Precision agriculture using Planet satellites: capturing daily water use and crop behavior over a growing season
Abstract
Recent advances in space-based observation have enabled a dramatic shift in how we monitor and model the earth system. With their launch of visible and near-infrared CubeSat constellations, Planet has overcome the spatiotemporal barriers that have limited operational insights in many earth surface processes. The capacity to observe the earth with high resolution in space and in time has particular relevance to dynamic in time responses, such as tracking vegetation growth and development or monitoring fast hydrological processes. Here we showcase some of these advantages with an application to precision agriculture. Focusing in on a single field, we illustrate the spatiotemporal advantage that 3 m and near-daily satellite retrievals can provide for mapping the crop behavior and development, both in terms of the dynamics of leaf area index and the variable water use consumed throughout a sample growing season. The CubeSat-Enabled Spatio-Temporal Enhancement Method (CESTEM) is used to exploit the unique features of the CubeSat systems, while ensuring consistency with research-grade platforms like Sentinel-2 and LandSat. A refined version of the Priestley-Taylor Jet Propulsion Laboratory (PT-JPL) model is employed to provide crop-water use estimates over a multi-week observation period. To benchmark satellite responses, ground based vegetation sampling and eddy-covariance data are used to evaluate model estimates. Using this new paradigm for Earth observation we illustrate the capacity to provide the data needed to drive precision agricultural insights at the within-field scale, offering operational capacity to an industry critical to ensuring food and water security in the future.
- Publication:
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AGU Fall Meeting Abstracts
- Pub Date:
- December 2019
- Bibcode:
- 2019AGUFM.B31E..04M
- Keywords:
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- 0402 Agricultural systems;
- BIOGEOSCIENCES;
- 0480 Remote sensing;
- BIOGEOSCIENCES;
- 1926 Geospatial;
- INFORMATICS;
- 1942 Machine learning;
- INFORMATICS