Water vapour flux, expressed as evapotranspiration (ET), is critical for understanding the earth climate system and the complex heat–water exchange mechanisms between the land surface and the atmosphere in the high-altitude Tibetan Plateau (TP) region. However, the performance of ET products over the TP has not been adequately assessed, and there is still considerable uncertainty in the magnitude and spatial variability in the water vapour released from the TP into the atmosphere. In this study, we evaluated 22 ET products in the TP against in situ observations and basin-scale water balance estimations. This study also evaluated the spatiotemporal variability of the total vapour flux and of its components to clarify the vapour flux magnitude and variability in the TP. The results showed that the remote sensing high-resolution global ET data from ETMonitor and PMLV2 had a high accuracy, with overall better accuracy than other global and regional ET data with fine spatial resolution (∼ 1 km), when comparing with in situ observations. When compared with water balance estimates of ET at the basin scale, ETMonitor and PMLV2 at finer spatial resolution and GLEAM and TerraClimate at coarse spatial resolution showed good agreement. Different products showed different patterns of spatiotemporal variability, with large differences in the central to western TP. The multi-year and multi-product mean ET in the TP was 333.1 mm yr−1, with a standard deviation of 38.3 mm yr−1. The ET components (i.e. plant transpiration, soil evaporation, canopy rainfall interception evaporation, open-water evaporation, and snow/ice sublimation) available from some products were also compared, and the contribution of these components to total ET varied considerably, even in cases where the total ET from different products was similar. Soil evaporation accounts for most of the total ET in the TP, followed by plant transpiration and canopy rainfall interception evaporation, while the contributions from open-water evaporation and snow/ice sublimation cannot be negligible.

The global climate has undergone unequivocal warming [...]

Spatiotemporal residual noise in terrestrial earth observation products, often caused by unfavorable atmospheric conditions, impedes their broad applications. Most users prefer to use gap-filled remote sensing products with time series reconstruction (TSR) algorithms. Applying currently available implementations of TSR to large-volume datasets is time-consuming and challenging for non-professional users with limited computation or storage resources. This study introduces a new open-source software package entitled ‘HANTS-GEE’ that implements a well-known and robust TSR algorithm, i.e. Harmonic ANalysis of Time Series (HANTS), on the Google Earth Engine (GEE) platform for scalable reconstruction of terrestrial earth observation data. Reconstruction tasks can be conducted on user-defined spatiotemporal extents when raw datasets are available on GEE. According to site-based and regional-based case evaluation, the new tool can effectively eliminate cloud contamination in the time series of earth observation data. Compared with traditional PC-based HANTS implementation, the HANTS-GEE provides quite consistent reconstruction results for most terrestrial vegetated sites. The HANTS-GEE can provide scalable reconstruction services with accelerated processing speed and reduced internet data transmission volume, promoting algorithm usage by much broader user communities. To our knowledge, the software package is the first tool to support full-stack TSR processing for popular open-access satellite sensors on cloud platforms.

Land Use/Land Cover (LULC) change is a major global concern and a topic of scientific debate. In West Africa, the key trend among the changes of the past few years is the loss of natural vegetation related to changes in different LULC categories, e.g., water bodies, wetland, and bare soil. However, not all detected changes in these LULC categories are relevant for LULC change management intervention in a resource-constrained continent, as a massive change in the dominant LULC types may be due to errors in the LULC maps. Previous LULC change analysis detected large discrepancies in the existing LULC maps in Africa. Here, we applied an open and synergistic framework to update and improve the existing LULC maps for West Africa at five-year intervals from 1990 to 2020—updating them to a finer spatial resolution of 30 m. Next, we detected spatial–temporal patterns in past and present LULC changes with the intensity analysis framework, focusing on the following periods: 1990–2000, 2000–2010, and 2010–2020. A faster annual rate of overall transition was detected in 1990–2000 and 2010–2020 than in 2000–2010. We observed consistent increases in shrubland and grassland in all of the periods, which confirms the observed re-greening of rangeland in West Africa. By contrast, forestland areas experienced consistent decreases over the entire period, indicating deforestation and degradation. We observed a net loss for cropland in the drought period and net gains in the subsequent periods. The settlement category also gained actively in all periods. Net losses of wetland and bare land categories were also observed in all of the periods. We observed net gains in water bodies in the 1990–2000 period and net losses in the 2010–2020 period. We highlighted the active forestland losses as systematic and issued a clarion call for an intervention. The simultaneous active gross loss and gain intensity of cropland raises food security concerns and should act as an early warning sign to policy makers that the food security of marginal geographic locations is under threat, despite the massive expansion of cropland observed in this study area. Instead of focusing on the dynamics of all the LULC categories that may be irrelevant, the intensity analysis framework was vital in identifying the settlement category relevant for LULC change management intervention in West Africa, as well as a cost-effective LULC change management approach.

