Abstract
Water shortages in the Western United States will continue to be one of the foremost American agricultural challenges in the coming years. As agriculture is the largest consumer of water in the western US, improvements in irrigation scheduling and modeling are needed to maximize production under limited water. Various satellite-based remote sensing models have been developed to estimate crop water use. However, water balance-based evapotranspiration (ET) models need field-scale irrigation information for initiating the seasonal soil water balance. This initialization has been challenging due to the lack of remotely sensed irrigation event data. In this study, we utilized a recently launched satellite constellation (Planet) with high temporal and spatial resolution data (daily, ~ 3 m) to evaluate if Planet data can facilitate early season irrigation detection. We utilized normalized difference vegetation index (NDVI), moisture index, and individual spectral bands to detect moisture and ultimately infer irrigation. As part of this comparison, a hybrid two-source energy and water balance model BAITSSS (Backward-Averaged Iterative Two-Source Surface temperature and energy balance Solution) was used to estimate ET with Planet-based vegetation indices and irrigation information. We also compared the results to eddy covariance (EC) located at lettuce fields in Yuma, Arizona in the lower Colorado River basin between 2016 and 2020. Overall, the results indicated that Planet’s data helped to establish the field-scale onset of irrigation, which assisted to initiate soil water balance in the BAITSSS model, thus ultimately improving ET. Further, these results should support the development of near-real-time landscape-scale ET and should be highly beneficial to agricultural communities for sub-field-scale effective water management.
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Data availability
Weather data can be found (https://ag.arizona.edu/azmet/). We thank the United States Department of Agriculture (USDA) Agricultural Research Service (ARS) and the University of Arizona for providing the Eddy Covariance data.
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Acknowledgements
This work is supported by Agriculture and Food Research Initiative Competitive Grant no. 2020-69012-31914 from the USDA National Institute of Food and Agriculture. This research was supported in part by the U.S. Department of Agriculture, Agricultural Research Service (project numbers 2036-61000-018-000-D and 2020-13660-008-000-D). This research used resources provided by the SCINet project of the USDA Agricultural Research Service, ARS (project number 0500-00093-001-00-D). The U.S. Department of Agriculture prohibits discrimination in all its programs and activities on the basis of race, color, national origin, age, disability, and where applicable, sex, marital status, familial status, parental status, religion, sexual orientation, genetic information, political beliefs, reprisal, or because all or part of an individual’s income is derived from any public assistance program (Not all prohibited bases apply to all programs). Persons with disabilities who require alternative means for communication of program information (Braille, large print, audiotape, etc.) should contact USDA’s TARGET Center at (202) 720-2600 (voice and TDD). To file a complaint of discrimination, write to USDA, Director, Office of Civil Rights, 1400 Independence Avenue, S.W., Washington, D.C. 20250-9410, or call (800) 795-3272 (voice) or (202) 720-6382 (TDD). USDA is an equal opportunity provider and employer.
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RD, RGA, AF, and TS designed and performed research, analyzed data, and originated manuscript; TS, MS and CAS contributed data; ES guided project development. All authors reviewed and revised the manuscript.
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Dhungel, R., Anderson, R.G., French, A.N. et al. Early season irrigation detection and evapotranspiration modeling of winter vegetables based on Planet satellite using water and energy balance algorithm in lower Colorado basin. Irrig Sci 42, 15–27 (2024). https://doi.org/10.1007/s00271-023-00874-7
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DOI: https://doi.org/10.1007/s00271-023-00874-7