometer (MODIS) was 15%. In this study, SIE was defined as the sum of the areas where the AMSR-E SIC was >15%. When retrieving SIE, we adjusted the SIC threshold for each PMR to be consistent with the SIE calculated based on the 15% SIC threshold for AMSR-E. We then calculated a time-series of the SIE trends over approximately 45 years using the adjusted SIE data. Therefore, we revealed the dramatic decrease in global sea ice extent since 1978. This technique enables retrieval of more accurate long-term sea ice trends for more than half a century in the future. [ABSTRACT FROM AUTHOR]

Changes in the ecological environment quality (EEQ) in the main inland Tarim River Basin in China substantially impact the regional development. Indeed, comprehensive ecological environment measures have been implemented in the Tarim River Basin since 2000. In this context, the main objective of the present study was to investigate the spatiotemporal evolution of the EEQ and monitor the effectiveness of ecological restoration measures in the Tarim River Basin over the 2000–2020 period using remote sensing data. First, a Remote Sensing Ecological Index (RSEI) was constructed based on the Moderate Resolution Imaging Spectroradiometer remote sensing data. Second, the spatial distributions and factors of the RSEI were analyzed by using Moran's Index and Geodetector. The results indicated that the overall RSEI values for the Tarim River Basin increased from 0.22 in 2000 to 0.25 in 2020. Moreover, the values for areas with poor EEQ decreased from 50.7% to 44.73%, while those with moderate EEQ increased from 11.45% to 16.91%. Therefore, the results demonstrated a slight overall improvement in the EEQ of the study area over the 2000–2020 period. On the other hand, the EEQ in the Tarim River Basin exhibited a significant spatial autocorrelation in the 2000–2020 period, with a relatively stable overall spatial distribution. Areas with high–high aggregation were distributed in the high-elevation mountainous areas in the western, northern, and southern parts of the study area. In contrast, areas with low–low aggregation were observed in the central and eastern low-elevation desert areas. The EEQ in the Tarim River Basin was driven by the interactions of several factors, including the normalized difference vegetation index, land surface moisture, land surface temperature, normalized differential build-up and bare soil index, and elevation. In particular, heat was the main driving factor that severely impacted the EEQ in the study area. Indeed, increase in the heat values could directly enhance meltwater runoff from glaciers in the basin, thereby resulting in short-term improvement in the basin EEQ. Furthermore, rapid urbanization from 2015 to 2020 resulted in a decrease in the average RSEI value of the Tarim River Basin by 0.1 over this period, consequently, the EEQ level decreased slightly. Briefly, the EEQ in the Tarim River Basin showed an overall increasing trend from 2000 to 2020, further demonstrating the effectiveness of a series of implemented ecological restoration measures in the Tarim River Basin over this period. [ABSTRACT FROM AUTHOR]

Aerosol–cloud–precipitation interaction (ACPI) plays a pivotal role in the global and regional water cycle and the earth's energy budget; however, it remains highly uncertain due to the different underlying physical mechanisms. Therefore, this study aims to systematically analyze the effects of aerosols and meteorological factors on ACPI in the co-located precipitating clouds (PCs) and non-precipitating clouds (NPCs) in winter and summer seasons by employing the long-term (2001–2021) retrievals from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Tropical Rainfall Measuring Mission (TRMM) coupled with the National Center for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalysis-II datasets over the Indo-Gangetic Plain (IGP). The results exhibit a decadal increase in aerosol optical depth (AOD) over Lahore (5.2 %), Delhi (9 %), Kanpur (10.7 %), and Gandhi College (22.7 %) and a decrease over Karachi (- 1.9 %) and Jaipur (- 0.5 %). The most stable meteorology with high values of lower-tropospheric stability (LTS) is found in both seasons over Karachi. In the summer season, the occurrence frequency of clouds is high (74 %) over Gandhi College, 60 % of which are PCs. Conversely, the lowest number of PCs is found over Karachi. Similarly, in the winter season, the frequency of cloud occurrence is low over Karachi and high over Lahore and Gandhi College. The analysis of cloud top pressure (CTP) and cloud optical thickness (COT) indicates high values of cloud fraction (CF) for thick and high-level clouds over all study areas except Karachi. The microphysical properties such as cloud effective radius (CER) and cloud droplet number concentration (CDNC) bear high values (CER ≳ 15 µm and CDNC ≳ 50 cm -3) for both NPCs and PCs in summer. The AOD–CER correlation is good (weak) for PCs (NPCs) in winter. Similarly, the sensitivity value of the first indirect effect (FIE) is high (ranging from 0.2 ± 0.13 to 0.3 ± 0.01 in winter and from 0.19 ± 0.03 to 0.32 ± 0.05 in summer) for PCs and low for NPCs. The sensitivity value for the second indirect effect (SIE) is relatively higher (such as 0.6 ± 0.14 in winter and 0.4 ± 0.04 in summer) than FIE. Sensitivity values of the aerosol–cloud interaction (ACI) are low (i.e., - 0.06 ± 0.09) for PCs in summer. Furthermore, the precipitation rate (PR) exhibits high values in the summer season, primarily due to the significant contribution from optically thick clouds with lower CDNC (≲ 50 cm -3) and larger CER and the intermediate contribution from optically thick clouds with higher CDNC (≳ 50 cm -3). [ABSTRACT FROM AUTHOR]

