Intelligent Recognition of Corn Residue Cover Area by Time-Series
Sentinel-2A Images
TAO Wan-cheng1, 2, ZHANG Ying1, 2, XIE Zi-xuan1, 2, WANG Xin-sheng1, 2, DONG Yi1, 2, ZHANG Ming-zheng 1, 2, SU Wei1, 2*, LI Jia-yu1, 2, XUAN Fu1, 2
1. College of Land Science and Technology, China Agricultural University, Beijing 100083, China
2. Key Laboratory of Remote Sensing for Agri-Hazards, Ministry of Agriculture and Rural Affairs, Beijing 100083, China
Abstract：Crop residue covering is an important way to reduce soil erosion and increase soil organic carbon, which is very important for black soil protection. Therefore, the accurate and rapid identification of corn residue cover area plays an important role in local government monitoring and promoting conservation tillage. The study area is located in Siping City, Jilin Province. Moreover, the time-series Sentinel-2A images collected from GEE (Google Earth Engine) cloud platform are used to capture spectral index based on the characteristics of the corn growing season and after harvest. Index features include Normalized Difference Vegetation Index (NDVI) and Normalized Difference Residue Index (NDRI). The time series feature values are sorted by size, and the quantile method is used to select quartile (QT) features at 0%, 25%, 50%, 75%, and 100% to construct datasets. On this basis, the random forest method after parameter optimization is applied to train and verify the sample datasets divided according to 7∶3, and then the datasets are classified, combined with the connected domain calibration method to remove the small connected domains generated in the classification process, and further optimize the global result. Through the quantitative and qualitative evaluation of Kappa and Overall Accuracy (OA), the experimental results show that: (1) The quantitative evaluation results of the classification model (M1/M2/M3/M4/M5) based on the dataset composed of the different feature are superior 90%. Among them, the classification model M5 of the dataset designed in this paper has the best performance, of which Kappa and OA are 97.41% and 97.91%, respectively. Compared with the classification model M2 without the QT feature, the Kappa and OA are increased by 4.52% and 3.64%, respectively. At the same time, the M5 recognition result can effectively retain edge detail information; (2) For QT feature of different time scales, using the QT feature classification model M5_6/M5 of time series remote sensing images from May to November can greatly restrain another crop residue. Compared with the Kappa and OA of the M5_1 model classification result using only the QT features of the time series images in November, the Kappa and OA increased by 3.9% and 3.12%, respectively; (3) Based on the M5 model, the Kappa and OA of the classification model M6 combined with the connected domain calibration method are 96.76% and 97.36%, respectively, second only to the recognition results of the M5 model. The model M6 avoids fine-grained patches while ensuring high accuracy and optimizing the classification visualization effect. Therefore, the M6 model proposed in this paper is suitable for identifying areas covered by corn residue in the study area. This method can be quickly implemented in the GEE cloud platform environment and is suitable for popularization and application in a corn residue covered areas in Northeast China.
Key words：Crop residue cover area; GEE cloud platform; Time series Sentinel-2A image; Random forest; Connected domain
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