Evaluation and analysis of AMSR‐2, SMOS, and SMAP soil moisture products in the Genhe area of China

• Published in: Journal of geophysical research. Atmospheres 2017-08, Vol.122 (16), p.8650-8666
• Main Authors: Cui, Huizhen, Jiang, Lingmei, Du, Jinyang, Zhao, Shaojie, Wang, Gongxue, Lu, Zheng, Wang, Jian
• Format: Article
• Language: English
• Subjects: Algorithms; Bands; Bias; Brightness; Brightness temperature; C band; Carbon capture and storage; Carbon emissions; Carbon sequestration; Dynamic analysis; Evaluation; Exploration; Genhe; Geophysics; in situ; Mathematical models; Moisture; Products; Radiometers; Remote sensing; Root-mean-square errors; Soil; Soil dynamics; Soil moisture; Soil temperature; Surface radiation temperature; surface temperature; Temperature data; vegetation optical depth
• ISSN: 2169-897X; 2169-8996
• DOI: 10.1002/2017JD026800

High‐precision soil moisture products play an important role in estimating forest carbon storage and carbon emissions in Genhe, China. In this paper, we evaluated the Soil Moisture and Ocean Salinity (SMOS) L3 product, the Soil Moisture Active Passive (SMAP) L3 product, and four soil moisture products derived from the Advanced Microwave Scanning Radiometer 2 (AMSR‐2), i.e., the Dual Channel Algorithm based on the Qp model (QDCA) product, the Japan Aerospace Exploration Agency (JAXA) L3 product, and the Land Parameter Retrieval Model (LPRM) C band and X band products in the Genhe area of China. The results indicated that the root‐mean‐square error (RMSE) and bias of the QDCA product were lower than those of the other AMSR‐2 products, although the QDCA still fell outside of the acceptable range with a volumetric error of no greater than 6%. The JAXA product underestimated the soil moisture and had a constant bias of 0.089–0.099 m3 m−3. The LPRM C‐band and X‐band products had a constant variable season bias of 0.261–0.576 m3 m−3. The quality of the SMOS was better than that of the AMSR‐2 products; however, the results were noisy and unstable. The SMAP was closest to the ground measurements and presented a low RMSE (0.039–0.063 m3 m−3) and bias (0.022–0.050 m3 m−3). Finally, an assessment was performed on the parameters in these soil moisture algorithms. Key Points We used the AMSR‐2 brightness temperature data to produce a long‐term soil moisture product (2012–2016) using the Dual‐Channel Algorithm based on the Qp model Long‐term in situ observations from northeastern China were acquired for dynamic analysis of remotely sensed information This research assessed six soil moisture products to obtain a long‐term time series and high‐precision soil moisture product for China