Crop type classification with combined spectral, texture, and radar features of time-series Sentinel-1 and Sentinel-2 data. Issue 4 (16th February 2023)

• Record Type: Journal Article
• Authors: Cheng, Gang; Ding, Huan; Yang, Jie; Cheng, Yushu
• Is Part Of: International journal of remote sensing. Volume 44:Issue 4(2023)
• Page Start: 1215
• Page End: 1237
• Publication Date: 2023-02-16
• Subjects: crop type classification -- variable selection -- texture feature -- multi-time series -- multi-source remote sensing; Remote sensing -- Periodicals; Télédétection -- Périodiques; 621.3678
• DOI: 10.1080/01431161.2023.2176723
• Languages: English
• ISSNs: 0143-1161

Abstract

ABSTRACT: Crop type mapping visualizes the spatial distribution pattern and proportion of planting areas of different crop types, which is the basis for subsequent agricultural applications. Although optical remote sensing has been widely used to monitor crop dynamics, data are not always available due to cloud and other atmospheric effects on optical sensors. Satellite microwave systems such as Synthetic Aperture Radar (SAR) have all-time and all-weather advantages in monitoring ground and crop conditions, combining optical imagery and SAR imagery for crop type classification is of great significance. Our study mainly proposes seven feature combination schemes based on the combination of multi-temporal spectral features and texture features of Sentinel-2 (S2), and radar backscattering features of Sentinel-1 (S1) evaluate the influence of different data sources and different features on classification accuracy, obtains the optimal classification strategy and analyses the contribution of different features to classification result, in the aim of providing a new technical approach for the fine identification of crops from multi-source remote-sensing data. Results show that the crop classification accuracy of combined multi-time series spectral, texture, and radar features is higher than that of combining two types of features. The features subset selected by multi-period spectral, texture, and radar features have the best classification result, the overall accuracy (OA) and kappa coefficients reach 96.40% and 0.93, respectively. The study provides a method reference for future research on larger-scale remote-sensing crop precise extraction.