Evaluating the potential of temporal Sentinel-1A data for paddy rice discrimination at local scales

ABSTRACT

A mapping algorithm is proposed in this letter on the application of Sentinel-1A data in discriminating paddy rice from other land-cover categories at local scales. The study region is Chongming Island located in the Shanghai metropolitan area, southeast China. We have acquired five temporal images of the new Sentinel-1A satellite in interferometric wide swath (IW) mode, covering a critical period of the 2015 paddy rice growing season in Chongming Island. Temporal backscatter at vertical transmitted and horizontal received (VH) polarization of Sentinel-1A was exploited and we observed that from early June to mid September, temporal backscatter profiles of the classes water/pond, built/urban, trees/forest and others were relatively stable. On the other hand, paddy rice exhibited a marked change in temporal backscatter coefficients, increasing steeply from flooding/planting to tillering/booting, and decreasing slightly at heading. Backscatter profiles also differ between paddy rice fields of different flooding/planting periods. These observed temporal microwave (radar) backscatter dynamics were employed in a decision tree mapping algorithm to discriminate paddy rice from other land-cover classes. An overall classification accuracy and Kappa statistic of 88.3% and 0.85 were recorded, respectively, which demonstrates the operational applicability of temporal Sentinel-1A data in paddy rice discrimination at local or district scales.​​​

Acknowledgements

The authors would like to thank students in the Key Laboratory of Agricultural Remote Sensing and Information Systems of Zhejiang University for their assistance during the field campaigns. We equally express our sincere appreciation to the three anonymous reviewers of this letter.

Disclosure statement

The authors declare no conflict of interest.

Additional information

Funding

This study was supported by the National Key Research and Development Plan of China (Grant No. 2016FYD0300601). ​​​