Search Results (718)

Winter wheat is a cornerstone of China’s grain production, contributing substantially to national food security and overall cereal output. This study modeled the nonlinear associations between multitemporal remote sensing variables and winter wheat yield. To produce high-spatiotemporal-resolution inputs, we used the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) to integrate Sentinel-2 normalized difference vegetation index (NDVI) data with MODIS NDVI data, generating NDVI composites at 8-day intervals with a 10-m spatial resolution. The NDVI, actual evapotranspiration (ET), land surface temperature (LST), precipitation (PRE), and soil moisture (SM) were selected as predictors for yield estimation because they are closely associated with winter wheat growth and yield formation during primary growth stages. By integrating the local temporal feature-learning capacity of a one-dimensional convolutional neural network (1-D CNN) with the strength of a bidirectional long short-term memory (BiLSTM) model in capturing temporal dependencies within time series, a BiLSTM–CNN model was constructed for wheat yield estimation and prediction. The BiLSTM–CNN model showed higher estimation accuracy than individual BiLSTM and 1-D CNN models, with an R² of 0.69 and root mean square error (RMSE) of 478.68 kg/hm². The use of all the parameters produced the best estimation performance among all the parameter combinations. Approximately two months before harvest, the model still provided satisfactory yield prediction accuracy. This study provides an important theoretical basis for high-accuracy regional winter wheat yield estimation and pre-harvest forecasting. Full article

Multi-decadal trajectories of large arid-zone reservoirs are seldom described by integrated satellite observation. This study characterises the optical and thermal variability of Lake Nasser, Egypt, over 2005–2025. Eight monthly MODIS indicators—NDVI, EVI, NDWI, NDTI, a near-infrared reflectance index, white-sky albedo, and day- and night-time land surface temperature (LST)—together with the derived diurnal temperature range (DTR), were compiled in Google Earth Engine over 186 cells of a 5 km grid and analysed by non-parametric trend, change-point and correlation procedures under false-discovery-rate control, correction for serial correlation, and effective-sample-size significance testing. Because a fixed polygon cannot separate environmental change from shoreline migration, every trend was recomputed on four domains of decreasing shoreline exposure. Three signals survive on all four: night-time LST rises (+0.51 to +0.76 °C decade⁻¹), DTR contracts (−0.83 to −1.95 °C decade⁻¹) and albedo declines (−0.0034 to −0.0193 decade⁻¹). The NDVI, EVI, near-infrared and day-time LST declines that the fixed polygon reports are not reproduced under the control, whereas NDWI declines (−0.048 to −0.077 decade⁻¹) only once it is applied. Change-point tests date a shift in level to 2016–2017, three years before the first filling of the Grand Ethiopian Renaissance Dam. Full article

Vegetation dynamics are highly sensitive to climate variability and constitute a key indicator of ecosystem condition, particularly in arid and semi-arid environments where field observations are often limited. This study investigated the spatiotemporal dynamics of vegetation and their relationships with climatic factors in the Quaraqum Watershed during 2001–2022 using multi-source remote sensing datasets. Vegetation dynamics were characterized using the Normalized Difference Vegetation Index (NDVI) derived from MODIS imagery, Land Surface Temperature (LST) was obtained from MODIS products, and precipitation data were obtained from the CHIRPS dataset. Spatial and temporal patterns and trends were examined using Geographic Information System (GIS) techniques, while the relationships between NDVI and climatic variables were quantified using Pearson correlation and multiple linear regression analyses. The results revealed a pronounced west-to-east decrease in precipitation accompanied by increasing temperature across the watershed. Annual precipitation reached maximum values of 389.20 mm in 2019 and 370.79 mm in 2009, whereas the lowest values, 172.50 mm and 171.50 mm, were recorded in 2008 and 2021, respectively. Spring was the wettest season, whereas summer was the driest. Vegetation cover was highest in spring and exhibited an increasing long-term trend, while autumn and winter showed the lowest vegetation cover and declining trends. Correlation analysis revealed a strong positive relationship between NDVI and precipitation and a weaker negative relationship between NDVI and LST. These results suggest that precipitation availability was more strongly associated with vegetation variability than thermal conditions. Multiple regression analysis further indicated that precipitation was the primary climatic variable explaining annual NDVI variability, while the inclusion of LST did not substantially increase the explained variance of the model. Overall, these findings highlight the dominant role of moisture availability in shaping vegetation dynamics in the Quaraqum Watershed, with thermal conditions representing an additional environmental factor affecting vegetation responses. Full article