Study region: Senegal river (SRB), Niger river (NRB), and Lake Chad basins (LCB). Study focus: We investigated the impacts of land use/land cover change (LULC) and climate variability on the water balance components from 1990 to 2020. We applied the Soil and Water Assessment Tool (SWAT) coupled with remote sensing retrievals of actual evapotranspiration (ETa) and surface soil moisture (SSM). To separate the impacts of the two aforementioned factors, two numerical experiments were designed: (i) climate variability effects by applying frozen LULC while changing the climate; (ii) LULC change impacts by applying frozen climate while changing LULC. New hydrological insights for the region: Overall, at the basin level, the results indicated that climate variability had the dominant role in increasing groundwater recharge, surface runoff, groundwater return flow and lateral flow in LCB and SRB. These increases triggered the recovery of lake area and higher water table in LCB and increased in SRB streamflow, while water scarcity increased in NRB. In contrast, the separate effect of LULC change, specifically natural vegetation expansion, increased actual ET and decreased the surface runoff, which could be a reason for lake area depletion in LCB and decreasing SRB and NRB streamflow. At the sub-basin level, LULC change, i.e. a gain in cropland and urban areas at the expense of forests in some sub-basins in NRB, led to a local increase in surface runoff. This implies a better redistribution of water in downstream and compensates the deficit in surface runoff caused by natural vegetation expansion in some other catchments. These changes, simultaneously with high intensity and long-duration precipitation, may increase the likelihood of inundation in some small catchments in the Niger river basin. These outcomes give useful hydrological insights into water and land management by emphasizing the crucial role of water recycling.

Information on crop yield is important for food security, in particular under the conditions of climate change and growing population worldwide. We developed a new fully distributed, high spatial resolution, model of biomass accumulation and crop yield applicable to a highly heterogeneous desert-oasis agroecosystem. The bulk of required input data is obtained by retrieving pixel-wise biogeophysical variables from a suite of very diverse satellite data. Both temperature and water stress conditions at field-scale are given full consideration, while the model was designed to strike a balance between model applicability and satisfactory characterization of the heterogeneous desert-oasis system to estimate field-scale yield. The development of this model relies on three main innovations. First, the start and end of the growing season were estimated for each pixel by calibrating the high spatial and temporal resolution observations of Normalized Difference Vegetation Index (NDVI) by Sentinal-2 (S2) MSI (Multi-Spectral Instrument) against limited local phenological information. Second, to monitor crop water stress, account taken of irrigation, a process-based water and energy balance model was applied to estimate the actual evapotranspiration (ET). This requires knowledge of soil water availability, which is characterized by downscaling the ASCAT (Advanced SCATterrometer) soil moisture data product. To capture the dominant features of the eco-hydrological conditions in the desert and oasis agroecosystem, ET was further downscaled from the 1 km resolution. Third, likewise the water stress indicator, the air temperature stress indicator was mapped after characterizing the thermal contrast and heterogeneity of the desert-oasis system, by generating time series of air temperature at 1 km spatial resolution using the MODIS (Moderate Resolution Imaging Spectroradiometer) Land Surface Temperature (LST) data product. In the temporal dimension, gaps were mitigated by applying time series analysis techniques to reconstruct cloud-free time series of LST, NDVI, fAPAR and albedo. These innovations add up to a high resolution characterization of crop response to the geospatial variability of weather and climate forcing in the desert-oasis agroecosystem. The model was applied to the dominant crops, i.e., spring wheat, maize, sunflower, and melon, in the oases of the Shiyang River Basin (northwestern China) characterized by a rather fragmented land use. The high resolution of pixel-wise ecohydrological parameters, i.e., crop phenology, temperature stress and water stress factors successfully reflect differences of crops with different phenology and location in the oases. The relative errors for wheat and maize yields compared to the census data are less than 5% at district level. At the county level, the relative errors of wheat yields of Liangzhou, Minqin, Gulang, Jinchuan, and Yongchang equal to 0.87%, 24.2%, 9.7%, 12.5%, and 7.2%. For maize, the dominant crop, the error on estimated yields was less than 5%, except in Gulang. The relative error on estimated yield for sunflower was less than 10% compared to agricultural census data. The relative error on estimated melon yield was 16%. This performance highlights the applicability of the model to estimate field-scale yields in agroecosystems characterized by fragmented land use.

