Scientific Data · 2024 · 21 citations · 65 references
AgricultureAgronomyCrop SciencesCrop ManagementRemote SensingYield MappingCrop ClassificationRemote Sensing For Crop ManagementRemote Sensing For Yield EstimationYield Map GenerationYield PredictionCrop Yield MappingField-scale Yield MappingGrain Yield MappingHarvest Yield EstimationRandom Forest ClassificationSpatial Yield AnalysisYield Map AnalysisYield Map Construction
Winter wheat constitutes approximately 20% of China's total cereal production. However, calculations of total production based on multiplying the planted area by the yield have tended to produce overestimates. In this study, we generated sample points from existing winter wheat maps and obtained samples for different years using a temporal migration method. Random forest classifiers were then constructed using optimized features extracted from spectral and phenological characteristics and elevation information. Maps of the harvested and planted areas of winter wheat in Chinese eight provinces from 2018 to 2022 were then produced. The resulting maps of the harvested areas achieved an overall accuracy of 95.06% verified by the sample points, and the correlation coefficient between the CROPGRIDS dataset is about 0.77. The harvested area was found to be about 13% smaller than the planted area, which can primarily be attributed to meteorological hazards. This study represents the first attempt to map the winter wheat harvested area at 10-m resolution in China, and it should improve the accuracy of yield estimation.
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