Climate change, population growth, and economic development exacerbate water scarcity. This study investigates the impact of drought on water availability in the Belt and Road region using high-resolution remote sensing data from 2001 to 2020. The results revealed an average water availability (precipitation minus evapotranspiration) of 249 mm/year and a declining trend in the Belt and Road region. Approximately 13% of the Belt and Road region faces water deficits (evapotranspiration exceeds precipitation), primarily in arid and semi-arid regions with high drought frequency. The area in the water deficit is expanding, and the intensity of the water deficit is increasing. The annual trend of water availability is strongly related to the frequency of droughts, i.e. water availability decreases with increased drought frequency. Drought exacerbates seasonal water stress in approximately one-third of the Belt and Road region, mainly in Europe and northern Asia, where drought frequently occurs during seasons with low water availability. The more severe the drought, the larger the negative anomaly in water availability. The critical role of evapotranspiration in seasonal water availability variability is also highlighted. This research underscores the importance of understanding drought-induced changes in water availability, which is crucial for sustainable water resource management.

Water volume, a fundamental characteristic of lakes, serves as a crucial indicator for understanding regional climate, ecological systems, and hydrological processes. However, limitations in existing estimation methods and datasets for water depth, such as the insufficient observation of small and medium-sized lakes and unclear temporal information, have hindered a comprehensive understanding of global lake water volumes. To address these challenges, this study develops a machine learning (ML)-based approach to estimate the dynamic water depths of global lakes. By incorporating various lake features and employing multiple innovative water depth extraction methods, we generated an extensive water depth dataset to train the model. Validation results demonstrate the model’s high accuracy, with the bias of −0.08 m, a MAE of 1.09 m, an RMSE of 4.78 m, and an R² of 0.95. The proposed method provides dynamic monthly estimates of global lake water depths and volumes in 2000~2020. This study offers a cost-effective and efficient solution for estimating global lake water dynamics, providing reliable data to support the monitoring, analysis, and management of regional and global lake systems.

Time series of spatially continuous satellite data are increasingly used for environmental studies. Among these, land surface temperature (LST), retrieved from data such as the MODerate resolution Imaging Spectroradiometer (MODIS), plays a vital role in numerous applications. However, cloud cover significantly reduces the number of usable pixelwise LST observations. Despite various documented methods for reconstructing missing LST pixels, challenges remain regarding their flexibility to handle varying gap percentages and reliance on multiple ancillary datasets. This study presents a flexible and automated technique to reconstruct missing LST pixels without relying on ancillary data. The approach combines three innovative techniques: global regression analysis, local regression analysis, and geospatial analysis. The missing pixels percentage of each day determines the appropriate technique to fill the gaps. The method was applied to daily Terra MODIS LST datasets (MOD11A1) at 1 km spatial resolution from 2002 to 2022. Two evaluation methods were conducted: comparing with in-situ measurements and introducing artificial gaps. The validation was demonstrated over the Heihe River basin in China and in four experimental areas worldwide with available ground measurements from FLUXNET. Validation with artificial gaps produced average root-mean-square error (RMSE) and mean absolute error (MAE) of 2.33 K and 1.76 K, respectively. In-situ measurements indicated superior performance with R ², RMSE, and MAE of 0.85, 4 K, and 3.4 K, outperforming two existing methods. The study demonstrates that the model accurately reconstructs missing pixels on heterogeneous surfaces under diverse conditions, effectively handling large datasets and complex gaps.