The potential drivers of vegetation changes in the Sudano-Sahelian region of Africa remain poorly understood due to complex interactions between climatic and anthropogenic processes. In this study, we analyzed the vegetation greenness trends in relation to rainfall variability that we considered the essence of climatic effects on vegetation in a well-known water-limited environment by using time series of satellite data in the Sudano-Sahelian region during 2001–2020. We quantified in more detail the relative contributions of rainfall variability (climatic factor), land use/land cover (LULC) change, and fire occurrence change (non-climatic factors) to vegetation greenness trends in selected sub-regions. The results showed that vegetation greening was widespread (26.9% of the total study area), while vegetation browning was more clustered in central West Africa (5% of the total study area). About half of the vegetation greening area can be explained by long-term rainfall variability during 2001–2020, but most of the area characterized by a browning trend was unrelated to rainfall variability. An analysis of the relative importance showed that LULC changes had significant local effects on vegetation greenness and that these changes were characterized by a strong spatial heterogeneity in specific sub-regions. Gains in cropland and natural vegetation related to positive land management were probably the dominant drivers of greening in Senegal and Ethiopia. Also, the combined impacts of rainfall variability and LULC changes contributed to greening trends in the arid zone, particularly in Mali and Sudan. In contrast, vegetation browning in central West Africa appeared to be driven by cropland gain and natural vegetation loss associated with extensive agricultural production activities. Furthermore, we found that repeated fires for agricultural expansion in central West Africa intensified vegetation browning. These results advanced our understanding of vegetation dynamics in response to climatic and non-climatic factors in Sudano-Sahelian drylands characterized by increasing pressures on land resources.

Glacier surface albedo dominates glacier energy balance, thus strongly affecting the glacier mass balance. Glaciers in the Western Nyainqentanglha Mountains (WNM) experienced large mass losses in the past two decades, but long-term changes of glacier albedo and its drivers are less understood. In this study, we retrieved glacier albedo with MODIS reflectance data to characterize the spatiotemporal variability of albedo from 2001 to 2020. Air temperature, rainfall, snowfall and deposition of light-absorbing impurities (LAIs) were evaluated as potential drivers of the observed variability in glacier albedo. The results showed that: (1) the glacier albedo experienced large inter-annual fluctuations, with the mean albedo being 0.552 ± 0.002 and a clear decreasing trend of 0.0443 ± 2 × 10-4 dec-1 in the WNM. The fastest decline was observed in autumn and in the vicinity of the equilibrium line altitude, indicating an extended melt season and an expansion of the ablation region to higher elevation; (2) local meteorology and LAIs deposition are the main drivers of glacier albedo change, but their effects on seasonal albedos are different due to different glacier processes. Both air temperature and the balance between liquid and solid precipitation affect summer and autumn albedos due to glacier ablation. Air temperature is the main driver of spring and winter albedos due to sublimation and metamorphism of snow, while snowfall carried by westerlies has limited influence on these two seasonal albedos due to less snowfall. LAIs mainly affect spring albedo due to high concentration coupled with the southerly wind in spring. These findings highlight the significance of changes in glacier albedo and the key role of local meteorology and LAIs deposition in determining such changes, which play an important role in glaciological and cryosphere processes.

