Search

Advanced Very High-Resolution Radiometer (AVHRR) satellite observations have provided the longest global daily records from 1980s, but the remaining temporal inconsistency in vegetation index datasets has hindered reliable assessment of vegetation greenness trends. To tackle this, we generated novel global long-term Normalized Difference Vegetation Index (NDVI) and Near-Infrared Reflectance of vegetation (NIRv) datasets derived from AVHRR and Moderate Resolution Imaging Spectroradiometer (MODIS). We addressed residual temporal inconsistency through three-step post processing including cross-sensor calibration among AVHRR sensors, orbital drifting correction for AVHRR sensors, and machine learning-based harmonization between AVHRR and MODIS. After applying each processing step, we confirmed the enhanced temporal consistency in terms of detrended anomaly, trend and interannual variability of NDVI and NIRv at calibration sites. Our refined NDVI and NIRv datasets showed a persistent global greening trend over the last four decades (NDVI: 0.0008 yr−1; NIRv: 0.0003 yr−1), contrasting with those without the three processing steps that showed rapid greening trends before 2000 (NDVI: 0.0017 yr−1; NIRv: 0.0008 yr−1) and weakened greening trends after 2000 (NDVI: 0.0004 yr−1; NIRv: 0.0001 yr−1). These findings highlight the importance of minimizing temporal inconsistency in long-term vegetation index datasets, which can support more reliable trend analysis in global vegetation response to climate changes.

UNLABELLED: On 15 January 2022, Hunga volcano erupted, creating an extensive and high-reaching umbrella cloud over the open ocean, hindering traditional isopach mapping and fallout volume estimation. In MODIS satellite imagery, ocean surface water was discolored around Hunga following the eruption, which we attribute to ash fallout from the umbrella cloud. By relating intensity of ocean discoloration to fall deposit thicknesses in the Kingdom of Tonga, we develop a methodology for estimating airfall volume over the open ocean. Ash thickness measurements from 41 locations are used to fit a linear relationship between ash thickness and ocean reflectance. This produces a minimum airfall volume estimate of 1.8-0.4+0.3 km3. The whole eruption produced > 6.3 km3 of uncompacted pyroclastic material on the seafloor and a caldera volume change of 6 km3 DRE. Our fall estimates are consistent with the interpretation that most of the seafloor deposits were emplaced by gravity currents rather than fall deposits. Our proposed method does not account for the largest grain sizes, so is thus a minimum estimate. However, this new ocean-discoloration method provides an airfall volume estimate consistent with other independent measures of the plume and is thus effective for rapidly estimating fallout volumes in future volcanic eruptions over oceans. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00445-024-01744-6.

This study aimed to evaluate the dynamics driving seasonal and interannual surface chlorophyll-a (chl-a) variability in the Karimata Strait (KS). The analysis shows that high chl-a concentrations were observed in the KS during December – February (northwest monsoon season) extending northwestward along the eastern coast of Peninsular Malaysia and northeastward along the northern coast of Borneo Island. This high chl-a concentration contrasts with the low chl-a concentration observed along the southern coast of Sumatra and Java during the same season. A substantial upwelling signal was identified in the central-eastern section of the South China Sea and along the northern coast of Java and the eastern coast of Sumatra from December to February. The positive wind stress curl forced downwelling in the southern section of the KS and the northern half of the Java Sea off the coast of Kalimantan. The elevated chl-a concentration in the KS during the northwest monsoon season could not be attributed to wind dynamics alone. The increase in surface chl-a concentration during the northwest monsoon season was associated with an increase in allochthonous nutrients from river discharges caused by increased precipitation over land. On an interannual timescale, a high chl-a concentration was observed during the southeast monsoon season when the La Niña event occurred in the tropical Pacific. The warm SST, associated with favourable downwelling winds, was observed over the entire the KS. It is suggested that anomalous chl-a that bloomed during the La Niña event was associated with anomalously high precipitation over land that transported nutrients to KS through river discharge. [ABSTRACT FROM AUTHOR]

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]

The start of the growing season (SOS) in grasslands is a critical factor that significantly affects grassland dynamics, production and quality. In the context of grassland fodder production, the exact detection of the start of the growing season allows farmers and land managers to precisely plan harvest schedules for hay production or silage making. However, the NDVI time-series based detection of the SOS for grassland poses challenges due to the gradual onset of greenness and the wide variety of environmental conditions. These challenges emphasize the need for novel approaches to improve satellite-based SOS detection accuracy. This study establishes a new methodology for identifying the SOS specifically tailored to grassland regions in Austria. To accomplish this, a synergistic approach combining remote sensing (MODIS NDVI at 250 m) and weather data analysis was adopted. Results indicate that the integrated approach outperforms alternative methods in estimating SOS. The study demonstrated a more robust and accurate estimates of SOS that were generally consistent with the phenological observations of a reference species (Forsythia suspensa) and in agreement with the observed SOS dates based on visual interpretation of 10 m resolution Sentinel-2 images. For future studies, the integration of additional validation techniques is crucial. [ABSTRACT FROM AUTHOR]

The significance of land surface temperature (LST) and near-surface air temperature (TAIR) extends to various applications, including the exploration of urban heat islands. Understanding urban heat islands is crucial for comprehending the intricate interactions among urbanization, climate dynamics, and human well-being. However, many aspects of these topics remain understudied. In this study, we conducted a comprehensive analysis of LST and TAIR, covering day and night and spanning all four seasons of a full year. We used global datasets and applied non-spatial and spatial analysis techniques in the Amman-Zarqa urban region, a typical arid to semiarid environment. The study had three primary objectives: (1) Assess how different human settlement types influence the variations in LST and TAIR across space and time. (2) Examine the spatial and temporal attributes of the relationships between TAIR and LST. (3) Synthesize insights regarding the spatial and temporal characteristics of urban heat islands in arid to semiarid environments. The findings unveiled that urban centers consistently exhibit the lowest daytime LST and maximum and minimum TAIR, across all seasons when compared to other human settlement types. Nighttime LST displayed more variable patterns. Urban centers act as surface urban cool islands during the day and canopy layer urban cool islands both day and night throughout the seasons. The presence of surface urban heat or cool islands at night is barely noticeable. Daytime and nighttime LST play a significant role in explaining the variability in maximum and minimum TAIR across all seasons, with the relationships exhibiting variations ranging from positive to non-significant to negative, influenced by location and seasonal changes. During the daytime, LST consistently exceeds TAIR across all seasons, whereas this relationship displays greater variability at night. The findings of this study hold significant implications for sustainable urban planning and efforts to combat the effects of urban heat islands. [ABSTRACT FROM AUTHOR]

Context: Characterizing landscape ecological complexity and change requires integrated description of spatial and temporal landscape organization and dynamics, as suggested by the shifting mosaic concept. Remotely sensed land surface phenology allows the detection of even small differences among landscape patches and through time, allowing for the analysis of landscapes as shifting mosaics. Objectives: We sought to quantify aspects of the complex landscape behaviors that are implied by spatiotemporal variation in land surface phenology. We adapted an information-theoretic (IT) framework from ecosystem ecology to capture landscape-level spatiotemporal complexity and organization and map these properties across large areas. Methods: Phenology data were derived from remotely sensed, pixel-level time series of a vegetation greenness index, across a large portion of North America. We summarized multi-year, multi-pixel dynamics in transition matrices, calculated IT metrics from the matrices, and used matrix projection to quantify disequilibrium dynamics and long-term trajectories of the metrics. Results: Mapping the IT metrics and their disequilibria revealed gradients in the spatiotemporal complexity and organization of multi-year land surface phenology dynamics at continental to local scales. These gradients suggest influences of biophysical and biogeographic setting, ecological development and disturbances, land use, and other drivers of landscape ecological dynamics. The spatiotemporal IT metrics were influenced by both year-to-year dynamics and spatial landscape heterogeneity, but correlations with spatial and temporal complexity measures varied among the IT metrics. Landscapes showing the strongest disequilibrium dynamics were mostly in the western part of the continent and appeared to be associated with large-scale disturbances including severe fire, forest pathogens, climate variability, and land use change—important subjects for further study. Conclusions: This approach reveals novel features of the shifting landscape mosaic, with implications for understanding landscape resilience and sustainability. Resulting spatial data products describing long-term landscape dynamics have potential applications in broad-scale ecological modeling, monitoring, assessment, and prediction. [ABSTRACT FROM AUTHOR]

