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A good water budget involving four variables, including precipitation (P), evapotranspiration (ET), streamflow (R), and terrestrial water storage change (TWSC), is reflected in two aspects: a high accuracy against observations for each budget component and the low water budget closure residual error (ΔRes). Due to the lack of consideration of observations of budget components in existing water budget closure assessment methods (BCMs), when the ΔRes of budget components is low, their error against respective observations may still be high. In this study, we assess the water budget closure accuracy of satellite/reanalysis-based hydrological data products over mainland China based on six popular P products and multiple datasets of additional budget components (ET, R, and TWSC). The results indicated that the ΔRes changes between ±15 mm over mainland China. Satellite P products such as GPM IMERG showed better performance by comparing them with rain gauge-based observations. However, reanalysis P products such as GLDAS and FLDAS showed a better water budget closure since the selected datasets of additional budget components (ET and R) are also derived from reanalysis datasets. This indicates that these same data sources for budget components make it easier to close the water budget. The further development of satellite P products should consider the closure of the water budget with other water cycle variables. [ABSTRACT FROM AUTHOR]

Sufficient historical flood inventory data (FID) are crucial for accurately predicting flood susceptibility using supervised machine learning models. However, historical FID are insufficient in many regions. Remote sensing provides a promising opportunity to expand the FID. However, whether the FID expanded by remote sensing can improve the accuracy of flood susceptibility modeling needs further study. In this study, a framework was proposed for improving the accuracy of flood susceptibility prediction (FSP) by combining machine learning models and the expanded FID using Sentinel-1A radar images. Five widely used machine learning models were employed to verify the accuracy of the proposed method by taking Wuhan City as a case study, including the random forest (RF), gradient boosting decision tree (GBDT), k-nearest neighbor (KNN), support vector machine (SVM), and artificial neural network (ANN) models. Sentinel-1A images from time points before, during, and after flood events were used to expand the FID for training the machine learning models. The results showed that the performance of the machine learning models for predicting flood susceptibility was improved greatly by considering the expanded FID, being improved by approximately 1.14–19.74% based on the area under the receiver operating characteristic curve (AUC). Among the used machine learning models, taking into account all the statistical indicators, the ANN showed the best performance, while the SVM showed the best generalization performance in Wuhan City. According to the results of the ANN model, approximately 19% of the area in Wuhan City, mainly distributed near rivers and lakes, is at a high flood susceptibility level. This study provides an essential reference for flood susceptibility analyses in regions with limited flood sampling data. [ABSTRACT FROM AUTHOR]

• A new method was proposed to merge multiple satellite-based precipitation products (SPPs) • The method estimates grid scale errors of SPPs based on water budget closure. • The method was verified by comparing with original SPPs and three merging methods. • The performance of SPPs was slightly reduced when enforcing water budget closure. Merging multiple satellite/reanalysis-based precipitation products (SPPs) is a critical way to improve the accuracy of spatial precipitation (P) estimation. In this paper, a new method was proposed to merge multiple SPPs by considering rain gauge-based observations and water budget closure. Based on the errors of SPPs at gauged regions, the proposed method first estimates the mesh-based errors of SPPs as the intermediate variable using the error in water budget closure and then calculates the respective merging weights of SPPs in data merging. The performance of this proposed method was verified by comparing its results with the input of original SPPs and the data merged by five existing merging methods in mainland China at mesh scale and in nine major basins. Two scenarios were designed for the period from 2003.02 to 2016.12 at a monthly temporal resolution − with and without water budget closure constraints. Seven popular SPPs were considered, including GPM IMERG, PERSIANN, GSMaP, CHIRPS, ERA5, CFSV2, and TerraClimate. The results showed that the proposed method improved the P estimates in terms of CC, RMSE, MAE, PBIAS and KGE in both scenarios, and that closing the terrestrial water budget would slightly reduce the accuracy of SPPs against observations. The reduction percentage is about 5% in term of CC and KGE, and 12% in term of RMSE. This study is the first attempt to merge SPPs and gauge-based rain observation with consideration of water budget closure. It provides not only an effective and robust tool for the merging of multiple SPPs, but also important insights into the further development of SPP merging methods by considering water budget closure. [ABSTRACT FROM AUTHOR]

