Articles | Volume 15, issue 5
https://doi.org/10.5194/essd-15-2055-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-15-2055-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
23 May 2023
Data description paper |
| 23 May 2023
| 23 May 2023
Generation of global 1 km daily soil moisture product from 2000 to 2020 using ensemble learning
Yufang Zhang
School of Remote Sensing and Information Engineering, Wuhan
University, Wuhan 430079, China
Department of Geography, The University of Hong Kong, Hong Kong
999077, China
Department of Geography, The University of Hong Kong, Hong Kong
999077, China
School of Remote Sensing and Information Engineering, Wuhan
University, Wuhan 430079, China
Qian Wang
State Key Laboratory of Remote Sensing Science, Beijing Normal
University, Beijing 100875, China
Bing Li
Key Research Institute of Yellow River Civilization and Sustainable
Development and Collaborative Innovation Center on Yellow River
Civilization of Henan Province, Henan University, Kaifeng 475001, China
Jianglei Xu
School of Remote Sensing and Information Engineering, Wuhan
University, Wuhan 430079, China
Guodong Zhang
School of Remote Sensing and Information Engineering, Wuhan
University, Wuhan 430079, China
Xiaobang Liu
School of Remote Sensing and Information Engineering, Wuhan
University, Wuhan 430079, China
Changhao Xiong
School of Remote Sensing and Information Engineering, Wuhan
University, Wuhan 430079, China
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Hydrol. Earth Syst. Sci., 17, 2121–2129, https://doi.org/10.5194/hess-17-2121-2013, https://doi.org/10.5194/hess-17-2121-2013, 2013
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Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hessd-10-3927-2013, https://doi.org/10.5194/hessd-10-3927-2013, 2013
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This paper presents the first comprehensive dataset of 528 large dams in Italy. It contains structural characteristics of the dams, such as coordinates, reservoir surface areas and volumes, together with a range of geomorphological, climatological, extreme rainfall, land cover and soil-related attributes of their upstream catchments.
Yueli Chen, Yun Xie, Xingwu Duan, and Minghu Ding
Earth Syst. Sci. Data, 17, 1265–1274, https://doi.org/10.5194/essd-17-1265-2025, https://doi.org/10.5194/essd-17-1265-2025, 2025
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Rainfall erosivity maps are crucial for identifying key areas of water erosion. Due to the limited historical precipitation data, there are certain biases in rainfall erosivity estimates in China. This study develops a new rainfall erosivity map for mainland China using 1 min precipitation data from 60 129 weather stations, revealing that areas exceeding 4000 MJ mm ha−1 h−1yr−1 of annual rainfall erosivity are mainly concentrated in southern China and on the southern Tibetan Plateau.
Jan Magnusson, Yves Bühler, Louis Quéno, Bertrand Cluzet, Giulia Mazzotti, Clare Webster, Rebecca Mott, and Tobias Jonas
Earth Syst. Sci. Data, 17, 703–717, https://doi.org/10.5194/essd-17-703-2025, https://doi.org/10.5194/essd-17-703-2025, 2025
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In this study, we present a dataset for the Dischma catchment in eastern Switzerland, which represents a typical high-alpine watershed in the European Alps. Accurate monitoring and reliable forecasting of snow and water resources in such basins are crucial for a wide range of applications. Our dataset is valuable for improving physics-based snow, land surface, and hydrological models, with potential applications in similar high-alpine catchments.
Haoxuan Yang, Jia Yang, Tyson E. Ochsner, Erik S. Krueger, Mengyuan Xu, and Chris B. Zou
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-55, https://doi.org/10.5194/essd-2025-55, 2025
Revised manuscript accepted for ESSD
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We developed a 3-hour, 1-km surface soil moisture dataset for the contiguous United States from 2015 to 2023 using the spatio-temporal fusion method. This dataset effectively combines the distinct advantages of two long-term SSM datasets, which is also the first hour-level 1-km soil moisture dataset at the continental US scale. The new dataset could provide new insight into the fast changes in soil moisture along with drought and wet spell occurrences.
Nikunj K. Mangukiya, Kanneganti Bhargav Kumar, Pankaj Dey, Shailza Sharma, Vijaykumar Bejagam, Pradeep P. Mujumdar, and Ashutosh Sharma
Earth Syst. Sci. Data, 17, 461–491, https://doi.org/10.5194/essd-17-461-2025, https://doi.org/10.5194/essd-17-461-2025, 2025
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We introduce CAMELS-IND (Catchment Attributes and MEteorology for Large-sample Studies – India), which provides daily hydrometeorological time series and static catchment attributes representing the location, topography, climate, hydrological signatures, land use, land cover, soil, geology, and anthropogenic influences for 472 catchments in Peninsular India to foster large-sample hydrological studies in India and promote the inclusion of Indian catchments in global hydrological research.
Bennett J. McAfee, Aanish Pradhan, Abhilash Neog, Sepideh Fatemi, Robert T. Hensley, Mary E. Lofton, Anuj Karpatne, Cayelan C. Carey, and Paul C. Hanson
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-27, https://doi.org/10.5194/essd-2025-27, 2025
Revised manuscript accepted for ESSD
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LakeBeD-US is a dataset of lake water quality data collected by multiple long-term monitoring programs around the United States. This dataset is designed to foster collaboration between lake scientists and computer scientists to improve predictions of water quality. By offering a way for computer models to be tested against real-world lake data, LakeBeD-US offers opportunities for both sciences to grow and to give new insights into the causes of water quality changes.
Aloïs Tilloy, Dominik Paprotny, Stefania Grimaldi, Goncalo Gomes, Alessandra Bianchi, Stefan Lange, Hylke Beck, Cinzia Mazzetti, and Luc Feyen
Earth Syst. Sci. Data, 17, 293–316, https://doi.org/10.5194/essd-17-293-2025, https://doi.org/10.5194/essd-17-293-2025, 2025
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This article presents a reanalysis of Europe's river streamflow for the period 1951–2020. Streamflow is estimated through a state-of-the-art hydrological simulation framework benefitting from detailed information about the landscape, climate, and human activities. The resulting Hydrological European ReAnalysis (HERA) can be a valuable tool for studying hydrological dynamics, including the impacts of climate change and human activities on European water resources and flood and drought risks.
Daniel Kovacek and Steven Weijs
Earth Syst. Sci. Data, 17, 259–275, https://doi.org/10.5194/essd-17-259-2025, https://doi.org/10.5194/essd-17-259-2025, 2025
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We made a dataset for British Columbia describing the terrain, soil, land cover, and climate of over 1 million watersheds. The attributes are often used in hydrology because they are related to the water cycle. The data are meant to be used for water resources problems that can benefit from lots of watersheds and their attributes. The data and instructions needed to build the dataset from scratch are freely available. The permanent home for the data is https://doi.org/10.5683/SP3/JNKZVT.
Andrea Galletti, Soroush Zarghami Dastjerdi, and Bruno Majone
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-521, https://doi.org/10.5194/essd-2024-521, 2025
Revised manuscript accepted for ESSD
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We propose IAR-HP, a detailed inventory of large hydropower systems in Italy's Alpine Region, aimed at improving hydrological modeling for climate impact studies by providing the most relevant information with a consistent level of detail. It includes structural, geographical, and operational data for over 300 hydropower plants and their related reservoirs and water intakes. Validated through modeling, IAR-HP accurately reproduces observed hydropower, capturing 96.2 % of actual production.
Bennet Juhls, Anne Morgenstern, Jens Hölemann, Antje Eulenburg, Birgit Heim, Frederieke Miesner, Hendrik Grotheer, Gesine Mollenhauer, Hanno Meyer, Ephraim Erkens, Felica Yara Gehde, Sofia Antonova, Sergey Chalov, Maria Tereshina, Oxana Erina, Evgeniya Fingert, Ekaterina Abramova, Tina Sanders, Liudmila Lebedeva, Nikolai Torgovkin, Georgii Maksimov, Vasily Povazhnyi, Rafael Gonçalves-Araujo, Urban Wünsch, Antonina Chetverova, Sophie Opfergelt, and Pier Paul Overduin
Earth Syst. Sci. Data, 17, 1–28, https://doi.org/10.5194/essd-17-1-2025, https://doi.org/10.5194/essd-17-1-2025, 2025
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The Siberian Arctic is warming fast: permafrost is thawing, river chemistry is changing, and coastal ecosystems are affected. We aimed to understand changes in the Lena River, a major Arctic river flowing to the Arctic Ocean, by collecting 4.5 years of detailed water data, including temperature and carbon and nutrient contents. This dataset records current conditions and helps us to detect future changes. Explore it at https://doi.org/10.1594/PANGAEA.913197 and https://lena-monitoring.awi.de/.