Estimates of precipitation, evaporation, river runoff, potential evaporation, and components of basin water reserves were obtained for the territories of Crimea and/or several its river basins. The period of 2014–2021 was characterized by higher aridity than 2001–2013, as can be seen from a decrease in precipitation and river runoff and an increase in the evaporation depth. The balance of the channel flow tends to decrease from the upper parts of the basin to its lower parts. The only exception is the Belbek River basin, where runoff losses are concentrated in the middle part of the drainage area. The analysis of changes of the measured and climatic runoff over the two periods showed an increase in runoff losses not associated with evaporation in many mountain and piedmont areas, which is likely due to a decrease in groundwater inflow and an increase in water intake. It is possible that in the lowland parts of the catchment areas of the Alma, Chernaya, Belbek, and Kacha, as well as the upper reaches of the Salgir, the river runoff increased due to groundwater extraction and the further release of wastewater into the rivers. The decrease in runoff in the Salgir basin by 0–40 mm can be partly explained by the closure of the North Crimean Canal in 2014. The decrease in groundwater reserves in Crimea over 2003–2020 was estimated at about 150 mm. [ABSTRACT FROM AUTHOR]

Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature (LST) products are essential data sources for global and regional climate change research. Currently, several versions of the MODIS LST product have been released, yet the performance differences and uncertainties they introduce in land surface studies remain insufficiently addressed. To bridge this gap, this study focuses on four distinct versions of the LST product: MxD11A1 Collection 5 (C5), Collection 6 (C6), Collection 6.1 (C6.1), and MxD21A1 Collection 6.1 (MxD21). The spatial resolution of all product generations is 1 km, and the temporal resolution is 0.5 days. This study provides a comprehensive analysis of the errors arising from different generations of these products in various land surface process studies. The error assessment includes cross-comparisons between product versions and evaluations of the absolute errors generated. Absolute errors in evaluation data were collected from 13 surface sites within the Heihe Watershed Allied Telemetry Experimental Research (HiWATER) project during the period 2013–2018. Cross-validation results show that the largest difference between C5 and C6.1 occurs over bare land, with an RMSE of approximately 1.45 K, while there is no significant change between C6 and C6.1. MOD21 shows considerable variation compared to C6.1 at night across different land cover types, with RMSE over cropland exceeding 2 K. The temperature difference between MOD21 and C6.1 is more pronounced at night (2.01 K) than during the day (0.30 K). Validation results based on temperature indicate that C5 has greater uncertainty compared to C6, especially over bare land, where errors are 2.06 K and 1.06 K, respectively. Furthermore, MxD21 demonstrates significant day–night performance discrepancies, with an average bias of 0.10 K at night, while daytime errors over bare land can reach 2 K, potentially influenced by atmospheric conditions. Based on the research in this paper, it is possible to clarify the performance of different versions of MODIS products, reflecting the appropriateness of their past applications; on the other hand, it is recommended to prioritize the use of the MxD11A1 C6 and C6.1 products for monitoring and applications in bare soil areas to ensure higher accuracy. Furthermore, for day and night monitoring, it may be beneficial to alternate between the MxD11A1 and MxD21A1 products to fully leverage their respective advantages and enhance overall monitoring effectiveness. [ABSTRACT FROM AUTHOR]

Understanding uncertainty in drought characteristics is vital in water resource management. To demonstrate an effective presentation of the uncertainty in agricultural drought derived from the evapotranspiration deficit indices (ETDIs), this study simulated ETDIs using the general ETDI formula with 1000 parameter combinations at different locations in the Ruvu River basin, Tanzania. Then, principal component analysis was used to reduce ETDI simulations to proxies of simulations. Thirdly, the evaluation of Kendall tau correlations between the proxies of simulations and ETDI simulations produced behavioural simulations, which were used to calculate the 95% prediction uncertainty (95PPU) of ETDIs and median ETDI. Finally, the 95PPU of ETDIs and median ETDI were characterized into drought durations. The findings show that the 95PPU bands of ETDIs and drought durations were very narrow but enclosed their median values. Therefore, the 95PPU and median from this approach comprehensively present drought characteristics derived from several ETDIs. [ABSTRACT FROM AUTHOR]

Satellite remote sensing is the best way to derive sea surface skin temperature (SSTskin) in the Arctic. However, as surface temperature retrieval algorithms in the infrared (IR) part of the electromagnetic spectrum are designed to compensate for atmospheric effects mainly due to water vapor, MODIS SSTskin retrievals have larger uncertainties at high latitudes where the atmosphere is very dry and cold, which is an extreme in the distribution of global conditions. MODIS R2019 SSTskin fields are currently derived using latitudinally and monthly dependent algorithm coefficients, including an additional band above 60°N to better represent the effects of Arctic atmospheres. However, the R2019 processing of MODIS SSTskin still has some unrevealed error characteristics. This study uses 21 years (2002–2022) of collocated, simultaneous satellite brightness temperature (BT) data from Aqua MODIS and in situ buoy-measured subsurface temperature data from iQuam for validation. Unlike elsewhere over the oceans, the 11 μm and 12 μm BT differences are poorly related to the column water vapor at high latitudes, resulting in poor atmospheric water vapor correction. Anomalous BT difference signals are identified, caused by the temperature and humidity inversions in the lower troposphere, which are especially significant during the summer. Although the existence of negative BT differences is physically reasonable, this makes the retrieval algorithm lose its effectiveness. Moreover, the statistics of the MODIS SSTskin data when compared with the iQuam buoy temperature data show large differences (in terms of mean and standard deviation) for the matchups at the Northern Atlantic and Pacific sides of the Arctic due to the disparity of in situ measurements and distinct surface and vertical atmospheric conditions. Therefore, it is necessary to further improve the retrieval algorithms to obtain more accurate MODIS SSTskin data to study surface ocean processes and climate change in the Arctic. [ABSTRACT FROM AUTHOR]

The urban heat island (UHI) effect is one of the most prominent surface climate changes driven by human activities. This study examines the UHI characteristics and influencing factors in the Southern Xinjiang urban agglomeration using MODIS satellite data combined with observational datasets. Our results reveal a significant increase in impervious surfaces in the region between 1995 and 2015, with the most rapid expansion occurring from 2010 to 2015. This urban expansion is the primary driver of changes in UHI intensity. The analysis from 2000 to 2015 shows substantial spatial variation in UHI effects across cities. Hotan recorded the highest annual average daytime UHI intensity of 3.7 °C, while Aksu exhibited the lowest at approximately 1.6 °C. Daytime UHI intensity generally increased during the study period, with the highest intensities observed in the summer. However, nighttime UHI trends varied across cities, with most showing an increase in intensity. Temperature, precipitation, and aerosol optical depth (AOD) were identified as the main factors influencing annual average daytime UHI intensity, while PM10 concentration showed a weak and inconsistent correlation with UHI intensity, varying by city and season. [ABSTRACT FROM AUTHOR]