The coordination of social economy, water, and ecology is an important foundation for sustainable development. However, the interaction among social economy, water, and ecology are complex. The data scales between socioeconomic factors (normally at the spatial scale of the administrative region and the temporal scale of a year or month) and river water quality and ecological factors (normally at the spatial scale of a sub-basin and the temporal scale of an instantaneous value) do not match. Therefore, many current regulation methods do not sufficiently consider the interaction mechanisms between socioeconomic, water, and ecological factors or cannot adequately deal with the scale of the data. In this study, we proposed a new framework to assess the coordinated development of social economy, water, and ecology (SWE) by integrating the distributed social economy-water-ecological model (SEWE) and the harmonious regulation model. We built the SEWE model by coupling a rainfall-runoff model, river water quality, and ecological models; we built the harmonious regulation model based on the harmony theory method. We applied the proposed method and framework to the Shaying River Basin, which exhibited typical characteristics of multiple sluices and dams, high pollution, and significant human activities. Results indicated that the coordinated development level of the SWE in the Shaying River Basin was poor, with the average coordination degree varying from 0.39 to 0.60. The average growth rate of the coordination degree of socioeconomic indicators was greater than that of the river water quality and ecological indicators. The coordination degree of water ecological indicators was small with a decreasing trend. These results indicated the increasing impact of socioeconomic development on river water quality and ecology. Therefore, the Shaying River Basin should focus on ecological and environmental restoration, such as the effective control of pollution draining into rivers. The proposed methods and framework could contribute to the integrated water resource management of the basin. [ABSTRACT FROM AUTHOR]

Abstract The quantitative assessment of the impact of socioeconomic development on river water environment is important to the scientific management of river basins. However, current methods have high data requirements or are difficult to deal with the impact between systems (which is defined by a collection of indicators). This paper first uses canonical correlation analysis (CCA) to understand the relationship between socialeconomic system (defined by a set of indicators reflecting socioeconomic development) and river system (defined by a set of indicators reflecting river water environment), and then proposes a method to assess the impact of socioeconomic system on river system by integrating CCA and the degrees of influence of river system indicators. The proposed method and framework are applied to the Shaying River Basin with the characteristics of multi-sluices, high pollution, and dense population based on data from 2000 to 2015. Results indicate that socioeconomic and river systems are highly related to each other with the average influence degree of greater than 0.9, indicating very close relationships between socioeconomic and river systems. The changes of influence degree vary between 0.19 and 0.79 with a turning point in 2010. Most of the influence levels are "moderate" (influence degree between 0.4 and 0.6) or "high" (influence degrees between 0.6 and 0.8) before 2010 but become to "low" (influence degrees between 0.2 and 0.4) since then. In addition, the influence degree shows a significant increase from upstream to downstream with Zhoukou Station as the turning point, meaning that the stronger the human activity is, the greater the impact of the socioeconomic system on the river system is. The main influential factors are population density and sewage treatment rate. The proposed method contributes to the research in river management with limited data availability and the results can serve as an important reference for basin management. Graphical abstract Unlabelled Image Highlights • A new method of river system impact assessment was proposed. • The impact of socio-economic development on the Shaying River Basin was assessed. • The CCA method was explored for assessing socio-economic impact of river system. • The key influential factors were identified for management purposes. [ABSTRACT FROM AUTHOR]

In order to study river health status from harmonic relationship between human and natural environment, a river health evaluation method was proposed from the aspects of ecosystem integrity and human service demand, and the understanding of river health connotation. The proposed method is based on the harmony theory and two types of river health assessment methods (the forecasting model and index evaluation). A new framework for assessing river water health was then formed from the perspective of harmony and dynamic evolution between human service demand and river ecosystem integrity. As a case study, the method and framework were applied to the Shaying River Basin, a tributary of the most polluted Huaihe River Basin in China. The health status of the river's ecosystem and its effect on the mainstream of Huaihe River were evaluated based on water ecological experiment. The results indicated that: (1) the water ecological environment in Shaying River was generally poor and showed a gradual changing pattern along the river. The river health levels were generally “medium” in the upstream but mostly “sub-disease” in the midstream and downstream, indicating that the water pollution in Shaying River were mainly concentrated in the midstream and downstream; (2) the water pollution of Shaying River had great influence on the ecosystem of Huaihe River, and the main influencing factors were TN, followed by TP and COD Mn ; (3) the natural attribute of river was transferring toward to the direction of socialization due to the increasing human activities. The stronger the human activity intervention is, the faster the transfer will be and the more river's attributes will match with human service demand. The proposed framework contributes to the research in water ecology and environment management, and the research results can serve as an important reference for basin management in Shaying River and Huaihe River. [ABSTRACT FROM AUTHOR]