Ralf Loritz, Alexander Dolich, Eduardo Acuña Espinoza, Pia Ebeling, Björn Guse, Jonas Götte, Sibylle K. Hassler, Corina Hauffe, Ingo Heidbüchel, Jens Kiesel, Mirko Mälicke, Hannes Müller-Thomy, Michael Stölzle, and Larisa Tarasova
Earth Syst. Sci. Data, 16, 5625–5642, https://doi.org/10.5194/essd-16-5625-2024, https://doi.org/10.5194/essd-16-5625-2024, 2024
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The CAMELS-DE dataset features data from 1582 streamflow gauges across Germany, with records spanning from 1951 to 2020. This comprehensive dataset, which includes time series of up to 70 years (median 46 years), enables advanced research on water flow and environmental trends and supports the development of hydrological models.
Einara Zahn and Elie Bou-Zeid
Earth Syst. Sci. Data, 16, 5603–5624, https://doi.org/10.5194/essd-16-5603-2024, https://doi.org/10.5194/essd-16-5603-2024, 2024
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Quantifying water and CO2 exchanges through transpiration, evaporation, net photosynthesis, and soil respiration is essential for understanding how ecosystems function. We implemented five methods to estimate these fluxes over a 5-year period across 47 sites. This is the first dataset representing such large spatial and temporal coverage of soil and plant exchanges, and it has many potential applications, such as examining the response of ecosystems to weather extremes and climate change.
Lauranne Alaerts, Jonathan Lambrechts, Ny Riana Randresihaja, Luc Vandenbulcke, Olivier Gourgue, Emmanuel Hanert, and Marilaure Grégoire
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-529, https://doi.org/10.5194/essd-2024-529, 2024
Revised manuscript accepted for ESSD
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We created the first comprehensive, high-resolution, and easily-accessible bathymetry dataset for the three main branches of the Danube Delta. By combining four data sources, we obtained a detailed representation of the riverbed, with resolutions ranging from 2 to 100 m. This dataset will support future studies on water and nutrient exchanges between the Danube and the Black Sea, and provide insights into the Delta’s buffer role within the understudied Danube-Black Sea continuum.
Wangyipu Li, Zhaoyuan Yao, Yifan Qu, Hanbo Yang, Yang Song, Lisheng Song, Lifeng Wu, and Yaokui Cui
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-460, https://doi.org/10.5194/essd-2024-460, 2024
Revised manuscript accepted for ESSD
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Due to shortcomings such as extensive data gaps and limited observation durations in current ground-based latent heat flux (LE) datasets, we developed a novel gap-filling and prolongation framework for ground-based LE observations, establishing a benchmark dataset for global evapotranspiration (ET) estimation from 2000 to 2022 across 64 sites at various time scales. This comprehensive dataset can strongly support ET modelling, water-carbon cycle monitoring, and long-term climate change analysis.
Juliette Godet, Pierre Nicolle, Nabil Hocini, Eric Gaume, Philippe Davy, Frederic Pons, Pierre Javelle, Pierre-André Garambois, Dimitri Lague, and Olivier Payrastre
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-472, https://doi.org/10.5194/essd-2024-472, 2024
Revised manuscript accepted for ESSD
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This paper describes a dataset that includes input, output, and validation data for the simulation of flash flood hazards and three specific flash flood events in the French Mediterranean region. This dataset is particularly valuable as flood mapping methods often lack sufficient benchmark data. Additionally, we demonstrate how the hydraulic method we used, named Floodos, produces highly satisfactory results.
Ling Zhang, Yanhua Xie, Xiufang Zhu, Qimin Ma, and Luca Brocca
Earth Syst. Sci. Data, 16, 5207–5226, https://doi.org/10.5194/essd-16-5207-2024, https://doi.org/10.5194/essd-16-5207-2024, 2024
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This study presented new annual maps of irrigated cropland in China from 2000 to 2020 (CIrrMap250). These maps were developed by integrating remote sensing data, irrigation statistics and surveys, and an irrigation suitability map. CIrrMap250 achieved high accuracy and outperformed currently available products. The new irrigation maps revealed a clear expansion of China’s irrigation area, with the majority (61%) occurring in the water-unsustainable regions facing severe to extreme water stress.
Dominik Paprotny, Paweł Terefenko, and Jakub Śledziowski
Earth Syst. Sci. Data, 16, 5145–5170, https://doi.org/10.5194/essd-16-5145-2024, https://doi.org/10.5194/essd-16-5145-2024, 2024
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Knowledge about past natural disasters can help adaptation to their future occurrences. Here, we present a dataset of 2521 riverine, pluvial, coastal, and compound floods that have occurred in 42 European countries between 1870 and 2020. The dataset contains available information on the inundated area, fatalities, persons affected, or economic loss and was obtained by extensive data collection from more than 800 sources ranging from news reports through government databases to scientific papers.
Paulina Bartkowiak, Bartolomeo Ventura, Alexander Jacob, and Mariapina Castelli
Earth Syst. Sci. Data, 16, 4709–4734, https://doi.org/10.5194/essd-16-4709-2024, https://doi.org/10.5194/essd-16-4709-2024, 2024
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This paper presents the Two-Source Energy Balance evapotranspiration (ET) product driven by Copernicus Sentinel-2 and Sentinel-3 imagery together with ERA5 climate reanalysis data. Daily ET maps are available at 100 m spatial resolution for the period 2017–2021 across four Mediterranean basins: Ebro (Spain), Hérault (France), Medjerda (Tunisia), and Po (Italy). The product is highly beneficial for supporting vegetation monitoring and sustainable water management at the river basin scale.
Fanny J. Sarrazin, Sabine Attinger, and Rohini Kumar
Earth Syst. Sci. Data, 16, 4673–4708, https://doi.org/10.5194/essd-16-4673-2024, https://doi.org/10.5194/essd-16-4673-2024, 2024
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Nitrogen (N) and phosphorus (P) contamination of water bodies is a long-term issue due to the long history of N and P inputs to the environment and their persistence. Here, we introduce a long-term and high-resolution dataset of N and P inputs from wastewater (point sources) for Germany, combining data from different sources and conceptual understanding. We also account for uncertainties in modelling choices, thus facilitating robust long-term and large-scale water quality studies.
Shanti Shwarup Mahto, Simone Fatichi, and Stefano Galelli
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-441, https://doi.org/10.5194/essd-2024-441, 2024
Revised manuscript accepted for ESSD
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The MSEA-Res database offers an open-access dataset tracking absolute water storage for 185 large reservoirs across Mainland Southeast Asia from 1985–2023. It provides valuable insights into how reservoir storage has grown by 130 % between 2008 and 2017, driven by dams in key river basins. Our data also reveal how droughts, like the 2019–2020 event, significantly impacted water reservoirs. This resource can aid water management, drought planning, and research globally.
Karl Nicolaus van Zweel, Laurent Gourdol, Jean François Iffly, Loïc Léonard, François Barnich, Laurent Pfister, Erwin Zehe, and Christophe Hissler
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-259, https://doi.org/10.5194/essd-2024-259, 2024
Revised manuscript accepted for ESSD
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Our study monitored groundwater in a Luxembourg forest over a year to understand water and chemical changes. We found seasonal variations in water chemistry, influenced by rainfall and soil interactions. This data helps predict environmental responses and manage water resources better. By measuring key parameters like pH and dissolved oxygen, our research provides valuable insights into groundwater behavior and serves as a resource for future environmental studies.
Rohit Mukherjee, Frederick Policelli, Ruixue Wang, Elise Arellano-Thompson, Beth Tellman, Prashanti Sharma, Zhijie Zhang, and Jonathan Giezendanner
Earth Syst. Sci. Data, 16, 4311–4323, https://doi.org/10.5194/essd-16-4311-2024, https://doi.org/10.5194/essd-16-4311-2024, 2024
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Global water resource monitoring is crucial due to climate change and population growth. This study presents a hand-labeled dataset of 100 PlanetScope images for surface water detection, spanning diverse biomes. We use this dataset to evaluate two state-of-the-art mapping methods. Results highlight performance variations across biomes, emphasizing the need for diverse, independent validation datasets to enhance the accuracy and reliability of satellite-based surface water monitoring techniques.
Lei Huang, Yong Luo, Jing M. Chen, Qiuhong Tang, Tammo Steenhuis, Wei Cheng, and Wen Shi
Earth Syst. Sci. Data, 16, 3993–4019, https://doi.org/10.5194/essd-16-3993-2024, https://doi.org/10.5194/essd-16-3993-2024, 2024
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Timely global terrestrial evapotranspiration (ET) data are crucial for water resource management and drought forecasting. This study introduces the VISEA algorithm, which integrates satellite data and shortwave radiation to provide daily 0.05° gridded near-real-time ET estimates. By employing a vegetation index–temperature method, this algorithm can estimate ET without requiring additional data. Evaluation results demonstrate VISEA's comparable accuracy with accelerated data availability.