Surface Soil Moisture (SSM) information is needed for agricultural water resource management, hydrology and climate analysis applications. Temporal and spatial sampling by the space-borne instruments designed to retrieve SSM is, however, limited by the orbit and sensors of the satellites. We produced a Global Daily-scale Soil Moisture Fusion Dataset (GDSMFD) with 25 km spatial resolution (2011~2018) by applying the Triple Collocation Analysis (TCA) and Linear Weight Fusion (LWF) methods. Using five metrics, the GDSMFD was evaluated against in-situ soil moisture measurements from ten ground observation networks and compared with the prefusion SSM products. Results indicated that the GDSMFD was consistent with in-situ soil moisture measurements, the minimum of root mean square error values of GDSMFD was only 0.036 cm³/cm³. Moreover, the GDSMFD had a good global coverage with mean Global Coverage Fraction (GCF) of 0.672 and the maximum GCF of 0.837. GDSMFD performed well in accuracy and global coverage fraction, making it valuable in applications to the global climate change monitoring, drought monitoring and hydrological monitoring.

The region-wide spatial pattern of the drivers of vegetation trends in the African Sahel-Sudano-Guinean region, one of the main drylands of the world, has not been fully investigated. Time-series satellite earth observation datasets were used to investigate spatiotemporal patterns of the vegetation greenness changes in the region and then a principal component regression method was applied to identify the region-wide spatial pattern of driving factors. Results find that vegetation greening is widespread in the region, while vegetation browning is more clustered in central West Africa. The dominant drivers of vegetation greenness have a distinct spatial pattern. Climatic factors are the primary drivers, but the impacts of precipitation decrease from north to south, while the impacts of temperature are contrariwise. Coupled with climatic drivers, land cover changes lead to greening trends in the arid zone, especially in the western Sahelian belt. However, the cluster of browning trends in central West Africa can primarily be attributed to the human-induced land cover changes, including an increasing fractional abundance of agriculture. The results highlight the spatial pattern of climatic and anthropic factors driving vegetation greenness changes, which helps natural resources sustainable use and mitigation of climate change and human activities in global dryland ecosystems.

Satellite-based Earth Observation systems archived a variety of vegetation products during the last 50 years, which can reveal regional to global ecosystem dynamics across diverse spatiotemporal scales. The anomaly metrics such as Vegetation Condition Index (VCI) defined by comparing the current vegetation growth condition to historical average status based on long-term EO-based vegetation products were widely used to delineate abnormal vegetation variation exerted by either climatic or anthropogenic factors (e.g., droughts, wildfires). However, currently available long-term vegetation products may differ from each other in terms of sensors (observational platform or spectral bands), bio-physical definitions (e.g., NDVI, EVI, LAI, and VOD), spatiotemporal resolution, as well as the time-spans, which results in inconsistency across these vegetation products. Taking the VCI as an example, this study evaluated the uncertainty of vegetation anomalies detected based on different vegetation products over the middle reach of the Yangtze River by explicitly considering the effect of sensors, biophysical definitions, and time-spans. The preliminary results showed that VCI derived from NDVI products from different sensors (AVHRR vs. MODIS) induced significant inconsistent anomalies over most landscapes. The differences resulting from products with different biophysical definitions (NDVI vs. EVI, LAI, and VOD) are much lower than those from different sensors but still significant over specific areas. As for the time-spans, the 20-year NDVI based VCI presented a considerable reduction in variance over the study area on average compared to VCI calculated based on 5-year NDVI. In summary, caution should be taken when applying EO-based vegetation products for vegetation anomalies mapping, especially for quantitative assessment.