Drought is a prevalent complex natural disaster due to its environmental extent and can severely impact global ecosystems. For the purpose of monitoring droughts and assessing their effects on a regional and global level, usually remote sensing data with an appropriate temporal and spatial resolution can be accessed. This research utilized the Moderate Resolution Imaging Spectroradiometer (MODIS)-based normalized difference vegetation index (NDVI), land surface temperature (LST), gross primary productivity (GPP) and vegetation health index (VHI) to investigate the historical duration, severity and recovery period for drought in selected districts of Balochistan. The Pearson correlation was used to determine the local link between the duration and severity of the drought between 2001 and 2021. The results showed that 2001, 2002, and 2004 were the driest years in which extreme to mild drought occurred with severity of 36%, 48% and 48% respectively. On the other hand, the drought duration result revealed 80–275 days, 160–275 days, and 176–275 days for 2001, 2002, and 2004 respectively. The result also indicated that crop land, water bodies, grass land and forest land, were positive correlation while shrub land was negative correlation with drought severity. On the other hand, crop land, water bodies, grass land and forest land, were negative correlation while shrub land was the positive correlation with drought duration. The drought recovery period analysis resulted in 16–66 days, 18–67 days, and 17–66 days for the years 2001, 2002, and 2004 respectively. With every aspect considered, the study offers insightful information on drought resistance for improved management. [ABSTRACT FROM AUTHOR]

Soil moisture is a vital parameter in the study of hydrology, agriculture and meteorology. The estimation of soil moisture is important for crop yield estimation, crop growth analysis and water resource management. Remote sensing is a significant way of mapping and monitoring crop fields' soil moisture content globally, using optical and microwave satellite datasets. In previous literature, many attempts have been made to compute soil moisture using optical and microwave-based remote sensing datasets. However, the applicability of optical data is limited due to the presence of atmospheric/cloud effects, while microwave applications are restricted due to limited resolution. In this article, a fusion-based change detection approach has been proposed to detect the soil moisture variation with multispectral and microwave satellite datasets. This study has been conducted in three stages i.e., (a) image-fusion of moderate resolution imaging spectroradiometer (MODIS) and scatterometer satellite (SCATSAT-1) at HH and VV polarization using different fusion algorithms i.e., nearest neighbour-based fusion (NNF), Gram–Schmidt (GS), Brovey transformation (BT) and principal component (PC) spectral; (b) Neural Net based classification of fused datasets to deliver the thematic maps, and (c) perform the post-classification change detection (PCD) to develop the change maps. The classified and change maps have been further utilized to detect the level of soil moisture. From the experimental outputs, it has been evaluated that the NNF-based PCD performed well enough in the development of the change maps as compared to other methods i.e., GD, BT and PC spectral. The present work can aid crop yield estimation, agricultural water and precision irrigation management. [ABSTRACT FROM AUTHOR]

Snow cover has a key role in balancing the Earth's surface temperature and can help in filling rivers and reservoirs. In this study, 8-day MOD10A2 images are employed to monitor the spatiotemporal changes in snow cover in the Sefid-Rud basin and its eleven sub-basins during 2000–2019. The non-parametric Mann–Kendall (MK) test and its associated Sen's slope estimator are utilized to estimate the trends in annual, seasonal, and monthly snow cover changes. The Sen's slope results show a decrease in the snow cover for the basin, statistically significant toward the central and southern parts of the basin. In the winter season, a decreasing trend is observed, where its decreasing rate is higher than the annual rate. The trends in the calendar months are like the seasons, i.e., December, January, and February exhibit a decreasing trend, like the winter season. The Goltapeh-Zarinabad and Ghorveh-Dehgolan sub-basins show decreasing snow cover rates of − 0.51 and − 0.68 (%/year) during 2000–2019, respectively, the only two sub-basins whose gradients are statistically significant at the 95% confidence level. The Pearson correlation analysis between elevation and snow cover for each year shows that the highest and lowest correlations are 0.81 for 2007 and 0.59 for 2017. Finally, analysis of the MCD12Q1 land cover data shows that a significant portion of non-vegetated lands have turned into grasslands, mainly in the central part of the basin, where the significant gradual snow cover decline is observed. The results can guide stakeholders and policymakers in the development of a sustainable environment in the face of climate change. [ABSTRACT FROM AUTHOR]

Due to its proximity to major sources of dust (the deserts of Iraq, Syria and Saudi Arabia) in the west, Iran has always faced dust storms and their sequences. Moreover, the Tigris and Euphrates basin (TEB) is considered one of the main centers of dust in the region. Hence, it can lead to adverse environmental, social and economic consequences, especially in the dry areas of southwestern Iran, and cause problems in human activities. Thus, identifying and evaluating different dimensions of this phenomenon is of vital importance. Accordingly, the aim of the current study is to investigate the temporal and spatial patterns of dust distribution in the southwest of Iran and to identify its relationship with the trend of surface water changes in the TEB. In this regard, the JRC Landsat dataset was used to monitor surface water changes and an integrated approach based on visual interpretation and assessment of aerosol optical depth (AOD) of MODIS images was used, and the processing was done on the platform of Google Earth Engine (GEE). The findings have shown that the occurrence of dust was largely correlated with the temporal pattern of surface water, and there was a negative correlation of 0.412. Accordingly, with the beginning of the decreasing trend of water in 2008, which has reduced its area to about 8199 km2, the dust storm has intensified and the highest annual frequency of dust has been observed with 2612 cases. Therefore, the role of water changes has been highlighted in this context. [ABSTRACT FROM AUTHOR]

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

The use of high-altitude remote sensing (RS) data from aerial and satellite platforms presents considerable challenges for agricultural monitoring and crop yield estimation due to the presence of noise caused by atmospheric interference, sensor anomalies, and outlier pixel values. This paper introduces a "Quartile Clean Image" pre-processing technique to address these data issues by analyzing quartile pixel values in local neighborhoods to identify and adjust outliers. Applying this technique to 20,946 Moderate Resolution Imaging Spectroradiometer (MODIS) images from 2002 to 2015, improved the mean peak signal-to-noise ratio (PSNR) to 40.91 dB. Integrating Quartile Clean data with Convolutional Neural Networks (CNN) models with exponential decay learning rate scheduling achieved RMSE improvements up to 5.88% for soybeans and 21.85% for corn, while Long Short-Term Memory (LSTM) models demonstrated RMSE reductions up to 11.52% for soybeans and 29.92% for corn using exponential decay learning rates. To compare the proposed method with state-of-the-art technique, we introduce the Vision Transformer (ViT) model for crop yield estimation. The ViT model, applied to the same dataset, achieves remarkable performance without explicit pre-processing, with R2 scores ranging from 0.9752 to 0.9875 for soybean and 0.9540 to 0.9888 for corn yield estimation. The RMSE values range from 7.75086 to 9.76838 for soybean and 26.25265 to 34.20382 for corn, demonstrating the ViT model's robustness. This research contributes by (1) introducing the Quartile Clean Image method for enhancing RS data quality and improving crop yield estimation accuracy, and (2) comparing it with the state-of-the-art ViT model. The results demonstrate the effectiveness of the proposed approach and highlight the potential of the ViT model for crop yield estimation, representing a valuable advancement in processing high-altitude imagery for precision agriculture applications.Article Highlights: Novel Quartile Clean Image technique improves MODIS data quality, enhancing crop yield estimation accuracy for CNN and LSTM models. Application of Vision Transformer for crop yield estimation using multi-spectral remote sensing data, explored for the first time. Comparative analysis of Quartile Clean Image method and Vision Transformer for crop yield prediction from remote sensing data. [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]