Sibylle Kathrin Hassler, Rafael Bohn Reckziegel, Ben du Toit, Svenja Hoffmeister, Florian Kestel, Anton Kunneke, Rebekka Maier, and Jonathan Paul Sheppard
Earth Syst. Sci. Data, 16, 3935–3948, https://doi.org/10.5194/essd-16-3935-2024, https://doi.org/10.5194/essd-16-3935-2024, 2024
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Agroforestry systems (AFSs) combine trees and crops within the same land unit, providing a sustainable land use option which protects natural resources and biodiversity. Introducing trees into agricultural systems can positively affect water resources, soil characteristics, biomass and microclimate. We studied an AFS in South Africa in a multidisciplinary approach to assess the different influences and present the resulting dataset consisting of water, soil, tree and meteorological variables.
Keirnan J. A. Fowler, Ziqi Zhang, and Xue Hou
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-263, https://doi.org/10.5194/essd-2024-263, 2024
Revised manuscript accepted for ESSD
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This paper presents Version 2 of the Australian edition of the Catchment Attributes and Meteorology for Large-sample Studies (CAMELS) series of datasets. CAMELS-AUS v2 comprises data for an increased number (561) of catchments, each with with long-term monitoring, combining hydrometeorological time series with attributes related to geology, soil, topography, land cover, anthropogenic influence and hydroclimatology. It is freely downloadable from https://zenodo.org/doi/10.5281/zenodo.12575680.
Kaihao Zheng, Peirong Lin, and Ziyun Yin
Earth Syst. Sci. Data, 16, 3873–3891, https://doi.org/10.5194/essd-16-3873-2024, https://doi.org/10.5194/essd-16-3873-2024, 2024
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We develop a globally applicable thresholding scheme for DEM-based floodplain delineation to improve the representation of spatial heterogeneity. It involves a stepwise approach to estimate the basin-level floodplain hydraulic geometry parameters that best respect the scaling law while approximating the global hydrodynamic flood maps. A ~90 m resolution global floodplain map, the Spatial Heterogeneity Improved Floodplain by Terrain analysis (SHIFT), is delineated with demonstrated superiority.
Yuzhong Yang, Qingbai Wu, Xiaoyan Guo, Lu Zhou, Helin Yao, Dandan Zhang, Zhongqiong Zhang, Ji Chen, and Guojun Liu
Earth Syst. Sci. Data, 16, 3755–3770, https://doi.org/10.5194/essd-16-3755-2024, https://doi.org/10.5194/essd-16-3755-2024, 2024
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We present the temporal data of stable isotopes in different waterbodies in the Beiluhe Basin in the hinterland of the Qinghai–Tibet Plateau (QTP) produced between 2017 and 2022. In this article, the first detailed stable isotope data of 359 ground ice samples are presented. This first data set provides a new basis for understanding the hydrological effects of permafrost degradation on the QTP.
Bernhard Lehner, Mira Anand, Etienne Fluet-Chouinard, Florence Tan, Filipe Aires, George H. Allen, Pilippe Bousquet, Josep G. Canadell, Nick Davidson, C. Max Finlayson, Thomas Gumbricht, Lammert Hilarides, Gustaf Hugelius, Robert B. Jackson, Maartje C. Korver, Peter B. McIntyre, Szabolcs Nagy, David Olefeldt, Tamlin M. Pavelsky, Jean-Francois Pekel, Benjamin Poulter, Catherine Prigent, Jida Wang, Thomas A. Worthington, Dai Yamazaki, and Michele Thieme
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-204, https://doi.org/10.5194/essd-2024-204, 2024
Revised manuscript accepted for ESSD
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The Global Lakes and Wetlands Database (GLWD) version 2 distinguishes a total of 33 non-overlapping wetland classes, providing a static map of the world’s inland surface waters. It contains cell fractions of wetland extents per class at a grid cell resolution of ~500 m. The total combined extent of all classes including all inland and coastal waterbodies and wetlands of all inundation frequencies—that is, the maximum extent—covers 18.2 million km2, equivalent to 13.4 % of total global land area.
Hordur Bragi Helgason and Bart Nijssen
Earth Syst. Sci. Data, 16, 2741–2771, https://doi.org/10.5194/essd-16-2741-2024, https://doi.org/10.5194/essd-16-2741-2024, 2024
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LamaH-Ice is a large-sample hydrology (LSH) dataset for Iceland. The dataset includes daily and hourly hydro-meteorological time series, including observed streamflow and basin characteristics, for 107 basins. LamaH-Ice offers most variables that are included in existing LSH datasets and additional information relevant to cold-region hydrology such as annual time series of glacier extent and mass balance. A large majority of the basins in LamaH-Ice are unaffected by human activities.
Chengcheng Hou, Yan Li, Shan Sang, Xu Zhao, Yanxu Liu, Yinglu Liu, and Fang Zhao
Earth Syst. Sci. Data, 16, 2449–2464, https://doi.org/10.5194/essd-16-2449-2024, https://doi.org/10.5194/essd-16-2449-2024, 2024
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To fill the gap in the gridded industrial water withdrawal (IWW) data in China, we developed the China Industrial Water Withdrawal (CIWW) dataset, which provides monthly IWWs from 1965 to 2020 at a spatial resolution of 0.1°/0.25° and auxiliary data including subsectoral IWW and industrial output value in 2008. This dataset can help understand the human water use dynamics and support studies in hydrology, geography, sustainability sciences, and water resource management and allocation in China.
Nehar Mandal, Prabal Das, and Kironmala Chanda
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-109, https://doi.org/10.5194/essd-2024-109, 2024
Revised manuscript accepted for ESSD
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Optimal features among hydroclimatic variables and land surface model (LSM) outputs are selected using a novel Bayesian network (BN) approach for simulating Terrestrial Water Storage Anomalies (TWSA). TWSA is simulated using ML models (CNN, SVR, ETR, and Stacking Ensemble Regression), and gridwise leader models are identified globally. TWSA is reconstructed (BNML_TWSA) with the selected leader models from January 1960 to December 2022 to generate a continuous global gridded dataset.
Pierre-Antoine Versini, Leydy Alejandra Castellanos-Diaz, David Ramier, and Ioulia Tchiguirinskaia
Earth Syst. Sci. Data, 16, 2351–2366, https://doi.org/10.5194/essd-16-2351-2024, https://doi.org/10.5194/essd-16-2351-2024, 2024
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Nature-based solutions (NBSs), such as green roofs, have appeared as relevant solutions to mitigate urban heat islands. The evapotranspiration (ET) process allows NBSs to cool the air. To improve our knowledge about ET assessment, this paper presents some experimental measurement campaigns carried out during three consecutive summers. Data are available for three different (large, small, and point-based) spatial scales.
Ralph Bathelemy, Pierre Brigode, Vazken Andréassian, Charles Perrin, Vincent Moron, Cédric Gaucherel, Emmanuel Tric, and Dominique Boisson
Earth Syst. Sci. Data, 16, 2073–2098, https://doi.org/10.5194/essd-16-2073-2024, https://doi.org/10.5194/essd-16-2073-2024, 2024
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The aim of this work is to provide the first hydroclimatic database for Haiti, a Caribbean country particularly vulnerable to meteorological and hydrological hazards. The resulting database, named Simbi, provides hydroclimatic time series for around 150 stations and 24 catchment areas.
Changming Li, Ziwei Liu, Wencong Yang, Zhuoyi Tu, Juntai Han, Sien Li, and Hanbo Yang
Earth Syst. Sci. Data, 16, 1811–1846, https://doi.org/10.5194/essd-16-1811-2024, https://doi.org/10.5194/essd-16-1811-2024, 2024
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Using a collocation-based approach, we developed a reliable global land evapotranspiration product (CAMELE) by merging multi-source datasets. The CAMELE product outperformed individual input datasets and showed satisfactory performance compared to reference data. It also demonstrated superiority for different plant functional types. Our study provides a promising solution for data fusion. The CAMELE dataset allows for detailed research and a better understanding of land–atmosphere interactions.
Yuhan Guo, Hongxing Zheng, Yuting Yang, Yanfang Sang, and Congcong Wen
Earth Syst. Sci. Data, 16, 1651–1665, https://doi.org/10.5194/essd-16-1651-2024, https://doi.org/10.5194/essd-16-1651-2024, 2024
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We have provided an inaugural version of the hydrogeomorphic dataset for catchments over the Tibetan Plateau. We first provide the width-function-based instantaneous unit hydrograph (WFIUH) for each HydroBASINS catchment, which can be used to investigate the spatial heterogeneity of hydrological behavior across the Tibetan Plateau. It is expected to facilitate hydrological modeling across the Tibetan Plateau.
