Abstract
Timely and accurate mapping of winter crop planting areas in China is important for food security assessment at a national level. Time-series of vegetation indices, such as the normalized difference vegetation index (NDVI), are widely used for crop mapping, as they can characterize the growth cycle of crops. However, with the moderate spatial resolution optical imagery acquired by Landsat and Sentinel-2, it is difficult to obtain complete time-series curves for vegetation indices due to the influence of the revisit cycle of the satellite and weather conditions. Therefore, in this study, we propose a method for compositing the multi-temporal NDVI, in order to map winter crop planting areas with the Landsat-7 and -8 and Sentinel-2 optical images. The algorithm composites the multi-temporal NDVI into three key values, according to two time-windows—a period of low NDVI values and a period of high NDVI values—for the winter crops. First, we identify the two time-windows, according to the time-series of the NDVI obtained from daily Moderate Resolution Imaging Spectroradiometer observations. Second, the 30 m spatial resolution multi-temporal NDVI curve, derived from the Landsat-7 and -8 and Sentinel-2 optical images, is composited by selecting the maximal value in the high NDVI value period, and the minimal and median values in the low NDVI value period, using an algorithm of the Google Earth Engine. Third, a decision tree classification method is utilized to perform the winter crop classification at a pixel level. The results indicate that this method is effective for the large-scale mapping of winter crops. In the study area, the area of winter crops in 2018 was determined to be 207,641 km2, with an overall accuracy of 96.22% and a kappa coefficient of 0.93. The method proposed in this paper is expected to contribute to the rapid and accurate mapping of winter crops in large-scale applications and analyses.
Highlights
And accurate updates of crop planting area are very important for assessing national food security, for agricultural management, and for the evaluation of ecological functions [1,2,3]
There were some differences in the winter crop normalized difference vegetation index (NDVI) curves in different regions, their trends were similar
For the MODIS images with a spatial resolution of 250 m, the overall accuracy was 82.11% and the kappa coefficient was 0.57, 14.11% less than the 96.22% accuracy derived from the composite images
Summary
And accurate updates of crop planting area are very important for assessing national food security, for agricultural management, and for the evaluation of ecological functions [1,2,3]. Regional crop-type mapping provides basic data for crop growth monitoring and yield forecasting [4,5,6]. These crop maps can assist decision-makers and end-users in identifying the crop areas and estimating the biomass production, irrigation needs, water productivity, and scheduling management strategies [7]. Obtaining a timely and precise map of the winter crop plantings is critical for China and the rest of the globe [7,11]. Global land cover products are available in China
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