Moderate-Resolution Imaging Spectroradiometer (MODIS) Nadir Bidirectional Reflectance Distribution Function (BRDF)-Adjusted Reflectance (NBAR) products are being increasingly used for the quantitative remote sensing of vegetation. However, the assumption underlying the MODIS NBAR product's inversion model—that surface anisotropy remains unchanged over the 16-day retrieval period—may be unreliable, especially since the canopy structure of vegetation undergoes stark changes at the start of season (SOS) and the end of season (EOS). Therefore, to investigate the MODIS NBAR product's temporal effect on the quantitative remote sensing of crops at different stages of the growing seasons, this study selected typical phenological parameters, namely SOS, EOS, and the intervening stable growth of season (SGOS). The PROBA-V bioGEOphysical product Version 3 (GEOV3) Fractional Vegetation Cover (FVC) served as verification data, and the Pearson correlation coefficient (PCC) was used to compare and analyze the retrieval accuracy of FVC derived from the MODIS NBAR product and MODIS Surface Reflectance product. The Anisotropic Flat Index (AFX) was further employed to explore the influence of vegetation type and mixed pixel distribution characteristics on the BRDF shape under different stages of the growing seasons and different FVC; that was then combined with an NDVI spatial distribution map to assess the feasibility of using the reflectance of other characteristic directions besides NBAR for FVC correction. The results revealed the following: (1) Generally, at the SOSs and EOSs, the differences in PCCs before vs. after the NBAR correction mainly ranged from 0 to 0.1. This implies that the accuracy of FVC derived from MODIS NBAR is lower than that derived from MODIS Surface Reflectance. Conversely, during the SGOSs, the differences in PCCs before vs. after the NBAR correction ranged between –0.2 and 0, suggesting the accuracy of FVC derived from MODIS NBAR surpasses that derived from MODIS Surface Reflectance. (2) As vegetation phenology shifts, the ensuing differences in NDVI patterning and AFX can offer auxiliary information for enhanced vegetation classification and interpretation of mixed pixel distribution characteristics, which, when combined with NDVI at characteristic directional reflectance, could enable the accurate retrieval of FVC. Our results provide data support for the BRDF correction timescale effect of various stages of the growing seasons, highlighting the potential importance of considering how they differentially influence the temporal effect of NBAR corrections prior to monitoring vegetation when using the MODIS NBAR product. [ABSTRACT FROM AUTHOR]

Accurate monitoring of irrigation areas is of great significance to ensure national food security and rational utilization of water resources. The low resolution of the Moderate Resolution Imaging Spectroradiometer and Landsat data makes the monitoring accuracy insufficient for actual demand. Thus, this paper proposes a method of extracting the irrigated area in arid regions based on Sentinel-2 long time-series imagery to realize the accurate monitoring of irrigation areas. In this paper, a typical irrigation area in the arid region of Northwest China–Xinjiang Santun River is selected as the study area. The long time series Sentinel-2 remote sensing data are used to classify the land use of the irrigation area. The random forest, CART decision tree, and support vector machine algorithms are used to combine the field collection of the typical irrigation point and non-irrigated sample points. The irrigation area is extracted by calculating the Normalized Vegetation Index (NDVI), Soil-Adjusted Vegetation Index (SAVI), and Optimized Soil-Adjusted Vegetation Index (OSAVI) time series data as the classification parameters. The results show that (1) the irrigated area of the dryland irrigation region can be effectively extracted using the SAVI time-series data through an object-oriented approach combined with the random forest algorithm. (2) The extracted irrigated areas were 44,417, 42,915, 43,411, 48,908, and 47,900 hm2 from 2019 to 2023, and the overall accuracies of the confusion matrix validation were 94.34%, 90.22%, 92.03%, 93.23%, and 94.63%, with kappa coefficients of 0.9011, 0.8887, 0.8967, 0.9009, and 0.9265, respectively. The errors of the irrigated area compared with the statistical data were all within 5%, which demonstrated the effectiveness of the method in extracting the irrigated area. This method provides a reference for extracting irrigated areas in arid zones. [ABSTRACT FROM AUTHOR]

Advancements in remote sensing technology have significantly contributed to the improvement of models for estimating terrestrial gross primary productivity (GPP). However, discrepancies in the spatial distribution and interannual variability within GPP datasets pose challenges to a comprehensive understanding of the terrestrial carbon cycle. In contrast to previous models that rely on remote sensing and environmental variables, we developed an ensemble model based on the random forest method (denoted ERF model). This model used GPP outputs from established models: Eddy Covariance Light Use Efficiency (EC-LUE), GPP estimate model based on Kernel Normalized Difference Vegetation Index (GPP-kNDVI), GPP estimate model based on Near-Infrared Reflectance of Vegetation (GPP-NIRv), Revised-EC-LUE, Vegetation Photosynthesis Model (VPM), and GPP estimate model based on the Moderate Resolution Imaging Spectroradiometer (MODIS). These outputs were used as inputs to estimate GPP. The ERF model demonstrated superior performance, explaining 85.1 % of the monthly GPP variations at 170 sites and surpassing the performance of selected GPP estimate models (67.7 %–77.5 %) and an independent random forest model using remote sensing and environmental variables (81.5 %). Additionally, the ERF model improved accuracy across each month and with various subranges, mitigating the issue of "high-value underestimation and low-value overestimation" in GPP estimates. Over the period from 2001 to 2022, the global GPP estimated by the ERF model was 132.7 PgC yr -1 , with an increasing trend of 0.42 PgC yr -2 , which is comparable to or slightly better than the accuracy of other mainstream GPP datasets in terms of validation results of GPP observations independent of FLUXNET (i.e., ChinaFLUX). Importantly, for a growing number of GPP datasets, our study provides a way to integrate these GPP datasets, which may lead to a more reliable estimate of global GPP. [ABSTRACT FROM AUTHOR]

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Savannas are widespread biomes with highly valued ecosystem services. To successfully manage savannas in the future, it is critical to better understand the long-term dynamics of their productivity and phenology. However, accurate large-scale gross primary productivity (GPP) estimation remains challenging because of the high spatial and seasonal variations in savanna GPP. China's savanna ecosystems constitute only a small part of the world's savanna ecosystems and are ecologically fragile. However, studies on GPP and phenological changes, while closely related to climate change, remain scarce. Therefore, we simulated savanna ecosystem GPP via a satellite-based vegetation photosynthesis model (VPM) with fine-resolution harmonized Landsat and Sentinel-2 (HLS) imagery and derived savanna phenophases from phenocam images. From 2015 to 2018, we compared the GPP from HLS VPM (GPPHLS-VPM) simulations and that from Moderate-Resolution Imaging Spectroradiometer (MODIS) VPM simulations (GPPMODIS-VPM) with GPP estimates from an eddy covariance (EC) flux tower (GPPEC) in Yuanjiang, China. Moreover, the consistency of the savanna ecosystem GPP was validated for a conventional MODIS product (MOD17A2). This study clearly revealed the potential of the HLS VPM for estimating savanna GPP. Compared with the MODIS VPM, the HLS VPM yielded more accurate GPP estimates with lower root-mean-square errors (RMSEs) and slopes closer to 1:1. Specifically, the annual RMSE values for the HLS VPM were 1.54 (2015), 2.65 (2016), 2.64 (2017), and 1.80 (2018), whereas those for the MODIS VPM were 3.04, 3.10, 2.62, and 2.49, respectively. The HLS VPM slopes were 1.12, 1.80, 1.65, and 1.27, indicating better agreement with the EC data than the MODIS VPM slopes of 2.04, 2.51, 2.14, and 1.54, respectively. Moreover, HLS VPM suitably indicated GPP dynamics during all phenophases, especially during the autumn green-down period. As the first study that simulates GPP involving HLS VPM and compares satellite-based and EC flux observations of the GPP in Chinese savanna ecosystems, our study enables better exploration of the Chinese savanna ecosystem GPP during different phenophases and more effective savanna management and conservation worldwide. [ABSTRACT FROM AUTHOR]