Chang Liao, Darren Engwirda, Matthew Cooper, Mingke Li, and Yilin Fang
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2023-398, https://doi.org/10.5194/essd-2023-398, 2024
Revised manuscript accepted for ESSD
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Discrete Global Grid systems, or DGGs, are digital frameworks that help us organize information about our planet. Although scientists have used DGGs in areas like weather and nature, using them in the water cycle has been challenging because some core datasets are missing. We created a way to generate these datasets. We then developed the datasets in the Amazon Basin, which plays an important role in our planet's climate. These datasets may help us improve our water cycle models.
Lingbo Li, Hong-Yi Li, Guta Abeshu, Jinyun Tang, L. Ruby Leung, Chang Liao, Zeli Tan, Hanqin Tian, Peter Thornton, and Xiaojuan Yang
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-43, https://doi.org/10.5194/essd-2024-43, 2024
Revised manuscript accepted for ESSD
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We have developed a new map that reveals how organic carbon from soil leaches into headwater streams over the contiguous United States. We use advanced artificial intelligence techniques and a massive amount of data, including observations at over 2,500 gauges and a wealth of climate and environmental information. The map is a critical step in understanding and predicting how carbon moves through our environment, hence a useful tool for tackling climate challenges.
Ziyun Yin, Peirong Lin, Ryan Riggs, George H. Allen, Xiangyong Lei, Ziyan Zheng, and Siyu Cai
Earth Syst. Sci. Data, 16, 1559–1587, https://doi.org/10.5194/essd-16-1559-2024, https://doi.org/10.5194/essd-16-1559-2024, 2024
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Large-sample hydrology (LSH) datasets have been the backbone of hydrological model parameter estimation and data-driven machine learning models for hydrological processes. This study complements existing LSH studies by creating a dataset with improved sample coverage, uncertainty estimates, and dynamic descriptions of human activities, which are all crucial to hydrological understanding and modeling.
Pierluigi Claps, Giulia Evangelista, Daniele Ganora, Paola Mazzoglio, and Irene Monforte
Earth Syst. Sci. Data, 16, 1503–1522, https://doi.org/10.5194/essd-16-1503-2024, https://doi.org/10.5194/essd-16-1503-2024, 2024
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FOCA (Italian FlOod and Catchment Atlas) is the first systematic collection of data on Italian river catchments. It comprises geomorphological, soil, land cover, NDVI, climatological and extreme rainfall catchment attributes. FOCA also contains 631 peak and daily discharge time series covering the 1911–2016 period. Using this first nationwide data collection, a wide range of applications, in particular flood studies, can be undertaken within the Italian territory.
Wei Jing Ang, Edward Park, Yadu Pokhrel, Dung Duc Tran, and Ho Huu Loc
Earth Syst. Sci. Data, 16, 1209–1228, https://doi.org/10.5194/essd-16-1209-2024, https://doi.org/10.5194/essd-16-1209-2024, 2024
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Dams have burgeoned in the Mekong, but information on dams is scattered and inconsistent. Up-to-date evaluation of dams is unavailable, and basin-wide hydropower potential has yet to be systematically assessed. We present a comprehensive database of 1055 dams, a spatiotemporal analysis of the dams, and a total hydropower potential of 1 334 683 MW. Considering projected dam development and hydropower potential, the vulnerability and the need for better dam management may be highest in Laos.
Chuanqi He, Ci-Jian Yang, Jens M. Turowski, Richard F. Ott, Jean Braun, Hui Tang, Shadi Ghantous, Xiaoping Yuan, and Gaia Stucky de Quay
Earth Syst. Sci. Data, 16, 1151–1166, https://doi.org/10.5194/essd-16-1151-2024, https://doi.org/10.5194/essd-16-1151-2024, 2024
Short summary
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The shape of drainage basins and rivers holds significant implications for landscape evolution processes and dynamics. We used a global 90 m resolution topography to obtain ~0.7 million drainage basins with sizes over 50 km2. Our dataset contains the spatial distribution of drainage systems and their morphological parameters, supporting fields such as geomorphology, climatology, biology, ecology, hydrology, and natural hazards.
Jingyu Lin, Peng Wang, Jinzhu Wang, Youping Zhou, Xudong Zhou, Pan Yang, Hao Zhang, Yanpeng Cai, and Zhifeng Yang
Earth Syst. Sci. Data, 16, 1137–1149, https://doi.org/10.5194/essd-16-1137-2024, https://doi.org/10.5194/essd-16-1137-2024, 2024
Short summary
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Our paper provides a repository comprising over 330 000 observations encompassing daily, weekly, and monthly records of surface water quality spanning the period 1980–2022. It included 18 distinct indicators, meticulously gathered at 2384 monitoring sites, ranging from inland locations to coastal and oceanic areas. This dataset will be very useful for researchers and decision-makers in the fields of hydrology, ecological studies, climate change, policy development, and oceanography.
Ana M. Ricardo, Rui M. L. Ferreira, Alberto Rodrigues da Silva, Jacinto Estima, Jorge Marques, Ivo Gamito, and Alexandre Serra
Earth Syst. Sci. Data, 16, 375–385, https://doi.org/10.5194/essd-16-375-2024, https://doi.org/10.5194/essd-16-375-2024, 2024
Short summary
Short summary
Floods are among the most common natural disasters responsible for severe damages and human losses. Agueda.2016Flood, a synthesis of locally sensed data and numerically produced data, allows complete characterization of the flood event that occurred in February 2016 in the Portuguese Águeda River. The dataset was managed through the RiverCure Portal, a collaborative web platform connected to a validated shallow-water model.
Jiawei Hou, Albert I. J. M. Van Dijk, Luigi J. Renzullo, and Pablo R. Larraondo
Earth Syst. Sci. Data, 16, 201–218, https://doi.org/10.5194/essd-16-201-2024, https://doi.org/10.5194/essd-16-201-2024, 2024
Short summary
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The GloLakes dataset provides historical and near-real-time time series of relative (i.e. storage change) and absolute (i.e. total stored volume) storage for more than 27 000 lakes worldwide using multiple sources of satellite data, including laser and radar altimetry and optical remote sensing. These data can help us understand the influence of climate variability and anthropogenic activities on water availability and system ecology over the last 4 decades.
Menaka Revel, Xudong Zhou, Prakat Modi, Jean-François Cretaux, Stephane Calmant, and Dai Yamazaki
Earth Syst. Sci. Data, 16, 75–88, https://doi.org/10.5194/essd-16-75-2024, https://doi.org/10.5194/essd-16-75-2024, 2024
Short summary
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As satellite technology advances, there is an incredible amount of remotely sensed data for observing terrestrial water. Satellite altimetry observations of water heights can be utilized to calibrate and validate large-scale hydrodynamic models. However, because large-scale models are discontinuous, comparing satellite altimetry to predicted water surface elevation is difficult. We developed a satellite altimetry mapping procedure for high-resolution river network data.
Cited articles
Abbaszadeh, P., Moradkhani, H., and Zhan, X.: Downscaling SMAP radiometer
soil moisture over the CONUS using an ensemble learning method, Water
Resour. Res., 55, 324–344, https://doi.org/10.1029/2018WR023354, 2019.
Al Bitar, A., Mialon, A., Kerr, Y. H., Cabot, F., Richaume, P., Jacquette, E., Quesney, A., Mahmoodi, A., Tarot, S., Parrens, M., Al-Yaari, A., Pellarin, T., Rodriguez-Fernandez, N., and Wigneron, J.-P.: The global SMOS Level 3 daily soil moisture and brightness temperature maps, Earth Syst. Sci. Data, 9, 293–315, https://doi.org/10.5194/essd-9-293-2017, 2017.
Al-Yaari, A., Wigneron, J.-P., Dorigo, W., Colliander, A., Pellarin, T.,
Hahn, S., Mialon, A., Richaume, P., Fernandez-Moran, R., Fan, L., Kerr, Y.
H., and De Lannoy, G.: Assessment and inter-comparison of recently
developed/reprocessed microwave satellite soil moisture products using ISMN
ground-based measurements, Remote Sens. Environ., 224, 289–303,
https://doi.org/10.1016/j.rse.2019.02.008, 2019.
Anderson, W. B., Zaitchik, B. F., Hain, C. R., Anderson, M. C., Yilmaz, M. T., Mecikalski, J., and Schultz, L.: Towards an integrated soil moisture drought monitor for East Africa, Hydrol. Earth Syst. Sci., 16, 2893–2913, https://doi.org/10.5194/hess-16-2893-2012, 2012.
