Leaf area index estimation of wheat crop using modified water cloud model from the time-series SAR and optical satellite data

Abstract

The time-series synthetic aperture radar (SAR) and optical satellite data were used for the leaf area index (LAI) estimation of wheat crop using modified water cloud model (MWCM) in Varanasi district, India. In this study, MWCM was developed by including scale invariant vegetation fraction (f_veg) in the old WCM for the estimation of LAI. The non-linear least square optimization technique was applied to determine the optimum model parameters for the retrieval of LAI which was further validated with the observed LAI. The estimated values of LAI by MWCM at VV polarization shows good correspondence (R² = 0.901 and RMSE = 0.456 m²/m²) with the observed LAI values than at VH polarization (R² = 0.742 and RMSE = 0.521 m²/m²).The MWCM shows great potential for the LAI estimation of wheat crop by incorporating optical data (i.e. Sentinel-2) in terms of f_veg with SAR data (i.e. Sentinel-1A).

Keywords:

• Backscattering coefficient
• LAI
• Sentinel-1A
• Sentinel-2
• MWCM

Acknowledgement

The authors would like to thank ESA (European Space Agency) for providing free access to the satellite data and tool used in the present study.

Disclosure statement

No potential conflict of interest was reported by the authors.