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1000
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Abstract/Summary
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<jats:p>Abstract. Evapotranspiration plays a key role in the terrestrial water cycle, climate extremes, and vegetation functioning. However, the understanding of spatio-temporal variability of evapotranspiration is limited by a lack of measurement techniques that are low cost and that can be applied anywhere at any time. Here we investigate the estimation of evapotranspiration and land surface energy-balance partitioning by only using observations made by smartphone sensors. Individual variables known to effect evapotranspiration as measured by smartphone sensors generally showed a high correlation with routine observations during a multiday field test. In combination with a simple multivariate regression model fitted on observed evapotranspiration, the smartphone observations had a mean RMSE of 0.10 and 0.05 mm h−1 during validation against lysimeter and eddy covariance observations, respectively. This is comparable to an error of 0.08 mm h−1 that is associated with estimating the eddy covariance ET from the lysimeter or vice versa. The results suggests that smartphone-based ET monitoring could provide a realistic and low-cost alternative for real-time ET estimation in the field.
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1000
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Hinweis
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DeepGreen-ID: 4493923b5fe6471f82980c498d51562f ; metadata provieded by: DeepGreen (https://www.oa-deepgreen.de/api/v1/), LIVIVO search scope life sciences (http://z3950.zbmed.de:6210/livivo), Crossref Unified Resource API (https://api.crossref.org/swagger-ui/index.html), to.science.api (https://frl.publisso.de/), ZDB JSON-API (beta) (https://zeitschriftendatenbank.de/api/), lobid - Dateninfrastruktur für Bibliotheken (https://lobid.org/resources/search)
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