The Normalised Difference Vegetation Index (NDVI) derived from optical satellite images plays a very important role in determining the state of plants' health. Also, it is an important parameter needed in various statistical/process-based models. However, the use of optical images is sometimes limited because of atmospheric conditions and cloud cover. On the other hand, synthetic aperture radar (SAR) remote sensing has been widely used for crop monitoring due to its high-resolution imaging and all-weather data acquisition capabilities. So, if the SAR backscatter response (σ⁰) and NDVI data could be correlated, it is possible to estimate NDVI (during complete or partial stages of crop development) under overcast situations. In this study, three different experiments have been performed to establish the relationship between NDVI-σ⁰_VV, NDVI-σ⁰_VH, and NDVI-σ⁰_VV/σ⁰_VH. Here, time-series σ⁰ (in VV and VH polarizations) and NDVI were extracted from Sentinel-1 and Senitnel-2, respectively. Based on the analysis, it is found that the NDVI is more closely correlated with the ratio σ⁰_VV/σ⁰_VH than it is with σ⁰_VV and σ⁰_VH when data points from the start of cropping season up to the start of the maturity stage of the crop, were considered (referred to as experiment 2 and experiment 3). This is opposed to experiment 1, which took into account all data points related to the crop's development i.e. start of cropping season up to the harvesting stage of the crop. The best results were obtained from experiment 3 in which higher-order polynomial regressions were developed between NDVI and σ⁰_VV/σ⁰_VH. A significant correlation ranging from R² = 0.81 to 0.98 were observed for NDVI-σ⁰_VV/σ⁰_VH. The study was conducted on selected farms located in the same agro-climatic zone during the Rabi season of 2018–19.