Mapping paddy rice with multitemporal ALOS/PALSAR imagery in southeast China

Abstract

Mapping rice cropping areas with optical remote sensing is often a challenge in tropical and subtropical regions because of frequent cloud cover and rainfall during the rice growing season. Synthetic aperture radar (SAR) is a potential alternative for rice mapping because of its all-weather imaging capabilities. The recent Phased Array-type L-band SAR (PALSAR) sensor onboard the Advanced Land Observing Satellite (ALOS) acquires multipolarization and multitemporal images that are highly suitable for rice mapping. In this pilot study, we demonstrate the feasibility of this sensor in mapping the rice planting area in Zhejiang Province, southeast China. High-resolution ALOS/PALSAR images were acquired at three rice growing stages (transplanting, tillering and heading) and were applied in a support vector machine (SVM) classifier to map rice and other land use surfaces. The results show that, based on the 1:10 000 land use/land cover (LULC) survey map, the rice fields can be mapped with a conditional Kappa value of 0.87 and at user's and producer's accuracies of 90% and 76%, respectively. The large commission error primarily came from confusion between rice and dryland crops or orchards because of their similar backscatter amplitudes in the rice growing season. The relatively high rice mapping accuracy in this study indicates that the new ALOS/PALSAR data could provide useful information in rice cropping management in subtropical regions such as southeast China.

Acknowledgements

This research was supported by the China Natural Science Foundation (grant no. 40341010) and China National 973 Project (no. 2002CB410810) funded at Zhejiang University, China. It was also partially supported by a NASA grant (NNG05GD49G) at Michigan State University and the Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Science. Partial support was also from the Student Exchange Programme of the Ministry of Education of China. We thank JAXA for providing the PALSAR data through their ALOS Kyoto and Carbon Initiative.