Improving the spatiotemporal resolution of remotely sensed ET information for water management through Landsat, Sentinel-2, ECOSTRESS and VIIRS data fusion

Jie Xue; Martha C Anderson; Feng Gao; Christopher Hain; Kyle R Knipper; Yun Yang; William P Kustas; Yang Yang; Nicolas Bambach; Andrew J McElrone; Sebastian J Castro; Joseph G Alfieri; John H Prueger; Lynn G McKee; Lawrence E Hipps; María Del Mar Alsina

doi:10.1007/s00271-022-00799-7

Improving the spatiotemporal resolution of remotely sensed ET information for water management through Landsat, Sentinel-2, ECOSTRESS and VIIRS data fusion

Irrig Sci. 2022;40(4-5):609-634. doi: 10.1007/s00271-022-00799-7. Epub 2022 May 21.

Authors

Jie Xue¹, Martha C Anderson¹, Feng Gao¹, Christopher Hain², Kyle R Knipper³, Yun Yang^{1

4}, William P Kustas¹, Yang Yang¹, Nicolas Bambach⁵, Andrew J McElrone^{6

7}, Sebastian J Castro^{6

7}, Joseph G Alfieri¹, John H Prueger⁸, Lynn G McKee¹, Lawrence E Hipps⁹, María Del Mar Alsina¹⁰

Affiliations

¹ Hydrology and Remote Sensing Laboratory, USDA-ARS, 10300 Baltimore Avenue, Beltsville, MD 20705 USA.
² Earth Science Office, Marshall Space Flight Center, NASA, Huntsville, AL 35805 USA.
³ Sustainable Agricultural Water Systems Unit, USDA-ARS, Davis, CA 95616 USA.
⁴ Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD 20742 USA.
⁵ Department of Land, Air and Water Resources, University of California, Davis, CA USA.
⁶ Crops Pathology and Genetics Research Unit, USDA-ARS, Davis, CA USA.
⁷ Department of Viticulture and Enology, University of California, Davis, CA USA.
⁸ National Laboratory for Agriculture and the Environment, USDA-ARS, Ames, IA USA.
⁹ Department of Plants Soils and Climate, Utah State University, Logan, UT USA.
¹⁰ E & J Gallo Winery, Viticulture, Chemistry and Enology, Modesto, CA USA.

Abstract

Robust information on consumptive water use (evapotranspiration, ET) derived from remote sensing can significantly benefit water decision-making in agriculture, informing irrigation schedules and water management plans over extended regions. To be of optimal utility for operational usage, these remote sensing ET data should be generated at the sub-field spatial resolution and daily-to-weekly timesteps commensurate with the scales of water management activities. However, current methods for field-scale ET retrieval based on thermal infrared (TIR) imaging, a valuable diagnostic of canopy stress and surface moisture status, are limited by the temporal revisit of available medium-resolution (100 m or finer) thermal satellite sensors. This study investigates the efficacy of a data fusion method for combining information from multiple medium-resolution sensors toward generating high spatiotemporal resolution ET products for water management. TIR data from Landsat and ECOSTRESS (both at ~ 100-m native resolution), and VIIRS (375-m native) are sharpened to a common 30-m grid using surface reflectance data from the Harmonized Landsat-Sentinel dataset. Periodic 30-m ET retrievals from these combined thermal data sources are fused with daily retrievals from unsharpened VIIRS to generate daily, 30-m ET image timeseries. The accuracy of this mapping method is tested over several irrigated cropping systems in the Central Valley of California in comparison with flux tower observations, including measurements over irrigated vineyards collected in the GRAPEX campaign. Results demonstrate the operational value added by the augmented TIR sensor suite compared to Landsat alone, in terms of capturing daily ET variability and reduced latency for real-time applications. The method also provides means for incorporating new sources of imaging from future planned thermal missions, further improving our ability to map rapid changes in crop water use at field scales.