A dynamic soil endmember spectrum selection approach for soil and crop residue linear spectral unmixing analysis

文献类型： 外文期刊

第一作者： Yue, Jibo

作者： Yue, Jibo;Tian, Qingjiu;Tang, Shaofei;Xu, Kaijian;Yue, Jibo;Tian, Qingjiu;Yue, Jibo;Zhou, Chengquan

作者机构：

关键词： Crop residue; Soil endmember; Soil moisture; Linear spectral unmixing analysis; Remote sensing

期刊名称：INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION （ 影响因子：5.933； 五年影响因子：6.225 ）

ISSN： 0303-2434

年卷期： 2019 年 78 卷

页码：

收录情况： SCI

摘要： Crop residue deposited on the soil surface helps protect against water and wind erosion, improve soil quality and increase soil organic matter and soil carbon storage. Linear spectral mixture analysis (LSMA) is an important technique in field crop residue estimation. Traditionally, only one single or fixed standard crop residue and soil endmember spectrum is performed for each of the presented endmember classes or field. Because the variation in soil endmember spectrum signatures significantly changes with soil moisture (SM), spectral unmixing with fixed endmember spectra can lead to poor accuracy of the abundance of the spectral constituents of pure crop residue. Herein, this paper presents a dynamic soil endmember spectrum selection approach for improving the performance of soil and rice residue spectral unmixing analysis in rice residue cover (RRC) estimation. This new approach uses SM and soil spectral reflectance model to modify soil endmember spectra in each spectral unmixing analysis. Two validation datasets computed results have verified the feasibility and correctness of the dynamic soil endmember method. Results indicated that the distribution of SM in farmland was crucial for RRC estimation. Compared with traditional fixed min and fixed mean soil endmember spectrum methods, the results of the method developed herein showed this method significantly improved RRC estimation accuracy for an SM content lower than 20% (volumetric water content) over the traditional methods tested. Therefore, our proposed approach can be used to improve RRC estimation accuracy in harvest field.

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