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Zhang Xiaochun,Cao Zequn,Yang Dan,Wang Qiuhao,Wang Xiugui,Xiong Qinxue.Extraction and spatio-temporal analysis of county-level crop planting patterns based on HJ-1 CCD[J].Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE) ,2021,37(6):168-181
Extraction and spatio-temporal analysis of county-level crop planting patterns based on HJ-1 CCD
Received:August 12, 2020  Revised:October 20, 2020
Foundation item:National Key Research and Development Program of China (2018YFC1508301, 2018YFC1508302); National Natural Science Foundation of China (31871516); Hubei Natural Science Foundation (2019CFB507)
Author NameAffiliation
Zhang Xiaochun 1.State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan 430072, China 
Cao Zequn 1.State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan 430072, China 
Yang Dan 3.State Grid Zhejiang Electric Power Co., Ltd. Jinshuitan Hydropower Plant, Lishui 323000, China 
Wang Qiuhao 1.State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan 430072, China 
Wang Xiugui 1.State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan 430072, China 
Xiong Qinxue 2.College of Agriculture, Yangtze University, Jingzhou 434000, China 
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Abstract: Crop-type classification and spatio-temporal change detection support rational agricultural management; perennial crop maps reflect changes in crop planting patterns and are useful for economic and social analysis. The satellite images used for mapping crops, however, are not at high spatial and high temporal resolution. They do not provide sufficient data for all stages of growth when mapping spatial distributions of crops in areas with a great variety of agricultural practices and products. A cost-effective solution using short repeat cycle Huan Jing (HJ)-1 Charge-Coupled Device (CCD) imagery and the freely available Landsat-8 imagery was proposed to produce annual crop maps reflecting spatio-temporal changes in planting areas in Jianli County, China. Phenological metrics such as maximum Normalized Difference Vegetation Index (NDVI) values, dates, and the number of days in the growth stages of the different crops were defined from time-series NDVI curves and used for crop classification. Typical planting areas were extracted from the 15 m pan-sharpened Landsat-8 images. The NDVI and time thresholds for phenological metrics were obtained from the NDVI time series curves during the crop growth stage of typical planting areas. Classification rules were established to create crop maps from 2009 to 2016 and applied land-use changes for different crops based on multi-year crop classification maps reflecting the distribution of dominant crops. The high-spatial-resolution China satellite images and crop area data from Jianli statistical yearbook were used to perform an accurate assessment. The average classification accuracy rate was 84% when compared with the high-spatial-resolution imagery, and the classification area matched up to 81.60% with the statistical crop area data. These results indicated that this method provided a possible means for classification permitting regular mapping of crop distributions in complex areas like Jianli County. By the spatio-temporal analysis of summer-harvest crops, it could be found that fewer farmers were willing to plant oilseed rape because of the high labor cost caused by this crop's low agricultural mechanization level. For autumn-harvest crops, the government set the standard of the lowest purchase price for the middle-season rice, which greatly reduced the risk of planting the middle-season rice for farmers. This might guide farmers' decisions and lead to small changes in the middle-season rice area. Meteorological data indicated that it had continuous rainfall and waterlogging in the summer of 2016, which led to the reduction of the middle-season rice area, and especially the big reduction of cotton area.
KeyWord:crops  remote sensing  decision trees  NDVI  HJ-1 CCD data  spatio-temporal analysis
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