Research and Implementation of Crop Identification Based on Sentinel-2 Time Series Data in Shijiazhuang City

Abstract

Using accurate remote sensing technology to monitor the spatial distribution of crops is of great practical significance for food security and sustainable development of agriculture. In this study, Shijiazhuang city, Hebei province in China was taken as the study area, and 13 sceneries Sentinel-2 from June to September 2017 was used as the data source. The time series data set of three indices which included enhanced vegetation index (EVI), land surface water index (LSWI) and red edge position (REP) was constructed. Combined with the multispectral data from Sentinel-2 imagery, corn, soybean, peanut, pear tree and walnut tree in this study area were identified by classification and regression tree (CART) algorithm and random forest (RF) algorithm on the Google Earth Engine (GEE) cloud computing platform. The results showed that the combination of EVI and REP achieved the best overall accuracy and kappa coefficient in two classification algorithms and the crop classification from the RF classifier had the higher accuracy than CART classifier, its overall accuracy and kappa coefficient were 95.09% and 0.94, respectively. Therefore, this method provided important reference value for the classification of crops in large area.