Synergy of dual – polarimetric radar vegetation descriptor and Gaussian processes regression algorithm for estimation of leaf area index

ABSTRACT

Gaussian Process Regression (GPR) emerged as a powerful algorithm since last decade in many applications. However, it is not fully explored in remote sensing applications, especially for predicting plant biophysical variables in the heterogeneous environment of India. This kernel-based machine learning technique has effectively replaced conventional approaches for estimating vegetation characteristics from remotely sensed data. In this work, an attempt has been made to test the ability of GPR to estimate the leaf area index (LAI) of wheat crops using the Sentinel-1 (S1) derived Dual-Polarized Radar Vegetation Index (DpRVI) and Sentinel-2 (S2) Top of Atmosphere (TOA) products. Further, the ability of the atmospheric correction procedure was tested by S2 Bottom of Atmosphere (BOA) images. To accomplish this, the field measurements of LAI were carried out from January to March 2020. Further comparisons of the GPR’s performance were made with the Artificial Neural Network (ANN) coupled with the PROSAIL (PROSPECT+SAIL) radiative transfer model, available through the Sentinel Application Platform (SNAP) Biophysical processor. The accuracy of the estimated LAI was evaluated using the statistical indicators, for example, coefficient of determination (R²), Root Mean Square Error (RMSE), and Nash Sutcliffe Efficiency (NSE). The results showed that the synergy of the GPR and DpRVI provided the most accurate result (R² = 0.822, RMSE = 0.503 m² m⁻², NSE = 0.831) as compared to the GPR and TOA (R² = 0.816, RMSE = 0.596 m² m⁻², NSE = 0.803) and SNAP biophysical processor (based on ANN) (R² = 0.696, RMSE = 0.760 m² m⁻², NSE = 0.578). Therefore, the study demonstrated the importance of S1 SAR images and GPR as an alternative tool among the other well-established machine learning algorithms to estimate the crop biophysical parameters.

KEYWORDS:

• LAI
• Sentinel -1
• Sentinel -2
• Gaussian processes, ANN, DpRVI

Acknowledgements

The authors would like to acknowledge the Department of Science and Technology (DST), New Delhi, for their financial assistance. The authors also thank the ESA (European Space Agency) for free access to S1 and S2 data through their data-portal and SNAP tool used in this study.

Disclosure statement

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