High-spatiotemporal-resolution evapotranspiration (ET) is critical for precision irrigation management and water resource regulation. Regarding the existing spatiotemporal fusion methods suffering from sparse high-resolution observations and coarse land surface temperature (LST), this study took winter wheat in Luancheng District, Hebei Province, as the research object, and proposed a remote sensing ET retrieval method based on the LST sharpening model. The Data Mining Sharpener (DMS) algorithm combined with Sentinel-2 multispectral data was used to downscale MODIS LST from 1000 m to 10 m, with auxiliary variables (DEM, albedo, NDVI, land cover) integrated into the Cubist regression tree to improve the physical rationality and spatial details of MODIS LST. The 10 m resolution ET was estimated from 10 m sharpened LST and Sentinel-2 multispectral data using the surface energy balance model, and the unmixing–weight ET image fusion model (UWET) was adopted to fuse the 10 m resolution ET with MODIS low-resolution ET to generate a daily 10 m ET dataset covering the entire winter wheat growing season. Validation with eddy covariance flux measurements showed that the correlation coefficient R = 0.921, RMSE = 0.779 mm/day during 2019–2020, and R = 0.900, RMSE = 0.831 mm/day during 2020–2021. The results demonstrate that auxiliary variables significantly enhance the spatial reality of LST, LST sharpening effectively improves the spatial heterogeneity of ET, and Sentinel-2 data compensates for the temporal deficiency of Landsat, thereby greatly promoting the accuracy of spatiotemporal fusion. This method can provide reliable high-spatiotemporal-resolution data support for refined farmland irrigation management and water resources regulation. Full article

As the largest inland river basin in China’s extremely arid region, the stability of the groundwater–vegetatifon system in the Tarim River Basin is crucial for the consolidation of the ecological security barrier in the northwest. To reveal the evolution law of groundwater storage in the watershed from 2003 to 2024 and its response mechanism to vegetation dynamics, this study is based on GRACE gravity satellite, GLDAS land surface assimilation and MODIS remote sensing data. The Theil Sen trend analysis, Hurst index, spatiotemporal Granger causality test, and standardized multiple linear regression model are integrated to systematically analyze the spatiotemporal heterogeneity, future evolution trend, and multi-driving factor contribution pattern of groundwater storage (GWSA) in the watershed. The results showed that: (1) During the study period, the GWSA of the watershed showed a significant downward trend, with a rate of −3.5 mm/a, and experienced a spatial redistribution process of “comprehensive loss local recovery southern compensation northern loss”. The northern and peripheral regions faced new depletion risks. (2) The vegetation condition continues to improve, and the VCI gradually rises from the low to medium range, but the spatial heterogeneity increases synchronously; there is a significant spatial positive correlation between VCI and GWSA, with only a strong lag driving effect in the southwestern region (F > 40). The explanatory power of vegetation factors for groundwater in other regions is limited. (3) Future trend predictions show that over 70% of the region will continue in the direction of historical changes, and the continuous loss trend in the north is difficult to reverse. (4) There is significant spatial differentiation in the contribution rate of driving factors: vegetation conditions (VCI) are the dominant factor, controlling 57.53% of the watershed edge and eastern region; precipitation and temperature dominate the central region (24.94%) and southwestern desert areas (17.53%), respectively. The research results can provide scientific basis for differentiated ecological water delivery and refined management of water resources in the Tarim River Basin. Full article