Oceanic water quality monitoring is essential for environmental protection, resource management, and ecosystem vitality. Optical remote sensing from space plays a pivotal role in global surveillance of oceanic water quality. However, the spatial resolution of current ocean color data products falls short of scrutinizing intricate small-scale marine features. This study introduces a hybrid model that fuses MODIS (Moderate Resolution lmaging Spectroradiometer) ocean color products with Sentinel-2 's remote sensing reflectance data to generate high-resolution ocean color imagery, specifically investigating the diffuse attenuation coefficient at a wavelength of 490 nm (Kd490). To address the intricacies of coastal environments, we propose two complementary strategies to improve the accuracy of inversion. The first strategy leverages MODIS ocean color products alongside a geographic segmentation model to perform distinct inversions for separate marine zones, enhancing spatial resolution and specificity in coastal regions. The second strategy bolsters model interpretability during training by integrating predictions from conventional physical models into a Random Forestbased Regression Ensemble (RFRE) model. This study focuses on the coastal regions surrounding the Beibu Gulf, near Hainan Island in China. Our findings exhibit a strong concordance with MODIS products, achieving a monthly average coefficient of determination (R²) of 0.90, peaking at 0.97, and sustaining a monthly average rootmean-square error (RMSE) of less than 0.02. These results substantiate the model's efficacy. Moreover, the annual trend analysis and localized assessment of the reconstructed Kd490 offer nuanced insights that surpass MODIS data, establishing a robust foundation for high-resolution water quality monitoring in coastal zones. [ABSTRACT FROM AUTHOR]

The estimation of spatially resolved near-surface air temperature (NSAT) has been extensively performed in previous studies using satellite-derived land surface temperature (LST) from MODIS. However, there remains a need for estimating daily NSAT based on LST data from other satellites, which has important implications for integrating multi-source LST in estimating NSAT and ensuring the continuity of satellite-derived estimates of NSAT over long-term periods. In this study, we conducted a comprehensive comparison of LST derived from Metop with MODIS LST in the modeling and mapping of daily NSAT. The results show that Metop LST achieves consistent predictive performance with MODIS LST in estimating daily NSAT, and models based on Metop LST or MODIS LST have overall predictive performance of about 1.2–1.4 K, 1.5–2.0 K, and 1.8–1.9 K in RMSE for estimating Tavg, Tmax, and Tmin, respectively. Compared to models based on nighttime LST, daytime LST can improve the predictive performance of Tmax by about 0.26–0.28 K, while performance for estimating Tavg or Tmin using different schemes of LST is comparable. Models based on Metop LST also exhibit high consistency with models utilizing MODIS LST in terms of the variability in predictive performance across months, with RMSE of 1.03–1.82 K, 1.3–2.49 K, and 1.26–2.66 K for Tavg, Tmin, and Tmax, respectively. This temporal variability in performance is not due to sampling imbalance across months, which is confirmed by comparing models trained using bootstrapped samples in balance, and our results imply that sampling representativeness, complicated by retrieval gaps in LST, is an important issue when analyzing the variability in predictive performance for estimating NSAT. To fully assess the predictive capability of Metop LST in estimating daily NSAT, more studies need to be performed using different methods across areas with a range of scales and geographical environments. [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]

Plant phenology regulates ecosystem functions at diverse scales but is impacted by micro and macro climatic variations, and climate change. In India, precise estimations of pheno-phase transition dates remain scarce at different spatial and temporal scales, necessitating comprehensive research efforts. This study aims to gather continuous intra-day ground data about vegetation and climate conditions using PhenoCam (optical RGB and IR images) along with meteorological sensors, at Dalma Wildlife Sanctuary (DWS), Jharkhand. To derive phenological metrics, different indices were computed from images captured by PhenoCam sensors and Satellite derived Normalized Difference Vegetation Index (NDVI). Since the PhenoCam covers diverse vegetation species in the frame, the analysis was performed over three specific subset Region of Interests (ROI): Bombax ceiba (Semal) tree, background cluster of vegetation and a sample tree. MODIS NDVI data revealed that most of the area is highly deciduous with major greening in the 1st half of April and senescence during 2nd half of March. The study found that Green Chromatic Coordinate Index (GCC) and Blue Chromatic Coordinate Index (BCC) results could reveal greening and senescence phases correctly. The timing of start of leaf flush (SOLF), end of leaf flush (EOLF) and end of leaf maturity (EOLM) estimated based on inflection point method from Pheno-Cam images are: for Semal tree: 5th April, 2nd May, 10th June, 2022; for background vegetation: 15th March, 28th March and 2nd May, 2022; and for sample tree: 15th March, 28th March and 25th April, 2022, respectively. The dates of SOLF differed in 2023 and it occurred twice for Semal and background vegetation: for Semal tree: 20th February and 3rd April 2023, and for background vegetation: 20th January and 8th March, 2023, respectively. The rate of leaf flush and rate of leaf maturity was not similar in different years as the rates were much higher in 2023 than in 2022. The temperature and rainfall during winter and spring played an important role in greening, senescence, and its sustenance. These findings revealed the micro-climatic effect on plant phenology in the Dalma Wildlife Sanctuary, as well as the importance of integrating PhenoCam and satellite data in accurate monitoring of phenological phases. [ABSTRACT FROM AUTHOR]

Over the past few years, forest ecosystems' ability to store carbon has been significantly impacted by Land use and Land cover (LULC), and climate change. Thus, it is crucial to understand how these change-causing factors impact carbon sequestration (CS). Due to a limited number of carbon storage monitoring methods and the shorter period of remote sensing data, it is difficult to continually analyze carbon storage in large areas. These issues can be solved by using AVHRR (Advanced Very High-Resolution Radiometer) and MODIS (Moderate Resolution Imaging Spector radiometer) remote sensing data. The main objective of this research is to measure the spatial and temporal patterns of carbon storage across the state of Karnataka's vegetative and non-vegetated terrains, between 2003 and 2021. To assess the effects of potential land use and land cover scenarios, our work uses spatial maps to estimate the storage of carbon sequestration from various land use patterns. To assess the spatio-temporal effects of land use and land cover (LULC) change on the availability and value of carbon storage. This research focuses on the entire Karnataka state as a study region to compute carbon storage utilizing online platforms like GEE (Google Earth Engine) using GPP (Gross Primary Productivity), and NPP (Net Primary Productivity) is an important measure to evaluate vegetation productivity using Decision Tree (DT) machine learning techniques. Statistical models like Pearson's correlation coefficient, standardized coefficients, and Root Mean Square Error (RMSE) methods are used for the model's performance with different indices and carbon storage. The findings show the Uttara Kannada district contains between 250 gCm − 2 and 300 gCm − 2 of carbon storage, which is relatively significant as compared to the other parts of the districts in the state. [ABSTRACT FROM AUTHOR]

Soil temperature (Ts) and vegetation temperature (Tv) provide significant information for various practical applications such as evapotranspiration estimation, water requirement analysis for vegetation, and drought monitoring during the crop growth. In this study, we estimated Ts and Tv with a land surface temperature (LST)-vegetation index (VI) trapezoidal space-based method (hereafter abbreviated as the trapezoidal method) and a dual-angle algorithm in which the estimation equations for Ts and Tv were established with thermal infrared observations from two angles and solved to using the least squares method. The Ts and Tv estimations were conducted using Moderate Resolution Imaging Spectroradiometer (MODIS) and Sentinel-3 data from the Google Earth Engine platform, together with auxiliary meteorological data from eight sites with various land cover types in the Heihe River Basin in 2019, and compared with corresponding in-situ measurements and temperatures from Landsat. The results showed that (1) both estimation methods outperformed the Ts retrievals compared to those of Tv; (2) the trapezoidal method overall had a similar but slightly better performance compared to the dual-angle algorithm, with the root mean square error for Ts retrievals of 3.7 K and 5.7 K, and for Tv retrievals of 6.5 K and 7.2 K, respectively, in comparison with temperatures from Landsat; (3) the trapezoidal method had higher accuracy over sites with grassland, desert, or meadow for Ts retrieval and sites with forests for Tv retrieval. This study provides advice about the method for Ts and Tv estimations and might be useful for rational utilization and more accurate monitoring of soil and vegetation. [ABSTRACT FROM AUTHOR]