The annual flood and the alteration in hydrological regimes are the most vital concerns in the Vietnamese Mekong Delta (VMD). Although synthetic aperture radar (SAR) Sentinel-1 imagery is widely used for water management, only a few studies have used Sentinel-1 data for mapping surface water and monitoring flood events in the VMD. This study developed an algorithm to implement (i) automatic Otsu threshold on a series of Sentinel-1 images to extract surface water and (ii) time series analyses on the derived surface water maps to detect flood water extent in near-real-time (NRT). Specifically, only cross-polarized VH was selected after an assessment of different Sentinel-1 polarizations. The dynamic Otsu thresholding algorithm was applied to identify an optimal threshold for each pre-processed Sentinel-1 VH image to separate water from non-water pixels for producing a time series of surface water maps. The derived Sentinel-1 surface water maps were visually compared with the Sentinel-2 Full Resolution Browse (FRB) and statistically examined with the Sentinel-2 Multispectral Instrument (MSI) surface water maps, which were generated by applying the Otsu threshold on the normalized difference water index (NDWI) and modified normalized difference water index (MNDWI) images. The visual comparison showed a strong correspondence between the Sentinel-1 surface water maps and Sentinel-2 FRB images in three periods, including rice’s sowing season, flood period, and rice’s maturation stage. A good statistical agreement suggested that the performance of the dynamic Otsu thresholding algorithm on Sentinel-1 image time series to map surface water is effective in river areas (R2 = 0.97 and RMSE = 1.18%), while it is somewhat lower in paddy field areas (R2 = 0.88 and RMSE = 3.88%). Afterward, a flood mapping algorithm in NRT was developed by applying the change-detection-based time series analyses on the derived Sentinel-1 surface water maps. Every single pixel at the time is respectively referred to its state in the water/non-water and flooded/non-flooded maps at the previous time to be classified into a flooded or non-flooded pixel. The flood mapping algorithm enables updates at each time step to generate temporal flood maps in NRT for monitoring flood water extent in large-scale areas. This study provides a tool to rapidly generate surface water and flood maps to support water management and risk reduction in the VMD. The future improvement of the current algorithm is discussed.

Model calibration and validation are challenging in poorly gauged basins. We developed and applied a new approach to calibrate hydrological models using distributed geospatial remote sensing data. The Soil and Water Assessment Tool (SWAT) model was calibrated using only twelve months of remote sensing data on actual evapotranspiration (ETa) geospatially distributed in the 37 sub-basins of the Lake Chad Basin in Africa. Global sensitivity analysis was conducted to identify influential model parameters by applying the Sequential Uncertainty Fitting Algorithm–version 2 (SUFI-2), included in the SWAT-Calibration and Uncertainty Program (SWAT-CUP). This procedure is designed to deal with spatially variable parameters and estimates either multiplicative or additive corrections applicable to the entire model domain, which limits the number of unknowns while preserving spatial variability. The sensitivity analysis led us to identify fifteen influential parameters, which were selected for calibration. The optimized parameters gave the best model performance on the basis of the high Nash–Sutcliffe Efficiency (NSE), Kling–Gupta Efficiency (KGE), and determination coefficient (R²). Four sets of remote sensing ETa data products were applied in model calibration, i.e., ETMonitor, GLEAM, SSEBop, and WaPOR. Overall, the new approach of using remote sensing ETa for a limited period of time was robust and gave a very good performance, with R² > 0.9, NSE > 0.8, and KGE > 0.75 applying to the SWAT ETa vs. the ETMonitor ETa and GLEAM ETa. The ETMonitor ETa was finally adopted for further model applications. The calibrated SWAT model was then validated during 2010–2015 against remote sensing data on total water storage change (TWSC) with acceptable performance, i.e., R² = 0.57 and NSE = 0.55, and remote sensing soil moisture data with R² and NSE greater than 0.85.

Past Land Use Land Cover (LULC) transitions analysis at the sub-continental scale of West Africa revealed spatial reallocation, i.e., simultaneous losses and gains of the LULC categories at different locations. We applied the component analysis approach to separate the total change into three major components, i.e., quantity (net change), exchange and shift (allocation change) as a way to analyse such spatial reallocation and identify the paired categories that accounted for the largest exchange and shift through time. Quantity change is the absolute value of the category's gross gains minus the category's gross losses. An exchange occurs when for example, a natural vegetation patch evolves to cropland at a location concurrently with an equal extent of cropland evolving into natural vegetation at a different location. A shift occurs when the LULC categories involved in the exchange are more than two. The amount of exchange and shift and locations that these exchanges occurred are very useful information for land policies appraisal and the long term contested re-greening of Africa as it may signal simultaneous regrowth and degradation of natural vegetation at different locations in the same landscape and also possible misclassification errors. The results revealed large exchanges in the landscape of West Africa between 1975 and 2000 for arid and humid eco-regions in West Africa. Overall, the exchange and shift components between wetland, water bodies and some other LULC categories such as forestland, other vegetation and cropland were the highest. The exchange between natural vegetation and cropland was considerable, which confirms regrowth despite the massive degradation revealed by the previous studies. Here, the large exchange in 1975–2000 highlighted large spatial reallocation of the LULC categories. The highest net change was experienced in the period between 2000 and 2013 at all spatial aggregations. Settlement and cropland experienced the highest positive net change whilst forestland and other vegetation experienced the highest negative net change. Shift was absent in the category of settlements indicating persistence over time. This analysis provided useful information on the contested re-greening of West Africa.