• A method was proposed for enforcing water budget closure by combining observations of budget components. • The method was verified in nine basins in mainland China by comparing with existing methods. • Proposed method improved budget corrected data by approximately 5-14% based on RMSE, MAE, KGE, and POD. Enforcing water budget closure is critical for providing consistent estimates of budget components to understand water movement between the atmosphere and the terrestrial land surface. However, existing water budget closure correction (BCC) methods do not consider improving the performance of budget-corrected datasets when closing the water budget. This study proposes a method for enforcing terrestrial water budget closures and improving the estimation of budget components by combining budget component measurements and introducing an error adjustment factor of ET. The proposed method first corrects raw budget component datasets based on their measurements and then enforces the water budget closure of the pre-corrected datasets using existing BCC methods. The proposed method was verified by comparing it with existing BCC methods in nine major basins in mainland China. Nine precipitation (P), four evapotranspiration (ET), two simulated runoff (R), and four terrestrial water storage change datasets were selected to comprehensively evaluate the performance of the proposed method. The results showed that the water budget closure constraint based on existing BCC methods improved the performance of datasets with low accuracy. However, the performance is not significantly improved or even reduced for datasets with high accuracy. Compared with existing BCC methods, the performance of budget-corrected datasets using the proposed method improved by approximately 8, 86, 8, 5, and 14% according to the statistical metrics root mean square error, percent bias, mean absolute error, Kling–Gupta efficiency, and probability of detection, respectively. [ABSTRACT FROM AUTHOR]

• A framework was proposed for assessing the integrated effects and contribution of multiple DSs on HR&EF as the number of DS increases. • The framework separates the impacts of climate variability, DSs, and land-use change on HR&EF. • Impact on HR&EF increases as the number of DSs increases but only for some aspects of HR. • DSs contribute the most to the alteration in HR&EF in Shaying River Basin, followed by climate variability and land-use change. Numerous studies have revealed dam- and sluice- (DS) induced modifications in the natural hydrologic regime and ecological flow (HR&EF) by identifying the pre- and post-impact periods. However, few studies have separated the impacts of climate variability, DSs, and land-use change on the HR&EF in rivers. In addition, the question of the integrated effect and contribution of multiple DSs on HR&EF as the number of DSs increases and their position difference remains unanswered. This study attempts to provide a framework for assessing the impact of integrated regulation with different numbers of multiple DSs on the alterations in HR&EF by separating disturbances from climate variability and land-use change based on streamflow observations and hydrological modeling. The Shaying River Basin (SRB), one of the basins with the most severe DS regulations in China, was selected as the study area for this case study. The 33 flow regime indicators, as well as the ecosurplus and ecodeficit indicators, were applied to comprehensively reflect the impacts of multiple DSs on HR&EF. The results indicated that DSs contributed the most to the alterations in HR&EF in the SRB, followed by climate variability, whereas land-use change contributed the least. The integrated regulation of multiple DSs has a significant effect on HR&EF in rivers, and this effect increases as the number of DSs increases. The low- and high-flows were more seriously affected by the DSs than the middle-flow, resulting in an increase in the frequency of the low-flow but an obvious decrease in the frequency of the high-flow. The seasonal variation in the ecodeficit showed that ecological flow during Spring, Summer, and Winter was greater than that in Autumn in the basin. The proposed framework provides insights into the methodology for separating the impacts of climate variability, DSs, and land-use change on HR&EF and is valuable in understanding the change in HR&EF induced by different numbers of multiple DSs at different locations in DS-dominated basins. [ABSTRACT FROM AUTHOR]

