Crop type classification with combined spectral, texture, and radar features of time-series Sentinel-1 and Sentinel-2 data

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.

KEYWORDS:

• crop type classification
• variable selection
• texture feature
• multi-time series
• multi-source remote sensing

Acknowledgement

The images of Sentinel-1 and Sentinel-2 come from ESA, and the authors would like to thank the above institutions for their help and support.

Disclosure statement

The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

Data availability statement

The observed crop distribution thematic map of Wen county in 2020 that support the findings of this study are available from the corresponding author, Huan Ding, upon reasonable request.

Additional information

Funding

This research was funded by the Fundamental Research Funds for the Universities of Henan Province[NSFRF180329], the MOE (Ministry of Education in China) Project of Humanities and Social Sciences [15YJCZH018], and Science and Technology Project of Henan Province[162102210063].