An Ensemble Mean Method for Remote Sensing of Actual Evapotranspiration to Estimate Water Budget Response across a Restoration Landscape. Petrakis, R. E., Norman, L. M., Villarreal, M. L., Senay, G. B., Friedrichs, M. O., Cassassuce, F., Gomis, F., & Nagler, P. L. Remote Sensing, 16(12):2122, January, 2024. Number: 12 Publisher: Multidisciplinary Digital Publishing Institute![An Ensemble Mean Method for Remote Sensing of Actual Evapotranspiration to Estimate Water Budget Response across a Restoration Landscape [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex Estimates of actual evapotranspiration (ETa) are valuable for effective monitoring and management of water resources. In areas that lack ground-based monitoring networks, remote sensing allows for accurate and consistent estimates of ETa across a broad scale—though each algorithm has limitations (i.e., ground-based validation, temporal consistency, spatial resolution). We developed an ensemble mean ETa (EMET) product to incorporate advancements and reduce uncertainty among algorithms (e.g., energy-balance, optical-only), which we use to estimate vegetative water use in response to restoration practices being implemented on the ground using management interventions (i.e., fencing pastures, erosion control structures) on a private ranch in Baja California Sur, Mexico. This paper describes the development of a monthly EMET product, the assessment of changes using EMET over time and across multiple land use/land cover types, and the evaluation of differences in vegetation and water distribution between watersheds treated by restoration and their controls. We found that in the absence of a ground-based monitoring network, the EMET product is more robust than using a single ETa data product and can augment the efficacy of ETa-based studies. We then found increased ETa within the restored watershed when compared to the control sites, which we attribute to increased plant water availability.
@article{petrakis_ensemble_2024,
title = {An {Ensemble} {Mean} {Method} for {Remote} {Sensing} of {Actual} {Evapotranspiration} to {Estimate} {Water} {Budget} {Response} across a {Restoration} {Landscape}},
volume = {16},
copyright = {http://creativecommons.org/licenses/by/3.0/},
issn = {2072-4292},
url = {https://www.mdpi.com/2072-4292/16/12/2122},
doi = {10.3390/rs16122122},
abstract = {Estimates of actual evapotranspiration (ETa) are valuable for effective monitoring and management of water resources. In areas that lack ground-based monitoring networks, remote sensing allows for accurate and consistent estimates of ETa across a broad scale—though each algorithm has limitations (i.e., ground-based validation, temporal consistency, spatial resolution). We developed an ensemble mean ETa (EMET) product to incorporate advancements and reduce uncertainty among algorithms (e.g., energy-balance, optical-only), which we use to estimate vegetative water use in response to restoration practices being implemented on the ground using management interventions (i.e., fencing pastures, erosion control structures) on a private ranch in Baja California Sur, Mexico. This paper describes the development of a monthly EMET product, the assessment of changes using EMET over time and across multiple land use/land cover types, and the evaluation of differences in vegetation and water distribution between watersheds treated by restoration and their controls. We found that in the absence of a ground-based monitoring network, the EMET product is more robust than using a single ETa data product and can augment the efficacy of ETa-based studies. We then found increased ETa within the restored watershed when compared to the control sites, which we attribute to increased plant water availability.},
language = {en},
number = {12},
urldate = {2024-06-28},
journal = {Remote Sensing},
author = {Petrakis, Roy E. and Norman, Laura M. and Villarreal, Miguel L. and Senay, Gabriel B. and Friedrichs, MacKenzie O. and Cassassuce, Florance and Gomis, Florent and Nagler, Pamela L.},
month = jan,
year = {2024},
note = {Number: 12
Publisher: Multidisciplinary Digital Publishing Institute},
keywords = {NALCMS},
pages = {2122},
}
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