文献类型： 外文期刊

作者： Liang, Bingkun ¹ ; Zhao, Rongkun ² ; Tan, Jieyang ⁴ ; Xia, Lang ² ; Cao, Hong ² ; Wu, Shangrong ² ; Yang, Peng ² ;

作者机构： 1.Sun Yat Sen Univ, Sch Geog & Planning, Guangzhou 510275, Peoples R China

2.Chinese Acad Agr Sci, Inst Agr Resources & Reg Planning, State Key Lab Efficient Utilizat Arid & Semiarid A, Beijing 100081, Peoples R China

3.Chinese Acad Agr Sci, Inst Agr Resources & Reg Planning, Key Lab Agr Remote Sensing, Minist Agr & Rural Affairs, Beijing 100081, Peoples R China

4.Hunan Acad Agr Sci, Inst Agr Econ & Reg Planning, Changsha 410125, Peoples R China

关键词： Planting area; polarization decomposition; radar vegetation index (RVI); rape; sentinel-1 data

期刊名称：IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING （ 影响因子：5.5； 五年影响因子：5.5 ）

ISSN： 1939-1404

年卷期： 2023 年 16 卷

页码：

收录情况： SCI

摘要： The rape planting plots are fragmented and have the same temporal variation characteristics as other land types, the rape planting area is cloudy and rainy, and the optical remote sensing images are limited, all make it difficult to extract the rape planting areas. Aiming at the above-mentioned problems, this study used microwave remote sensing data combined with the compact polarimetric technique to construct a dual-polarization radar index to classify rape planting areas and analyzed the influence of time series combinations on the extraction accuracy of rape in South China. First, based on m-? compact polarimetric decomposition, a dual-polarization radar vegetation index (RVI) was constructed. Then, based on Sentinel-1 data, the dynamic time warping (DTW) threshold classification method was used to extract rape planting areas in six counties in the main rape-producing areas in the middle and lower reaches of the Yangtze River, China. Finally, a random forest algorithm was used to analyze and screen the optimal time series combination of the extracted regional rape planting areas. Among the 94 rape samples obtained in the field, 74 samples were accurately classified as rape, and the overall accuracy was 78.72%. Six ground samples were used to verify the accuracy of the rape planting area extraction results, and the F-1 score was 81.00%. The above-mentioned results indicated that the rape planting area extraction approach based on the RVIm-? and DTW threshold classification methods yields high accuracy in regional rape planting area extraction from dual-polarization SAR data, and RVIm-? is characterized by certain regional scalability and stability.