Extreme meteorological events hit our planet with increasing frequency, resulting in an ever-increasing number of natural disasters. Flash floods generated by intense and violent rains are among the most dangerous natural disasters that compromise crops and cause serious damage to infrastructure and human lives. In the case of such a kind of disastrous events, timely and accurate information about the location and extent of the affected areas can be crucial to better plan and implement recovery and containment interventions. Satellite systems may efficiently provide such information at different spatial/temporal resolutions. Several authors have developed satellite techniques to detect and map inundated areas using both Synthetic Aperture Radar (SAR) and a new generation of high-resolution optical data but with some accuracy limits, mostly due to the use of fixed thresholds to discriminate between the inundated and unaffected areas. In this paper, the RST-FLOOD fully automatic technique, which does not suffer from the aforementioned limitation, has been exported for the first time to the mid–high-spatial resolution (20 m) optical data provided by the Copernicus Sentinel-2 Multi-Spectral Instrument (MSI). The technique was originally designed for and successfully applied to Advanced Very High Resolution Radiometer (AVHRR), Moderate Resolution Imaging Spectroradiometer (MODIS), and Visible Infrared Imaging Radiometer Suite (VIIRS) satellite data at a mid–low spatial resolution (from 1000 to 375 m). The processing chain was implemented in a completely automatic mode within the Google Earth Engine (GEE) platform to study the recent strong flood event that occurred in May 2023 in Emilia Romagna (Italy). The outgoing results were compared with those obtained through the implementation of an existing independent optical-based technique and the products provided by the official Copernicus Emergency Management Service (CEMS), which is responsible for releasing information during crisis events. The comparisons carried out show that RST-FLOOD is a simple implementation technique able to retrieve more sensitive and effective information than the other optical-based methodology analyzed here and with an accuracy better than the one offered by the CEMS products with a significantly reduced delivery time. [ABSTRACT FROM AUTHOR]

Clouds are a crucial regulator in the Earth's energy budget through their radiative properties, both at the top of the atmosphere and at the surface; hence, determining key factors like their vertical extent is of essential interest. While the cloud top height is commonly retrieved by satellites, the cloud base height is difficult to estimate from satellite remote sensing data. Here, we present a novel method called ORABase (Ordinal Regression Auto-encoding of cloud Base), leveraging spatially resolved cloud properties from the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument to retrieve the cloud base height over marine areas. A machine learning model is built with two components to facilitate the cloud base height retrieval: the first component is an auto-encoder designed to learn a representation of the data cubes of cloud properties and to reduce their dimensionality. The second component is developed for predicting the cloud base using ground-based ceilometer observations from the lower-dimensional encodings generated by the aforementioned auto-encoder. The method is then evaluated based on a collection of collocated surface ceilometer observations and retrievals from the CALIOP satellite lidar. The statistical model performs similarly on both datasets and performs notably well on the test set of ceilometer cloud bases, where it exhibits accurate predictions, particularly for lower cloud bases, and a narrow distribution of the absolute error, namely 379 and 328 m for the mean absolute error and the standard deviation of the absolute error, respectively. Furthermore, cloud base height predictions are generated for an entire year over the ocean, and global mean aggregates are also presented, providing insights into global cloud base height distributions and offering a valuable dataset for extensive studies requiring global cloud base height retrievals. The global cloud base height dataset and the presented models constituting ORABase are available from Zenodo (Lenhardt et al., 2024). [ABSTRACT FROM AUTHOR]

This comprehensive study analyzed aerosol products from six low-Earth orbit (LEO) and geostationary Earth orbit (GEO) sensors. LEO sensors like the MODerate resolution Imaging Spectroradiometer (MODIS) and VIsible InfraRed Suite (VIIRS) provide one to two daily global measurements, while GEO sensors (Advanced Himawari Imager: AHI, Advanced Baseline Imager: ABI) offer high-frequency data (∼ 10 min) over specific regions. The combination of LEO and GEO capabilities offers expanded coverage of the global aerosol system if aerosol retrievals are applied consistently across all sensors and packaged in an easy-to-use product. The Dark Target aerosol retrieval algorithm was applied to the six sensors, and the resulting Level 2 aerosol optical depth (AOD) products were gridded and merged into a Level 3 quarter-degree latitude–longitude grid with a 30 min temporal resolution, providing the necessary consistency and packaging. Validation of this packaged Level 3 AOD product against Aerosol Robotics NETwork (AERONET) measurements across global locations showcased the merged product's robustness with a correlation coefficient of 0.83, revealing a global mean bias of approximately ±0.05 , with 65.5 % of retrievals falling within an expected uncertainty range, underlining the reliability of the dataset. The new gridded Level 3 dataset significantly improved daily global coverage to nearly 45 %, overcoming the limitations of individual sensors, which typically range from 12 % to 25 %. Furthermore, this merged dataset approximates the diurnal cycle of AOD observed by AERONET, thus offering insights into diurnal signatures retrieved elsewhere. The resulting dataset's high spatiotemporal resolution and improved global coverage, especially in regions covered by GEO sensors (Americas and Asia), make it a valuable tool for diverse applications. Tracking aerosol transport from phenomena like wildfires and dust storms is gaining precision, enabling enhanced air quality forecasting and hindcasting. Additionally, the study positions the merged dataset as a significant asset for evaluating and intercomparing regional or global model simulations, which was previously unattainable in such a gridded format. The dataset and fusion framework layout in this study have the potential to include data from recently (future) launched other GEO (FCI, AMI) and LEO (PACE, VIIRS-JPSS) sensors. [ABSTRACT FROM AUTHOR]

