AUTS: A Novel Approach to Mapping Winter Wheat by Automatically Updating Training Samples Based on NDVI Time Series

Abstract

Accurate and rapid access to crop distribution information is a significant requirement for the development of modern agriculture. Improving the efficiency of remote sensing monitoring of winter wheat planting area information, a new method of automatically updating training samples (AUTS), is proposed herein. Firstly, based on the Google Earth Engine (GEE) platform, a Sentinel-2 image with a spatial resolution of 10 m was selected to extract the distribution map of winter wheat in the city of Shijiazhuang in 2017. Secondly, combined with the NDVI time series, the weighted correlation coefficients from 2017, 2018, and 2019 were calculated. Then, the 2017 winter wheat distribution map and its most significant relevant areas were used to extract sample points from 2018 and 2019 automatically. Finally, the distribution map of winter wheat in Shijiazhuang in 2018 and 2019 was generated. In addition, to test the applicability of the automatically updating training sample at different scales and regions, the proposed method was applied to Landsat 8 image data with a spatial resolution of 30 m, as well as to Handan and Baoding. The results showed that the calculated winter wheat planting area is comparable with the officially published statistics, based on Sentinel-2, extracting three years of winter wheat, the R2 values for all three years were above 0.95. The R2 values for 2018 and 2019, based on Landsat 8 extractions, were 0.95 and 0.90, respectively. The R2 values extracted from Handan and Baoding in 2018 were 0.94 and 0.86, respectively. These results indicate that the proposed method has high accuracy and can provide technical support and reference for winter wheat area monitoring and yield estimation.

Keywords

• winter wheat
• random forests
• training sample updated
• Sentinel-2
• Google Earth Engine (GEE)