Wind power in China is experiencing accelerating growth to meet the need for structural reform of the energy supply, however its impact on the ecological environment urgently needs to be taken into account. In this study, the impact of a wind farm in Gonghe County, Qinghai Province, China, on local land surface temperature (LST) was explored by removing the temporal background using year-by-year LST averaging and filtering the spatial background using pixel-by-pixel LST averaging based on Moderate Resolution Imaging Spectroradiometer (MODIS) and the European Centre for Medium-Range Weather Forecast's ERA5 data. The main findings are as follows: (1) From a long-term series perspective, the LST on the wind farm and the adjacent areas shows an increasing trend, reaching 0.060K/19a in spring; (2) the wind farm exerts a warming effect on the local LST, with a maximum of 7.04 K and a minimum of 1.63 K, and there is a significant spatial matching with the wind farm boundaries, which is more obvious in spring and winter, and less pronounced in summer and autumn; (3) the construction of wind farms also has a potential upward impact on the LST in the adjacent areas, especially in autumn and winter, and the warming effect gradually decreases with increasing distance; (4) the warming effect is most remarkable when the predominant wind directions of the upwind and downwind are close to consistent and the differences in wind volumes are significant. Research findings on the effect of wind farms on local LST are vitally important for strengthening the establishment of wind farms, site selection of wind farms, and the scientific and reasonable utilization of wind energy resources. Key policy highlights: Impacts of wind farms on local land surface temperature. Using the MODIS and ERA5 data whose temporal and spatial backgrounds have been removed. [ABSTRACT FROM AUTHOR]

Atmospheric pollution affects air quality and can pose a serious risk to public health. Traditional PM2.5 monitoring is limited by the uneven distribution of ground stations, which makes it difficult to obtain spatially continuous and accurate PM2.5 concentrations information. Using aerosol optical depth (AOD) retrieved from satellite remote sensing to study the temporal and spatial variation characteristics of PM2.5 can accurately predict the PM2.5 concentrations in a wide range and provide a basis for atmospheric pollution prevention and control. Considering the spatio-temporal correlations among the data, this study uses AOD data, introduces geographic location data, temporal data, meteorological data, and elevation data to construct an Optimized XGBoost (O-XGBoost) model with spatial and temporal characteristics to estimate PM2.5 concentrations in the Guanzhong region from 2019 to 2021 and analyse its spatial and temporal distribution characteristics. The results show that compared with the random forest (RF) and XGBoost models, the O-XGBoost model has higher estimation accuracy, with $$ {R^2}$$ R 2 , $$ RMSE$$ RMSE , and $$MAE$$ MAE of 0.873, 11.460 $$\mu g\cdot{m^{ - 3}}$$ μg ⋅ m − 3 , and 8.061 $$\mu g\cdot{m^{ - 3}}$$ μg ⋅ m − 3 , respectively. The estimation of PM2.5 concentrations based on the O-XGBoost model makes up for the uneven distribution of ground monitoring stations and improves the estimation accuracy. [ABSTRACT FROM AUTHOR]

Remote sensing and process-coupled ecological models are widely used for the simulation of GPP, which plays a key role in estimating and monitoring terrestrial ecosystem productivity. However, most such models do not differentiate the C3 and C4 photosynthetic pathways and neglect the effect of nitrogen content on V max and J max , leading to considerable bias in the estimation of gross primary productivity (GPP). Here, we developed a model driven by the leaf area index, climate, and atmospheric CO 2 concentration to estimate global GPP with a spatial resolution of 0.1° and a temporal interval of 1 day from 2000 to 2022. We validated our model with ground-based GPP measurements at 128 flux tower sites, which yielded an accuracy of 72.3%. We found that the global GPP ranged from 116.4 PgC year − 1 to 133.94 PgC year − 1 from 2000 to 2022, with an average of 125.93 PgC year − 1 . We also found that the global GPP showed an increasing trend of 0.548 PgC year − 1 during the study period. Further analyses using the structure equation model showed that atmospheric CO 2 concentration and air temperature were the main drivers of the global GPP changes, total associations of 0.853 and 0.75, respectively, while precipitation represented a minor but negative contribution to global GPP. [ABSTRACT FROM AUTHOR]

Snow cover is a relevant component of the Earth's climate system, influencing water supply, ecosystem health, and natural hazard management. This study aims to monitor daily snow cover in the Cantabrian Mountains using Sentinel-2, Landsat (5–8), and MODIS data processed in Google Earth Engine (GEE). The main purpose is to extract metrics on snow cover extent, duration, frequency, and trends. Key findings reveal significant spatial and temporal variability in Snow-Cover Days (SCDs) across the region. Over the past 23 years, there has been a notable overall decrease in snow-cover days (−0.26 days per year, and −0.92 days per year in areas with a significant trend). Altitudes between 1000–2000 m a.s.l. showed marked decreases. The analysis of Snow-Cover Fraction (SCF) indicates high interannual variability and records the highest values at the end of January and the beginning of February. The effectiveness of satellite data and GEE is highlighted in providing detailed, long-term snow cover analysis, despite some limitations in steep slopes, forests, and prolonged cloud-cover areas. These results underscore the capacity for continuous monitoring with satellite imagery, especially in areas with sparse snow observation networks, where studies could be enhanced with more localized studies or additional ground-based observations. [ABSTRACT FROM AUTHOR]

SPAtial EFficiency (SPAEF) metric is one of the most thoroughly used metrics in hydrologic community. In this study, our aim is to improve SPAEF by replacing the histogram match component with other statistical indices, i.e. kurtosis and earth mover's distance, or by adding a fourth or fifth component such as kurtosis and skewness. The existing spatial metrics i.e. SPAtial efficiency (SPAEF), structural similarity (SSIM) and spatial pattern efficiency metric (SPEM) were compared with newly proposed metrics to assess their converging performance. The mesoscale hydrologic model (mHM) of the Moselle River is used to simulate streamflow (Q) and actual evapotranspiration (AET). The two-source energy balance AET during the growing season is used as monthly reference maps to calculate the spatial performance of the model. The moderate resolution imaging spectroradiometer based leaf area index is utilized by the mHM via pedo-transfer functions and multi-scale parameter regionalization approach to scale the potential ET. In addition to the real monthly AET maps, we also tested these metrics using a synthetic true AET map simulated with a known parameter set for a randomly selected day. The results demonstrate that the newly developed four-component metric i.e. SPAtial Hybrid 4 (SPAH4) slightly outperforms conventional three-component metric i.e. SPAEF (3% better). However, SPAH4 significantly outperforms the other existing metrics i.e. 40% better than SSIM and 50% better than SPEM. We believe that other fields such as remote sensing, change detection, function space optimization and image processing can also benefit from SPAH4. [ABSTRACT FROM AUTHOR]

