崔慧珍等： Evaluation and analysis of AMSR-2, SMOS, and SMAP soil moisture products in the Genhe area of China

作者:Cui, HZ (Cui, Huizhen)[ 1,2 ] ; Jiang, LM (Jiang, Lingmei)[ 1,2 ] ; Du, JY (Du, Jinyang)[ 3 ] ; Zhao, SJ (Zhao, Shaojie)[ 4 ] ; Wang, GX (Wang, Gongxue)[ 1,2 ] ; Lu, Z (Lu, Zheng)[ 4 ] ; Wang, J (Wang, Jian)[ 1,2 ]

JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES

卷: 122  期: 16  页: 8650-8666

DOI: 10.1002/2017JD026800

出版年: AUG 27 2017

摘要

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 m(3) m(-3). The LPRM C-band and X-band products had a constant variable season bias of 0.261-0.576 m(3) 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 m(3) m(-3)) and bias (0.022-0.050 m(3) m(-3)). Finally, an assessment was performed on the parameters in these soil moisture algorithms.

通讯作者地址: Jiang, LM (通讯作者)

Beijing Normal Univ, Inst Remote Sensing Sci & Engn, Fac Geog Sci, State Key Lab Remote Sensing Sci, Beijing, Peoples R China.

通讯作者地址: Jiang, LM (通讯作者)

Joint Ctr Global Change Studies, Beijing, Peoples R China.

地址:

[ 1 ] Beijing Normal Univ, Inst Remote Sensing Sci & Engn, Fac Geog Sci, State Key Lab Remote Sensing Sci, Beijing, Peoples R China

[ 2 ] Joint Ctr Global Change Studies, Beijing, Peoples R China

[ 3 ] Univ Montana, Coll Forestry & Conservat, Numer Terradynam Simulat Grp, Missoula, MT 59812 USA

[ 4 ] Beijing Normal Univ, Coll Resources Sci & Technol, Fac Geog Sci, State Key Lab Remote Sensing Sci, Beijing, Peoples R China