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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]

• 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]

• 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]

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]