Babaeian, E., Sadeghi, M., Jones, S. B., Montzka, C., Vereecken, H., and
Tuller, M.: Ground, Proximal, and Satellite Remote Sensing of Soil Moisture,
Rev. Geophys., 57, 530–616, https://doi.org/10.1029/2018RG000618, 2019.
Balenzano, A., Mattia, F., Satalino, G., Lovergine, F. P., Palmisano, D.,
and Davidson, M. W. J.: Dataset of Sentinel-1 surface soil moisture time
series at 1 km resolution over Southern Italy, Data Br., 38, 107345,
https://doi.org/10.1016/J.DIB.2021.107345, 2021.
Bartalis, Z., Wagner, W., Naeimi, V., Hasenauer, S., Scipal, K., Bonekamp,
H., Figa, J., and Anderson, C.: Initial soil moisture retrievals from the
METOP-A Advanced Scatterometer (ASCAT), Geophys. Res. Lett., 34, L20401,
https://doi.org/10.1029/2007GL031088, 2007.
Bauer-Marschallinger, B., Freeman, V., Cao, S., Paulik, C., Schaufler, S.,
Stachl, T., Modanesi, S., Massari, C., Ciabatta, L., Brocca, L., and Wagner,
W.: Toward Global Soil Moisture Monitoring With Sentinel-1: Harnessing
Assets and Overcoming Obstacles, IEEE T. Geosci. Remote, 57,
520–539, https://doi.org/10.1109/TGRS.2018.2858004, 2019.
Beaudoing, H. and Rodell, M.: GLDAS Noah Land Surface Model L4 3 hourly 0.25
x 0.25 degree V2.1, Goddard Earth Sciences Data and Information Services
Center [data set], https://doi.org/10.5067/E7TYRXPJKWOQ, 2020.
Belgiu, M. and Drãguþ, L.: Random forest in remote sensing: A review
of applications and future directions, ISPRS J. Photogramm., 114, 24–31,
https://doi.org/10.1016/j.isprsjprs.2016.01.011, 2016.
Berg, A. and Sheffield, J.: Climate change and drought: the soil moisture
perspective, Current Climate Change Report, 4, 180–191,
https://doi.org/10.1007/s40641-018-0095-0, 2018.
Bindlish, R., Jackson, T., Sun, R., Cosh, M., Yueh, S., and Dinardo, S.:
Combined Passive and Active Microwave Observations of Soil Moisture During
CLASIC, IEEE Geosci. Remote S., 6, 644–648,
https://doi.org/10.1109/LGRS.2009.2028441, 2009.
Breiman, L.: Random forests, Mach. Learn., 45, 5–32,
https://doi.org/10.1023/A:1010933404324, 2001.
Brocca, L., Ciabatta, L., Massari, C., Camici, S., and Tarpanelli, A.: Soil
Moisture for Hydrological Applications: Open Questions and New
Opportunities, Water, 9, 140, https://doi.org/10.3390/w9020140, 2017.
Brocca, L., Filippucci, P., Hahn, S., Ciabatta, L., Massari, C., Camici, S., Schüller, L., Bojkov, B., and Wagner, W.: SM2RAIN–ASCAT (2007–2018): global daily satellite rainfall data from ASCAT soil moisture observations, Earth Syst. Sci. Data, 11, 1583–1601, https://doi.org/10.5194/essd-11-1583-2019, 2019.
Chan, S. K., Bindlish, R., O'Neill, P. E., Njoku, E., Jackson, T.,
Colliander, A., Chen, F., Burgin, M., Dunbar, S., Piepmeier, J., Yueh, S.,
Entekhabi, D., Cosh, M. H., Caldwell, T., Walker, J., Wu, X., Berg, A.,
Rowlandson, T., Pacheco, A., McNairn, H., Thibeault, M., Martínez, J.,
González, Á., Seyfried, M., Bosch, D., Starks, P., Goodrich, D.,
Prueger, J., Palecki, M., Small, E. E., Zreda, M., Calvet, J., Crow, W. T.,
and Kerr, Y.: Assessment of the SMAP passive soil moisture product, IEEE
T. Geosci. Remote, 54, 4994–5007,
https://doi.org/10.1109/TGRS.2016.2561938, 2016.
Chan, S. K., Bindlish, R., O'Neill, P., Jackson, T., Njoku, E., Dunbar, S.,
Chaubell, J., Piepmeier, J., Yueh, S., Entekhabi, D., Colliander, A., Chen,
F., Cosh, M. H., Caldwell, T., Walker, J., Berg, A., McNairn, H., Thibeault,
M., Martínez-Fernández, J., Uldall, F., Seyfried, M., Bosch, D.,
Starks, P., Holifield Collins, C., Prueger, J., van der Velde, R., Asanuma,
J., Palecki, M., Small, E. E., Zreda, M., Calvet, J., Crow, W. T., and Kerr,
Y.: Development and assessment of the SMAP enhanced passive soil moisture
product, Remote Sens. Environ., 204, 931–941,
https://doi.org/10.1016/j.rse.2017.08.025, 2018.
Chen, T. and Guestrin, C.: XGBoost: A Scalable Tree Boosting System, in:
Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge
Discovery and Data Mining, 785–794,
https://doi.org/10.1145/2939672.2939785, 2016.
Colliander, A., Jackson, T. J., Bindlish, R., Chan, S., Das, N., Kim, S. B.,
Cosh, M. H., Dunbar, R. S., Dang, L., Pashaian, L., Asanuma, J., Aida, K.,
Berg, A., Rowlandson, T., Bosch, D., Caldwell, T., Caylor, K., Goodrich, D.,
al Jassar, H., Lopez-Baeza, E., Martínez-Fernández, J.,
González-Zamora, A., Livingston, S., McNairn, H., Pacheco, A.,
Moghaddam, M., Montzka, C., Notarnicola, C., Niedrist, G., Pellarin, T.,
Prueger, J., Pulliainen, J., Rautiainen, K., Ramos, J., Seyfried, M.,
Starks, P., Su, Z., Zeng, Y., van der Velde, R., Thibeault, M., Dorigo, W.,
Vreugdenhil, M., Walker, J. P., Wu, X., Monerris, A., O'Neill, P. E.,
Entekhabi, D., Njoku, E. G., and Yueh, S.: Validation of SMAP surface soil
moisture products with core validation sites, Remote Sens. Environ., 191,
215–231, https://doi.org/10.1016/j.rse.2017.01.021, 2017.
Crow, W. T., Berg, A. A., Cosh, M. H., Loew, A., Mohanty, B. P., Panciera,
R., De Rosnay, P., Ryu, D., and Walker, J. P.: Upscaling sparse ground-based
soil moisture observations for the validation of coarse-resolution satellite
soil moisture products, Rev. Geophys., 50, 1–20,
https://doi.org/10.1029/2011RG000372, 2012.
Crow, W. T., Chen, F., and Colliander, A.: Benchmarking downscaled
satellite-based soil moisture products using sparse, point-scale ground
observations, Remote Sens. Environ., 283, 113300,
https://doi.org/10.1016/j.rse.2022.113300, 2022.
Cui, D., Liang, S., Wang, D., and Liu, Z.: A 1 km global dataset of historical (1979–2013) and future (2020–2100) Köppen–Geiger climate classification and bioclimatic variables, Earth Syst. Sci. Data, 13, 5087–5114, https://doi.org/10.5194/essd-13-5087-2021, 2021.
Dai, Y., Shangguan, W., Wei, N., Xin, Q., Yuan, H., Zhang, S., Liu, S., Lu, X., Wang, D., and Yan, F.: A review of the global soil property maps for Earth system models, SOIL, 5, 137–158, https://doi.org/10.5194/soil-5-137-2019, 2019.
Das, N. N., Entekhabi, D., Dunbar, R. S., Chaubell, M. J., Colliander, A.,
Yueh, S., Jagdhuber, T., Chen, F., Crow, W., O'Neill, P. E., Walker, J. P.,
Berg, A., Bosch, D. D., Caldwell, T., Cosh, M. H., Collins, C. H.,
Lopez-Baeza, E., and Thibeault, M.: The SMAP and Copernicus Sentinel 1A/B
microwave active-passive high resolution surface soil moisture product,
Remote Sens. Environ., 233, 111380,
https://doi.org/10.1016/J.RSE.2019.111380, 2019.
