Retrieval of optical vegetation indices from SCATSAT-1 Ku-band backscatter: A comparative analysis with MODIS and Proba-V sensors

Abstract

The extraction of vegetation indices from radar backscatter measurements offers a solution to optical remote sensing, especially in cloud-dominated areas where optical satellite observations are poor. This research explores the capability of Scatsat-1 Ku-band backscatter observations for normalised difference vegetation index (NDVI) estimation from two optical satellite instruments namely Moderate Resolution Imaging Spectroradiometer (MODIS) and Proba-V. Total of 60 different study sites are considered for taking the observations of Scatsat-1 backscattering coefficients and NDVI from two different sensors. A nonlinear statistical model has been developed for the retrieval of NDVI (MODIS and Proba-V) using Scatsat-1 backscattering coefficients at horizontal transmit, horizontal receive (HH) and vertical transmit, vertical receive (VV) polarisation by least square optimisation techniques. The validation results demonstrate that VV-polarised backscatter yields better NDVI retrieval accuracy than HH polarisation, with a higher correlation (R = 0.796) and lower root mean square error (RMSE) (0.058) for MODIS NDVI compared to R = 0.579 and RMSE = 0.057 for Proba-V NDVI. The bias values are near zero, showing no strong systematic overestimation or underestimation of the retrieval models. Yet, retrieval errors are more evident in low NDVI situations, where vegetation sparsity adds variability to backscatter response. The research validates that Scatsat-1 Ku-band backscatter data can be utilised to estimate NDVI effectively, offering an alternative for vegetation monitoring when optical sensors are obscured by cloud cover. Future studies need to investigate multi-temporal data fusion, machine learning methods, and other vegetation indices to further improve the accuracy and reliability of radar-based NDVI retrieval models. © 2026 Elsevier Ltd. All rights reserved..