Search Results (652)

Terrestrial vegetation cover is a critical component of the global carbon cycle, annually assimilating a substantial fraction of anthropogenic CO₂ emissions. However, regional estimates of gross primary production (GPP) and net primary production (NPP) remain insufficiently studied, especially for ecologically sensitive areas such as the sub-Mediterranean landscapes of southeastern Crimea. The aim of this study is to calculate and map the spatio-temporal distribution of GPP and NPP across southeastern Crimea over the period 2001–2025 using Earth remote sensing data and geoinformation modeling. This study employed MODIS products (MOD17A2H collection 061) processed in the Google Earth Engine cloud platform, together with temperature and precipitation data (ClimateEU, CHIRPS). Statistical analysis included calculation of the carbon use efficiency (CUE) coefficient and correlation analysis. The results show that the mean GPP for southeastern Crimea is 1.13 kg C/m² and the mean NPP is 0.61 kg C/m², which exceed global average values. Maximum productivity is characteristic of natural forest communities (sessile oak, beech and juniper forests), whereas anthropogenically transformed landscapes (agricultural land, urban coenoses) exhibit the lowest values. The mean CUE is 0.54, with the highest values (0.63–0.66) recorded for agrocoenoses and steppes, and the lowest (0.49–0.57) for forests. A positive correlation between productivity and precipitation and a negative correlation with air temperature were identified, especially for forest ecosystems. This study fills a gap in regional primary productivity assessments and can serve as a basis for ecosystem monitoring under climate change and anthropogenic pressure. Full article

Global climate change has intensified drought risk, threatening water resources, agriculture, and ecosystems. This study evaluated four Moderate Resolution Imaging Spectroradiometer (MODIS)-based drought indices in Hebei Province: the evapotranspiration-to-potential-evapotranspiration-based Crop Water Stress Index (ET/PET-based CWSI), Temperature Condition Index (TCI), Vegetation Condition Index (VCI), and Temperature Vegetation Dryness Index (TVDI). During 2001–2020, the Standardized Precipitation Index (SPI), the Standardized Precipitation Evapotranspiration Index (SPEI), the 1 km gridded soil-moisture product SMCI1.0, Taylor statistics, classification metrics, and typical drought events were used for multi-source consistency evaluation. Although the classification metrics for CWSI indicated moderate drought discrimination and relatively high false-alarm rates, the multi-source consistency evaluation showed that CWSI had the most stable overall performance among the four tested indices. Sen’s slope, the Mann–Kendall test, the Optimal Parameters-based Geographical Detector (OPGD), and random forest were used to examine trends and spatially associated factors. CWSI-derived drought intensity generally decreased, with severe and extreme drought areas decreasing and mild and moderate drought areas increasing. Severe-and-above drought was most prominent in spring. Temperature showed the strongest association with CWSI spatial differentiation, followed by elevation and socioeconomic variables. The interaction between temperature and soil type had the highest explanatory power. These results provide a reference for regional drought monitoring and water resource management. Full article

The Yellow River Basin (YRB) is a key ecological barrier and socio-economic region in China, but the spatiotemporal dynamics of its ecosystem carbon sink and the non-linear effects of environmental drivers remain insufficiently understood. This study estimated Net Ecosystem Productivity (NEP) in the YRB from 2001 to 2024 using MODIS Net Primary Productivity (NPP) data and an empirical soil heterotrophic respiration model, analyzed NEP trends with the Theil–Sen estimator and Mann–Kendall test, and quantified non-linear responses to climatic, temporal, and spatial factors using a Generalized Additive Model (GAM). The YRB acted as a persistent and strengthening net carbon sink, with annual total NEP increasing significantly from 39.85 Tg C yr⁻¹ in 2001 to 176.44 Tg C yr⁻¹ in 2024, at a rate of 5.65 Tg C yr⁻¹. NEP showed a clear southeast-to-northwest decreasing gradient, and 85.6% of the basin exhibited increasing trends, particularly on the Loess Plateau. The GAM captured non-linear associations of NEP with temperature, precipitation, solar radiation, relative humidity, year, and spatial location, and achieved a moderate pooled spatial block cross-validated R² of 0.723. NEP displayed a unimodal association with temperature—with a fitted peak near 0 °C reflecting the spatial transition from cold high-altitude to warmer water-limited regions—and a saturation-type response to precipitation, highlighting the joint control of hydrothermal conditions and pervasive water limitation. The fitted spatial smooth further revealed residual spatially structured variation that may be partly associated with irrigation and land management. These findings improve the understanding of carbon-sink dynamics in the YRB and provide scientific support for climate-adaptive ecosystem management and the regional implementation of China’s “dual carbon” goals. Full article