Since 2008, the Yellow Sea has experienced the world's largest-scale marine disaster, the green tide, marked by the rapid proliferation and accumulation of large floating algae. Leveraging advanced artificial intelligence (AI) models, namely AlgaeNet and GANet, this study comprehensively extracted and analyzed green tide occurrences using optical Moderate Resolution Imaging Spectroradiometer (MODIS) images and microwave Sentinel-1 synthetic aperture radar (SAR) images. However, due to cloud and rain interference and the varying observation frequencies of the two types of satellites, the daily green tide coverage time series throughout the entire life cycle often contain large gaps and missing frames, resulting in discontinuity and limiting their use. Therefore, this study presents a continuous and seamless weekly average green tide coverage dataset with a resolution of 500 m, by integrating highly precise daily optical and SAR data for each week during the green tide breakout. The uncertainty assessment shows that this weekly product conforms to the life pattern of green tide outbreaks and exhibits parabolic-curve-like characteristics, with a low uncertainty (R2=0.89 and RMSE=275 km2). This weekly dataset offers reliable long-term data spanning 15 years, facilitating research in forecasting, climate change analysis, numerical simulation, and disaster prevention planning in the Yellow Sea. The dataset is accessible through the Oceanographic Data Center, Chinese Academy of Sciences (CASODC), along with comprehensive reuse instructions provided at 10.12157/IOCAS.20240410.002 (Gao et al., 2024). [ABSTRACT FROM AUTHOR]

The present work demonstrates a methodology for acquiring high-resolution soil moisture information, namely at 1 km at a daily time step, utilizing data from a sparse network of soil moisture sensors and remotely sensed Land Surface Temperature (LST). Building on previous research and highlighting the strong correlation between surface soil moisture and LST, as a result of the thermal inertia, we first evaluated the correlation between Moderate Resolution Imaging Spectroradiometer (MODIS) LST and ground-based soil moisture information from soil moisture sensors installed in a pilot area in Northeastern Greece. Second, a regression formula was developed for three out of six soil moisture sensors, keeping the three remaining monitoring stations serving as a validation set. Furthermore, regression coefficients were interpolated at 1 km and the regression equations were applied for the entire study area, thus acquiring soil moisture information at a spatial resolution of 1 km at the daily time step. The verification process indicated a reasonable accuracy, with a mean absolute error (MAE) of,0.02 m³/m³. The results were considerably better than using a simple interpolation or downscaled daily 1-km SMAP soil moisture. [ABSTRACT FROM AUTHOR]

Solar-induced chlorophyll fluorescence (SIF) serves as a proxy indicator for vegetation photosynthesis and can directly reflect the growth status of vegetation. Using SIF for drought monitoring offers greater potential compared to traditional vegetation indices. This study aims to develop and validate a novel approach, the improved Temperature Fluorescence Dryness Index (iTFDI), for more accurate drought monitoring in Henan Province, China. However, the low spatial resolution, data dispersion, and short temporal sequence of SIF data hinder its direct application in drought studies. To overcome these challenges, this study constructs a random forest SIF downscaling model based on the TROPOspheric Monitoring Instrument SIF (TROPOSIF) and the Moderate-resolution Imaging Spectroradiometer (MODIS) data. Assuming an unchanging spatial scale relationship, an improved SIF (iSIF) product with a temporal resolution of 500 m over the period March to September, 2010–2022 was obtained for Henan Province. Subsequently, using the retrieved iSIF and the surface temperature difference data, the iTFDI was proposed, based on the assumption that under the same vegetation cover conditions, lower soil moisture and a greater diurnal temperature range of the surface indicate more severe drought. Results showed that: (1) The accuracy of the TROPOSIF downscaling model achieved coefficient of determination (R2), mean absolute error (MAE), and root mean square error (RMSE) values of 0.847, 0.073 mW m−2 nm−1 sr−1, and 0.096 mW m−2 nm−1 sr−1, respectively. (2) The 2022 iTFDI drought monitoring results indicated favorable soil moisture in Henan Province during March, April, July, and August, while extensive droughts occurred in May, June, and September, accounting for 70.27%, 71.49%, and 43.61%, respectively. The monitored results were consistent with the regional water conditions measured at ground stations. (3) The correlation between the Standardized Precipitation Evapotranspiration Index (SPEI) and iTFDI at five stations was significantly stronger than the correlation with the Temperature Vegetation Dryness Index (TVDI), with the values −0.631, −0.565, −0.612, −0.653, and −0.453, respectively. (4) The annual Sen's slope and Mann–Kendall significance test revealed a significant decreasing trend in drought severity in the southern and western regions of Henan Province (6.74% of the total area), while the eastern region showed a significant increasing trend (4.69% of the total area). These results demonstrate that the iTFDI offers a significant advantage over traditional indices, providing a more accurate reflection of regional drought conditions. This enhances the ability to identify drought trends and supports the development of targeted drought management strategies. In conclusion, the iTFDI constructed using the downscaled iSIF data and surface temperature differential data shows great potential for drought monitoring. [ABSTRACT FROM AUTHOR]