Dorigo, W., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L.,
Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, P. D.,
Hirschi, M., Ikonen, J., de Jeu, R., Kidd, R., Lahoz, W., Liu, Y. Y.,
Miralles, D., Mistelbauer, T., Nicolai-Shaw, N., Parinussa, R., Pratola, C.,
Reimer, C., van der Schalie, R., Seneviratne, S. I., Smolander, T., and
Lecomte, P.: ESA CCI Soil Moisture for improved Earth system understanding:
State-of-the art and future directions, Remote Sens. Environ., 203,
185–215, https://doi.org/10.1016/J.RSE.2017.07.001, 2017.
Dorigo, W., Himmelbauer, I., Aberer, D., Schremmer, L., Petrakovic, I., Zappa, L., Preimesberger, W., Xaver, A., Annor, F., Ardö, J., Baldocchi, D., Bitelli, M., Blöschl, G., Bogena, H., Brocca, L., Calvet, J.-C., Camarero, J. J., Capello, G., Choi, M., Cosh, M. C., van de Giesen, N., Hajdu, I., Ikonen, J., Jensen, K. H., Kanniah, K. D., de Kat, I., Kirchengast, G., Kumar Rai, P., Kyrouac, J., Larson, K., Liu, S., Loew, A., Moghaddam, M., Martínez Fernández, J., Mattar Bader, C., Morbidelli, R., Musial, J. P., Osenga, E., Palecki, M. A., Pellarin, T., Petropoulos, G. P., Pfeil, I., Powers, J., Robock, A., Rüdiger, C., Rummel, U., Strobel, M., Su, Z., Sullivan, R., Tagesson, T., Varlagin, A., Vreugdenhil, M., Walker, J., Wen, J., Wenger, F., Wigneron, J. P., Woods, M., Yang, K., Zeng, Y., Zhang, X., Zreda, M., Dietrich, S., Gruber, A., van Oevelen, P., Wagner, W., Scipal, K., Drusch, M., and Sabia, R.: The International Soil Moisture Network: serving Earth system science for over a decade, Hydrol. Earth Syst. Sci., 25, 5749–5804, https://doi.org/10.5194/hess-25-5749-2021, 2021.
Dorigo, W. A., Xaver, A., Vreugdenhil, M., Gruber, A., Hegyiová, A.,
Sanchis-Dufau, A. D., Zamojski, D., Cordes, C., Wagner, W., and Drusch, M.:
Global Automated Quality Control of In Situ Soil Moisture Data from the
International Soil Moisture Network, Vadose Zone J., 12, vzj2012.0097,
https://doi.org/10.2136/vzj2012.0097, 2013.
Entekhabi, D., Reichle, R. H., Koster, R. D., and Crow, W. T.: Performance
metrics for soil moisture retrievals and application requirements, J.
Hydrometeorol., 11, 832–840, https://doi.org/10.1175/2010JHM1223.1, 2010.
Friedl, M. and Sulla-Menashe, D.: MCD12Q1 MODIS/Terra+Aqua Land Cover Type
Yearly L3 Global 500m SIN Grid V006, NASA EOSDIS Land Processes DAAC [data
set], https://doi.org/10.5067/MODIS/MCD12Q1.006, 2019.
Friedman, J. H.: Greedy function approximation: a gradient boosting machine,
Ann. Stat., 29, 1189–1232, https://doi.org/10.1214/aos/1013203451, 2001.
Ghulam, A., Qin, Q., Teyip, T., and Li, Z.-L.: Modified perpendicular
drought index (MPDI): a real-time drought monitoring method, ISPRS J.
Photogramm., 62, 150–164,
https://doi.org/10.1016/j.isprsjprs.2007.03.002, 2007.
Gislason, P. O., Benediktsson, J. A., and Sveinsson, J. R.: Random Forests
for land cover classification, Pattern Recognit. Lett., 27, 294–300,
https://doi.org/10.1016/j.patrec.2005.08.011, 2006.
Gruber, A., Dorigo, W. A., Zwieback, S., Xaver, A., and Wagner, W.:
Characterizing Coarse-Scale Representativeness of in situ Soil Moisture
Measurements from the International Soil Moisture Network, Vadose Zone J.,
12, vzj2012.0170, https://doi.org/10.2136/vzj2012.0170,
2013.
Gruber, A., Su, C.-H., Zwieback, S., Crow, W., Dorigo, W., and Wagner, W.:
Recent advances in (soil moisture) triple collocation analysis, Int. J.
Appl. Earth Obs., 45, 200–211,
https://doi.org/10.1016/j.jag.2015.09.002, 2016.
Gruber, A., Scanlon, T., van der Schalie, R., Wagner, W., and Dorigo, W.: Evolution of the ESA CCI Soil Moisture climate data records and their underlying merging methodology, Earth Syst. Sci. Data, 11, 717–739, https://doi.org/10.5194/essd-11-717-2019, 2019.
Gruber, A., De Lannoy, G., Albergel, C., Al-Yaari, A., Brocca, L., Calvet,
J.-C., Colliander, A., Cosh, M., Crow, W., Dorigo, W., Draper, C., Hirschi,
M., Kerr, Y., Konings, A., Lahoz, W., McColl, K., Montzka, C.,
Muñoz-Sabater, J., Peng, J., Reichle, R., Richaume, P., Rüdiger, C.,
Scanlon, T., van der Schalie, R., Wigneron, J.-P., and Wagner, W.:
Validation practices for satellite soil moisture retrievals: What are (the)
errors?, Remote Sens. Environ., 244, 111806,
https://doi.org/10.1016/j.rse.2020.111806, 2020.
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A.,
Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D.,
Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P.,
Biavati, G., Bidlot, J., Bonavita, M., De Chiara, G., Dahlgren, P., Dee, D.,
Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer,
A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková,
M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., de Rosnay,
P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.-N.: The ERA5
global reanalysis, Q. J. Roy. Meteor. Soc., 146, 1999–2049,
https://doi.org/10.1002/qj.3803, 2020.
Holzman, M. E., Rivas, R., and Piccolo, M. C.: Estimating soil moisture and
the relationship with crop yield using surface temperature and vegetation
index, Int. J. Appl. Earth Obs., 28, 181–192,
https://doi.org/10.1016/j.jag.2013.12.006, 2014.
Humphrey, V., Berg, A., Ciais, P., Gentine, P., Jung, M., Reichstein, M.,
Seneviratne, S. I., and Frankenberg, C.: Soil moisture–atmosphere feedback
dominates land carbon uptake variability, Nature, 592, 65–69,
https://doi.org/10.1038/s41586-021-03325-5, 2021.
Karthikeyan, L. and Mishra, A. K.: Multi-layer high-resolution soil moisture
estimation using machine learning over the United States, Remote Sens.
Environ., 266, 112706, https://doi.org/10.1016/J.RSE.2021.112706, 2021.
Kerr, Y. H., Al-Yaari, A., Rodriguez-Fernandez, N., Parrens, M., Molero, B.,
Leroux, D., Bircher, S., Mahmoodi, A., Mialon, A., Richaume, P., Delwart,
S., Al Bitar, A., Pellarin, T., Bindlish, R., Jackson, T. J., Rüdiger,
C., Waldteufel, P., Mecklenburg, S., and Wigneron, J. P.: Overview of SMOS
performance in terms of global soil moisture monitoring after six years in
operation, Remote Sens. Environ., 180, 40–63,
https://doi.org/10.1016/j.rse.2016.02.042, 2016.
Kim, S., Zhang, R., Pham, H., and Sharma, A.: A Review of Satellite-Derived
Soil Moisture and Its Usage for Flood Estimation, Remote Sens. Earth Syst.
Sci., 2, 225–246, https://doi.org/10.1007/s41976-019-00025-7, 2019.
Li, B., Liang, S., Liu, X., Ma, H., Chen, Y., Liang, T., and He, T.:
Estimation of all-sky 1 km land surface temperature over the conterminous
United States, Remote Sens. Environ., 266, 112707,
https://doi.org/10.1016/J.RSE.2021.112707, 2021.
Li, X., Wigneron, J.-P., Fan, L., Frappart, F., Yueh, S. H., Colliander, A.,
Ebtehaj, A., Gao, L., Fernandez-Moran, R., Liu, X., Wang, M., Ma, H., Moisy,
C., and Ciais, P.: A new SMAP soil moisture and vegetation optical depth
product (SMAP-IB): Algorithm, assessment and inter-comparison, Remote Sens.
Environ., 271, 112921,
https://doi.org/10.1016/j.rse.2022.112921, 2022.
Liang, S. and Wang, J. (Eds.): Chapter 18 – Soil moisture contents, in:
Advanced Remote Sensing, 2nd Edn., Academic Press, 685–711,
https://doi.org/10.1016/B978-0-12-815826-5.00018-0, 2020.
