Random Forest Classification of Rice Planting Area Using Multi-Temporal Polarimetric Radarsat-2 Data

Peng, Wanshan; Li, Shihua; He, Ze; Ning, Silan; Liu, Yuhan; Su, Zhonghua

doi:10.1109/igarss.2019.8898654

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

hi@scite.ai

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Random Forest Classification of Rice Planting Area Using Multi-Temporal Polarimetric Radarsat-2 Data

Cited by 8 publications

References 13 publications

Crop Yield Prediction in India: A Comparative Analysis of Ensemble Techniques

Crop Yield Prediction in India: A Comparative Analysis of Ensemble Techniques

Exploiting Sentinel-1 data and machine learning–based random forest for collectively mapping rice fields in Taiwan

A phenological object-based approach for rice crop classification using time-series Sentinel-1 Synthetic Aperture Radar (SAR) data in Taiwan

Contact Info

Product

Resources

About