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ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Hydrology and Remote Sensing Laboratory » Research » Publications at this Location » Publication #386419

Research Project: Integrating Remote Sensing, Measurements and Modeling for Multi-Scale Assessment of Water Availability, Use, and Quality in Agroecosystems

Location: Hydrology and Remote Sensing Laboratory

Title: Influence of modeling domain and meteorological forcing data on daily evapotranspiration estimates from a Shuttleworth-Wallace model using Sentinel-2 surface reflectance data

Author
item Bhattarai, Nishan
item D'URSO, G. - The University Of Naples Federico Ii
item Kustas, William - Bill
item BAMBACH, N. - University Of California, Davis
item Anderson, Martha
item Gao, Feng
item ALSINA, M. - E & J Gallo Winery
item ABOUTALEBI, M. - E & J Gallo Winery
item McKee, Lynn
item Alfieri, Joseph
item MCELRONE, A. - University Of California, Davis
item Prueger, John
item BELFIORE, O. - The University Of Naples Federico Ii

Submitted to: Irrigation Science
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 1/10/2022
Publication Date: 2/7/2022
Citation: Bhattarai, N., D'Urso, G., Kustas, W.P., Bambach, N., Anderson, M.C., Gao, F.N., Alsina, M., Aboutalebi, M., McKee, L.G., Alfieri, J.G., McElrone, A., Prueger, J.H., Belfiore, O. 2022. Influence of modeling domain and meteorological forcing data on spectral-based Shuttleworth-Wallace derived daily evapotranspiration estimates using Sentinel-2. Irrigation Science. https://doi.org/10.1007/s00271-022-00768-0.
DOI: https://doi.org/10.1007/s00271-022-00768-0

Interpretive Summary: Precision irrigation management tools in viticulture are critically needed as extreme and prolonged drought and heatwave events are predicted to increase in the western U.S., particularly in the California Central Valley where a much of wine and table grapes are produced. The widespread availability of remotely sensed thermal data at 30 m resolution from Landsat and advanced tools to process and combine multiple images has potential for operational applications in monitoring vine water use (evapotranspiration, ET) and stress at the sub-field scale. However, the low temporal resolution of Landsat (16-day revisit) precludes its operational use, especially in vineyards that are often highly dynamic in timing and amount of irrigated water applied. The Sentinel-2 satellite shows potential for use in the operational monitoring vineyard ET because of its high spatial (10 m pixel) and temporal (5 day revisit) resolutions. An ET modeling approach using Sentinel-2 satellite data was evaluated at vineyard validation sites as part of the GRAPEX (Grape Remote-sensing Atmospheric Profile and Evapotranspiration eXperiment) network. The findings from this study indicate that merging the ET model using Sentinel-2 with those from less frequent thermal-based ET model products using Landsat has the potential to improve the frequency and accuracy of ET monitoring of California vineyards.

Technical Abstract: Efficient use of available water resources is key to sustainable viticulture management, which can be augmented by remote and frequent field-scale information on vineyard water status. Though the Sentinel-2 sensor offers good spatial (10-60m) and temporal (~5 days) coverages, its utility in monitoring vineyard evapotranspiration (ET) has not been thoroughly evaluated primarily due to the lack of a thermal band. Recently, a new spectral-based Shuttleworth Wallace (SW) ET model using Sentinel-2 (SW-S2) showed promising results when tested over a GRAPEX (Grape Remote-sensing Atmospheric Profile and Evapotranspiration eXperiment) site in California (CA). However, current knowledge on its applicability across a climate gradient in CA and how the selections of modeling domain and meteorological data influence model outputs are limited. To this end, the current research expands the evaluation of the SW-S2 model across multiple domains and meteorological inputs covering all three GRAPEX sites over three recent growing seasons (2018-2020). Results reveal a relatively weak influence of the modeling domain on model performance though the model appears to work slightly better under a larger domain (root mean squared error, RMSE, within 1.03-1.11 mm/d and mean biases within 2%). However, the meteorological forcing data and in particular vapor pressure deficit (VPD) and wind speed (u) were found to have a strong influence on model outputs as indicated by the poor performance of the model with less accurate regional and coarse-scale gridded meteorological inputs. Results suggest that simple regression for local bias correction of VPD and u significantly improved model performance. Overall, the findings from this study indicate that merging SW-S2 model output with those from less frequent thermal-based ET model products has the potential to reduce latency in ET monitoring of California vineyards.