Evaluating the performance of irrigation water use is essential for efficient and sustainable water resource management. However, existing approaches often lack systematic quantification of irrigation water consumption and fail to differentiate between the use of precipitation and anthropogenic appropriation of water flows. Building on the green–blue water concept, consumptive water use, assumed equal to actual evapotranspiration (ET_a), was partitioned into green ET (GET) and blue ET (BET) using remote sensing data and the Budyko hypothesis. A novel BET metric of consumptive irrigation water use was developed and applied to the irrigated lands in northwest China to evaluate the performance of irrigation from 2001 to 2021. The results showed that in terms of total available water resources (precipitation + gross irrigation water (GIW)) compared to irrigation water demand, estimated as reference evapotranspiration (ET₀), Ningxia has sufficient water supply to meet irrigation demand, while the Hexi Corridor faces increasing risks of unsustainable water use. The Hetao irrigation scheme has shifted from a fragile supply–demand balance to a situation where water demand far exceeds availability. In Xinjiang, the balance between water supply and demand is tight. Furthermore, when considering the available water (GIW) relative to the net irrigation water demand (ET₀-GET), the Hexi Corridor faces significant water deficits, and Ningxia and Xinjiang are close to meeting local irrigation water demands by relying on current water availability and efficient irrigation practices. It is noteworthy that the BET remains lower than the GIW in northwest China (excluding the Hexi Corridor in recent years). The ratio of the BET to GIW is an estimate of the scheme irrigation efficiency, which was equal to 0.54 for all irrigation schemes taken together. In addition, the irrigation water use efficiency, estimated as the ratio of BET to net irrigation water, was evaluated in detail, and it was found that in the last 10 years the irrigation water use efficiency improved in Ningxia, the Hetao irrigation scheme, and Xinjiang. However, the Hexi Corridor continues to face severe net irrigation water deficits, suggesting the likelihood of groundwater use to sustain irrigated agriculture. BET innovatively separates consumptive use of precipitation (green water) and consumptive use of irrigation (blue water), a critical advancement beyond conventional approaches’ estimates that merge these distinct hydrological components to help quantifying water use efficiency.

The estimation of water requirements constitutes a critical prerequisite for delineating water scarcity hotspots and mitigating intersectoral competition, particularly in endorheic basins in arid or semi-arid regions where hydrological closure exacerbates resource allocation conflicts. Under conditions of water scarcity, water supplied locally by precipitation and shallow groundwater bodies should be taken into account to estimate the net water requirements to be met with water conveyed from off-site sources. This concept is embodied in the distinction of blue ET (BET) and green ET (GET). In this study, the Budyko hypothesis (BH) method was optimized to partition the total ET into GET and BET during 2001–2018 in the Heihe River Basin. In this region, a better knowledge of net water requirements is even more important due to water allocation policies which reduced water supply to irrigated lands in the last 15 years. This study proposes a modified BH method based on a new vegetation-specific parameter ((Formula presented.)) which was optimized for different vegetation types using precipitation and actual ET data obtained from remote sensing observations. The results show that the BH method partitioned GET and BET reasonably well, with a percent bias of 23.8% and 37.4% and a root mean square error of 84.8 mm/a and 113.6 mm/a, respectively, when compared with reported data, which are superior to that of the precipitation deficit and soil water balance methods. A sensitivity experiment showed that the BH method exhibits a low sensitivity to uncertainties of input data. The results documented differences in the contribution of GET and BET to total ET across different land cover types in the Heihe River Basin. As expected, rainfed forest and grassland ecosystems are predominantly governed by GET, with 81.3% and 87.2% of total ET, respectively. In contrast, croplands and shrublands are primarily regulated by BET, with contributions of 61.5% and 84.3% to total ET. The improved BH method developed in this study paves the way for further analyses of the net water requirements in arid and semi-arid regions.