Accurate mapping of Soil moisture (SM) is essential for effectively monitoring agricultural drought. However, the coarse spatial resolution of passive microwave products, including the 9 km Soil Moisture Active Passive (SMAP) retrievals, limits their effectiveness at regional and local scales. To address this limitation, three machine learning-based downscaling frameworks were compared to improve SMAP SM resolution from 9 km to 1 km over Békés County, Hungary. The study period covered the growing seasons (April to October) from 2020 to 2023. A set of multi-temporal MODIS-derived variables, including vegetation indices (NDVI, EVI), daytime and night-time land surface temperature, and evapotranspiration, along with land cover classification and topographic elevation, were combined as auxiliary predictor variables. Three machine learning algorithms, Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Gradient Boosting Machine (GBM), were trained and evaluated. The results showed that (1) the RF model had the highest accuracy during the testing (R² = 0.71, RMSE = 0.0295 m³/m³) phase and validation against four in situ monitoring stations with confirmed reliable SM estimation at the local scale; (2) daytime LST was the most important predictor in all models, underscoring the strong thermal–moisture coupling that governs surface SM dynamics; and (3) the validated RF model produced 1 km Standardized Soil Moisture Index (SSI) maps that effectively captured inter-annual drought variability, identifying the severe drought of July 2022. Overall, this study presents a downscaling approach for generating high-resolution SM data suitable for Central European agricultural environments. The resulting 1 km SM and SSI products provide valuable tools for decision-makers to enhance planning during drought periods and reduce agricultural losses through improved irrigation scheduling. Full article

Wildfire in African savannas reflects coupled surface thermal, hydroclimatic, vegetation, and disturbance processes, but basin-scale time-series analyses can overstate mechanisms when temporal dependence and spatial heterogeneity are ignored. We assembled a monthly 2003–2024 multi-source environmental dataset for the Zambezi River Basin ($n=263$) and nine Level-4 HydroBASINS units, using quality-controlled MODIS burned area, normalized difference vegetation index (NDVI), daytime land surface temperature (LST), and evapotranspiration (ET), together with CHIRPS precipitation and GLDAS-2.1 Noah 0–10 cm soil moisture. Primary inference used heteroskedasticity- and autocorrelation-consistent regressions, expanding-window Random Forest validation, false-discovery-rate-controlled distributed-lag tests, and sub-basin robustness; vector autoregression was retained as a secondary diagnostic. Current-month burned-area anomalies were positively associated with LST ($\beta =0.340$, $p=0.0015$) and negatively associated with near-surface soil moisture ($\beta =-0.326$, $p=0.0005$). Over 1–9 months, precipitation and soil-moisture histories were jointly supported after multiplicity correction, whereas basin-wide NDVI, ET, and LST histories were not. Burned-area history was followed by cumulative 0–6 month declines in NDVI ($\beta =-0.280$, 95% CI $[-0.452,-0.107]$) and ET ($\beta =-0.214$, 95% CI $[-0.419,-0.008]$). Directions were broadly consistent across sub-basins, although magnitudes varied. The evidence supports time-scale-specific conditional associations without structural-causal claims. Full article

Surface urban heat island (SUHI) behavior in dryland cities can reverse sign when hot, bare peripheral surfaces exceed urban-core temperatures, yet long monthly records across contrasting climates remain scarce. This study reconstructs land surface temperature (LST) and standardized urban-core–periphery thermal contrasts for five Moroccan cities from 1995 to 2024 using Landsat, ERA5-Land, NDVI, and the annual MODIS MCD12Q1 land-cover product. The diagnostic analysis of SUHI and Urban Heat Sink (UHS) occurrence is explicitly separated from the forecasting component: SARIMA, Random Forest, XGBoost, and LSTM models predict monthly LST rather than UHI intensity. Tangier exhibits a persistent positive SUHI (mean: 4.6 °C; UHS frequency: 3.0%), whereas Béni Mellal, Ifrane, Laayoune, and Taza have negative mean contrasts of −1.4, −2.4, −1.0, and −0.7 °C, respectively; Ifrane records the highest UHS frequency (90.5%). No city shows a statistically significant monotonic UHI trend, and Sen’s slopes remain close to zero. Forecasting skill is city-dependent: SARIMA performs best in Ifrane and Tangier (R² = 0.94 and 0.93), while Random Forest performs best in Taza and Laayoune (R² = 0.90 and 0.79). Approximate 95% empirical uncertainty half-widths derived from held-out RMSE range from ±3.78 to ±15.48 °C, indicating substantial model- and city-specific uncertainty. The results support the hypothesis that local climate and peripheral land-cover context can outweigh urban fraction as controls on SUHI sign and amplitude. Generalization is limited by fixed reference distances, possible urbanization of the outer ring, clear-sky satellite sampling, the distinction between LST and air temperature, and the 36-month recursive forecasting chain used to display the 2026–2027 outlook. Full article