A drought-induced water storage deficit index (D-WSDI) is proposed to quantify the response of GRACE-based terrestrial water storage change to meteorological drought and the impact of drought on water storage deficit. D-WSDI is defined as the normalized residual component of GRACE time-series data after removing the long-term trend and seasonal components. The evaluation based on the Emergency Events Database (EM-DAT) showed that more than 90% of global drought events from 2002 to 2019 led to a water storage deficit, which can be detected by the proposed D-WSDI. The severity of the water storage deficit caused by drought increases with the extending drought duration. An average of 73% of water storage deficit months at the global scale is related to precipitation shortages. The cumulative precipitation deficit in relatively short periods of less than 9 months can lead to the water storage deficit in low-latitude regions, whereas a longer time scale is required to lead to a water storage deficit in high-latitude regions. The negative monthly precipitation anomaly of about -20% can lead to a water storage deficit in high rainfall regions, whereas the negative precipitation anomaly can reach -80% in arid and semiarid areas. D-WSDI holds the capability to quantify the water storage deficit caused by drought, especially in the regions with terrestrial water storage change influenced by the long-term trends in climate and anthropogenic activities, and can be used as an index of drought monitoring with similar or superior performance compared to some traditional drought indices.

Terrestrial remote sensing data products retrieved from radiometric measurements in the optical and thermal infrared spectrum such as vegetation spectral indices can be heavily contaminated by atmospheric conditions, including cloud and aerosol layers. This contamination results in gaps or noisy observations. The harmonic analysis of time series (HANTS) has been widely used for time series reconstruction of remote sensing imagery in recent decades. To use HANTS model, a series of parameters, such as number of frequencies (NF), fitting error tolerance (FET), degree of over-determinedness (DoD), and regularization factor (Delta), need to be defined by users. These parameters provide flexibilities, but also make it difficult for non-expert users to determine appropriate settings for specific applications. This study systematically evaluated the reconstruction performance of the model under different parameter setting scenarios by simulating pixel-wise reference and noisy NDVI time series. The results of these numerical experiments were further used to identify optimal settings and improve global NDVI reconstruction performance. The results suggested optimal settings for different areas (local optimization). If a user opts to use unique settings for global reconstruction, the setting NF = 4, FET = 0.05, DoD = 5, and Delta = 0.5 can produce the best performance across all setting scenarios (global optimization). In addition, several internal improvements, such as dynamic weighting scheme, polynomial and inter-annual harmonic components, and ancillary attributes of input data can be used to further improve the performance of reconstruction. With these results, future non-expert users can easily determine appropriate settings of HANTS for specific applications in different regions.

