Integrating UAV Multispectral Data into a Combined Crop-Radiative Transfer Model for Processing Tomatoes Using a Particle Filter

The proliferation of remote sensing (RS) data and advancements in mechanistic crop modeling and data assimilation techniques necessitate a framework that digitally represents cropping systems and their spectral properties. Such a framework would enable crop growth simulation, scenario testing, and timely prediction updates using RS data.

In this study, we develop a comprehensive coupling scheme that links a crop model (DSSAT-CROPGRO) with a radiative transfer model (RTMo module in SCOPE). This integration allows for the utilization of reflectance data from all measured spectral bands during data assimilation (DA) into the crop model.

We apply this coupled crop-radiative-transfer model in a DA experiment using a novel particle filter scheme. The assimilated data consists of observed reflectance measurements obtained by a multispectral camera mounted on an unmanned aerial vehicle (UAV). Using multispectral data with a high spatial resolution for analyzing a row crop required a dedicated analysis to fit model simulations to measurements. The suggested DA scheme was implemented in an irrigation and fertilization trial with processing tomatoes to evaluate its effectiveness.

The results showed that applying the DA scheme improved the NRMSE of the Leaf Area Index (LAI) from 59% to 41.8% and yield from 63.6% to 35.4%. The DA scheme performed best when the treatment that included the most severe stress was excluded from weight calculation, resulting in NRMSE of 34.1% and 15.5% for LAI and yield, respectively. After showing promising results, the suggested data assimilation scheme should be tested in large-scale, commercial fields using space-borne RS data to examine its applicability in various scenarios.