Integrating Sentinel-1 SAR and Sentinel-2 optical imagery with a crop structure dynamics model to track crop condition

Abstract

Sentinel-1 and Sentinel-2 satellites are delivering data at the spatial resolutions and geographies needed for operational crop monitoring. Of particular interest, Sentinel-1 Synthetic Aperture Radar (SAR) data in Single Look Complex (SLC) format can be processed to provide polarimetric parameters sensitive to crop development. This study created a Sentinel-1 based SAR vegetation index for canola, calibrated to Normalized Difference Vegetation Index (NDVI) (SARcal-NDVI) values calculated from Sentinel-2. A Random Forest Regressor (RFR) modelled the SARcal-NDVI, from four selected polarimetric features (degree of linear polarization (DoLP), normalized Shannon entropy (SE), the second eigenvalue of the coherency matrix (l2) and the ellipticity angle ((Formula presented.). The coefficient of determination (R2) between the Sentinel-2 NDVI and the SARcal-NDVI was 0.89. RFR-generated SARcal-NDVI estimates, based on the four SLC generated polarimetric parameters, were then used with a Canopy Structure Dynamics Model (CSDM) and with Growing Degree Days (GDD) to estimate the condition of canola at a daily time step. The SARcal--NDVI time series estimated from the CSDM model was correlated to the ground measured biomass. During the rapid accumulation of biomass from early to mid-season, correlations of the SARcal-NDVI to wet biomass were strong (R2 of 0.88). Correlations were still significant albeit weaker during pod development and throughout the period of canola senescence (R2 of 0.42). As climate variability drives uncertainty in the agricultural sector, sensors like the Sentinels can be leveraged to track changes in crop acreages and crop productivity. The next step in this research is to extend to other economically important crops such as corn, soybeans and wheat and test the ability of a Sentinel-1-based vegetation index to inform crop yield estimates.