Due to Sharkia population's rapid growth, the crises of atmospheric pollutants are causing significant concern. These circumstances have positive and negative impacts on both environment and climate. This research aims to investigate and evaluate changes in land and air characteristics of the governorate using remotely sensed data from several satellites (Landsat, Aqua, Terra, and Sentinel-5P) in conjunction with geographic information system (GIS) techniques. Three multitemporal Landsat images were acquired in July 2002, 2012, and 2022, to monitor land use/land cover (LULC) changes during the past 20 years. In addition, aerosol optical depth (AOD) data were obtained daily over 2022, and Particulate Matter (PM2.5) was calculated and then averaged seasonally. Sentinel-5P was utilized for CO, NO2, and SO2 retrievals. Results showed that the governorate was continuously subjected to spatiotemporal changes in LULC during the whole study period. It was mainly occupied by vegetation with a total area of 3711.64, 3851.05, and 3794.59 km2 in 2002, 2012, and 2022, respectively. Seasonal means of PM2.5 followed this order: spring (77.59 µg/m3) > summer (50.76 µg/m3) > winter (49.29 µg/m3) > autumn (42.71 µg/m3). However, the winter season recorded the highest mean values for all gases, CO (946.58 µg/m3), NO2 (9.70 µg/m3), and SO2 (13.07 µg/m3). It was concluded that the southern region of the governorate is more vulnerable to environmental stresses, which can reduce biodiversity and ultimately affect the study area's climate. In order to manage fragile ecosystems sustainably, this study recommends the creation of efficient land and air management information systems and regulations. [ABSTRACT FROM AUTHOR]

This study is the first to examine vegetation change on Kilimanjaro from 2000 to 2022, using the Normalised Difference Vegetation Index (NDVI) at a spatial resolution of 250 m via the Moderate Resolution Imaging Spectrometer (MODIS). There is a long-term increase in vegetation on an annual basis, but with decreases in the long/short rains at low/high elevations. Temperature significantly influences NDVI across all elevations and seasons. Precipitation effects are more variable and often delayed, especially in the forest zone. The study explores how El Nino Southern Oscillation (ENSO) events affect NDVI differently at contrasting elevations, indicating the need for further elevation-specific remote sensing research and field data. [ABSTRACT FROM AUTHOR]

Water transparency, as indicated by the Secchi disk depth (Zsd), is a key parameter for assessing the quality of aquatic environments, reflecting the ability of light to penetrate through the water column. In the Tibetan Plateau (TP), where lakes are abundant yet remote and challenging to access, remote sensing techniques offer a promising approach for monitoring Zsd over large spatial scales. In this study, we used the semianalytical -based Zsd algorithm to study the temporal and spatial dynamics of water transparency over TP during the period from 2003 to 2022. The results show that the 173 lakes have a mean value of Zsd is 3.64 ± 2.4 m for long term, and generallywith the significantly increasing change trends in the past 20 years. In the central Tibetan Plateau (CTP) region, lake transparency showed a positive correlation with lake surface temperature (r = 0.73) and a negative correlation with precipitation (r = -0.54), highlighting the region's heightened sensitivity tometeorological changes compared to other areas. The spike inwater clarity observed in the CTP region may be linked to alterations in lake hydrodynamics driven by the extremely climate events (i.e., El Niño). These results indicated the importance of considering regional climatic factors when interpreting fluctuations in water transparency. [ABSTRACT FROM AUTHOR]

The main purpose of the current research is to investigate the nighttime LST climatology in Iran. The study of the seasonal regime and annual temperature differences along with the identification of the role of different temperature thresholds and the spatial analysis of Iran's cold spots are among the climatic features studied in the current research. For this purpose, the MYD11A1 product (version 6.1) of the MODIS which is a key instrument onboard the Aqua Satellite with a horizontal resolution of 1000 meters has been used for a period of 19 years (2003-2021). This product is related to the nighttime passage of the satellite at 1:30 local time. Satellite data were validated using daily soil temperature data from 174 meteorological stations. The accuracy of satellite data showed that the area average values of root mean square error (RMSE) and mean bias error (MBE) compared with station data are 1.7℃ and 1.39℃ respectively. Also, the PBias statistics results show that the underestimation prevails in the night LST data compared to the soil temperature. In total, the MODIS sensor estimates LST in most regions of the country with an error of less than 10%, which indicates the high efficiency of the MYD11A1 product in assessing Iran's nighttime LST. The findings showed that the combination of altitude and higher latitude has significant importance in the seasonal changes of nighttime minimum LST in Iran. So, the combination of these two factors plays an important role in the occurrence of the lowest night temperatures in the country in the middle parts of central Alborz and the highlands of Azerbaijan. Thus, the longest period of dominance of low temperatures in Iran is related to 38°N and 36°N latitudes, respectively. The results also indicate that the prevailing temperature zones in Iran fluctuate from the minimum temperature in January to the maximum temperature in July. In the transitional seasons, only April and October have distinct and independent temperature identities, and the rest of the months of the transitional seasons are either connected to winter or connected to summer according to the pattern. Examining the course of intra-annual temperature changes shows that the highest intra-annual temperature concentration and the highest homogeneity are observed in the summer season. On the other hand, the highest level of heterogeneity and the lowest level of temperature concentration are related to the autumn season. Examining the average nighttime temperature difference in Iran also shows that the highest values of the earth surface temperature difference occur in the winter season and the lowest values in the summer season. In a general view, the temperature difference curve fluctuates between a maximum of 56.6℃ in January and a minimum of 46.5℃ in July. Spatial analysis of nighttime temperature difference showed that the Lut desert experiences the highest average values of nighttime temperature difference throughout the year. In general, lack of moisture, very low percentage of cloud cover, lack of vegetation cover, and very low soil moisture seem to have led to high nighttime temperature fluctuations in the Lut desert. On the other hand, a narrow strip along the southern coastline of the Caspian Sea experiences the lowest nighttime temperature difference in the country. Clearly, the high humidity of the southern coastline of the Caspian Sea plays the main role in reducing the temperature fluctuations of this region. The geographical analysis of cold spots indicates that the coldest spots of Iran are observed in the high peaks, located in high latitudes. Also, the highest geographical concentration of cold spots in Iran is related to the summer season. Meanwhile, the largest spatial distribution of Iran's cold spots can be seen in the winter season, especially in December. The findings also indicate the existence of a regular annual cycle in the spatial arrangement of cold spots in Iran. In this way, in the warm period of the year, the cold spots of Iran are concentrated only in the middle part of the central Alborz. With the beginning of the cold period of the year, we are witnessing a northwest shift of cold spots toward the highlands of Azerbaijan. So that a bimodal pattern replaces the summer concentrated pattern. The bimodal pattern continues at the height of winter with the dispersion of cold spots in Alborz and Azerbaijan, but with the arrival of the warm period of the year, a southeastward shift of cold spots towards the Alborz Mountains begins, with the cold spots completely concentrated on the Damavand peak, this annual cycle ends in the summer season. [ABSTRACT FROM AUTHOR]

Air quality is becoming a serious public health issue, affecting millions of people globally. In support of this fact, the World Health Organization predicts that approximately 2.4 million people die per year as a result of the health impacts of air pollution. So, to recognize the impacts of air pollution, we must first investigate their physical properties. In this article, we used the Ultraviolet Aerosol Index (UVAI) and Multi-Angle Implementation of Atmospheric Correction (MAIAC) aerosol optical depth (AOD) from January 2005 to December 2021 obtained by the Ozone Monitoring Instrument (OMI) and MODIS respectively to investigate the Spatio-temporal patterns, annually and seasonal variations of absorbing aerosols, and interaction of aerosols with various meteorological parameters (rainfall, temperature, wind speed, e.g.) over Saudi Arabia (SA). Using the Hybrid Single Particle Langrangian Integrated Trajectory (HYSPLIT) model, we also identified pollution sources in SA's main cities. We also go through the natural and manmade factors that influence absorbing aerosols. Significant UVAI and MAIAC AOD values were observed high in the eastern and central regions of SA and low in the northern and western regions. Over SA, the average UVAI and MAIAC AOD are increasing at 0.93% and 0.83% per year respectively. UVAI has a favorable relationship with temperature in SA's eastern regions. In SA, UVAI has a positive and negative correlation with energy consumption and secondary industries of 0.787 and -0.52, respectively. Therefore, this study will help policymakers to identify the major hotspots and variability of aerosols in SA. Moreover, the contribution of different anthropogenic activities in polluting the atmosphere will also be analyzed in this study. Furthermore, depending on the findings of this study, various techniques such as plantation promotion, excellent fuel efficiency, a ban on the use of old and outdated vehicles, and so on can be employed to minimize the concentration of particle pollution. [ABSTRACT FROM AUTHOR]