Rainfall is a key driver of terrestrial vegetation. Clarifying the response mechanism of vegetation to rainfall can advance the understanding of expected changes in ecosystems under projected rainfall scenarios. Besides the rainfall amount over a period of time, the frequency, duration and intensity are of importance in driving ecosystem processes. The pulsating nature of rainfall forcing and of vegetation response applies particularly to arid and semi – arid regions and led to conceptualize the “pulse-reserve” paradigm, which we used to explain the approach we propose in this study. We introduce the Transfer Function Analysis (TFA) method in the frequency domain to capture the response of vegetation to rainfall at multiple temporal scales. The TFA method determines coherence, gain, and phase to characterize the existence, strength, and time-lag, respectively, of the vegetation response to rainfall at different temporal scales. Specifically, the coherence measures the existence of the response and only with a significant response are the gain and phase values significant and valuable for further analysis. The gain measures the strength of the relationship between fluctuations in vegetation growth and fluctuations in rainfall, while the phase value (i.e. the time-lag) measures how fluctuations in vegetation growth lag (or lead) fluctuations in rainfall. The TFA method was applied to the 34-years (1982–2015) NDVI3g and CHIRPS precipitation dataset in the Sahel-Sudano-Guinean region (20°W ~ 60°E, 0–25°E). The Sahelian zone was characterized by a significant vegetation response to rainfall across all inter- and intra- annual time-scales, while the Sudano-Guinean zone was dominated by significant response at annual or 6-month scales. The negative phase lag indicated that rainfall variation normally led NDVI change for most areas and across timescales. However, a positive phase observed in part of the tropical rainforest area indicated that NDVI changes led rainfall variations, which may be caused by the strong vegetation-rainfall feedback through recycling of precipitation by evapotranspiration. In summary, these results suggested that the TFA method is a powerful tool to quantify the vegetation-rainfall response regime across a range of timescales, as conceptualized by the “pulse-reserve” paradigm. Unraveling the response of fluctuations in vegetation growth to separate components of the forcing by precipitation might improve our understanding of environmental change in the past decades in the Sahel - Sudan - Guinean region.

Glacier albedo determines the net shortwave radiation absorbed at the glacier surface and plays a crucial role in glacier energy and mass balance. Remote sensing techniques are efficient means to retrieve glacier surface albedo over large and inaccessible areas and to study its variability. However, corrections of anisotropic reflectance of glacier surface have been established for specific shortwave bands only, such as Landsat 5 Thematic Mapper (L5/TM) band 2 and band 4, which is a major limitation of current retrievals of glacier broadband albedo. In this study, we calibrated and evaluated four anisotropy correction models for glacier snow and ice, applicable to visible, near-infrared and shortwave-infrared wavelengths using airborne datasets of Bidirectional Reflectance Distribution Function (BRDF). We then tested the ability of the best-performing anisotropy correction model, referred to from here on as the ‘updated model’, to retrieve albedo from L5/TM, Landsat 8 Operational Land Imager (L8/OLI) and Moderate Resolution Imaging Spectroradiometer (MODIS) imagery, and evaluated these results with field measurements collected on eight glaciers around the world. Our results show that the updated model: (1) can accurately estimate anisotropic factors of reflectance for snow and ice surfaces; (2) generally performs better than prior approaches for L8/OLI albedo retrieval but is not appropriate for L5/TM; (3) generally retrieves MODIS albedo better than the MODIS standard albedo product (MCD43A3) in both absolute values and glacier albedo temporal evolution, i.e., exhibiting both fewer gaps and better agreement with field obser-vations. As the updated model enables anisotropy correction of a maximum of 10 multispectral bands and is implemented in Google Earth Engine (GEE), it is promising for observing and analyz-ing glacier albedo at large spatial scales.

A distributed hydrological energy-water-balance model (FEST-EWB) is calibrated over the Heihe Basin, a mainly desertic basin in China, employing remotely-sensed Land Surface Temperature (LST) (MODIS, 1-km resolution) as calibration variable. This approach overcomes the problem of model parameters characterization, which are usually difficult to define especially over large basins, allowing a pixel-by-pixel calibration, preserving the spatial heterogeneity. Hence, the spatial distribution of the modelled LST, but also of soil moisture (SM) and evapotranspiration (ET) is improved. The accuracy of the calibration process is documented through common statistical indexes. The modelled ET is compared locally against two eddy covariance stations in the agricultural area, while distributively against the ET estimates of the ETMonitor model and some global re-analysis products (ERA-Interim, GLDAS2, GLEAM and MERRA-2). Calibration and validation performed in this study prove that a considerable model accuracy is attainable even in extremely arid environments. An average LST bias of 2.6 °C is obtained over the basin. A good adaptation of FEST-EWB is also obtained against eddy-covariance stations ET with a little bias around −1 mm/d. On the other hand, the reanalysis products display a much worse performance, with higher absolute biases (around −3.5 mm/d), although with high variability among the models.