In data-scarce regions, prudent planning and precise decision-making for sustainable development, especially in agriculture, remain challenging due to the lack of correct information. Remotely sensed satellite images provide a powerful source for assessing land use and land cover (LULC) classes and crop identification. Applying remote sensing (RS) in conjunction with the Geographical Information System (GIS) and modern tools/algorithms of artificial intelligence (AI) and deep learning has been proven effective for strategic planning and integrated resource management. The study was conducted in the canal command area of the Lower Chenab Canal system in Punjab, Pakistan. Crop features/classes were assessed using the Normalized Difference Vegetation Index (NDVI) algorithm. The Moderate Resolution Imaging Spectroradiometer (MODIS) 250 m and Landsat 5 TM (thematic mapper) images were deployed for NDVI time-series analysis with an unsupervised classification technique to obtain LULC classes that helped to discern cropping pattern, crop rotation, and the area of specific crops, which were then used as key inputs for agricultural mechanization planning and resource management. The accuracy of the LULC map was 78%, as assessed by the error matrix approach. Limitations of high-resolution RS data availability and the accuracy of the results are the concerns observed in this study that could be managed by the availability of good quality local sources and advanced processing techniques, that would make it more useful and applicable for regional agriculture and environmental management. [ABSTRACT FROM AUTHOR]

Droughts are among the most costly hazards in Tay Nguyen (known as Vietnamese Central Highlands), causing significant threats to agriculture and vegetation ecosystems. This study investigated the spatial and temporal dynamics of vegetation-based drought in the Tay Nguyen Provinces of Dak Lak and Dak Nong, using a long-term series of Moderate Resolution Imaging Spectroradiometer (MODIS) Vegetation Condition Index (VCI) from 2000 to 2023. The results exhibited a high positive correlation (R=0.73) between VCI and soil moisture-based drought index in drought-detected areas. Monthly analysis revealed severe drought events during the dry months, notably in 2005, 2010, 2013, 2016, and 2019. In contrast, wetter conditions were primarily observed during 2017-2018 and 2022-2023. Despite temporal variability of drought, larger trends of decreasing and increasing vegetation-based drought were detected during the dry season. These trends remained a relatively stable during the rainy season. Among vegetation types, shrubland exhibited the lowest VCI trends. This research offers valuable insights for stakeholders and policymakers to develop targeted strategies for sustainable land management and regional drought resilience. [ABSTRACT FROM AUTHOR]

The terrestrial ecosystem stores a huge amount of carbon in biomass and acts as a major carbon sink. Gross primary productivity (GPP) measures the carbon assimilation rate in terrestrial ecosystems. Accurate quantification and spatiotemporal analysis of GPP have become the essential indicators of various management, policy developments, and restoration activities in recent decades with the installation of new ground stations, development of robust models, and use of Earth Observation satellite data. The model-estimated and satellite data-derived GPP values greatly differ from ground observations due to model structure and approach, input driving data, coefficients and parameters, and various assumptions. Consequently, considerable ambiguity prevails among datasets and their benchmarking. Moreover, the productivity of ecosystems is regulated by physiological traits coupled with the local environmental conditions. This review provides an overview of the environmental and anthropogenic variables that regulate productivity and pose challenges in GPP estimation and evaluation of the available GPP products. It also evaluates the various available GPP datasets/ products and estimation methods/ models and compares the ecosystem productivity in broad natural and human-modified ecosystems in South Asia. Moreover, this study includes a case study on evaluating five globally available GPP products with variable spatiotemporal resolutions, such as the Moderate Resolution Imaging Spectroradiometer (MODIS), the Vegetation Photosynthesis Model (VPM), the Global Land Surface Satellite (GLASS), Global OCO-2-based SIF product (GOSIF), and the Penman-Monteith-Leuning (PML) in three major land cover type of South Asia (forest, cropland, and grassland) comparing with eddy covariance (EC) flux tower data. Results showed a better performance of GOSIF and GLASS data than other GPP products. The study aims to provide an overview of the prevailing environmental conditions and carbon sequestration in different ecosystems and assist in developing sustainable landscape management strategies to reduce carbon emissions and mitigate climate change impact. Article highlights: • A critical review of the methods employed for GPP estimation and their limitations. • Impact of various management practices, extreme events, and climate change on GPP. • Comparison among available GPP datasets. [ABSTRACT FROM AUTHOR]

Long-term monitoring studies on the transition of different land cover units to barren areas are crucial to gain a better understanding of the potential challenges and threats that land surface ecosystems face. This study utilized the Moderate Resolution Imaging Spectroradiometer (MODIS) land cover products (MCD12C1) to conduct geospatial analysis based on the maximum extent (MaxE) concept, to assess the spatiotemporal changes in barren areas from 2001 to 2022, at global and continental scales. The MaxE area includes all the pixels across the entire period of observations where the barren land cover class was at least once present. The relative expansion or reduction of the barren areas can be directly assessed with MaxE, as any annual change observed in the barren distribution is comparable over the entire dataset. The global barren areas without any land change (UA) during this period were equivalent to 12.8% (18,875,284 km2) of the global land surface area. Interannual land cover changes to barren areas occurred in an additional area of 3,438,959 km2 (2.3% of the global area). Globally, barren areas show a gradual reduction from 2001 (91.1% of MaxE) to 2012 (86.8%), followed by annual fluctuations until 2022 (88.1%). These areas were mainly interchanging between open shrublands and grasslands. A relatively high transition between barren areas and permanent snow and ice is found in Europe and North America. The results show a 3.7% decrease in global barren areas from 2001 to 2022. Areas that are predominantly not barren account for 30.6% of the transitional areas (TAs), meaning that these areas experienced short-term or very recent transitions from other land cover classes to barren. Emerging barren areas hotspots were mainly found in the Mangystau region (Kazakhstan), Tibetan plateau, northern Greenland, and the Atlas Mountains (Morocco, Tunisia). [ABSTRACT FROM AUTHOR]