Mountain transition zones are highly sensitive to environmental change, yet the nonlinear coupling between topography and hydroclimate in controlling vegetation Net Primary Productivity (NPP) remains insufficiently constrained. Here, we reconstructed a 2003–2025 annual NPP time series for the West Qinling Mountains using a Carnegie–Ames–Stanford Approach (CASA)-based workflow that integrated Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation products, fifth-generation European Centre for Medium-Range Weather Forecasts reanalysis for land (ERA5-Land) meteorological data, Shuttle Radar Topography Mission (SRTM) topography, and an Aridity Index (AI) dataset. Product-based validation against the annual MOD17A3HGF dataset indicated strong agreement, with a 23-year mean spatial Spearman correlation of 0.823 and a mean annual Pearson correlation of 0.773. The reconstructed dataset showed that 98.50% of the study area experienced increasing NPP, including 50.26% with significant increases, and the domain-wide mean Sen slope reached approximately 3.08 g C m⁻² yr⁻¹. Factor detection further showed that radiation (q = 0.253), elevation (q = 0.252), and temperature (q = 0.249) were the dominant single controls, whereas Aridity–Temperature (q = 0.367) and Elevation–Aridity (q = 0.367) represented the strongest interactions. The concentration of the strongest gains in gentle-slope and moderate-aridity settings suggests that vegetation recovery is maximized where topographic buffering and water-energy balance are jointly optimized. These results strengthen the interpretation of NPP dynamics in mountainous climate-transition environments and provide a basis for spatially targeted ecological restoration, regional carbon-budget assessment, and climate adaptation planning. Full article

The increasing frequency and intensity of extreme weather are profoundly affecting the ecological carbon cycle in arid regions, yet there remains a lack of quantitative understanding regarding the nonlinear response of net ecosystem productivity (NEP) in Central Asia to changes in extreme weather conditions. This study focused on the five Central Asian countries and China’s Xinjiang region, where NEP was estimated using MODIS Net Primary Productivity and daily meteorological data, and 12 extreme climate indices (ECIs) were constructed. By combining the XGBoost model with the SHapley Additive exPlanations method, the key ECIs of NEP were identified for different regions, and their nonlinear responses and threshold characteristics were quantified. The results show that from 2000 to 2023, Central Asia overall acted as a weak carbon source, with NEP exhibiting a spatial pattern of increasing in the east and decreasing in the west. Extreme precipitation indices showed an overall declining trend, whereas extreme temperature indices increased significantly. There is significant spatial heterogeneity in the importance of ECIs across different regions. Specifically, the annual total precipitation (PRCPTOT) is most important in Kazakhstan, Kyrgyzstan, and Tajikistan, while the annual maximum of daily maximum temperature (TXx) shows greater importance in Turkmenistan and Xinjiang. The responses of NEP to ECIs exhibited significant nonlinear threshold characteristics. PRCPTOT showed a positive saturation effect in Kazakhstan, Kyrgyzstan, and Tajikistan, with a threshold range of 700–1000 mm in Kyrgyzstan. TXx exhibited a pronounced negative high-temperature effect in Turkmenistan and Xinjiang, with thresholds of approximately 42 °C and 30 °C, respectively. In Uzbekistan, Diurnal Temperature Range (DTR) showed a response trough near 10 °C. The study reveals the nonlinear response and spatial heterogeneity of the NEP in Central Asia to extreme weather change, offering theoretical support for ecosystem restoration and sustainable carbon management in arid regions. 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

