Dynamic modelling of rice leaf area index with quad-source optical imagery and machine learning regression models

Abstract

Optical satellite imagery has been widely used to monitor leaf area index (LAI). However, most studies have focussed on single- or dual-source data, thus making little use of a growing repository of freely available optical imagery. Hence this study has evaluated the feasibility of quad-source optical satellite imagery involving Landsat-8, Sentinel-2A, China’s environment satellite constellation (HJ-1 A and B) and Gaofen-1 (GF-1) in modelling rice green LAI over a test site located in southeast China at two growing seasons. With the application of machine learning regression models including Random Forest (RF), Support Vector Machine (SVM), k-Nearest Neighbour (k-NN) and Gradient Boosting Decision Tree (GBDT), results indicated that regression models based on an ensemble of decision trees (RF and GBDT) were more suitable for modelling rice green LAI. The current study has demonstrated the feasibility of quad-source optical imagery in modelling rice green LAI and this is relevant for cloudy areas.

Keywords:

• Paddy rice
• green LAI
• optical satellite
• quad-source imagery
• machine learning

Acknowledgments

The authors express their sincere gratitude to students of the Key Laboratory of Agricultural Remote Sensing and Information Systems at Zhejiang University for their assistance during field work. The advices of peer-reviewers are also appreciated.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This study was funded by the National Key R&D Programme of China under the thematic areas ‘Monitoring methods of paddy rice agro-meteorological disasters in the middle and lower reaches of the Yangtze River (Grant No. 2016YFD0300601)’ and ‘Monitoring and prediction methods of paddy rice and winter wheat in the middle and lower reaches of the Yangtze River (Grant No. 2016YFD0300603-5)’.