In this study, we applied a multivariate logistic regression model to identify deforested areas and evaluate the current effects on environmental variables in the Brazilian state of Rondônia, located in the southwestern Amazon region using data from the MODIS/Terra sensor. The variables albedo, temperature, evapotranspiration, vegetation index, and gross primary productivity were analyzed from 2000 to 2022, with surface type data from the PRODES project as the dependent variable. The accuracy of the models was evaluated by the parameters area under the curve (AUC), pseudo R2, and Akaike information criterion, in addition to statistical tests. The results indicated that deforested areas had higher albedo (25%) and higher surface temperatures (3.2 °C) compared to forested areas. There was a significant reduction of the EVI (16%), indicating water stress, and a decrease in GPP (18%) and ETr (23%) due to the loss of plant biomass. The most precise model (91.6%) included only surface temperature and albedo, providing important information about the environmental impacts of deforestation in humid tropical regions. [ABSTRACT FROM AUTHOR]

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

The soil in Northern Iran exhibits significant variability, showing notable responses to variations in climatic and environmental conditions. In a representative area covering 936.7 km2, we measured 18 soil properties at 73 sampling locations. These properties were then screened using Principal Component Analysis (PCA) which identified five PCs with eigenvalues greater than 1.0 and correlation analysis to construct the Nemro Soil Quality Index (SQI) with a mean of 0.27 ± 0.04. The predictability of SQI was modeled using the Generalized Additive Model (R2 = 0.669, explained deviance = 69.7%), indicating that elevated Normalized Difference Vegetation Index (NDVI; p value = 0.000) and daytime Land Surface Temperature (LST; p value = 0.000) enhance SQI, while higher slopes (p value = 0.020) have a diminishing effect. The model was also utilized to illustrate potential future alterations in SQI for the year 2040. For this purpose, the 2040 MODIS data (NDVI and LST) were projected using various regression models (0.09 < R2 < 0.69) applied to historical mean annual MODIS data spanning from 2002 to 2022. The majority of projected changes in SQI exhibited a negative trend, primarily attributed to the depletion of vegetation cover at the peripheries of forest borders. These findings underscore the imperative need for strategic future management plans, emphasizing the preservation of soil in marginal forest lands. [ABSTRACT FROM AUTHOR]

Mountain springs are the only water source for most of the communities in the Kashmir Himalayas. However, over the past two decades, the discharge of these springs has decreased, and some springs have dried up. The current study mapped springs from the Sindh basin of the Kashmir Himalaya via a GPS-based Field survey. The dynamics of spring discharge were evaluated vis-à-vis climatic variability, land use changes, snow cover area, and any major developmental projects in the catchment. The observed temperature and precipitation data were subjected to trend analysis using the Mann Kendall's tau test and Sen's slope estimator. MODIS data were used to understand the dynamics of the Snow Cover Area (SCA) from 2010 to 2021. Landsat satellite imagery was processed to quantify the changes in Land use/cover (LULC). The study found that 13% of the springs below 1600 masl, 22% from 1600 to 2500 masl, and 28.9% above 2500 masl have witnessed a high decrease in discharge. The seasonality of the spring discharge implies snowmelt is the predominant source. The time series analysis shows a weak decreasing trend in the precipitation during the growing season (Spring and Summer). However, both minimum and maximum temperatures show a statistically significant upward trend and result in the early melting of the snow, also confirmed by the analysis of SCA. The study reveals that climate change and LULC change are responsible for the drying of the springs. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Abstract The use of high-altitude remote sensing (RS) data from aerial and satellite platforms presents considerable challenges for agricultural monitoring and crop yield estimation due to the presence of noise caused by atmospheric interference, sensor anomalies, and outlier pixel values. This paper introduces a "Quartile Clean Image" pre-processing technique to address these data issues by analyzing quartile pixel values in local neighborhoods to identify and adjust outliers. Applying this technique to 20,946 Moderate Resolution Imaging Spectroradiometer (MODIS) images from 2002 to 2015, improved the mean peak signal-to-noise ratio (PSNR) to 40.91 dB. Integrating Quartile Clean data with Convolutional Neural Networks (CNN) models with exponential decay learning rate scheduling achieved RMSE improvements up to 5.88% for soybeans and 21.85% for corn, while Long Short-Term Memory (LSTM) models demonstrated RMSE reductions up to 11.52% for soybeans and 29.92% for corn using exponential decay learning rates. To compare the proposed method with state-of-the-art technique, we introduce the Vision Transformer (ViT) model for crop yield estimation. The ViT model, applied to the same dataset, achieves remarkable performance without explicit pre-processing, with R 2 scores ranging from 0.9752 to 0.9875 for soybean and 0.9540 to 0.9888 for corn yield estimation. The RMSE values range from 7.75086 to 9.76838 for soybean and 26.25265 to 34.20382 for corn, demonstrating the ViT model's robustness. This research contributes by (1) introducing the Quartile Clean Image method for enhancing RS data quality and improving crop yield estimation accuracy, and (2) comparing it with the state-of-the-art ViT model. The results demonstrate the effectiveness of the proposed approach and highlight the potential of the ViT model for crop yield estimation, representing a valuable advancement in processing high-altitude imagery for precision agriculture applications.

The significance of land surface temperature (LST) and near-surface air temperature (TAIR) extends to various applications, including the exploration of urban heat islands. Understanding urban heat islands is crucial for comprehending the intricate interactions among urbanization, climate dynamics, and human well-being. However, many aspects of these topics remain understudied. In this study, we conducted a comprehensive analysis of LST and TAIR, covering day and night and spanning all four seasons of a full year. We used global datasets and applied non-spatial and spatial analysis techniques in the Amman-Zarqa urban region, a typical arid to semiarid environment. The study had three primary objectives: (1) Assess how different human settlement types influence the variations in LST and TAIR across space and time. (2) Examine the spatial and temporal attributes of the relationships between TAIR and LST. (3) Synthesize insights regarding the spatial and temporal characteristics of urban heat islands in arid to semiarid environments. The findings unveiled that urban centers consistently exhibit the lowest daytime LST and maximum and minimum TAIR, across all seasons when compared to other human settlement types. Nighttime LST displayed more variable patterns. Urban centers act as surface urban cool islands during the day and canopy layer urban cool islands both day and night throughout the seasons. The presence of surface urban heat or cool islands at night is barely noticeable. Daytime and nighttime LST play a significant role in explaining the variability in maximum and minimum TAIR across all seasons, with the relationships exhibiting variations ranging from positive to non-significant to negative, influenced by location and seasonal changes. During the daytime, LST consistently exceeds TAIR across all seasons, whereas this relationship displays greater variability at night. The findings of this study hold significant implications for sustainable urban planning and efforts to combat the effects of urban heat islands.

Abstract Background Ecosystem models are valuable tools to make climate-related assessments of change when ground-based measurements of water and carbon fluxes are not adequately detailed to realistically capture geographic variability. The Carnegie-Ames-Stanford Approach (CASA) is one such model based on satellite observations of monthly vegetation cover to estimate net primary production (NPP) of terrestrial ecosystems. Results CASA model predictions from 2015 to 2022 for Western Europe revealed several notable high and low periods in growing season NPP totals in most countries of the region. For the total land coverage of France, Greece, Italy, Portugal, and Spain, 2018 was the year with the highest terrestrial plant growth, whereas 2017 and 2019 were the years with the highest summed NPP across the UK, Germany, and Croatia. For most of Western Europe, 2022 was the year predicted with the lowest summed plant growth. Annual precipitation in most countries of Western Europe gradually declined from a high average rate in 2018 to a low average precipitation level in 2022. Conclusions The CASA model predicted decreased growing season NPP of between − 25 and − 60% across all of Spain, southern France, and northern Italy from 2021 to 2022, and much of that plant production loss was detected in the important cropland regions of these nations.