Accurate river ice mapping is crucial for predicting and managing floods caused by ice jams and for the safe operation of hydropower and water resource facilities. Although satellite multispectral images are widely used for river ice mapping, atmospheric contamination limits their effectiveness. This study developed river ice mapping models for the Han River in South Korea using atmospherically uncorrected Landsat-8 Operational Land Imager (OLI) multispectral reflectance data, addressing atmospheric influences with a Random Forest (RF) classification approach. The RF-based river ice mapping models were developed by implementing various combinations of input variables, incorporating the Landsat-8 multispectral top-of-atmosphere (TOA) reflectance, normalized difference indices for snow, water, and bare ice, and atmospheric factors such as aerosol optical depth, water vapor content, and ozone concentration from the Moderate Resolution Imaging Spectroradiometer observations, as well as surface elevation from the GLO-30 digital elevation model. The RF model developed using all variables achieved excellent performance in the classification of snow-covered ice, snow-free ice, and water, with an overall accuracy and kappa coefficient exceeding 98.4% and 0.98 for test samples, and higher than 83.7% and 0.75 when compared against reference river ice maps generated by manually interpreting the Landsat-8 images under various atmospheric conditions. The RF-based river ice mapping model for the atmospherically corrected Landsat-8 multispectral surface reflectance was also developed, but it showed very low performance under atmospheric conditions heavily contaminated by aerosol and water vapor. Aerosol optical depth and water vapor content were identified as the most important variables. This study demonstrates that multispectral reflectance data, despite atmospheric contamination, can be effectively used for river ice monitoring by applying machine learning with atmospheric auxiliary data to mitigate atmospheric effects. [ABSTRACT FROM AUTHOR]

This study investigates the impacts of summer monsoon activity on aerosols over the Indian region. We analyze the variability of aerosols during active and break monsoon phases, as well as strong and weak monsoon years, using data from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO). Our findings show a clear distinction in aerosol distribution between active and break phases. During active phases, the Aerosol Optical Depth (AOD) and aerosol extinction are lower across the Indian region, while break phases are associated with higher AOD and extinction. Furthermore, we observed a significant increase in AOD over Central India during strong monsoon years, compared to weak monsoon years. Utilizing the vertical feature mask (VFM) data from CALIPSO, we identified polluted dust and dusty marine aerosols as the dominant types during both active/break phases and strong/weak monsoon years. Notably, the contributions of these pollutants are significantly higher during break phases compared to during active phases. Our analysis also reveals a shift in the origin of these aerosol masses. During active phases, the majority originate from the Arabian Sea; in contrast, break phases are associated with a higher contribution from the African region. [ABSTRACT FROM AUTHOR]

Drought, characterized by frequent occurrences, an extended duration, and a wide range of destruction, has become one of the natural disasters posing a significant threat to both socioeconomic progress and agricultural livelihoods. Large-scale geographical environments often exhibit obvious spatial heterogeneity, leading to significant spatial differences in drought's development and outcomes. However, traditional drought monitoring models have not taken into account the impact of regional spatial heterogeneity on drought, resulting in evaluation results that do not match the actual situation. In response to the above-mentioned issues, this study proposes the establishment of ecological–geographic zoning to adapt to the spatially stratified heterogeneous characteristics of large-scale drought monitoring. First, based on the principles of ecological and geographical zoning, an appropriate index system was selected to carry out ecological and geographical zoning for Yunnan Province. Second, based on the zoning results and using data from NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) and the Tropical Rainfall Measuring Mission (TRMM) 3B43, the vegetation condition index (VCI), the temperature condition index (TCI), the precipitation condition index (TRCI), and three topographic factors including the digital elevation model (DEM), slope (SLOPE), and aspect (ASPECT) were selected as model parameters. Multiple linear regression models were then used to establish integrated drought monitoring frameworks at different eco–geographical zoning scales. Finally, the standardized precipitation evapotranspiration index (SPEI) was used to evaluate the monitoring effects of the model, and the spatiotemporal variation patterns and characteristics of winter and spring droughts in Yunnan Province from 2008–2019 were further analyzed. The results show that (1) compared to the traditional non-zonal models, the drought monitoring model constructed based on ecological–geographic zoning has a higher correlation and greater accuracy with the SPEI and (2) Yunnan Province experiences periodic and seasonal drought patterns, with spring being the peak period of drought occurrence and moderate drought and light drought being the main types of drought in Yunnan Province. Therefore, we believe that ecological–geographic zoning can better adapt to geographical spatial heterogeneity characteristics, and the zonal drought monitoring model constructed can more effectively identify the actual occurrence of drought in large regions. This research finding can provide reference for the formulation of drought response policies in large-scale regions. [ABSTRACT FROM AUTHOR]

During the first two decades of the twenty‐first century, we analyze the expansion of urban land cover, urban heat island (UHI), and urban pollution island (UPI) in the Houston Metropolitan Area (HMA) using land cover classifications derived from Landsat and land/aerosol products from NASA's Moderate Resolution Imaging Spectroradiometer. Our approach involves both direct utilization and fusion with in situ observations for a comprehensive characterization. We also examined how social vulnerability within the HMA changed during the study period and whether the synergy of UHI, UPI, and social vulnerability enhances environmental inequalities. We found that urban land cover within the HMA increased by 1,345.09 km2 and is accompanied by a 171.92 (73.93) % expansion of the daytime (nighttime) UHI. While the UPI experienced an overall reduction in particulate pollution, the magnitude of change is smaller compared to the surroundings. Further, the UPI showed localized enhancement in particulate pollution caused by increases in vehicular traffic. Our analysis found that the social vulnerability of the HMA urban regions increased during the study period. Overall, we found that the urban growth during the first two decades of the twenty‐first century resulted in a synergy of UHI, UPI, and social vulnerability, causing an increase in environmental inequalities within the HMA. Plain Language Summary: We used a combination of satellite and ground‐based observations to study how urban area expansion in Houston, Texas affects urban heat, pollution, and social vulnerability. We found that in the past two decades, Houston has added to its urban land cover by almost twice the area of New York City. This resulted in warmer conditions during the daytime which persisted during the night. Although there was an overall decrease in particulate pollution, our research identified pockets of heightened traffic activity that offset these improvements. We also found social vulnerability increased during the last two decades. We find the combined effect of increased heat stress, pollution and vulnerability leads to increased environmental inequalities. Key Points: The rapid urbanization of Houston expands the urban heat and pollution islands, raising exposure to high temperatures and poor air qualityCommunities with highest social vulnerability are most impacted by the adverse effects of urban heat and pollution islandsThe Clean Air Act's efficacy is locally diminished by Houston's urban pollution island [ABSTRACT FROM AUTHOR]