Accurate knowledge of the at-surface solar irradiance (SSI) is essential for retrieving surface and atmospheric properties using satellite measurements of backscattered and reflected radiance. The latter is affected by surface-atmosphere interactions, including the effects of terrain. The SSI is affected by the same processes. This study proposes a method to estimate the components of instantaneous SSI: direct, isotropic and circumsolar diffuse, and terrain irradiance, which is expected to improve the simultaneous retrieval of aerosol optical depth (AOD) and surface reflectance. The method takes into account the coupled effects of topography and atmosphere by combining parameterization and the lookup table (LUT) approaches. The method was applied to rugged terrain over the Tibetan Plateau using Moderate Resolution Imaging Spectrometer (MODIS) atmosphere and surface data, the fifth generation European Centre for Medium-Range Weather Forecasts reanalysis (ERA5) data, Cloud-Aerosol Lidar With Orthogonal Polarization (CALIOP) aerosol data, and a digital elevation model (DEM). The results showed that the SSI estimates were in satisfactory agreement with ground observations at four stations over the Tibetan Plateau (TP) in 2018 with R2 values of 0.61, 0.44, 0.41, and 0.49, respectively, and root mean square error (RMSE) of 205.7, 176.9, 186.0, and 201.2 W/m2, respectively. Estimations of the diffuse irradiance were evaluated separately against the only available in situ observations at the Dali Station, and the results were better than our SSI estimates with R2, RMSE, and relative bias (BIAS) being 0.71, 94.98 W/m2, and 31%, respectively. The isotropic and circumsolar diffuse irradiances accounted for 37.57% and 7.68% of the total annual SSI, respectively, while diffuse irradiance accounted for 46.48% of the total annual SSI. Under clear skies, every 0.1 increase in AOD caused about a 35-W/m2 increase in diffuse irradiance and a decrease of about 25 W/m2 of SSI.

Improving irrigation water management is a key concern for the agricultural sector, and it requires extensive and comprehensive tools that provide a complete knowledge of crop water use and requirements. This study presents a novel methodology to explicitly estimate daily gross and net crop water requirements, actual crop water use, and irrigation efficiency of center pivot irrigation systems, by mainly utilizing the Sentinel-2 MultiSpectral Instrument (MSI) imagery at the farm scale. ETMonitor model is adapted to estimate actual water use (as the sum of canopy transpiration and evaporation of water intercepted by canopy and evaporation from soil) at daily/10-m resolution, benefiting from the high-resolution Sentinel-2 data and thus to assess the irrigation efficiency at the farm scale. The gross irrigation water requirement is estimated from the net crop water requirement and the water loss, including the water droplet evaporation directly into the air during application before droplets fall on the canopy and canopy interception loss. The method was applied to a pilot farmland with two major crops (wheat and potato) in the Inner Mongolia Autonomous Region of China, where modern equipment and appropriate irrigation methods are deployed for efficient water use. The estimated actual crop water use showed good agreement with the ground observations, e.g. the determination coefficients range from 0.67 to 0.81 and root mean square errors range from 0.56 mm/day to 1.24 mm/day for wheat and potato when comparing the estimated evapotranspiration with the measurement by the eddy covariance system. It also showed that the losses of total irrigated volume were 25.4% for wheat and 23.7% for potato, respectively, and found that the water allocation was insufficient to meet the water requirement in this irrigated area. This suggests that the amount of water applied was insufficient to meet the crop water requirement and the inherent water losses in the center pivot irrigation system, which imply the necessity to improve the irrigation practice to use the water more efficiently.

