Characterizing the accuracy of satellite-based products to detect soil moisture at the global scale
•A regular evaluation of satellite soil moisture (SM) products was conducted.•Their accuracies in detecting SM anomalies and seasonality were identified.•l-band products can effectively capture SM seasonality over forest covers.•Products had difficulties in detecting SM variations over organic-rich...
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Published in | Geoderma Vol. 432; p. 116388 |
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Main Authors | , , , , |
Format | Journal Article |
Language | English |
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Elsevier B.V
01.04.2023
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Abstract | •A regular evaluation of satellite soil moisture (SM) products was conducted.•Their accuracies in detecting SM anomalies and seasonality were identified.•l-band products can effectively capture SM seasonality over forest covers.•Products had difficulties in detecting SM variations over organic-rich soils.•AMSR2 showed higher accuracy to detect SM seasonality over arid areas.
Satellite-based soil moisture (SM) products exhibit distinct error patterns at various timescales. Knowledge of their accuracy characteristics for capturing different SM signals is significant for many real-world applications in soil sciences. This study conducted a comprehensive evaluation of the following eight satellite-based SM products: the Soil Moisture and Ocean Salinity-Institut National de la Recherche Agronomique and Centre d'Etudes Spatiales de la BIOsphere version 2 (SMOS-IC-V2), the Level 3 (L3) Soil Moisture Active Passive V7 (SMAP-V7), the combined European Space Agency Climate Change Initiative V06.1 (ESA CCI-V06.1), the L3 SMOS V3, the L3 Advanced Microwave Scanning Radiometer (AMSR2) V3 from the Japan Aerospace Exploration Agency (JAXA), the L3 Land Parameter Retrieval Model (LPRM) C1- and X- band AMSR2 V001, and the Chinese FengYun-3B V1. By using >1,500 sites from the International Soil Moisture Network and triple collocation analysis, this study aimed to identify not only the relative strengths of the products for detecting original SM signals, short-term SM anomalies, and long-term SM seasonality, but also the impacts of land cover, soil organic carbon (SOC), and soil texture on their retrieval accuracy. In terms of capturing the temporal dynamics of SM anomalies, SMAP-V7 outperformed SMOS-IC-V2 and ESA CCI-V06.1. In arid and semi-arid areas, LPRM C1-band AMSR2 V001 outperformed SMOS-ICV2 and SMAP-V7, in terms of detecting SM seasonality. SMAP-V7, SMOS-ICV2, and ESA CCI-V06.1 effectively captured SM seasonality over most forest zones and effectively detected short-term SM anomalies over croplands and grasslands. The l-band products showed the highest accuracy in terms of capturing the temporal dynamics of all the SM signals when the clay fraction was in the range of 20 %–25 %. Most products had difficulties in detecting SM variations over bare deserts, sandy and clay soils, and soils rich in organic matter (SOC content > 8.72 %). |
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AbstractList | •A regular evaluation of satellite soil moisture (SM) products was conducted.•Their accuracies in detecting SM anomalies and seasonality were identified.•l-band products can effectively capture SM seasonality over forest covers.•Products had difficulties in detecting SM variations over organic-rich soils.•AMSR2 showed higher accuracy to detect SM seasonality over arid areas.
Satellite-based soil moisture (SM) products exhibit distinct error patterns at various timescales. Knowledge of their accuracy characteristics for capturing different SM signals is significant for many real-world applications in soil sciences. This study conducted a comprehensive evaluation of the following eight satellite-based SM products: the Soil Moisture and Ocean Salinity-Institut National de la Recherche Agronomique and Centre d'Etudes Spatiales de la BIOsphere version 2 (SMOS-IC-V2), the Level 3 (L3) Soil Moisture Active Passive V7 (SMAP-V7), the combined European Space Agency Climate Change Initiative V06.1 (ESA CCI-V06.1), the L3 SMOS V3, the L3 Advanced Microwave Scanning Radiometer (AMSR2) V3 from the Japan Aerospace Exploration Agency (JAXA), the L3 Land Parameter Retrieval Model (LPRM) C1- and X- band AMSR2 V001, and the Chinese FengYun-3B V1. By using >1,500 sites from the International Soil Moisture Network and triple collocation analysis, this study aimed to identify not only the relative strengths of the products for detecting original SM signals, short-term SM anomalies, and long-term SM seasonality, but also the impacts of land cover, soil organic carbon (SOC), and soil texture on their retrieval accuracy. In terms of capturing the temporal dynamics of SM anomalies, SMAP-V7 outperformed SMOS-IC-V2 and ESA CCI-V06.1. In arid and semi-arid areas, LPRM C1-band AMSR2 V001 outperformed SMOS-ICV2 and SMAP-V7, in terms of detecting SM seasonality. SMAP-V7, SMOS-ICV2, and ESA CCI-V06.1 effectively captured SM seasonality over most forest zones and effectively detected short-term SM anomalies over croplands and grasslands. The l-band products showed the highest accuracy in terms of capturing the temporal dynamics of all the SM signals when the clay fraction was in the range of 20 %–25 %. Most products had difficulties in detecting SM variations over bare deserts, sandy and clay soils, and soils rich in organic matter (SOC content > 8.72 %). |
ArticleNumber | 116388 |
Author | Shangguan, YuLin Min, Xiaoxiao Li, Danlu Tian, Shuo Shi, Zhou |
Author_xml | – sequence: 1 givenname: Xiaoxiao surname: Min fullname: Min, Xiaoxiao organization: Institute of Agricultural Remote Sensing and Information Technology Application, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China – sequence: 2 givenname: Danlu surname: Li fullname: Li, Danlu organization: Institute of Agricultural Remote Sensing and Information Technology Application, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China – sequence: 3 givenname: YuLin surname: Shangguan fullname: Shangguan, YuLin organization: Institute of Agricultural Remote Sensing and Information Technology Application, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China – sequence: 4 givenname: Shuo surname: Tian fullname: Tian, Shuo organization: Institute of Agricultural Remote Sensing and Information Technology Application, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China – sequence: 5 givenname: Zhou orcidid: 0000-0003-3914-5402 surname: Shi fullname: Shi, Zhou email: shizhou@zju.edu.cn organization: Institute of Agricultural Remote Sensing and Information Technology Application, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China |
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