Accurate evapotranspiration (ET) estimation is crucial for sustainable water management in the diverse and water-scarce Mediterranean region. This study compares three prominent models (Simulator of Terrestrial Ecohydrological Processes and Systems (STEPS), Soil-Canopy-Observation of Photosynthesis and Energy fluxes (SCOPE), and Two-Source Energy Balance (TSEB)) with established global datasets (Moderate Resolution Imaging Spectroradiometer 8-day global terrestrial product (MOD16A2), Global Land Evaporation Amsterdam Model (GLEAM), and TerraClimate) at multiple spatial and temporal scales and validates model outcomes with eddy covariance based ground measurements. Insufficient ground-based observations limit comprehensive model validation in the eastern Mediterranean part (Turkey and Balkans). The results reveal significant discrepancies among models and datasets, highlighting the challenges of capturing ET variability in this complex region. Differences are attributed to variations in ecosystem type, energy balance calculations, and water availability constraints. Ground validation shows that STEPS performs well in some French and Italian forests and crops sites but struggles with seasonal ET patterns in some locations. SCOPE mostly overestimates ET due to detailed radiation flux calculations and lacks accurate water limitation representation. TSEB faces challenges in capturing ET variations across different ecosystems at a coarser 10 km resolution. No single model and global dataset accurately represent ET across the entire region. Model performance varies by region and ecosystem. As GLEAM and TSEB excel in semi-arid Savannahs, STEPS and SCOPE are better in grasslands, croplands, and forests in few locations (5 out of 18 sites) which indicates these models need calibration for other locations and ecosystem types. Thus, a region-specific model calibration and validation, sensitive to extremely humid and arid conditions can improve ET estimation across the diverse Mediterranean region. [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]

Forest fires pose significant environmental and socioeconomic threats, particularly in regions such as Central India, where forest ecosystems are vital for biodiversity and local livelihoods. Understanding forest fire dynamics and identifying fire risk zones are crucial for effective mitigation. The current study explores the spatiotemporal dynamics of forest fires in the Khandwa and North Betul forest divisions in the Central Indian region over 22 years using Mann–Kendall and Sen's slope tests on MODIS (Moderate Resolution Imaging Spectroradiometer) fire point data. We found a nonsignificant increase in forest fires in both divisions. Khandwa showed a nonsignificant slope rise of more than three events per year, while North Betul revealed an increase of around one event per year. The lack of statistical significance suggests that upward trends of forest fire events may result from random fluctuations rather than consistent patterns. Spatial autocorrelation analysis revealed significant clustering of fire incidents in both regions. Khandwa confirmed moderate clustering (Moran's I = 0.043), whereas North Betul showed robust clustering (Moran's I = 0.096). Kernel density estimation further identified high-risk clusters in both divisions, necessitating zonal-wise targeted fire management strategies. Fire risk zonation was developed using the analytic hierarchy process (AHP), combining 10 environmental and socioeconomic factors. The AHP model, validated using MODIS fire data, showed reliable accuracy. The results revealed many of both divisions in the high- to very high-risk categories. Approximately, 45% of the area of the Khandwa and nearly 50% of the area of North Betul fall under high to very high fire risk zones. Khandwa's high-risk areas mainly lie in the northern and southeastern parts, while North Betul lies in the northwestern and north-eastern regions. The identified fire-prone areas indicate the pressing need for local or region-specific fire prevention and mitigation strategies. Thus, the findings of this study provide valuable insights into forest fire risk management and contribute to more focused research and methodological developments. [ABSTRACT FROM AUTHOR]

This research proposes an optimized method for identifying high-quality vegetation areas, with a focus on forest ecosystems, using an improved Vegetation Health Index (VHI). The study introduces the Land Cover Vegetation Health Index (LCVHI), which integrates the Vegetation Condition Index (VCI) and the Temperature Condition Index (TCI) with land cover data. Utilizing MODIS (Moderate Resolution Imaging Spectroradiometer) satellite imagery and Google Earth Engine (GEE), the study assesses the impact of land cover changes on vegetation health, with particular attention to forested areas. The application of the LCVHI demonstrates that forests exhibit a VHI approximately 25% higher than that of croplands, and wetlands show an 18% higher index compared to grasslands. Analysis of data from 2012 to 2022 in Hubei Province, China, reveals an overall upward trend in vegetation health, highlighting the effectiveness of environmental protection and forest management measures. Different land cover types, including forests, wetlands, and grasslands, significantly impact vegetation health, with forests and wetlands contributing most positively. These findings provide important scientific evidence for regional and global ecological management strategies, supporting the development of forest conservation policies and sustainable land use practices. The research results offer valuable insights into the effective management of regional ecological dynamics. [ABSTRACT FROM AUTHOR]

A nearly continuous daily, global Environmental Science Data Record of NASA Standard MODerate-resolution Imaging Spectroradiometer (MODIS) snow-cover extent (SCE) data products has been available since 2000. When the MODIS record ends, the 'moderate resolution' SCE record will continue with NASA Standard Visible Infrared Imaging Radiometer Suite (VIIRS) SCE data products. The objective of this work is to evaluate and quantify the continuity between the MODIS and VIIRS SCE data products to enable the merging of the data product records. A climate data record (CDR) could be developed when 30 years of daily global moderate-resolution SCE become available if the continuity of the MODIS and VIIRS records can be established. Here, we focus on the daily cloud-gap-filled MODIS and VIIRS SCE NASA standard data products, MOD10A1F and VNP10A1F, respectively, for a case study in the Great Basin of the western United States during a period of sensor overlap. Using the methodologies described herein (daily percent of snow cover, duration of snow cover, average monthly number of days (Ndays) of snow cover, and trends in Ndays of snow cover, we show that the snow maps display excellent agreement. For example, the average monthly number of days of snow cover in the Great Basin calculated using MOD10A1F and VNP10A1F agrees with a Pearson's correlation coefficient of r = 0.99 for our 11-year study period from WY 2013 to 2023. Additionally, the SCE derived from each data product agrees very well with meteorological station data, with a Pearson's correlation coefficient of r = 0.91 and r = 0.92 for MOD10A1F and VNP10A1F, respectively. Our results support the eventual creation of a CDR. [ABSTRACT FROM AUTHOR]

