Location: Hydrology and Remote Sensing Laboratory
Project Number: 8042-13610-029-017-S
Project Type: Non-Assistance Cooperative Agreement
Start Date: Sep 1, 2015
End Date: Aug 31, 2020
Objective:
To determine the capabilities of integrating Unmanned Aircraft System (UAS) multispectral and thermal-infrared data at coarser (~1 meter) and finer (<0.5 meter) pixel resolutions with the two-source energy balance model (TSEB) and other remote sensing-based energy balance models for assessment of water use (evapotranspiration), root zone water availability and plant stress of vineyards. The image processing software developed by Utah State University can produce maps of fractional vegetation cover, greenness, and land surface temperature at the coarser pixel resolutions at near-real time while the finer pixel resolution requires significantly more processing time and therefore much more difficult to provide an operational product. The utility of having the finer pixel resolution information that takes considerably more time and effort to generate than the coarser pixel resolution imagery from the UAS will be evaluated for its efficacy in improving irrigation management while sustaining grape productivity and quality.
Approach:
Conduct intensive field experiments collecting evapotranspiration (ET), root zone soil moisture and biophysical data in concert with UAS remote sensing imagery provided by Utah State University (Cooperator) at both and coarser and finer pixel resolutions. The remote sensing imagery from UAS sensors at the different spatial resolutions will be used to evaluate and understand the added benefit of having very high resolution (<0.5 m) remote sensing data for input to ET models for quantifying vineyard water use and vine stress/condition at different vine physiological stages. The finer resolution UAS imagery can better distinguish vine and cover crop contributions to total water use and root zone water availability as well as be used to evaluate and refine thermal sharpening techniques that use coarser resolution data to provide separate vine and cover crop contributions to total ET. Thermal sharpening and upscaling/downscaling techniques involving such methodologies as statistical learning machines and wavelets will be evaluated as options for translating coarse-scale imagery into finer scales.