• SWAT model was modified to examine the capability of PET products in improving hydrological simulation accuracy. • Distributed input and higher spatial resolution of PET products are more conducive to hydrological modeling. • The input of PET products has a great impact on streamflow and baseflow, followed by surface runoff. • The accuracy of PET products should be improved from both improving PET products and considering water budget closure. Terrestrial evapotranspiration (ET), which links water and energy exchanges between atmosphere, land surface and underground water, significantly affects hydrological modelling. However, accurate ET estimation is still a great challenge for hydrological models. Currently, most hydrological models obtain ET mainly from their built-in potential evapotranspiration (PET) modules due to lack of observations. Note that the ET obtained in this way cannot be verified by observations and inevitably brings large model uncertainty. Fortunately, several PET products have been produced based on satellite remote sensing and/or reanalysis climate model output with global coverage and high spatiotemporal resolution, providing a great opportunity for tunning hydrological models. However, the accuracy of global gridded PET products varies in different regions. It is still unknown whether PET products can improve the accuracy of hydrological simulation, and how different resolutions and input types (lumped/distributed) of PET products affect the performance of hydrological models. The question about how hydrological models will be further developed in combination with PET products is also not well answered, given the complexity in interaction between ET and other water cycle variables. In this study, a modified Soil and Water Assessment Tool (SWAT) was chosen to examine the capability of PET products in improving hydrological simulation in terms of surface runoff, streamflow and baseflow, together with the Shaying River Basin in China as a case study. Two Moderate Resolution Imaging Spectroradiometer (MODIS) PET products were employed to form various PET scenarios. Results indicate that PET products are significantly different from the PET simulated by the SWAT built-in PET module in terms of spatial distribution and magnitudes. According to statistical metrics, the use of PET products to replace the SWAT built-in PET module can improve the accuracy of hydrological simulation compared with the original SWAT using its built-in PET module. The distributed input and higher spatial resolution of PET products are more conducive to improve the accuracy of hydrological simulation. Our research shows that both improving the accuracy of PET products and balancing water budget closure are critical for accurate hydrological modeling. These conclusions obtained based on MODIS products were further verified against a hourly PET data product (hPET). This study provides insights into understanding the use of PET products in improving the accuracy of hydrological simulation. The study findings can provide a beneficial reference for the further development of hydrological models in combination with PET products. [ABSTRACT FROM AUTHOR]

• Nearly 40 years of remote sensing-based water area data from Lake Chad are generated. • The total inundation area lags behind the open water area by approximately 2 – 3 months. • An interactive online application for inspecting yearly and monthly areas of Lake Chad is provided for the general public. As the second largest lake in Africa, the extent of Lake Chad has over 50% seasonal variations with large parts of water under aquatic vegetation. Although a great area shrinkage since the 1960s divided the lake into two parts, namely, the northern and southern basins, it still feeds millions of people from four countries around it. Given sparse in situ measurements, remote sensing with seamless spatial coverage has served for acquiring the long-term area series of Lake Chad. This study retrieves the open (unvegetated) surface water area series of Lake Chad and the total inundation area, including open water and water under macrophytes, combining multiple remote sensing data from around 1980 to 2020 with satellite-based water level and in situ observations at the Bol gauge as validation and auxiliary. Results show that the total inundation area continuously recovered at a rate of 145 km2/year from 1982 to 2020 with large annual fluctuations. Approximately two- to three-month time lags between the open surface water and the total inundation area reveal the hydrological asynchrony of Lake Chad, which can be attributed by the large area of rooted macrophytes. The overspill of the southern surface water flowing over the Great Barrier into the northern lake also has a one-month lag with the open surface water of the southern lake. The Google Earth Engine-based online application can be easily shared for use and data downloaded by the general public, and the open code can be adjusted for any other lakes worldwide. [ABSTRACT FROM AUTHOR]

