Remotely sensed vegetation index and LAI for parameter determination of the CSM-CROPGRO-Soybean model when in situ data are not available

An agricultural system is a complex combination of many different components that require different types of data for analysis and modeling. Remote sensing information is an alternative source of data for areas that only have a small amount of ground truth data. The goal of this study was to evaluate whether remotely sensed data can be used for calibration of genetic specific parameters (GSPs) with the ultimate goal of yield estimation. This study used the Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) from Moderate Resolution Imaging Spectroradiometer (MODIS) with measured Leaf Area Index (LAI) for soybean fields in Parana, Brazil and Iowa, USA, to calibrate the cultivar parameters of the CSM-CROPGRO-Soybean model. Three calibration methods were performed including field-measured LAI, remotely sensed derived LAI, and remotely sensed derived Light Interception. The cultivar parameters sensitive to LAI and LI were calibrated for yield with a mean error of -4.5 kg/ha (0.1%) and with a R-2 of 0.89 for Parana. The availability of crop growth measurements for Iowa resulted in an average RMSE of 895 kg/ha (average nRMSE of 6%), and Willmott agreement index of 0.98 for time-series biomass, and an average RMSE of 941 kg/ha (average nRMSE of 15%) for pod weight. This study showed that remotely sensed LAI and LI from NDVI data can be used for calibration of GSPs with the ultimate goal of improving yield predictions based on local dynamic temporal and spatial variability.