Liang, S., Cheng, J., Jia, K., Jiang, B., Liu, Q., Xiao, Z., Yao, Y., Yuan,
W., Zhang, X., Zhao, X., and Zhou, J.: The Global Land Surface Satellite
(GLASS) Product Suite, B. Am. Meteorol. Soc., 102, E323–E337,
https://doi.org/10.1175/BAMS-D-18-0341.1, 2021.
Liu, L., Gudmundsson, L., Hauser, M., Qin, D., Li, S., and Seneviratne, S.
I.: Soil moisture dominates dryness stress on ecosystem production globally,
Nat. Commun., 11, 4892, https://doi.org/10.1038/s41467-020-18631-1, 2020.
Liu, N. F., Liu, Q., Wang, L. Z., Liang, S. L., Wen, J. G., Qu, Y., and Liu, S. H.: A statistics-based temporal filter algorithm to map spatiotemporally continuous shortwave albedo from MODIS data, Hydrol. Earth Syst. Sci., 17, 2121–2129, https://doi.org/10.5194/hess-17-2121-2013, 2013.
Long, D., Bai, L., Yan, L., Zhang, C., Yang, W., Lei, H., Quan, J., Meng,
X., and Shi, C.: Generation of spatially complete and daily continuous
surface soil moisture of high spatial resolution, Remote Sens. Environ.,
233, 111364, https://doi.org/10.1016/j.rse.2019.111364, 2019.
Luo, P., Song, Y., Huang, X., Ma, H., Liu, J., Yao, Y., and Meng, L.:
Identifying determinants of spatio-temporal disparities in soil moisture of
the Northern Hemisphere using a geographically optimal zones-based
heterogeneity model, ISPRS J. Photogramm., 185, 111–128,
https://doi.org/10.1016/j.isprsjprs.2022.01.009, 2022.
Ma, H. and Liang, S.: Development of the GLASS 250-m leaf area index product
(version 6) from MODIS data using the bidirectional LSTM deep learning
model, Remote Sens. Environ., 273, 112985,
https://doi.org/10.1016/J.RSE.2022.112985, 2022.
Ma, H., Zeng, J., Zhang, X., Fu, P., Zheng, D., Wigneron, J.-P., Chen, N.,
and Niyogi, D.: Evaluation of six satellite- and model-based surface soil
temperature datasets using global ground-based observations, Remote Sens.
Environ., 264, 112605,
https://doi.org/10.1016/j.rse.2021.112605, 2021.
McColl, K. A., Vogelzang, J., Konings, A. G., Entekhabi, D., Piles, M., and
Stoffelen, A.: Extended triple collocation: Estimating errors and
correlation coefficients with respect to an unknown target, Geophys. Res.
Lett., 41, 6229–6236, https://doi.org/10.1002/2014GL061322,
2014.
Molero, B., Leroux, D. J., Richaume, P., Kerr, Y. H., Merlin, O., Cosh, M.
H., and Bindlish, R.: Multi-Timescale Analysis of the Spatial
Representativeness of In Situ Soil Moisture Data within Satellite
Footprints, J. Geophys. Res.-Atmos., 123, 3–21,
https://doi.org/10.1002/2017JD027478, 2018.
Muñoz-Sabater, J.: ERA5-Land hourly data from 1981 to present,
Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set],
https://doi.org/10.24381/cds.e2161bac, 2019.
Muñoz-Sabater, J.: ERA5-Land hourly data from 1950 to 1980, Copernicus
Climate Change Service (C3S) Climate Data Store (CDS) [data set],
https://doi.org/10.24381/cds.e2161bac, 2021.
Muñoz-Sabater, J., Dutra, E., Agustí-Panareda, A., Albergel, C., Arduini, G., Balsamo, G., Boussetta, S., Choulga, M., Harrigan, S., Hersbach, H., Martens, B., Miralles, D. G., Piles, M., Rodríguez-Fernández, N. J., Zsoter, E., Buontempo, C., and Thépaut, J.-N.: ERA5-Land: a state-of-the-art global reanalysis dataset for land applications, Earth Syst. Sci. Data, 13, 4349–4383, https://doi.org/10.5194/essd-13-4349-2021, 2021.
Naz, B. S., Kollet, S., Franssen, H.-J. H., Montzka, C., and Kurtz, W.: A 3
km spatially and temporally consistent European daily soil moisture
reanalysis from 2000 to 2015, Sci. Data, 7, 111,
https://doi.org/10.1038/s41597-020-0450-6, 2020.
Njoku, E. G., Jackson, T. J., Lakshmi, V., Chan, T. K., and Nghiem, S. V:
Soil moisture retrieval from AMSR-E, IEEE T. Geosci. Remote, 41,
215–229, https://doi.org/10.1109/TGRS.2002.808243, 2003.
O'Neill, P. E., Chan, S., Njoku, E. G., Jackson, T., Bindlish, R., and
Chaubell, J.: SMAP L3 Radiometer Global Daily 36 km EASE-Grid Soil Moisture,
Version 8, NASA National Snow and Ice Data Center Distributed Active Archive
Center [data set], https://doi.org/10.5067/OMHVSRGFX38O, 2021.
Owe, M., de Jeu, R., and Holmes, T.: Multisensor historical climatology of
satellite-derived global land surface moisture, J. Geophys. Res.-Earth, 113, F01002, https://doi.org/10.1029/2007JF000769, 2008.
Peng, J., Loew, A., Merlin, O., and Verhoest, N. E. C.: A review of spatial
downscaling of satellite remotely sensed soil moisture, Rev. Geophys., 55,
341–366, https://doi.org/10.1002/2016RG000543, 2017.
Peng, J., Albergel, C., Balenzano, A., Brocca, L., Cartus, O., Cosh, M. H.,
Crow, W. T., Dabrowska-Zielinska, K., Dadson, S., Davidson, M. W. J., de
Rosnay, P., Dorigo, W., Gruber, A., Hagemann, S., Hirschi, M., Kerr, Y. H.,
Lovergine, F., Mahecha, M. D., Marzahn, P., Mattia, F., Musial, J. P.,
Preuschmann, S., Reichle, R. H., Satalino, G., Silgram, M., van Bodegom, P.
M., Verhoest, N. E. C., Wagner, W., Walker, J. P., Wegmüller, U., and
Loew, A.: A roadmap for high-resolution satellite soil moisture applications
– confronting product characteristics with user requirements, Remote Sens.
Environ., 252, 112162, https://doi.org/10.1016/J.RSE.2020.112162, 2021.
Poggio, L., de Sousa, L. M., Batjes, N. H., Heuvelink, G. B. M., Kempen, B., Ribeiro, E., and Rossiter, D.: SoilGrids 2.0: producing soil information for the globe with quantified spatial uncertainty, SOIL, 7, 217–240, https://doi.org/10.5194/soil-7-217-2021, 2021.
Qu, Y., Liu, Q., Liang, S., Wang, L., Liu, N., and Liu, S.:
Direct-Estimation Algorithm for Mapping Daily Land-Surface Broadband Albedo
From MODIS Data, IEEE T. Geosci. Remote, 52, 907–919,
https://doi.org/10.1109/TGRS.2013.2245670, 2014.
Rahimzadeh-Bajgiran, P., Berg, A. A., Champagne, C., and Omasa, K.:
Estimation of soil moisture using optical/thermal infrared remote sensing in
the Canadian Prairies, ISPRS J. Photogramm., 83, 94–103,
https://doi.org/10.1016/j.isprsjprs.2013.06.004, 2013.
Rodell, M., Houser, P. R., Jambor, U., Gottschalck, J., Mitchell, K., Meng,
C. J., Arsenault, K., Cosgrove, B., Radakovich, J., Bosilovich, M., Entin,
J. K., Walker, J. P., Lohmann, D., and Toll, D.: The global land data
assimilation system, B. Am. Meteorol. Soc., 85, 381–394,
https://doi.org/10.1175/BAMS-85-3-381, 2004.
Senyurek, V., Lei, F., Boyd, D., Kurum, M., Gurbuz, A. C., and Moorhead, R.:
Machine Learning-Based CYGNSS Soil Moisture Estimates over ISMN sites in
CONUS, Remote Sens., 12, 1168, https://doi.org/10.3390/rs12071168, 2020.
Sheffield, J., Goteti, G., Wen, F., and Wood, E. F.: A simulated soil
moisture based drought analysis for the United States, J. Geophys. Res.,
109, 1–19, https://doi.org/10.1029/2004JD005182, 2004.
Shi, J., Zhao, T., Cui, Q., and Yao, P.: Airborne and Spaceborne Passive
Microwave Measurements of Soil Moisture, in: Observation and Measurement of
Ecohydrological Processes, edited by: Li, X. and Vereecken, H., Springer
Berlin Heidelberg, Berlin, Heidelberg, 71–105,
https://doi.org/10.1007/978-3-662-48297-1_3, 2019.
