Comparing the relationship between NDVI and SAR backscatter across different frequency bands in agricultural areas

Abstract

The objective of this study is to investigate the relationship between the Normalized Difference Vegetation Index (NDVI) and Synthetic Aperture Radar (SAR) data at multiple frequencies, focusing on S- and C-band data with additional analysis for X- and L-band. This is the foundation for the translation of SAR data into NDVI values, thereby enabling the filling of gaps in NDVI data due to cloud cover. This study encompasses three distinct study areas in Argentina, Australia, and Vietnam, which exhibit considerable climatic and agricultural differences. NovaSAR-1 S-band and Sentinel-1 C-band data were acquired for all areas, with the addition of COSMO-SkyMed X-band and SAOCOM L-band SAR data for one region. Following the processing of the SAR data and the derivation of NDVI values from optical Sentinel-2 data, the relationship between them is analyzed for field-wise aggregated data.

The relationship between S- and C-band SAR data and NDVI values is observed to be strong for all fields. Consequently, cross-polarized (HV or VH) data demonstrated this relationship for all fields with a Pearson correlation coefficient $\rho >0.5$, whereas for co-polarized data (HH or VV), this could only be shown for some fields and crops. In the case of rice paddy fields, however, a different relationship is observed. While both S- and C-band data demonstrate a good relationship, this is primarily evident in the case of co-polarized data, with cross-polarized data exhibiting a comparatively weaker relationship. A relationship was observed for X-band data, but no relationship could be attested for L-band data. Neither the cross-ratio nor the radar vegetation index (RVI) generally showed a stronger relationship with the NDVI compared to a single polarization.

The demonstrated relationship between NDVI values and SAR backscatter data allows for a translation to be feasible. Consequently, the planned launch of the NISAR satellite, comprising S- and L-band SAR sensors, will facilitate new opportunities for agricultural monitoring. However, the retrieval of NDVI values from SAR data is a complex topic, as numerous factors, including crop type, crop phenology, SAR geometry and frequency, and others, influence this relationship.