Flash droughts tend to cause severe damage to agriculture due to their characteristics of sudden onset and rapid intensification. Early detection of the response of vegetation to flash droughts is of utmost importance in mitigating the effects of flash droughts, as it can provide a scientific basis for establishing an early warning system. The commonly used method of determining the response time of vegetation to flash drought, based on the response time index or the correlation between the precipitation anomaly and vegetation growth anomaly, leads to the late detection of irreversible drought effects on vegetation, which may not be sufficient for use in analyzing the response of vegetation to flash drought for early earning. The evapotranspiration-based (ET-based) drought indices are an effective indicator for identifying and monitoring flash drought. This study proposes a novel approach that applies cross-spectral analysis to an ET-based drought index, i.e., Evaporative Stress Anomaly Index (ESAI), as the forcing and a vegetation-based drought index, i.e., Normalized Vegetation Anomaly Index (NVAI), as the response, both from medium-resolution remote sensing data, to estimate the time lag of the response of vegetation vitality status to flash drought. An experiment on the novel method was carried out in North China during March–September for the period of 2001–2020 using remote sensing products at 1 km spatial resolution. The results show that the average time lag of the response of vegetation to water availability during flash droughts estimated by the cross-spectral analysis over North China in 2001–2020 was 5.9 days, which is shorter than the results measured by the widely used response time index (26.5 days). The main difference between the phase lag from the cross-spectral analysis method and the response time from the response time index method lies in the fundamental processes behind the definitions of the vegetation response in the two methods, i.e., a subtle and dynamic fluctuation signature in the response signal (vegetation-based drought index) that correlates with the fluctuation in the forcing signal (ET-based drought index) versus an irreversible impact indicated by a negative NDVI anomaly. The time lag of the response of vegetation to flash droughts varied with vegetation types and irrigation conditions. The average time lag for rainfed cropland, irrigated cropland, grassland, and forest in North China was 5.4, 5.8, 6.1, and 6.9 days, respectively. Forests have a longer response time to flash droughts than grasses and crops due to their deeper root systems, and irrigation can mitigate the impacts of flash droughts. Our method, based on cross-spectral analysis and the ET-based drought index, is innovative and can provide an earlier warning of impending drought impacts, rather than waiting for the irreversible impacts to occur. The information detected at an earlier stage of flash droughts can help decision makers in developing more effective and timely strategies to mitigate the impact of flash droughts on ecosystems.

The West Sahel is facing significant threats to its vegetation and wildlife due to the land degradation and habitat fragmentation. It is crucial to assess the regional vegetation greenness dynamics in order to comprehensively evaluate the effectiveness of protection in the natural reserves. This study analyzes the vegetation greenness trends and the driving factors in the Dosso Partial Faunal Reserve in Niger and nearby unprotected regions—one of the most important habitats for endemic African fauna—using satellite time series data from 2001 to 2020. An overall vegetation browning trend was observed throughout the entire region with significant spatial variability. Vegetation browning dominated in the Dosso Reserve with 17.7% of the area showing a significant trend, while the area with significant greening was 6.8%. In a comparison, the nearby unprotected regions to the north and the east were found to be dominated by vegetation browning and greening, respectively. These results suggest that the vegetation protection practice was not fully effective throughout the Dosso Reserve. The dominant drivers were also diagnosed using the Random Forest model-based method and the Partial Dependence Plot tool, showing that water availability (expressed as soil moisture) and land use/land cover change were the most critical factors affecting vegetation greenness in the study region. Specifically, soil moisture stress and specific land management practices associated with logging, grazing, and land clearing appeared to dominate vegetation browning in the Dosso Reserve. In contrast, the vegetation greening in the central Dosso Reserve and the nearby unprotected region to the east was probably caused by the increase in shrubland/forest, which was related to the effective implementation of protection. These findings improve our understanding of how regional vegetation greenness dynamics respond to environmental changes in the Dosso Reserve and also highlight the need for more effective conservation planning and implementation to ensure sustainable socio-ecological development in the West Sahel.

Global-scale surface soil moisture (SSM) products (e.g. SMAP L3.0, ASCAT V3.0, ESA/CCI V7.1 and GLDAS V2.2) are vital for applications in hydrology, climate variability, and agriculture. This study uses a new SSM evaluation approach by combining temporal evolution, Coefficient of Variation (CV), Cumulative Distribution Function (CDF), evaluation metrics, and Triple Collocation Analysis (TCA) to assess SSM accuracy and spatial–temporal variability, particularly the impact of footprint mismatch when comparing retrieved SSM with in-situ measurements. Results revealed significant spatial variability and seasonal patterns in SSM, as indicated by the CV values and temporal evaluations at different resampling scales. The variability captured by in-situ measurements was comparable to that of SSM products. The impact of footprint mismatch between in-situ measurements and data products, particularly for SMAP and ASCAT SSM, was more significant and led to substantial differences in evaluation metrics between smaller and larger spatial scales. TCA alone cannot reliably assess the accuracy of global-scale SSM products without in-situ SSM measurements. Overall, our findings highlight the critical role of footprint mismatch on the estimated accuracy of SSM products and underscore the need to combine multiple evaluations into an overall scoring indicator, as proposed in this study.

