文献类型： 外文期刊
作者： Han, Dong 1 ; Yang, Hao 2 ; Qiu, Chunxia 1 ; Yang, Guijun 2 ; Chen, Erxue 3 ; Du, Ying 2 ; Yang, Wenpan 2 ; Zhou, Chengqu 1 ;
作者机构： 1.Xian Univ Sci & Technol, Coll Geomat, Xian, Shaanxi, Peoples R China
2.Minist Agr China, Beijing Res Ctr Informat Technol Agr, Key Lab Quantitat Remote Sensing Agr, Beijing, Peoples R China
3.Chinese Acad Forestry, Inst Forest Resources Informat Tech, Beijing, Peoples R China
期刊名称：REMOTE SENSING LETTERS （ 影响因子：2.583； 五年影响因子：2.601 ）
ISSN： 2150-704X
年卷期： 2019 年 10 卷 3 期
页码：
收录情况： SCI
摘要： Wheat is one of the staple crops of the world. With the wide application of remote-sensing methods in agriculture, the use of data from synthetic aperture radar has attracted increasing attention for monitoring wheat growth. Most of previous studies estimated wheat biomass based on a water cloud model (WCM). However, when no data are available on soil moisture content, the applicability of such models is greatly reduced because of insufficient parameters. Thus, this study proposed a new polarized water cloud model (PWCM) called APWCM, which is a physical model and no auxiliary ground data was needed including soil moisture data. APWCM and WCM model was compared to estimate the above ground biomass of wheat in two different study areas. The results revealed that the WCM has a slightly lower root mean squared error (RMSE = 131.63 g m(-2), and 645.17 g m(-2)) in two different study areas. However, the APWCM has lower relative error (RE = 17.91%, and 13.53%) for wheat biomass estimation in areas with higher biomsass. The final result indicates that the APWCM can replace the WCM for estimating wheat biomass based on Gaofen-3 (GF-3) data when soil-moisture data are not available.
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