Project description

This proposed project aims to prototype methods for routine production of high spatiotemporal resolution evapotranspiration (ET), vegetation index (VI) and derived phenology and yield products using a multi-sensor data fusion approach. This approach fuses moderate resolution, near-daily retrievals of ET and surface reflectance (SR) from sensors like MODIS and VIIRS with periodic finer scale data from Landsat, Sentinel-2, ECOSTRESS and other Landsat-like sensors to generate multi-year timeseries of gridded products at daily timesteps and 30m spatial resolution. ET will be estimated using a wellestablished surface energy balance algorithm, which uses thermal infrared (TIR) retrievals of land-surface temperature along with vegetation cover and albedo information from the SR bands. For high-resolution sensors like Sentinel-2 that lack TIR imaging capacity, a novel approach will be employed to sharpen 375m TIR data from VIIRS to 30m on Sentinel-2 overpass days using multi-band Sentinel-2 SR data. Similarly, ECOSTRESS TIR data will be supplemented by fused Landsat- Sentinel-2 SR timeseries. Collectively, the high spatiotemporal resolution ET and VI “datacubes” will provide valuable field-scale diagnostics of water use, moisture stress, phenology and biomass accumulation required for monitoring agricultural production systems and forecasting yield. The accuracy of these products will be evaluated over diverse agricultural landscapes, including crop, pasture and rangelands in the US and internationally. ET retrievals will be compared with flux tower measurements to assess absolute accuracy and ability to capture episodic changes in moisture conditions resulting from, e.g., rainfall, irrigation, harvest, and rapid stress onset. VI data and derived phenological metrics will be evaluated at full resolution in comparison with biophysical data collected in-field, and at larger scales using county and state-level crop progress reports. Finally, we will demonstrate utility of combining the 30m daily ET/VI data and derived phenology for operational agricultural assessments. Improvement in moisture stress and yield mapping capabilities will be demonstrated over highly managed agricultural systems in the US, Brazil, Czech Republic and Lebanon - where performance of coarser resolution datasets are known to be degraded due to mixed pixels and shifting phenology. We will also map water productivity (“crop-per-drop”) over these landscapes to quantify differences in efficiencies between regions, climates and cropping systems. This study will motivate the value to agriculture of routine, daily multi-sensor satellite products developed at Landsat scale, where crop water use and development can be differentiated by crop type and management strategy.

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