Coastal humid-heat and photochemical-oxidant episodes are commonly studied through concentration estimation, leaving it unclear whether satellite observations improve warning decisions under explicit false-alarm constraints. This study introduces CoAST-EWS Japan, a six-station, validation-locked hindcast benchmark across Osaka Bay and Tokyo Bay. Models were developed using June–July 2023 data, calibrated and thresholded on August 2023 predictions, and retrospectively evaluated on June–August 2025 station-hour observations. The strong non-satellite route combines recent ground history, ERA5 meteorology, CAMS composition, and static station geometry. Adding previous-day MODIS thermal context to an otherwise identical XGBoost route increased average precision from 0.3153 to 0.3429, reduced the Brier score from 0.05032 to 0.04874, and improved recall/F1 under a validation-locked budget of 0.5 false alarms per station-day (FPDs) from 0.1864/0.2511 to 0.2402/0.3042. Japan-local calendar-day intervals supported the improvements in Brier score, recall, and F1. In a dimension-matched comparison using the same 18 MODIS variables, previous-day context increased average precision over the same-day route by 0.0378 (95% CI: 0.0144–0.0603), demonstrating that the timing advantage was not attributable to a larger satellite feature set. The MODIS increment was strongest during high-heat issue times and in Osaka Bay, and its ranking value was reproduced by a 36 h Temporal FLOW model. Matched spatial controls identified distance-based coastal context as the most stable 24 h graph component, while wind-aligned information operated as a complementary route. These results establish latency-aware MODIS thermal context as a measurable decision input for neighborhood-scale coastal compound warning. Strict station-level localization, cross-bay transfer, and forecast-consistent deployment define the next validation frontier. Full article

Forest fires are among the most critical disturbance agents reshaping Mediterranean ecosystems under accelerating climate change. This study employs a multi-scale remote sensing approach to examine the relationship between post-fire vegetation dynamics and climate variability in high-fire-risk areas of southern İzmir, Türkiye. Burned areas were delineated using the Burned Area Index (BAI) and differenced Normalized Burn Ratio (dNBR) applied to Landsat imagery (1990–2024) and Sentinel-2 imagery (2017–2024). Post-fire vegetation recovery was quantified through the Normalized Difference Vegetation Index (NDVI), Soil-Adjusted Vegetation Index (SAVI), Vegetation Condition Index (VCI), Leaf Area Index (LAI), and Land Surface Temperature (LST) derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) products. Climate variables, including soil moisture, precipitation, and maximum, minimum, and mean air temperature, were derived from the TerraClimate dataset. Long-term spatiotemporal trends were assessed using the non-parametric Mann–Kendall (MK) test and Sen’s slope estimator for the 2000–2023 period. Results indicate a statistically significant increase in mean temperature (p < 0.05) and a concurrent decline in soil moisture over the past three decades, consistent with progressive atmospheric aridification. Vegetation indices exhibited marked seasonal asymmetry: significant declines in NDVI, SAVI, and LAI were recorded during summer months, whereas partial recovery was confined to the winter–spring wet season. A pronounced warm-dry shift was identified in the post-2015 period, characterized by positive Land Surface Temperature anomalies and compressed vegetation recovery windows. These findings highlight that increasing thermal stress and diminishing soil moisture collectively constrain post-fire ecosystem resilience in the Mediterranean climatic zone (MCZ). The integrated remote sensing framework developed here provides a robust and transferable basis for fire ecosystem monitoring and the formulation of climate adaptation strategies in fire-prone dryland regions. Full article

