Estimation of crop residue cover in rice paddies by a dynamic-quadripartite pixel model based on Sentinel-2A data

文献类型： 外文期刊

第一作者： Sun, Zhendong

作者： Sun, Zhendong;Zhu, Qilei;Deng, Shangqi;Li, Xu;Hu, Xueqian;Chen, Riqiang;Shao, Guowen;Yang, Hao;Yang, Guijun;Sun, Zhendong;Deng, Shangqi;Li, Xu;Hu, Xueqian

作者机构：

关键词： Quantitative remote sensing; Sentinel-2 MSI; Dynamic-quadripartite pixel model; Rice residue cover

期刊名称：INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION （ 影响因子：7.672； 五年影响因子：7.332 ）

ISSN： 1569-8432

年卷期： 2022 年 106 卷

页码：

收录情况： SCI

摘要： Crop residues left on the field after harvest increase soil organic matter content and improve soil quality. Linear spectral mixture analysis (LSMA) is an important technique for calculating crop residue cover. Traditionally, farmland has been considered to be composed solely of soil and crop residue endmembers. But rice paddy fields are often more complex than other fields. In the decomposition of pixels reflectance, leaving out potential endmembers greatly increases the variability of existing endmember reflectance. The error is then transferred to the rice residue endmember. In this paper, a dynamic-quadripartite pixel model (DQPM) is proposed to adapt LSMA to calculate rice residue cover (RRC) in complex paddy fields. This method considers that pixels in paddy fields are composed of four endmembers: soil, rice residues, green moss and white moss. With the approach, soil moisture can be calculated to automatically correct the reflectance of the soil endmembers of each pixel. The calculation results of our model were verified with field data and compared with those from the static-quadripartite pixel model (SQPM) without considering soil moisture (SM) content and with the dynamic-dimidiate pixel model (DDPM). Results confirm the feasibility of DQPM. The results show that with four end-members, RRC has a large range of improved computational accuracy with DQPM compared with SQPM and DDPM. DDPM has a large error under 1% < SM < 3% (gravimetric water content) and 60% < RRC < 70%. Only when SM is near 0.3, SQPM can achieve good accuracy. Among the three models, DQPM has the best robustness under different soil moisture and RRC scenarios. Therefore, our proposed method is useful for calculating RRC in complex paddy field scenarios.

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