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Gridded precipitation products with fine resolutions and qualities are of great importance for understanding the global water–carbon-energy cycles at various spatiotemporal scales. Though continuous developments in Satellite Remote Sensing fields have been providing great strengths for measuring the precipitation from space, merging precipitation products from different sources, especially the gauge observations, is still the optimal way for obtaining high-quality precipitation data. Currently, the mainstream merging methods mainly focus on merging the rain rates without the considerations of rain events. In this study, we propose a new assumption that both rain events and rain rates should be considered in the merging procedures rather than only the rain rates. To meet our assumption, a morphology-based adaptive spatio-temporal merging algorithm (MASTMA) for combining various precipitation products is proposed, in which the morphology theory is first introduced to comprehensively consider the influences from both rain events and rain rates. The multisource and multiscale precipitation products including the gauge-based data (CPC-U, 0.5°, daily), the satellite-based data [Global Satellite Mapping of Precipitation by Moving Vector with Kalman (GSMaP-MVK), 0.1°, hourly; integrated multisatellite retrievals for global precipitation measurement late run (IMERG-LR), 0.1°, half-hourly], and the reanalysis data (ERA5-land, 0.1°, hourly), have been comprehensively considered in MASTMA for generating the final estimates (MASTMA-F, 0.1°, hourly) over the southeastern regions of the Mainland China in the periods from 2016 to 2019. The main conclusions include but are not limited to: 1) considerations on rain events contribute significantly to the final merged results, especially when eliminating false extreme values over the regions where precipitation is greatly overestimated; 2) the MASTMA could optimally integrate the advantages from multisource precipitation products with different resolutions, particularly from the perspective of the spatial distributions; and 3) the final merged estimates using MASTMA outperform the contemporary state-of-the-art precipitation products especially in terms of modified Kling–Gupta Efficiency (mKGE) and critical success index (CSI). Additionally, the results of this study suggest that MASMTA is a new promising merging approach with great robustness and applicability, and has the foreseeable potentials for the operational run to generate the optimal global merged precipitation products. [ABSTRACT FROM AUTHOR]

Man-made reservoirs are key components of terrestrial hydrological systems. Identifying the location and number of reservoirs is the premise for studying the impact of human activities on water resources and environmental changes. While complete bottom-up censuses can provide a comprehensive view of the reservoir landscape, they are time-consuming and laborious and are thus infeasible on a global scale. Moreover, it is challenging to distinguish man-made reservoirs from natural lakes in remote sensing images. This study proposes a convolutional neural network (CNN)-based framework to recognize global reservoirs from Landsat 8 imageries. On the basis of the HydroLAKES dataset, a Landsat 8 cloud-free mosaic of 2017 was clipped for each feature (reservoir or lake) and was resized into 224 × 224 patches, which were collected as training and testing samples. Compared to other deep learning methods (Alexnet and VGG) and state-of-the-art traditional machine learning methods (support vector machine, random forest, gradient boosting, and bag-of-visual-words), we found that fine-tuning the pretrained CNN model, ResNet-50, could reach the highest accuracy (91.45%). Application cases in Kansas (USA, North America), Mpumalanga (South Africa, Africa), and Kostanay (Kazakhstan, Asia) resulted in classification accuracies of better than 99%, which showed the applicability of the proposed ResNet-50 model to the extraction of reservoirs from a vast amount of moderate resolution images. The framework that was developed in this paper is the first attempt to combine remote sensing big data and the deep learning technique to the recognition of reservoirs at a global scale. [ABSTRACT FROM AUTHOR]

National Oceanography and Atmosphere Administration's Advanced Very High Resolution Radiometer (AVHRR) (1970s–present, ∼1 km) and China's GaoFen-1 (GF-1) image series (2013–present, ∼16 m), can both provide extremely useful information of land use and land cover, due to the long history and, wide scanning width of AVHRR and the high resolution of GF-1. However, both data sets have apparent weaknesses of large and inconsistent geometric and geolocation errors (AVHRR: 1–20 pixels, GF-1: 1–10 pixels), prohibiting them from many interdisciplinary applications. This paper, based on feature point detection method, presents a new approach to improve the georeferencing of AVHRR and GF-1 data using inland water bodies. No need of a prior knowledge of the error source and only based on the image information and the base map, this method is able to automatically search and find the optimal water threshold value (reflectance), conduct feature point detecting and matching, and perform affine transformation. In the case study of the Tibetan Plateau, water body (i.e., lake) with its lower reflectance and relatively stable location and shape, is used as the referencing mask (i.e., base map) for detecting feature point pairs. The accuracy of the geometric correction is found within subpixel level, with average RMSE = 0.76 for AVHRR and RMSE = 0.91 for GF-1. The developed georeferencing method can be potentially applied to other less accessible areas that are difficult to derive ground control points for numerous applications with improved location and geometric accuracies. [ABSTRACT FROM AUTHOR]

The Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement mission (IMERG) has been widely evaluated. However, most of these studies focus on the ultimate merged satellite-gauge precipitation estimate and neglect the valuable intermediate estimates which directly guide the improvement of the IMERG product. This research aims to identify the error sources of the latest IMERG version 6 by evaluating the intermediate and ultimate precipitation estimates, and further examine the influences of regional topography and surface type on these errors. Results show that among six passive microwave (PMW) sensors, the Microwave Humidity Sounder (MHS) has outstanding comprehensive behavior, and Special Sensor Microwave Imager/Sounder (SSMIS) operates advanced at precipitation detection, while the Sounder for Atmospheric Profiling of Humidity in the Intertropics by Radiometry (SAPHIR) has the worst performance. More precipitation events are detected with larger quantitative uncertainty in low-lying places than in highlands, in urban and water body areas than in other places, and more in coastal areas than in inland regions. Infrared (IR) estimate has worse performance than PMW, and the precipitation detectability of IR is more sensitive to the factors of elevation and the distance to the coast, as larger critical successful index (CSI) over lowlands and coastal areas. PMW morphing and the mixing of PMW and IR algorithms partly reverse the conservative feature of the precipitation detection of PMW and IR estimates, resulting in higher probability of detection (POD) and false alert ratio (FAR). Finally, monthly gauge calibration improves most of the statistical indicators and reduces the influence of elevation and surface type factor on these errors. [ABSTRACT FROM AUTHOR]

Abstract Preparing experiment-ready images is a labor-intensive but necessary process for almost all remote sensing related studies. This short communication introduces a software to efficiently acquire large-scale high-quality and cloud-free Landsat images to support subsequent analysis and applications. The software fully leverages Google Earth Engine's processing of huge amount of remote sensing images, and the visualization on the availability and accessibility of Landsat images provided by heatmaps at both regional and global scales. The cloud percentage property and quality bands of Landsat surface reflectance products are used for masking clouds, cloud shadows, snow/ice, and low-quality pixels in this study. The software can be applied to effectively choose study areas and prepare data in different fields, such as hydrology, ecology, and human activities. This functioning open-source software can be accessible via GitHub webpages in both JavaScript and Python formats. [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]