Rationale: Chronic inflammation plays an important role in alveolar tissue damage in emphysema, but the underlying immune alterations and cellular interactions are incompletely understood. Objectives: To explore disease-specific pulmonary immune cell alterations and cellular interactions in emphysema. Methods: We used single-cell mass cytometry (CyTOF) to compare the immune compartment in alveolar tissue from 15 patients with severe emphysema and 5 control subjects. Imaging mass cytometry (IMC) was applied to identify altered cell-cell interactions in alveolar tissue from patients with emphysema (n = 12) compared with control subjects (n = 8). Measurements and Main Results: We observed higher percentages of central memory CD4 T cells in combination with lower proportions of effector memory CD4 T cells in emphysema. In addition, proportions of cytotoxic central memory CD8 T cells and CD127+CD27+CD69- T cells were higher in emphysema, the latter potentially reflecting an influx of circulating lymphocytes into the lungs. Central memory CD8 T cells, isolated from alveolar tissue from patients with emphysema, exhibited an IFN-γ response upon anti-CD3 and anti-CD28 activation. Proportions of CD1c+ dendritic cells, expressing migratory and costimulatory markers, were higher in emphysema. Importantly, IMC enabled us to visualize increased spatial colocalization of CD1c+ dendritic cells and CD8 T cells in emphysema in situ. Conclusions: Using CyTOF, we characterized the alterations of the immune cell signature in alveolar tissue from patients with chronic obstructive pulmonary disease stage III or IV emphysema versus control lung tissue. These data contribute to a better understanding of the pathogenesis of emphysema and highlight the feasibility of interrogating the immune cell signature using CyTOF and IMC in human lung tissue. Clinical trial registered with www.clinicaltrials.gov (NCT04918706).

Introduction: The vegetation dynamics of the Sahel-Sudan-Guinea region in Africa, one of the largest transition zones between arid and humid zones, is of great significance for understanding regional ecosystem changes. However, a time-unvarying trend based on linear assumption challenges the overall understanding of vegetation greenness evolution and of tracking a complex ecosystem response to climate in the Sahel-Sudan-Guinea region. Methods: This study first applied the ensemble empirical mode decomposition (EEMD) method to detect the time-varying trends in vegetation greenness based on normalized difference vegetation index (NDVI) data in the region during 2001–2020, and then identified the dominant climatic drivers of NDVI trends by employing explainable machine learning framework. Results: The study revealed an overall vegetation greening but a significant nonlinear spatio-temporal evolution characteristic over the region. Trend reversals, i.e., browning-to-greening and greening-to-browning, were dominant in approximately 60% of the study area. The browning-to-greening reversal was primarily observed in the southern Sahel, Congo Basin north of the Equator, and East Africa, with a breakpoint around 2008, while the greening-to-browning reversal was mainly observed in West Africa, with a breakpoint around 2011. The sustained greening primarily took place in northern Sahel, Central African Republic and South Sudan; while sustained browning clustered in central West Africa and Uganda, mainly in agricultural lands. Furthermore, the combination of Random Forest (RF) algorithm and the SHapley Additive exPlanations (SHAP) method could robustly model and reveal the relationships between the observed trends in NDVI and in climatic variables, also detected by applying EEMD. The results suggested that air temperature and precipitation were the most important climatic drivers controlling the NDVI trends across the Sahel-Sudan-Guinea region. The NDVI trends were more likely to have negative correlations with solar radiation and vapor pressure deficit in arid areas, while they could have positive correlations in humid areas. The study also found that large-scale climate changes induced by sea surface temperature (SST) anomalies had strong relationships with trend reversals in vegetation greenness at a sub-continental scale. These findings advanced the understanding of the impacts of climatic drivers on vegetation greenness evolution in the Sahel-Sudan-Guinea region.

