Evaluating Sentinel-1A datasets for rice leaf area index estimation based on machine learning regression models

Abstract

Three Sentinel-1A datasets in vertical transmitted and horizontal received (VH) and vertical transmitted and vertical received (VV) polarisations, and the linear combination of VH and VV (VHVV) are evaluated for rice green leaf area index (LAI) estimation using four machine learning regression models [Support Vector Machine (SVM), k-Nearest Neighbour (k-NN), Random Forest (RF) and Gradient Boosting Decision Tree (GBDT)]. Results showed that for the entire growing season, VV outperformed VH, recording an R² of 0.68 and an RMSE of 0.98 m²/m² with the k-NN model. However, VHVV produced the most accurate estimates with GBDT (R² of 0.82 and RMSE of 0.68 m²/m²), followed by that of VHVV with RF (R² of 0.78 and RMSE of 0.90 m²/m²). Our findings have further confirmed that combining VH and VV data can achieve improved rice growth modelling, and that tree-based algorithms can better handle data dimensionality.

Keywords:

• Paddy rice
• green LAI
• Sentinel-1A datasets
• machine learning

Disclosure statement

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

Additional information

Funding

This study was funded by the National Natural Science Foundation of China (41871328), and 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. 2017YFD0300402-3)” 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)”. Also, our gratitude goes to all students of the Key Laboratory of Agricultural Remote Sensing and Information Systems at Zhejiang University for their assistance during field work. Furthermore, this paper benefited from the comments and advices of peer reviewers.