Parametrization of Crop Models Using UAS Captured Data

Bilal Abughali, Purdue University

Abstract

Calibration of crop models is an expensive and time intensive procedure, which is essential to accurately predict the possible crop yields given changing climate conditions. One solution is the utilization of unmanned aircraft systems (UAS) deployed with Red Green Blue Composite (RGB), and multispectral sensors, which has the potential to measure and collect in field biomass and yield in a cost and time effective manner. The objective of this project was to develop a relationship between remotely sensed data and crop indices, similar to biomass, to improve the ability to parametrize crop models for local conditions, which in turn could potentially improve the quantification of the effect of hydrological extremes on predicted yield. An experiment consisting of 750 plots (350 varieties) was planted in 2018, and a subset of 18 plots (9 varieties) were planted in 2019. The in-situ above ground biomass along with multispectral and RGB imagery was collected for both experiments throughout the growing season. The imagery was processed through a custom software pipeline to produce spectrally corrected imagery of individual plots. A model was fit between spectral data and sampled biomass resulting in an R-square of 0.68 and RMSE of 160 g when the model was used to estimate biomass for multiple flight dates flights. The VICCropSyst model, a coupled hydrological and agricultural system model, was used to simulate crop biomass and yield for multiple years at the experiment location. Soybean growth was parametrized for the location using CropSyst’s Crop Calibrator tool. Biomass values generated from UAS imagery, along with the in-situ collected biomass values were used separately to parametrize soybean simulations in CropSyst resulting in very similar parameter sets that were distinct from the default parameter values. The parametrized crop files along with the default files were used separately to run the VIC-CropSyst model and results were evaluated by comparing simulated and observed values of yield and biomass values. Both parametrized crop files (using in-situ samples and UAS imagery) produced approximately identical results with a max difference of 0.03 T/Ha for any one year, compared to a base value of 3.6 T/Ha, over a 12-year period in which the simulation was ran. The parametrized runs produced yield estimates that were closer to in-situ measured yield, as compared to unparametrized runs, for both bulk varieties and the run experiments, with the exception of 2011, which was a flooding year. The parametrized simulations consistently produced simulated yield results that were higher than the measured bulk variety yields, whereas the default parameters produced consistently lower yields. Biomass was only assessed for 2019, and the results indicate that the biomass after parametrization is lower than the default, which is attributed to the radiation use efficiency parameter being lower in the parametrized files, 2.5 g/MJ versus 2.25 g/MJ. The improved accuracy of predicting yield is evidence that the UAS based methodology is a suitable substitute for the more labor intensive insitu sampling of biomass for soybean studies under similar environmental conditions.

Degree

M.Sc.

Advisors

Cherkauer, Purdue University.

Subject Area

Aerospace engineering|Robotics|Transportation

COinS