Smith, A. B., Walker, J. P., Western, A. W., Young, R. I., Ellett, K. M.,
Pipunic, R. C., Grayson, R. B., Siriwardena, L., Chiew, F. H. S., and
Richter, H.: The Murrumbidgee soil moisture monitoring network data set,
Water Resour. Res., 48, W07701,
https://doi.org/10.1029/2012WR011976, 2012.
Song, J.: Bias corrections for Random Forest in regression using residual
rotation, J. Korean Stat. Soc., 44, 321–326,
https://doi.org/10.1016/j.jkss.2015.01.003, 2015.
Song, P., Zhang, Y., Guo, J., Shi, J., Zhao, T., and Tong, B.: A 1 km daily surface soil moisture dataset of enhanced coverage under all-weather conditions over China in 2003–2019, Earth Syst. Sci. Data, 14, 2613–2637, https://doi.org/10.5194/essd-14-2613-2022, 2022.
Starks, P. J., Fiebrich, C. A., Grimsley, D. L., Garbrecht, J. D., Steiner,
J. L., Guzman, J. A., and Moriasi, D. N.: Upper Washita River Experimental
Watersheds: Meteorologic and Soil Climate Measurement Networks, J. Environ.
Qual., 43, 1239–1249,
https://doi.org/10.2134/jeq2013.08.0312, 2014.
Stoffelen, A.: Toward the true near-surface wind speed: Error modeling and
calibration using triple collocation, J. Geophys. Res.-Oceans, 103,
7755–7766, https://doi.org/10.1029/97JC03180, 1998.
Vergopolan, N., Chaney, N. W., Pan, M., Sheffield, J., Beck, H. E.,
Ferguson, C. R., Torres-Rojas, L., Sadri, S., and Wood, E. F.:
SMAP-HydroBlocks, a 30-m satellite-based soil moisture dataset for the
conterminous US, Sci. Data, 8, 264,
https://doi.org/10.1038/s41597-021-01050-2, 2021.
Wagner, W., Pathe, C., Doubkova, M., Sabel, D., Bartsch, A., Hasenauer, S.,
Blöschl, G., Scipal, K., Martínez-Fernández, J., and Löw,
A.: Temporal Stability of Soil Moisture and Radar Backscatter Observed by
the Advanced Synthetic Aperture Radar (ASAR), Sensors, 8, 1174–1197,
https://doi.org/10.3390/s80201174, 2008.
Wagner, W., Hahn, S., Kidd, R., Melzer, T., Bartalis, Z., Hasenauer, S.,
Figa-Saldaña, J., de Rosnay, P., Jann, A., Schneider, S., Komma, J.,
Kubu, G., Brugger, K., Aubrecht, C., Züger, J., Gangkofner, U.,
Kienberger, S., Brocca, L., Wang, Y., Blöschl, G., Eitzinger, J., and
Steinnocher, K.: The ASCAT Soil Moisture Product: A Review of its
Specifications, Validation Results, and Emerging Applications, Meteorol.
Z., 22, 5–33, https://doi.org/10.1127/0941-2948/2013/0399, 2013.
Wang, Y., Leng, P., Peng, J., Marzahn, P., and Ludwig, R.: Global
assessments of two blended microwave soil moisture products CCI and SMOPS
with in-situ measurements and reanalysis data, Int. J. Appl. Earth Obs., 94, 102234, https://doi.org/10.1016/J.JAG.2020.102234, 2021.
Wigneron, J.-P., Li, X., Frappart, F., Fan, L., Al-Yaari, A., De Lannoy, G.,
Liu, X., Wang, M., Le Masson, E., and Moisy, C.: SMOS-IC data record of soil
moisture and L-VOD: Historical development, applications and perspectives,
Remote Sens. Environ., 254, 112238,
https://doi.org/10.1016/j.rse.2020.112238, 2021.
Xu, X.: Evaluation of SMAP Level 2, 3, and 4 Soil Moisture Datasets over the
Great Lakes Region, Remote Sensing, 12, 3785, https://doi.org/10.3390/rs12223785,
2020.
Yamazaki, D., Ikeshima, D., Tawatari, R., Yamaguchi, T., O'Loughlin, F.,
Neal, J. C., Sampson, C. C., Kanae, S., and Bates, P. D.: A high-accuracy
map of global terrain elevations, Geophys. Res. Lett., 44, 5844–5853,
https://doi.org/10.1002/2017GL072874, 2017.
Yang, J., Zhang, P., Lu, N., Yang, Z., Shi, J., and Dong, C.: Improvements
on global meteorological observations from the current Fengyun 3 satellites
and beyond, Int. J. Digit. Earth, 5, 251–265,
https://doi.org/10.1080/17538947.2012.658666, 2012.
Yee, M. S., Walker, J. P., Rüdiger, C., Parinussa, R. M., Koike, T., and
Kerr, Y. H.: A comparison of SMOS and AMSR2 soil moisture using
representative sites of the OzNet monitoring network, Remote Sens. Environ.,
195, 297–312, https://doi.org/10.1016/j.rse.2017.04.019, 2017.
Yuan, Q., Xu, H., Li, T., Shen, H., and Zhang, L.: Estimating surface soil
moisture from satellite observations using a generalized regression neural
network trained on sparse ground-based measurements in the continental U.S,
J. Hydrol., 580, 124351, https://doi.org/10.1016/j.jhydrol.2019.124351,
2020.
Yue, J., Tian, J., Tian, Q., Xu, K., and Xu, N.: Development of soil
moisture indices from differences in water absorption between
shortwave-infrared bands, ISPRS J. Photogramm., 154, 216–230,
https://doi.org/10.1016/j.isprsjprs.2019.06.012, 2019.
Zappa, L., Forkel, M., Xaver, A., and Dorigo, W.: Deriving Field Scale Soil
Moisture from Satellite Observations and Ground Measurements in a Hilly
Agricultural Region, Remote Sensing, 11, 2596, https://doi.org/10.3390/rs11222596,
2019.
Zhang, Q., Yuan, Q., Li, J., Wang, Y., Sun, F., and Zhang, L.: Generating seamless global daily AMSR2 soil moisture (SGD-SM) long-term products for the years 2013–2019, Earth Syst. Sci. Data, 13, 1385–1401, https://doi.org/10.5194/essd-13-1385-2021, 2021.
Zhang, Y., Liang, S., Ma, H., He, T., Wang, Q., and Li, B.: A global 1 km
surface soil moisture product from 2000 to 2020, Zenodo [data set],
https://doi.org/10.5281/ZENODO.7172664, 2022a.
Zhang, Y., Liang, S., Zhu, Z., Ma, H., and He, T.: Soil moisture content
retrieval from Landsat 8 data using ensemble learning, ISPRS J. Photogramm., 185, 32–47,
https://doi.org/10.1016/j.isprsjprs.2022.01.005, 2022b.
Zhao, T., Shi, J., Entekhabi, D., Jackson, T. J., Hu, L., Peng, Z., Yao, P.,
Li, S., and Kang, C. S.: Retrievals of soil moisture and vegetation optical
depth using a multi-channel collaborative algorithm, Remote Sens. Environ.,
257, 112321, https://doi.org/10.1016/j.rse.2021.112321,
2021.
Zheng, C., Jia, L., and Zhao, T.: A 21-year dataset (2000–2020) of gap-free
global daily surface soil moisture at 1 km grid resolution, Sci. Data, 10,
139, https://doi.org/10.1038/s41597-023-01991-w, 2023.
Zheng, J., Zhao, T., Lü, H., Shi, J., Cosh, M. H., Ji, D., Jiang, L.,
Cui, Q., Lu, H., Yang, K., Wigneron, J.-P., Li, X., Zhu, Y., Hu, L., Peng,
Z., Zeng, Y., Wang, X., and Kang, C. S.: Assessment of 24 soil moisture
datasets using a new in situ network in the Shandian River Basin of China,
Remote Sens. Environ., 271, 112891,
https://doi.org/10.1016/j.rse.2022.112891, 2022.
Zhou, Z.-H.: Ensemble Learning, in: Machine Learning, Springer Singapore,
Singapore, 181–210,
https://doi.org/10.1007/978-981-15-1967-3_8, 2021.
Short summary
Soil moisture observations are important for a range of earth system applications. This study generated a long-term (2000–2020) global seamless soil moisture product with both high spatial and temporal resolutions (1 km, daily) using an XGBoost model and multisource datasets. Evaluation of this product against dense in situ soil moisture datasets and microwave soil moisture products showed that this product has reliable accuracy and more complete spatial coverage.
Soil moisture observations are important for a range of earth system applications. This study...
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