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 Spectro-radiometer (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 The middle and lower reaches of the Yangtze River are frequently affected by the Western Pacific Subtropical High (WPSH) in summer. This leads to phenomena including air subsidence, high temperatures, low rainfall, and weak winds, all of which affect the urban heat island (UHI) effect. Currently, there are few studies on the influence of WPSH on the UHI effect. In this study, we analysed the temporal and spatial distributions of the influence of WPSH on the UHI effect by establishing two scenarios: with and without WPSH. We calculated the UHI intensity and the urban heat island proportion index (UHPI) to analyse the temporal and spatial distributions of the UHI effect. The geographical detector method was then used to analyse the factors influencing UHI. The results indicate the strong heat island effect during the day in provincial capitals and some developed cities. The area of high UHI intensity was larger under the influence of WPSH than in the years without WPSH. WPSH affected UHPI at both day and night, although the effect was more pronounced at night. The factors affecting daytime UHI intensity are mainly POP and NTL, O3 plays a large role in the years with WPSH control. The main factors affecting the UHI intensity at night are AOD, POP and NTL were mainly factors in the years without WPSH control, POP and WPSH were mainly factors in the years with WPSH control. The interactions of the factors are mainly POP and multi-factors during the daytime, and DEM and multi-factors during the nighttime. It was found that the UHI intensity was enhanced under the control of the WPSH, and the influencing factors of the diurnal UHI differed with and without the WPSH control, which ultimately provides realistic suggestions for mitigating the intensity of the UHI in areas affected by the WPSH.

Abstract Droughts pose significant challenges to food security, affecting millions of people and hectares of land in India. Despite their widespread impact, assessing drought patterns at a high spatial resolution remains a challenge, particularly in regions with poor data availability. Standardised Precipitation Evapotranspiration Index (SPEI) is one of the most widely accepted drought indices. The publicly available SPEI reanalysis dataset has a poor spatial resolution for regional drought studies. Land Surface Temperature (LST) has the potential to act as an effective proxy for SPEI and can be used for high-resolution drought studies. The study considered six homogeneous drought regions (NWH, CPR, NEI, NCP, NWI, and SPR) over India, and the recent trend in LST and SPEI over these regions was estimated using modified Mann-Kendell (MMK) trend and Sen’s slope estimator at monthly, seasonal, and annual time scales. The correlation between SPEI and LST was also checked at a 95% confidence level. Results indicate statistically significant decreasing annual LST trends in NWH (slope = −0.10), CPR (slope = −0.09), and NWI (slope = −0.11) regions, while increasing SPEI trends were observed in NWI (slope = 0.06) and NCP (slope = 0.01) regions during 2001–2019. Additionally, NCP (p = 0.025), CPR (p = 0.002), and NWI (p = 0.020) exhibited significantly decreasing LST trends during winter, with a notable post-monsoon decrease observed in NWI. Significant correlations between annual LST and SPEI were observed only in CPR (r =−0.527) and NWI (r = −0.601), with seasonal associations found predominantly in winter across CPR, NEI, NCP, and NWI regions. Notably, a significant correlation was observed in all months except July over NWI, with consistent correlations during most of the winter months in NWH, CPR, NCP, and NWI regions. The observed trends and correlations can provide valuable insights for policymakers and stakeholders in formulating effective drought mitigation strategies.

Introduction: Offshore wind farms (OWFs) generate extensive wind wakes in their leeward areas, which can induce marine upwelling and downwelling. These processes significantly affect marine stratification and ecosystems, leaving detectable patterns on the sea surface. Materials and Methods: By utilizing MODIS data, we analyzed six representative OWFs worldwide to identify these wake signatures. Result: Notably, we observed pronounced signatures near an OWF located in the coastal waters of Jiangsu Province, China. Conversely, no coherent wake signatures were detected at other selected European and Chinese OWFs. Discussion: This absence may be attributed to inactive upwelling, weak marine stratification, unsteady wind directions, and land-sea distribution. This research offers a fresh perspective on the environmental impacts of OWFs and, for the first time, underscores the potential of global gridded satellite dataset in detecting OWF-induced wind wake signatures. [ABSTRACT FROM AUTHOR]

Background: Ecosystem models are valuable tools to make climate-related assessments of change when ground-based measurements of water and carbon fluxes are not adequately detailed to realistically capture geographic variability. The Carnegie-Ames-Stanford Approach (CASA) is one such model based on satellite observations of monthly vegetation cover to estimate net primary production (NPP) of terrestrial ecosystems. Results: CASA model predictions from 2015 to 2022 for Western Europe revealed several notable high and low periods in growing season NPP totals in most countries of the region. For the total land coverage of France, Greece, Italy, Portugal, and Spain, 2018 was the year with the highest terrestrial plant growth, whereas 2017 and 2019 were the years with the highest summed NPP across the UK, Germany, and Croatia. For most of Western Europe, 2022 was the year predicted with the lowest summed plant growth. Annual precipitation in most countries of Western Europe gradually declined from a high average rate in 2018 to a low average precipitation level in 2022. Conclusions: The CASA model predicted decreased growing season NPP of between − 25 and − 60% across all of Spain, southern France, and northern Italy from 2021 to 2022, and much of that plant production loss was detected in the important cropland regions of these nations. Plain language summary: Annual growth of terrestrial plant cover, also known as net primary production (NPP), must be maintained and managed by societies worldwide to meet their essential needs for food and fiber. A model of global NPP that uses monthly satellite images as inputs predicted that extreme drought in the years 2020 and 2021 across Western Europe, caused by greatly diminished precipitation totals and elevated temperatures, depressed plant production by between − 25 and − 60% compared to the previous five years in France, Italy, and Spain. [ABSTRACT FROM AUTHOR]

Albedo is a primary driver of the glacier surface energy balance and consequent melting. As glacier albedo decreases, it further accelerates glacier melting. Over the past 20 years, glaciers on the Tibetan Plateau have experienced significant melting. However, our understanding of the variations in glacier albedo and its driving factors in this region remains limited. This study used MOD10A1 data to examine the average characteristics and variations in glacier albedo on the Tibetan Plateau from 2001 to 2022; the MOD10A1 snow cover product, developed at the National Snow and Ice Data Center, was employed to analyze spatiotemporal variations in surface albedo. The results indicate that the albedo values of glaciers on the Tibetan Plateau predominantly range between 0.50 and 0.60, with distinctly higher albedo in spring and winter, and lower albedo in summer and autumn. Glacier albedo on the Tibetan Plateau decreased at an average linear regression rate of 0.06 × 10−2 yr−1 over the past two decades, with the fastest declines occurring in autumn at an average rate of 0.18 × 10−2 yr−1, contributing to the prolongation of the melting period. Furthermore, significant variations in albedo change rates with altitude were found near the snowline, which is attributed to the transformation of the snow and ice surface. The primary factors affecting glacier albedo on the Tibetan Plateau are temperature and snowfall, whereas in the Himalayas, black carbon and dust primarily influence glacier albedo. Our findings reveal a clear decrease in glacier albedo on the Tibetan Plateau and demonstrate that seasonal and spatial variations in albedo and temperature are the most important driving factors. These insights provide valuable information for further investigation into surface albedo and glacier melt. [ABSTRACT FROM AUTHOR]