Surface water is an important component of urban land-cover systems, but its relationship with land surface temperature (LST) is difficult to interpret in lake-rich urban agglomerations where water bodies, shorelines, vegetation, built-up land, and terrain are closely interwoven. This study examined the association between surface-water fraction and daytime LST in the Wuhan Urban Agglomeration, China, across six benchmark years from 2000 to 2025. MODIS daytime LST, Landsat-derived water maps, GHSL built-up data, vegetation indices, and terrain variables were integrated on a 250 m grid. Surface-water fraction was measured within each grid cell and surrounding windows from 250 to 2000 m. Summer and winter models were compared, and between-location differences were separated from within-location water-fraction changes. Spatial validation, city-specific models, and robustness checks were used to assess consistency. Higher surface-water fraction was associated with lower daytime LST in both seasons. The strongest standardized association occurred at 500 m in summer, whereas the winter association strengthened toward 2000 m. Between-location differences were much larger than within-location changes, and the negative association was retained across all cities. These results suggest that surface-water fraction should be interpreted as a scale- and season-dependent land-cover indicator rather than as a fixed cooling measure. Full article

Rapid urban expansion and agricultural development have substantially altered land use/land cover (LULC), surface thermal regimes, and ecosystem conditions in semi-arid cities. This study investigates the spatiotemporal dynamics of LULC and evaluates their associations with land surface temperature (LST), vegetation health, soil moisture, and drought conditions in Ghazni City, Afghanistan, between 2013 and 2023 using a Google Earth Engine (GEE)-based framework. Landsat 8 OLI/TIRS imagery was classified using a Random Forest (RF) algorithm, while the Normalized Difference Vegetation Index (NDVI), Soil Adjusted Vegetation Index (SAVI), Vegetation Condition Index (VCI), Temperature Condition Index (TCI), Vegetation Health Index (VHI), and LST were derived to evaluate environmental responses. To provide a comprehensive evaluation of urban–climate interactions, monthly Landsat-derived LST time series were analyzed and compared to MODIS and ERA5 datasets through a multi-source consistency assessment framework. The RF classification achieved overall accuracies of 95.56% (2013) and 97.77% (2023), with Kappa coefficients of 0.89 and 0.94, respectively. Results revealed a substantial expansion of built-up areas (5.4%) and vegetation/agricultural land (7.2%), accompanied by a decline in bare land. Urban and barren surfaces consistently exhibited higher LST values, whereas vegetated areas demonstrated a pronounced cooling effect. NDVI and SAVI analyses indicated improving vegetation conditions and soil moisture status over the study period. LST exhibited strong seasonal variability, with summer maxima reaching 49.74 °C and winter minima declining to −8.39 °C. Comparisons among the Landsat, MODIS, and ERA5 datasets demonstrated strong agreement, with a high correlation between Landsat- and MODIS-derived LST (R = 0.84), supporting the reliability of the Landsat-derived LST estimates. Generally, the findings demonstrate the critical role of vegetation in moderating surface temperatures and enhancing urban climate resilience, providing scientific evidence for sustainable land use planning and climate adaptation strategies in semi-arid cities. Full article

Satellite -derived environmental features can predict county-level life expectancy (LE) across the contiguous United States with a mean absolute error of 1.08 years over two decades, without using any census or sociodemographic inputs. We assembled 61,680 county-year observations across 3084 counties from 2000–2019, integrating features from 11 satellite and gridded data streams. The data streams include the Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature and vegetation indices, Sentinel-1 synthetic aperture radar, Sentinel-2 and Landsat optical imagery, the United States Department of Agriculture (USDA) Cropland Data Layer, the European Commission Joint Research Centre (JRC) Global Surface Water layer, the Copernicus Digital Elevation Model, the European Space Agency Climate Change Initiative (ESA CCI) soil moisture record, and the Food and Agriculture Organization (FAO) gridded livestock densities. After a supervised pruning step that removed low-importance variables, a Random Forest regressor was trained and evaluated using 5-fold cross-validation grouped by county. The grouping places all 20 years of each county exclusively in either the training set or the test set, which prevents spatial information leakage between folds. Coefficient of determination, mean absolute error, and root mean squared error are reported as $R^2=0.631\pm 0.013$, MAE $=1.08\pm 0.02$ years, and RMSE $=1.48\pm 0.04$ years. Moran’s I, a measure of residual spatial autocorrelation, is $0.0988$ ($p=0.001$), which supports geographic generalisation. Multimodal fusion reduces unexplained variance by approximately one-third relative to the strongest single-modality baseline (MODIS land surface temperature alone, $R^2=0.442$). TreeSHAP attribution analysis reveals a feature hierarchy in which nighttime land surface temperature features carry roughly $6.16\times$ the cumulative attribution weight of all daytime channels combined. The model response shows a protective inflection near a minimum overnight temperature of about 7.5 °C. Because all input streams are globally available, the framework is architecturally extensible to regions where civil registration and vital statistics systems are incomplete; however, the trained model and its thresholds require recalibration against local mortality data before application outside the contiguous United States. With that caveat, the approach supports satellite-based monitoring of United Nations Sustainable Development Goal (UN SDG) Target 3.9. Full article

