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<p>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 VIC-CropSyst 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 in-situ sampling of biomass for soybean
studies under similar environmental conditions.</p>
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| Identifer | oai:union.ndltd.org:purdue.edu/oai:figshare.com:article/17267138 |
| Date | 17 December 2021 |
| Creators | Bilal Jamal Abughali (11851874) |
| Source Sets | Purdue University |
| Detected Language | English |
| Type | Text, Thesis |
| Rights | CC BY 4.0 |
| Relation | https://figshare.com/articles/thesis/Parametrization_of_Crop_Models_Using_UAS_Captured_Data/17267138 |
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