Abstract
Northeast China is the leading grain production region in China where one-fifth of the national grain is produced; however, consistent and reliable crop maps are still unavailable, impeding crop management decisions for regional and national food security. Here, we produced annual 10-m crop maps of the major crops (maize, soybean, and rice) in Northeast China from 2017 to 2019, by using (1) a hierarchical mapping strategy (cropland mapping followed by crop classification), (2) agro-climate zone-specific random forest classifiers, (3) interpolated and smoothed 10-day Sentinel-2 time series data, and (4) optimized features from spectral, temporal, and texture characteristics of the land surface. The resultant maps have high overall accuracies (OA) spanning from 0.81 to 0.86 based on abundant ground truth data. The satellite estimates agreed well with the statistical data for most of the municipalities (R2 ≥ 0.83, p < 0.01). This is the first effort on regional annual crop mapping in China at the 10-m resolution, which permits assessing the performance of the soybean rejuvenation plan and crop rotation practice in China.
Highlights
Background & SummaryNortheast China has become the increasingly important grain bowl for the country[1]; the cropping systems in this region has changed significantly year by year due to the crop rotation practice and soybean rejuvenation plan targeting sustainable agricultural production and relieving pressure on international trade of soybeans, respectively[2]
Landsat images could provide more spatial details comparing to the previous efforts using coarse resolution MODIS data[7], the 16-day revisit cycle could not disentangle different crop phonologies, limiting the accuracy of the resulting maps[2,8]
The relatively short revisit cycle could provide more detailed phenological information related to individual crop types
Summary
Northeast China has become the increasingly important grain bowl for the country[1]; the cropping systems in this region has changed significantly year by year due to the crop rotation practice and soybean rejuvenation plan targeting sustainable agricultural production and relieving pressure on international trade of soybeans, respectively[2]. Landsat images could provide more spatial details comparing to the previous efforts using coarse resolution MODIS data[7], the 16-day revisit cycle could not disentangle different crop phonologies, limiting the accuracy of the resulting maps[2,8]. The effective classification features were not well documented when using high spectral, temporal, and spatial resolutions of satellite data (i.e. S2)[13]. (2) To alleviate the negative impacts of the spectral and phenological variability of a specific crop across space, we generated regionally independent classifiers by considering agro-climate zones (ACZs), which had regionally consistent cropping systems. The objective of this study is to produce annual crop maps in Northeast China from 2017 to 2019 at 10-m spatial resolution using (1) a hierarchical mapping strategy, (2) agro-climate zone-specific random forest classifiers, (3) interpolated and smoothed S2 time series, and (4) optimized features from spectral, temporal, and texture information. Our consistent crop maps can be utilized to monitor crop dynamics and to assess the effects of land-use policies
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