Satellite-based land surface temperature (LST) products are frequently affected by cloud cover and atmospheric conditions, resulting in missing data that significantly limits the continuous monitoring of the thermal environment in complex terrains, such as the Tibetan Plateau. Existing spatiotemporal interpolation methods face clear accuracy limitations when addressing extensive data gaps, while physical models often struggle due to insufficient meteorological inputs in complex landscapes. Moreover, conventional data-driven approaches usually overlook local spatial variations, resulting in smoothed thermal patterns and systematic errors. To overcome these issues, we propose a Physically Constrained Spatial Residual Learning framework. In this framework, we use the Enhanced Annual Temperature Cycle (EATC) model to capture the temporal baseline of LST first. Then, we integrate multi-source auxiliary data into the Geographical-XGBoost (G-XGBoost) algorithm to model spatial nonlinear residuals. Using simulated cloud masks on the 2017 MODIS LST dataset from the Qinghai–Tibet Engineering Corridor, we show that the hybrid model outperforms both individual physical models and global machine learning models in accuracy and spatial detail recovery. Validation results yield an R² of 0.88, an RMSE of 1.92 K, and a mean bias of 0.07 K. Seasonal evaluations indicate best performance in winter (RMSE = 1.19 K) with robust performance in summer. Furthermore, the framework reduces boundary artifacts and accurately reproduces thermal spatial patterns in complex terrain through adaptive local bandwidth and weight adjustments. This approach provides a reliable method for high-precision LST reconstruction over heterogeneous alpine surfaces. Full article

The Sea and Land Surface Temperature Radiometer (SLSTR) is a dual-view scanning radiometer onboard the Sentinel-3A and Sentinel-3B satellites. This sensor provides data from the visible to the thermal infrared, with a temporal resolution of approximately 12 h. In this work, we present an automated system using shortwave infrared (SWIR) bands at 500 m spatial resolution to monitor active volcanoes in near real time. The system implements a normalized hotspot index (NHI) to detect and characterize high-temperature volcanic features in daylight and nighttime conditions. During the first three months of operation (i.e., August–October 2025), the system successfully identified several eruptive activities, with a false positive rate around 2.0%. The latter includes also true hot pixels associated with vegetation fires and other high-temperature sources. Results were assessed through comparison with the Fire Information for Resource Management System (FIRMS), the Middle Infrared Observations of Volcanic Activity (MIROVA), MODVOLC, and the S3-L2 FRP product. The preliminary comparison with the MIROVA-MODIS dataset reveals a good correlation in the estimates of fire radiative power over Etna (Italy) and Kilauea (Hawaii, USA), although discrepancies in the magnitude of this parameter remain significant also because of the SWIR retrieval method, which was optimized for gas flares. Despite the impact of snow-covered surfaces and band co-registration on the accuracy of hotspot detection, this study shows that the NHI-SLSTR system may provide a relevant contribution to the surveillance of active volcanoes from space, integrating information from other systems performing globally. Full article

Reference evapotranspiration (ET₀) represents the atmospheric demand for water from a well-watered vegetated surface and is a key component of the hydrological cycle and agricultural water management. This study evaluated the performance of seven machine learning (ML) models: linear regression (LR), Random Forest (RF), Support Vector Machines (SVM), K-Nearest Neighbors (KNN), Extreme Gradient Boosting (XGBoost), Decision Trees (DT), and Cubist, for predicting monthly FAO-56 Penman–Monteith ET₀ in Benin. The target variable was calculated from data collected at six synoptic stations over the 2017–2021 period. Ten remote-sensing and topographic predictors were used: MODIS Land Surface Temperature (LST), six Sentinel-2 optical vegetation indices (NDVI, EVI, NDMI, NDWI, MSI, NDRE), elevation, and cyclic month encoding. Models were trained on the 2017–2019 period and evaluated on an independent temporal test set (2020–2021). All models showed positive predictive performance, with the BMA ensemble achieving the highest accuracy (RMSE = 7.0% of mean ET₀, R² = 0.802), followed by Cubist (RMSE = 7.3%, R² = 0.787) and DT (RMSE = 7.5%, R² = 0.776). The seven models were combined via Bayesian Model Averaging (BMA) with posterior weights estimated by the EM algorithm to produce 1 km monthly ET₀ maps for Benin for 2025. BMA-derived inter-model standard deviation provided spatially explicit uncertainty estimates, revealing that prediction uncertainty is greatest in the northern Sudanian zone during the dry season. The ET₀ target variable was constructed as a hybrid product combining station temperature observations with solar radiation, wind speed, and vapor pressure deficit extracted from the TerraClimate gridded reanalysis dataset; this methodological choice is discussed as a study limitation. Full article

