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《Journal of Natural Resources》 2019-05
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A study of winter rape extraction at sub-pixel fusing multi-source data based on Artificial Neural Networks:A case study of Jianghan and Dongting Lake Plain

LIU Wen-bin;TAO Jian-bin;XU Meng;CHEN Rui-qing;GUO Yang;The College of Urban and Environmental Sciences, Central China Normal University/Key Laboratory of Geographical Processes and Simulation of Geographical Processes;  
Rape is the fifth largest crop type and an important oil crop in China. Obtaining the distribution information of rape is of great significance to the development of edible oil market and food security. Jianghan and Dongting Lake Plain are important production bases of grain,cotton and edible oil in China. The crop rotation and intercropping are very common due to the fragmented cropland fields and diversity of landscape. Traditional remote sensing monitoring methods are difficult to assess the spatial distribution and interannual variation of planting areas. In this paper, an Artificial Neural Network-based method for extracting winter rape from sub-pixels was proposed, and the MODIS and GF-1 high resolution data were combined to obtain winter rape abundance on the JPDLP. First, the Sequential Forward Selection algorithm was used to select phenological feature from the MODIS time series dataset. Then an ANN model fusing multi-source data was built to estimate winter rape abundance. The results showed that the distribution information of winter rape obtained by this method has high accuracy(the extraction accuracies of ANN modeled versus GF-1 and census data were91.54% and 74.70%, respectively). This method showed that there is a great potential in the large-scale winter rape mapping using coarse resolution images. The results of this paper can provide technical methods for the spatial pattern evolution and spatial-temporal dynamics analysis of winter rape in China.
【Fund】: 湖北省自然科学基金项目(2017CFB434)
【CateGory Index】: S565.4;TP183
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