Synthetic aperture radar and optical satellite data for estimating the biomass of corn

Above ground biomass is an important crop biophysical parameter for monitoring crop condition and determining crop productivity, in particular if linked with phenological growth stage. Optical reflectance and Synthetic Aperture Radar (SAR) backscatter have been used to model above ground biomass for some crops. However, to date, direct comparisons of biomass retrievals from these two data sources, and more importantly an integration of biophysical products from optical and SAR satellite data, has not been fully explored. In this study, SAR and optical satellite data are assessed and compared for estimating the wet and dry biomass of corn. Wet and dry biomass are estimated from RADARSAT-2 using a calibrated Water Cloud Model (WCM) and from RapidEye using vegetation indices which include the Normalized Difference Vegetation Index (NDVI), Red-Edge Triangular Vegetation Index (RTVI), Simple Ratio (SR) and Red-Edge Simple Ratio (SRre). Data collected during the SMAP Validation Experiment 2012 (SMAPVEX12) are used for testing the model accuracies. The results show high accuracies for both SAR and optical sensors for dry biomass with a correlation coefficient (R) of 0.83, Root Mean Square Error (RMSE) of 0.16 kg m(-2) and Mean Absolute Error (MAE) of 0.15 kg m(-2) from RADARSAT-2 and R of 0.92, RMSE of 0.11 kg m(-2) and MAE of 0.07 kg m(-2) from RapidEye. For wet biomass, the accuracies are 0.78 (R), 1.25 kg m(-2) (RMSE) and 1.00 kg m(-2) (MAE) from RADARSAT-2 and for RapidEye, 0.92 (R), 0.71 kg m(-2) (RMSE) and 0.49 kg m(-2) (MAE). For dry biomass, the accuracies from RapidEye are slightly higher but the results from these two sensors are comparable. For wet biomass, the accuracies from RADARSAT-2 decreased, likely due to the effect of Vegetation Water Content (VWC) on backscatter intensity. This study also developed a neural network transfer function between the biomass derived from SAR and from optical data. The purpose of this function is to facilitate the integration of optical and SAR derived biomass in order to monitor corn throughout the growing season, regardless of source of satellite data. Integration of data from multiple sources is an important strategy to improve temporal coverage, in particular when clouds obscure optical observations. With this approach in mind, biomass estimates from RADARSAT-2 were cross-calibrated against those from RapidEye and their time-series biomass maps were generated.