Very high resolution (daily, 3 m) data cubes of surface reflectance synergizing observations from CubeSats, Sentinel-2, Landsat 8, and MODIS
Abstract
The recent emergence of new observational paradigms combined with advances in conventional spaceborne sensing has resulted in a proliferation of satellite sensor data. This geospatial information revolution constitutes a game changer in the ability to derive time-critical and location-specific insights into dynamic land surface processes. The production of analysis ready, scalable, and very high spatiotemporal resolution information feeds has an obvious role in advancing digital agriculture at broad scales with potentially far reaching societal and economic benefits. However, sensor interoperability issues and cross-calibration challenges present obstacles in realizing the full potential of these rich geospatial datasets.
In this study, the CubeSat-Enabled Spatio-Temporal Enhancement Method (CESTEM) is applied to enhance, harmonize, and inter-calibrate cross-sensor data streams leveraging rigorously calibrated 'gold standard' satellites (i.e., Sentinel, Landsat, MODIS) in synergy with superior resolution CubeSats from Planet. The end products are data cubes of spatially complete, very high resolution (daily, 3 m), and harmonized surface reflectance (SR) images for target AOIs and TOIs. Results will be presented for a variety of agricultural landscapes showcasing the utility of CESTEM for providing high fidelity field-scale information on spatio-temporal SR dynamics in a timely manner. CESTEM-based information feeds can provide time-critical insights into crop growth dynamics, developing plant stress, and crop disturbances, which will be key towards directing location-specific and sustainable management strategies for increasing agricultural efficiency, optimize productivity, and enhance profitability.- Publication:
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AGU Fall Meeting Abstracts
- Pub Date:
- December 2019
- Bibcode:
- 2019AGUFM.B54D..01H
- Keywords:
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- 0402 Agricultural systems;
- BIOGEOSCIENCES;
- 0434 Data sets;
- BIOGEOSCIENCES;
- 0480 Remote sensing;
- BIOGEOSCIENCES;
- 1842 Irrigation;
- HYDROLOGY