Gross primary productivity (GPP) is central to terrestrial carbon cycling and forest carbon sink assessment. Using Google Earth Engine, MODIS GPP, ERA5-Land meteorological data, and forest extent masks, this study examined GPP dynamics and climatic controls in China’s northeast, southern, and southwest forest regions from 2005 to 2025. GPP increased overall in all three regions, with higher values in the south and lower values in the north. Climatic drivers differed regionally: in the northeast, GPP responded positively to temperature, while VPD slightly exceeded temperature in the dominant-control area; in the southern region, temperature was the main driver but VPD remained important; in the southwest, temperature dominated larger areas, whereas moisture-related controls showed stronger spatial heterogeneity. Piecewise analysis identified temperature–VPD turning points of 11.74 °C, 10.43 °C, and 25.64 °C for the northeast, southwest, and southern regions, respectively. Two-dimensional temperature–VPD binning further revealed nonlinear GPP distributions and distinct optimal hydrothermal combinations across regions. These results show that warming effects on forest productivity are region-specific and constrained by atmospheric dryness, providing evidence for assessing China’s forest carbon sink responses to climate change. Full article

This study presents an integrated framework for agricultural drought monitoring in data-scarce regions, utilizing the Google Earth Engine (GEE) platform to analyze multisource Earth observation data over the South Wollo highlands, Ethiopia, from 2001 to 2024. The analysis was complemented by Mann–Kendall trend testing, Sen’s slope estimation, and Pettitt change-point detection to identify and quantify long-term trends and abrupt shifts in drought dynamics. The methodology integrates climatic and satellite-derived indicators within a hybrid analytical framework. It incorporates the standardized precipitation evapotranspiration index (SPEI), vegetation condition index (VCI), vegetation health index (VHI), temperature condition index (TCI), and land surface temperature (LST), which are derived from MODIS (NDVI, LST, PET) and CHIRPS precipitation datasets. The analysis focused on the main growing season (June–September) to capture critical crop growth and moisture-sensitive periods for agricultural production in the study area. The findings reveal pronounced interannual variability in drought occurrence and intensity across the study period. Severe agricultural drought conditions were most extensive in 2009 and 2014, with VHIs indicating 15% and 4% of the area under severe and extreme drought in 2009, respectively, and 2.6% and 2% in 2014, respectively. In contrast, 2001, 2005, 2020, and particularly 2024 were characterized by predominantly no-drought to mild-drought conditions, with no-drought coverage increasing from 86.7% (2009) to 98.0% (2024). Vegetation-based indices demonstrate that drought impacts are episodic rather than persistent and strongly controlled by rainfall timing and early-season moisture availability. The LST exhibited marked year-to-year variability (28.8 °C to 33.8 °C), with elevated temperatures coinciding with drought periods and suppressed evaporative cooling. Correlation analysis confirmed a strong positive relationship between the SPEI and VHI (r = 0.77), with moderate correlations for the VCI (r = 0.40) and TCI (r = 0.36), underscoring the sensitivity of integrated vegetation health to the climatic water balance. The study concludes that combining the SPEI with satellite-derived vegetation and thermal indices provides a robust, scalable approach for agricultural drought assessment in regions with limited ground-based observations. The integrated framework effectively captures both moisture deficits and thermal stress components, offering a scientific basis for improving drought early warning systems and climate-resilient agricultural planning in Ethiopia and similar environments. Full article