Climate change is increasingly altering forest ecosystems worldwide, reshaping species phenology, productivity, and resilience. In this study, we evaluate the phenoclimatic responses of European beech (Fagus sylvatica L.) forests across Europe by assessing their phenological responses to climate change across climatic zones and altitudinal gradients using remote-sensing data. We used 24 years of satellite-derived land-surface phenology and climate data to quantify phenological trends at 356 beech-dominant locations from the EUFGIS database, of which 274 remained after land-cover homogeneity and data-quality filtering. To reduce land-cover mixing at the MODIS resolution, we applied a land-cover homogeneity filter based on ESA WorldCover. The analysis was structured across the seven climatic zones in Europe. Phenological responses to climate change were assessed through climate–phenology sensitivity analyses and a composite phenoclimatic departure index integrating climatic trends, phenological shifts, and interannual variability. Phenological sensitivity varied across climatic zones and phenological phases. Temperature-related sensitivity was most evident in spring in several continental zones, whereas precipitation sensitivity was more apparent for growing-season length and autumn timing in some regions. The composite phenoclimatic departure analysis showed that regional profiles were not uniform across the European beech range. Although warming was widespread, precipitation trends, phenological shifts, and interannual variability differed strongly among zones. These findings demonstrate heterogeneous and location-specific phenoclimatic responses across Europe, but the departure index should not be interpreted as a direct measure of ecological vulnerability or risk. Full article

Forest cover change alters land–atmosphere exchanges of energy, water, and carbon, thereby influencing local and regional climate. This study assessed climatic patterns associated with afforestation in Khyber Pakhtunkhwa, Pakistan, from 2003 to 2023 using remote sensing data and Google Earth Engine. Land surface temperature (LST) was treated as the primary response variable, while evapotranspiration (ET) was analyzed as a secondary response variable. Air temperature; precipitation; vegetation indices, including the normalized difference vegetation index (NDVI) and the enhanced vegetation index (EVI); and elevation were used as supporting variables to interpret the broader climatic and biophysical responses of afforestation. MODIS land-cover, LST, ET, and vegetation-index products, together with climate research unit (CRU) climate data and ALOS-PALSAR DEM, were used to evaluate spatiotemporal trends and variable relationships. The results showed that mean LST increased by 0.520 ± 0.070 °C across KP during 2003–2023; however, areas classified as forest gain showed a localized cooling pattern of 0.490 ± 0.050 °C during the 2013–2023 forest-cover transition assessment window. Afforested areas also exhibited increased ET, whereas forest-loss areas showed reduced ET and higher LST. Specifically, ET increased by 0.013 ± 0.002 mm/8-day in afforested areas, whereas forest-loss areas showed a decline of 0.005 ± 0.001 mm/8-day. CRU-derived regional air temperature showed an increasing tendency of 0.310 ± 0.050 °C, whereas precipitation showed only a weak and statistically non-significant regional tendency; therefore, precipitation was used only as background climatic context. The NDVI and the EVI were negatively correlated with daytime LST, and elevation showed a strong negative relationship with LST. Overall, the findings indicate that forest-cover gain was associated with localized surface cooling patterns and improved vegetation–climate regulation indicators in the study area. Full article