Accurately evaluating drought and its effects on the natural environment is difficult in regions with limited climate monitoring stations, particularly in the hyper-arid region of the Sultanate of Oman. Rising global temperatures and increasing incidences of insufficient precipitation have turned drought into a major natural disaster worldwide. In Oman, drought constitutes a major threat to food security. In this study, drought indices (DIs), such as temperature condition index (TCI), vegetation condition index (VCI), and vegetation health index (VHI), which integrate data on drought streamflow, were applied using moderate resolution imaging spectroradiometer (MODIS) data and the Google Earth Engine (GEE) platform to monitor agricultural drought and assess the drought risks using the drought hazard index (DHI) during the period of 2001–2023. This approach allowed us to explore the spatial and temporal complexities of drought patterns in the Najd region. As a result, the detailed analysis of the TCI values exhibited temporal variations over the study period, with notable minimum values observed in specific years (2001, 2005, 2009, 2010, 2014, 2015, 2016, 2017, 2019, 2020, and 2021), and there was a discernible trend of increasing temperatures from 2014 to 2023 compared to earlier years. According to the VCI index, several years, including 2001, 2003, 2006, 2008, 2009, 2013, 2015, 2016, 2017, 2018, 2020, 2021, 2022, and 2023, were characterized by mild drought conditions. Except for 2005 and 2007, all studied years were classified as moderate drought years based on the VHI index. The Pearson correlation coefficient analysis (PCA) was utilized to observe the correlation between DIs, and a high positive correlation between VHI and VCI (0.829, p < 0.01) was found. Based on DHI index spatial analysis, the northern regions of the study area faced the most severe drought hazards, with severity gradually diminishing towards the south and east, and approximately 44% of the total area fell under moderate drought risk, while the remaining 56% was classified as facing very severe drought risk. This study emphasizes the importance of continued monitoring, proactive measures, and effective adaptation strategies to address the heightened risk of drought and its impacts on local ecosystems and communities. [ABSTRACT FROM AUTHOR]

Phenology and photosynthetic capacity both regulate carbon uptake by vegetation. Previous research investigating the impact of phenology on vegetation productivity has focused predominantly on the start and end of growing seasons (SOS and EOS), leaving the influence of peak phenology metrics—particularly in typical climatic transition zones—relatively unexplored. Using a 24-year (2000–2023) enhanced vegetation index (EVI) dataset from the Moderate Resolution Imaging Spectroradiometer (MODIS), we extracted and examined the spatiotemporal variation for peak of season (POS) and peak growth (defined as EVImax) of forest vegetation in the Funiu Mountain region, China. In addition to quantifying the factors influencing the peak phenology metrics, the relationship between vegetation productivity and peak phenological metrics (POS and EVImax) was investigated. Our findings reveal that POS and EVImax showed advancement and increase, respectively, negatively and positively correlated with vegetation productivity. This suggested that variations in EVImax and peak phenology both increase vegetation productivity. Our analysis also showed that EVImax was heavily impacted by precipitation, whereas SOS had the greatest effect on POS variation. Our findings highlighted the significance of considering climate variables as well as biological rhythms when examining the global carbon cycle and phenological shifts in response to climate change. [ABSTRACT FROM AUTHOR]

The global, near-real-time monitoring of volcano thermal activity has become feasible through thermal infrared sensors on various satellite platforms, which enable accurate estimations of volcanic emissions. Specifically, these sensors facilitate reliable estimation of Volcanic Radiative Power (VRP), representing the heat radiated during volcanic activity. A critical factor influencing VRP estimates is the identification of hotspots in satellite imagery, typically based on intensity. Different satellite sensors employ unique algorithms due to their distinct characteristics. Integrating data from multiple satellite sources, each with different spatial and spectral resolutions, offers a more comprehensive analysis than using individual data sources alone. We introduce an innovative Remote Sensing Data Fusion (RSDF) algorithm, developed within a Cloud Computing environment that provides scalable, on-demand computing resources and services via the internet, to monitor VRP locally using data from various multispectral satellite sensors: the polar-orbiting Moderate Resolution Imaging Spectroradiometer (MODIS), the Sea and Land Surface Temperature Radiometer (SLSTR), and the Visible Infrared Imaging Radiometer Suite (VIIRS), along with the geostationary Spinning Enhanced Visible and InfraRed Imager (SEVIRI). We describe and demonstrate the operation of this algorithm through the analysis of recent eruptive activities at the Etna and Stromboli volcanoes. The RSDF algorithm, leveraging both spatial and intensity features, demonstrates heightened sensitivity in detecting high-temperature volcanic features, thereby improving VRP monitoring compared to conventional pre-processed products available online. The overall accuracy increased significantly, with the omission rate dropping from 75.5% to 3.7% and the false detection rate decreasing from 11.0% to 4.3%. The proposed multi-sensor approach markedly enhances the ability to monitor and analyze volcanic activity. [ABSTRACT FROM AUTHOR]

Turbidity serves as a crucial indicator of coastal water health and productivity. Twenty years of remote sensing data (2003–2022) from the Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) satellite were used to analyze the spatial and temporal variations in turbidity, as measured by total suspended matter (TSM), in the Berau Coastal Shelf (BCS), East Kalimantan, Indonesia. The BCS encompasses the estuary of the Berau River and is an integral part of the Coral Triangle, renowned for its rich marine and coastal habitats, including coral reefs, mangroves, and seagrasses. The aim of this research is to comprehend the seasonal and interannual patterns of turbidity and their associations with met-ocean parameters, such as wind, rainfall, and climate variations like the El Niño–Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD). The research findings indicate that the seasonal spatial pattern of turbidity is strongly influenced by monsoon winds, while its temporal patterns are closely related to river discharge and rainfall. The ENSO and IOD climate cycles exert an influence on the interannual turbidity variations, with turbidity values decreasing during La Niña and negative IOD events and conversely increasing during El Niño and positive IOD events. Furthermore, the elevated turbidity during negative IOD and La Niña coincides with rising temperatures, potentially acting as a compound stressor on marine habitats. These findings significantly enhance our understanding of turbidity dynamics in the BCS, thereby supporting the management of marine and coastal ecosystems in the face of changing climatic and environmental conditions. [ABSTRACT FROM AUTHOR]