Land surface temperature (LST) is a key variable in the physics of land surface processes on both regional and global scales. This paper addresses the all-sky (clear-sky and cloudy-sky) LSTs retrieval from the data acquired by the Medium-Resolution Spectral Imager II on Fengyun 3D (FY-3D) satellite. First, an improved split-window algorithm to retrieve clear-sky LSTs is developed using numerical radiative transfer modeling experiments. Then, clear-sky LSTs are retrieved from MERSI-II/FY-3D data in January and July 2022 over an Asian area (70°E~130°E, 10°N~50°N), and cross-validated against MODIS/Aqua LST/emissivity (LST/E) Daily version 6 (MYD11C1 V6) product. Next, a hybrid method combining the eXtreme Gradient Boosting (XGBoost) model and the surface energy balance theory is developed to estimate cloudy-sky LSTs. After that, cloudy-sky LSTs are estimated from the MERSI-II data and validated with the China Meteorological Administration Land Data Assimilation System Version 2 (CLDAS V2) dataset. Against the MYD11C1 LSTs, the root mean square error (RMSE), bias and coefficient of determination (R²) of the retrieved clear-sky LSTs are 1.15 K, 0.01 ± 1.14 K, and 0.99, respectively. Against the CLDAS LSTs, the RMSE, bias and R² of the estimated hypothetical clear-sky LSTs are 4.05 K, 0.75 ± 3.98 K and 0.91, respectively, while they are 3.69 K, 0.36 ± 3.67 K, and 0.92 for the retrieved cloudy-sky LSTs, respectively, which indicates that the retrieval accuracy of cloudy-sky LSTs is improved after the cloud radiation effect correction. The all-sky LSTs retrieved in this study are accurate and consistent with the results in previous studies. Full article

High-resolution estimation of global near-surface air temperature (Ta) is essential for investigating microclimates, ecosystem processes, and agricultural suitability. However, sparse in situ observations do not capture local heterogeneity, whereas existing datasets lack fine-scale detail because of their coarse spatial resolution. To address this limitation, we developed an ensemble machine-learning framework using Landsat 8/9 data. Predictions from LightGBM, XGBoost, and CatBoost were combined through Bayesian model averaging (BMA), which assigns probabilistic weights to individual models to improve robustness. The models were trained using a globally distributed spatiotemporal matchup dataset that paired HadISD in situ Ta observations with MODIS/VIIRS products to support subsequent Landsat-based application. Key inputs included land surface temperature (LST), vegetation indices, elevation, solar zenith angle, and spatiotemporal features. The BMA ensemble achieved strong validation performance, with an RMSE of ~3 K, near-zero bias, and an R² of 0.92. Feature-importance analysis identified LST as the dominant predictor, underscoring the primary role of surface thermal state in estimating Ta. The proposed method can generate robust global Ta fields at 90 m resolution, revealing fine-scale thermal patterns that have previously been difficult to resolve at the global scale. Unlike many regional models calibrated for single study area or dependent on dynamic external auxiliary fields, our Landsat-predominant application framework supports operational mapping of clear-sky and overpass-time Ta. Such detailed instantaneous data can advance climate research, improve assessments of ecological responses and climate impacts, and support applications such as urban heat island monitoring and precision agriculture. Full article

Existing Google Earth Engine-based retrieval workflows often use fixed normalized difference vegetation index thresholds and coarse atmospheric water vapor inputs, which may limit their adaptability to regional surface and atmospheric conditions. This study evaluates how these two intermediate parameters influence Landsat 8 land surface temperature retrieval over northeastern Egypt using the generalized single-channel algorithm. Atmospheric water vapor was derived from MERRA-2 and NCEP reanalysis products, while land surface emissivity was estimated using ASTER Global Emissivity Dataset data and an NDVI-threshold framework. Reanalysis-derived total precipitable water was first compared with MODIS MOD05_L2. MERRA-2 showed a stronger correlation with MOD05_L2, whereas NCEP produced lower bias and root mean square error. The retrieved land surface temperature was then compared with the Landsat 8 Collection 2 Level 2 LST product as an internal consistency check. Using MERRA-2 reduced the overall root mean square error from 1.3977 K to 1.2615 K relative to NCEP, although it also increased the magnitude of the negative bias. A grid search of 24 normalized difference vegetation index threshold combinations showed that retrieval consistency was sensitive to threshold selection in cropland areas, while desert and built-up areas were largely insensitive. The best overall consistency with the Landsat product was obtained using a soil threshold of 0.20 and a vegetation threshold of 0.75, with a root mean square error of 1.2507 K and a bias of −0.7236 K. External validation at the Baseline Surface Radiation Network Gobabeb station showed a slight improvement when using MERRA-2 instead of NCEP, with root mean square error decreasing from 4.726 K to 4.441 K. Overall, the results show that intermediate parameter choices can affect Landsat land surface temperature retrieval, but the optimized settings should be interpreted as region-specific and relative to the Landsat product because independent validation remains limited. Full article