Accurate and continuous estimation of surface albedo is vital for assessing and understanding land–surface–atmosphere interactions. We developed a method for estimating instantaneous all-sky at-surface shortwave upwelling radiance and albedo over the Tibetan Plateau. The method accounts for the complex interplay of topography and atmospheric interactions and aims to mitigate the occurrence of data gaps. Employing an RTLSR-kernel-driven model, we retrieved surface shortwave albedo with a 1 km resolution, incorporating direct, isotropic diffuse; circumsolar diffuse; and surrounding terrain irradiance into the all-sky solar surface irradiance. The at-surface upwelling radiance and surface shortwave albedo estimates were in satisfactory agreement with ground observations at four stations in the Tibetan Plateau, with RMSE values of 56.5 W/m² and 0.0422, 67.6 W/m² and 0.0545, 98.6 W/m² and 0.0992, and 78.0 98.6 W/m² and 0.639. This comparison indicated an improved accuracy of at-surface upwelling radiance and surface albedo and significantly reduced data gaps. Valid observations increased substantially in comparison to the MCD43A2 data product, with the new method achieving an increase ranging from 40% to 200% at the four stations. Our study demonstrates that by integrating terrain, cloud properties, and radiative transfer modeling, the accuracy and completeness of retrieved surface albedo and radiance in complex terrains can be effectively improved.

In the Tibetan Plateau (TP) region, the foreseeable increase in air temperature may have profound and complex effects on the local hydrological cycle, and is likely to increase water loss from the land surface to the atmosphere through evapotranspiration (ET). Quantifying ET and its regulatory mechanisms are major challenges for understanding the water cycle and land-atmosphere interactions in the TP region. We evaluated the performance of several Earth observation-based ET datasets in the TP region, and explored the spatiotemporal variation of ET in the same region. The accuracy of different global ET datasets was evaluated, and ETMonitor and PML-V2 provide the best accuracy with overall high correlation, low bias, and low root mean square error. ETMonitor ET is also the only product with both high spatial (~1 km) and temporal (daily) resolution. ETMonitor ET may reflect the effect of mountain topography on ET better than other global products, i.e., ET values are higher in the humid valleys with denser vegetation cover and higher soil moisture, and ET values are lower on the mountain slopes at higher elevations with less vegetation cover and colder climate. Other ET products failed to capture the spatial patterns of ET in the mountainous regions, and this suggests that the spatial resolution is not the only dominant factor leading to the poorer performance of these ET products in the mountain regions of the TP. The results show that multi-year average ET is 339 mm/yr in the TP region during 2000-2021, which accounts for about 51% of the total precipitation in the TP region. From 2000 to 2021, ET over the Tibetan Plateau shows an overall increasing trend with large spatial variability.

Glaciers are crucial water resources in the Third Pole (the Tibetan Plateau and its surroundings) and are shrinking in response to climate change. Glacier albedo is an expression of glacier interactions with climate and dust/black carbon, and albedo reduction enhances glacier mass loss, but its changes and potential drivers remain poorly quantified. We leverage satellite observations to explore the variability of glacier albedo and understand its sensitivity to potential drivers and its future evolution. We find that glacier albedo has declined during 2001–2020, but high interannual variability is also an important signal. These variations are highly sensitive to air temperature and snow conditions and to nearby dust/black carbon emission sources. Future changes to these drivers will lead to further decreases of 2.9%–12.5% in glacier albedo by 2100 under different warming scenarios. These findings highlight the importance of albedo in glacier future evolution and the urgency of action to mitigate climate warming.