Assimilation of UAV multispectral imagery into a coupled DSSAT-CROPGRO − SCOPE model for processing tomatoes

The increasing availability of remote sensing (RS) data and advancements in data assimilation (DA) techniques facilitate the non-destructive calibration of mechanistic crop models but necessitate a framework that digitally represents cropping systems and their spectral properties. This study implemented a coupling scheme linking the outputs of a crop model (DSSAT-CROPGRO) with a radiative transfer model (RTMo module in SCOPE). Reflectance data acquired from a multispectral camera mounted on a UAV were assimilated into the coupled model. The DA scheme was tested in an irrigation and fertilization trial with processing tomatoes, a row crop, requiring the adjustment of the model in order to reflect the vegetation and soil pixel proportions. Examining the relative contribution of dynamically updating specific RTMo parameters showed that the coupled model performed better when parameters were adjusted than when using their nominal values. Applying the DA scheme improved the normalized root mean square error (NRMSE) of the Leaf Area Index (LAI) from 59% to 42% and yield from 64% to 35%. The best performance was achieved when the most water-stressed treatment was excluded, resulting in NRMSE of 34% for LAI and 16% for yield. Since the DA scheme presented here performed well at low to moderate water stress, it should be further tested in assimilating space-borne RS data into simulations of large-scale, commercial fields.