• A framework to compare DS-induced water quality and ecological changes was proposed. • The difference of water ecology in upstream and downstream of DSs was compared. • The relationship between DS discharges and water quality changes was investigated. • The long short-term water quality and ecological impact of DSs were summarized. The construction of dams and sluices (DSs) is conducive to human development, but affects river water quality (WQ) and water ecology (WE) seriously. While many studies have explored the DS-induced impact on hydrology, water quality and water ecology, the difference in DS regulations for different objectives and interests (such as flood control, power generation and ecological protection) are not taken into the account although their impacts on WQ and WE are different. Discharge is the key factor to measure different DS regulation and its variation directly affects the long-term WQ and WE in rivers. Thus, studying short-term multi-scenario DS regulation with different discharge is very important for providing insights to their long-term irregular management. Furthermore, the relationships between discharges and the change of WQ indicators are different in the upstream and downstream of DSs and at different distance to DSs in the downstream. However, it is not well understood how the WQ and WE indicators at upstream and downstream sites of DSs change under different regulation scenarios in long-term and short-term. Using the monitored WQ and WE indicators at twelve sampling sites (SPs) of six DSs in the Huai River Basin (HRB), this study investigates the impact of long-term irregular DS regulation on changes in WQ and WE indicators, i.e., the long-term regulation experiment (LTRE), and then the relationship between the alteration of DS discharge flow and changes in WQ indicators, i.e., the short-term regulation experiment (STRE) under four scenarios with different flow magnitudes at one of sluices (Huaidian Sluice). The results of long-term regulations show that the WQ at downstream sites of most DSs was worse than their corresponding upstream sites. The similarity of WE indicators at upstream and downstream sites of DSs was low, meaning the great impact of DS long-term regulation on WE. The results of short-term regulations show that small flow magnitude (20 m3/s in this study) helps release and reduce upstream pollution of DSs as it does not disturb downstream riverbed pollutions, resulting in an increase of downstream river pollution. Further increasing flow magnitude (40 m3/s in this study) helps reduce and release upstream pollution, but increases river pollution significantly at downstream due to the release of pollutions in the sediments of downstream riverbed, which may cause a great river ecological impact since pollutants cannot be diluted and degraded in time. A flow magnitude about 60 m3/s can help release and rapidly dilute and degrade sediment pollutions in downstream riverbed. However, a larger flow magnitude may also be inappropriate due to the strong downstream riverbed erosion except for special regulation requirements. This study can provide more insights for water resources management to develop long-term ecological DS regulation schemes for in HRB and other similar DS dominated basins. [ABSTRACT FROM AUTHOR]

• New method is proposed to assess accuracy of satellite products in ungauged basins. • Mutual cancellation in product errors is avoided in water budget closure. • Results of proposed method are examined by the application of FORM. • The proposed method showed lower dependence on precipitation. Remote sensing products have been widely used in water resources assessment and management. However, the accuracy varies in different products. Water budget closure provides an important breakthrough for better assessing the uncertainties and quantifying error sources of satellite-based data products using water budget imbalance, especially in ungauged basins where conventional verification methods are not applicable. However, the commonly used assessment methods mainly refer to closure residual of water budget Δ Re s or its proportion to corresponding mean precipitation (P) Δ Re s / P , which have apparent limitations due to the dimensional effects in the former method and the high dependence on precipitation in the latter one. More critically, these two methods do not consider measured values (referring to in-situ values in gauged basins or average values of multiple satellite products in ungauged basins as in this study) as benchmarks of water budget components in the assessment of satellite-based hydrological data products, resulting in a wrong choice because the closure result is subject to the mutual cancellation in the errors of water budget components from different satellite-based products. This study proposes a novel error-based method to assess the accuracy of satellite-based hydrological data products for both gauged and ungauged basins based on water budget equation, measured values, and the omission error (OE) which is generally overlooked in existing studies. The method is applied to a typical closed basin, the Tarim River Basin (TRB) of China, to verify the applicability and reliability of the proposed method. Sixty combinations of water budget components from various satellite sources can be designed based on five precipitation (P) products, four evapotranspiration (ET) products and three terrestrial water storage change (TWSC) products. First order reliability method (FORM) is employed to assess the reliability of water budget closure results caused by the errors of satellite sources. The results indicated that different combinations produced different annual Δ Re s and Δ Re s / P , indicating great water budget closure errors for different combinations due to errors in different satellite sources. The OE error from satellite-based hydrological data products of water budget components clearly showed a greater impact on water budget imbalance than water budget components, indicating that OE error is important for understanding quality of satellite products. By comparing the results of Δ Re s and Δ Re s / P , it can be seen that our proposed method had great advantages in assessment of satellite products with a low dependence on P, and a better comparability between different combinations. The value of our assessment criterion ranges from 0 to 1 which is easier to understand in practice. The combinations of water budget components from TRMM_3B43 (P), GPM_3IMERGHH (P), GLDAS_NOAH025 (ET), and GRACE (TWSC) products show satisfactory water budget closure results in TRB using our proposed method. The proposed method provides a new aspect for assessing the accuracy of satellite-based hydrological data products especially in ungauged basins, which provides insights into water resources management in basins. [ABSTRACT FROM AUTHOR]

