Wentao Yang , Fei Guo , Xiaohong Zhang , Yifan Zhu , Zheng Li , Zhiyu Zhang
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引用次数: 0
Abstract
Global Navigation Satellite System-Reflectometry (GNSS-R) has considerable potential for large-scale soil moisture (SM) monitoring. With the Fengyun-3 (FY-3) E, F, and G satellites currently in orbit, the FY-3 satellite series has formed the GNSS-R constellation. A comprehensive analysis and validation of the SM retrieval capability of the FY-3 GNSS-R constellation observations are essential. This study is the first to use FY-3 GNSS-R constellation observations to evaluate their performance in quasi-global daily SM retrieval. Specifically, this study proposed an effective SM retrieval method for obtaining an FY-3 GNSS-R SM with minimal ancillary data. Compared with the Soil Moisture Active Passive (SMAP) reference SM, the FY-3 SM exhibited a reasonable global spatial pattern as SMAP, with a root mean square error (RMSE) of 0.039 / in low vegetation areas. Validation results from over 200 independent in situ stations showed that the unbiased RMSE and correlation for FY-3 SM were 0.039 / and 0.60, respectively. Triple collocation (TC) analysis showed that the standard deviation and correlation for the FY-3 SM were 0.017 / and 0.62, respectively. Global and local validations indicate that the SM derived from the FY-3 GNSS-R constellation observations has well-defined accuracy and effectively captures spatiotemporal variations. Compared to the contemporaneous Cyclone GNSS official SM, the accuracy of the FY-3 SM retrieved using the proposed method improved by 17.1 %. Consequently, the SM from the FY-3 GNSS-R constellation observations can be an invaluable complement to the global SM dataset. Furthermore, this method effectively reduced systematic bias and random errors in SM retrievals (unbiased RMSE (ubRMSE) from 0.041to 0.034 /and TC standard deviation from 0.034to 0.017 /), which may provide a valuable reference for generating SM products from subsequent FY-3 GNSS-R constellations.
期刊介绍:
Remote Sensing of Environment (RSE) serves the Earth observation community by disseminating results on the theory, science, applications, and technology that contribute to advancing the field of remote sensing. With a thoroughly interdisciplinary approach, RSE encompasses terrestrial, oceanic, and atmospheric sensing.
The journal emphasizes biophysical and quantitative approaches to remote sensing at local to global scales, covering a diverse range of applications and techniques.
RSE serves as a vital platform for the exchange of knowledge and advancements in the dynamic field of remote sensing.