期刊
REMOTE SENSING
卷 11, 期 7, 页码 -出版社
MDPI
DOI: 10.3390/rs11070887
关键词
rice; SAR; Sentinel-1; random forest; decision tree; classification
类别
资金
- French Space Study Center (CNES, TOSCA 2018 project, POMME-V)
- National Research Institute of Science and Technology for Environment and Agriculture (IRSTEA)
- European Space Agency (ESA)
This study proposes an effective method to map rice crops using the Sentinel-1 SAR (Synthetic Aperture Radar) time series over the Camargue region, Southern France. First, the temporal behavior of the SAR backscattering coefficient over 832 plots containing different crop types was analyzed. Through this analysis, the rice cultivation was identified using metrics derived from the Gaussian profile of the VV/VH time series (3 metrics), the variance of the VV/VH time series (one metric), and the slope of the linear regression of the VH time series (one metric). Using the derived metrics, rice plots were mapped through two different approaches: decision tree and Random Forest (RF). To validate the accuracy of each approach, the classified rice map was compared to the available national data. Similar high overall accuracy was obtained using both approaches. The overall accuracy obtained using a simple decision tree reached 96.3%, whereas an overall accuracy of 96.6% was obtained using the RF classifier. The approach, therefore, provides a simple yet precise and powerful tool to map paddy rice areas.
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