• River water quality and ecology were investigated in dam dominated basin. • A framework was formed to assess impact of human activity on river water quality and ecology. • A significant seasonality of water quality was revealed with higher impact in winter than in summer. • Seasonality of the impact was weakened as human activity increased. • Seasonality of water ecology was almost not significant in our study. Improving water quality and ecological status is an important foundation for sustainability development. Increasing human activity and the corresponding land use change lead to the deterioration of river water quality (WQ) and water ecology (WE), but the pathway is complex, especially in dam dominated basins with the altered streamflow by the joint regulation of multiple dams and sluices that leads to changes in WQ seasonally. However, the research on this issue is inadequate and it is urgently needed since a large number of dams and sluices have been built worldwide for socioeconomic benefits, putting the basin's WQ and WE under pressure. Based on water ecological experiment, a framework was proposed to assess the impact of land use (LU) and urbanization on WQ and WE in different scenarios derived from human activities (low, and intense) and seasonality (winter, and summer) in a dam dominated basin. The framework was applied to the Shaying River Basin located in the Central China as a case study. The remote sensing of Landsat 8 OLI_TIRS images and Geographic Information System (GIS) were combined by using the support vector machine classification method to extract the land use patterns and to map the spatial distribution diagrams of WQ and WE indicators based on in-situ experimental data. A canonical correlation analysis method and Pearson correlation testing were adopted to evaluate the impact of LU and urbanization on WQ and WE in the basin. Results showed that LU and urbanization had a great impact on WE compared to WQ due to the slow reaction of ecosystem in the basin. Because the former was resulted by long-term regulation of dams and sluices, and the latter was related to the short-term changes of river environment. Moreover, the former was attributed to the latter largely. LU and urbanization impact on WQ had a significant seasonality with higher impact in winter than in summer, and the seasonality of the impact was weakened as the disturbance of LU and urbanization increased. The seasonal variation of WE indicators was almost not significant in our study due to their adaptability to surrounding environment. Our results provide insights to water resources management and ecological restoration in the Shaying River Basin and other similar basins with intensive dams and sluices. [ABSTRACT FROM AUTHOR]

Temporally continuous daily actual evapotranspiration (ET) data play a critical role in water resource management in arid areas. As a typical remotely sensed land surface temperature (LST)-based ET model, the surface temperature-vegetation index (Ts-VI) triangle model provides direct monitoring of ET, but these estimates are temporally discontinuous due to cloud contamination. In this work, we present a gap-filling algorithm (TSVI_DNN) using a deep neural network (DNN) with the Ts-VI triangle model to obtain temporally continuous daily actual ET at regional scale. The TSVI_DNN model is evaluated against in situ measurements in an arid area of China during 2009–2011 and shows good agreement with eddy covariance (EC) observations. The temporal coverage was improved from 16.1% with the original Ts-VI tringle model to 67.1% with the TSVI_DNN model. The correlation coefficient (R), root mean square error (RMSE), bias, and mean absolute difference (MAD) are 0.9, 0.86 mm d−1, −0.16 mm d−1, and 0.65 mm d−1, respectively. When compared with the National Aeronautics and Space Administration (NASA) official MOD16 version 6 ET product, estimates of ET using TSVI_DNN are improved by approximately 49.2%. The method presented here can potentially contribute to enhanced water resource management in arid areas, especially under climate change. [ABSTRACT FROM AUTHOR]

Surface soil moisture (SM) plays an essential role in the water and energy balance between the land surface and the atmosphere. Low spatio-temporal resolution, about 25–40 km and 2–3 days, of the commonly used global microwave SM products limits their application at regional scales. In this study, we developed an algorithm to improve the SM spatio-temporal resolution using multi-source remote sensing data and a machine-learning model named the General Regression Neural Network (GRNN). First, six high spatial resolution input variables, including Land Surface Temperature (LST), Normalized Difference Vegetation Index (NDVI), albedo, Digital Elevation Model (DEM), Longitude (Lon) and Latitude (Lat), were selected and gap-filled to obtain high spatio-temporal resolution inputs. Then, the GRNN was trained at a low spatio-temporal resolution to obtain the relationship between SM and input variables. Finally, the trained GRNN was driven by the high spatio-temporal resolution input variables to obtain high spatio-temporal resolution SM. We used the Fengyun-3B (FY-3B) SM over the Tibetan Plateau (TP) to test the algorithm. The results show that the algorithm could successfully improve the spatio-temporal resolution of FY-3B SM from 0.25° and 2–3 days to 0.05° and 1-day over the TP. The improved SM is consistent with the original product in terms of both spatial distribution and temporal variation. The high spatio-temporal resolution SM allows a better understanding of the diurnal and seasonal variations of SM at the regional scale, consequently enhancing ecological and hydrological applications, especially under climate change. [ABSTRACT FROM AUTHOR]