National Aeronautics and Space Administration Jet Propulsion Laboratory
National Aeronautics and Space Administration Jet Propulsion Laboratory California Institute of Technology Cube. Sat Derived Crop-Water Use for Precision Agriculture Bruno Aragon Figure 1: Evaluation scatterplots for the original (left) and modified (right) PT-JPL model. Fluxes, where the corresponding LAI value is larger than four, are displayed in red (n = 23), while the rest of the data points are shown in light blue (n = 26). Figure 2: Daily crop water use estimates in mm day− 1 for DOY 277 with a false color background, derived from high-resolution Cube. Sat (3 m) LAI and ground measured meteorological data. For this day, the 34 crops under planting are using an estimated 72, 900 m 3 or approximately 2150 m 3 per field. Fields in brown are bare and not included in this estimate. Science: Planet Cube. Sat constellation of satellites provide near daily monitoring of the Earth surface at 3 m pixel resolution. Although not designed for scientific missions, they represent an unprecedented resource to derive hydrological variables such as evapotranspiration. Here, we combine Cube. Sat imagery with the well-established PTJPL evapotranspiration model to evaluate the capacity of Cube. Sats to provide crop water use information for precision agriculture. We also assess the performance of a modified PT-JPL scheme for dry an hot environments against a time-series of tower-based flux measurements. Results: The PT-JPL Cube. Sat fusion worked very well straight out of the box, but required some calibration to adjust to site characteristics (Figure 1). We derived extremely high resolution satellite-based crop water use retrievals (Figure 2). Significance: Given that food demand is expected to continue growing and that agriculture accounts for 70% of the global fresh water use, having accurate evapotranspiration retrievals is of paramount importance. The revisit time and spatial resolution of Cube. Sats offers the possibility to better monitor and improve agricultural demands and management practices. Aragon, B. ; Houborg, R. ; Tu, K. ; Fisher, J. B. ; Mc. Cabe, M. Cube. Sats Enable High Spatiotemporal Retrievals of Crop-Water Use for Precision Agriculture. Remote Sens. 2018, 10, 1867. B. A was supported by the King Abdullah University of Science and Technology (KAUST). R. H. acknowledges research support by the South Dakota State University. K. T. recognizes support by NASA THP. J. B. F. was supported in part by NASA programs: THP, SUSMAP and ECOSTRESS.
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