Uncertainty Assessment of Satellite Remote Sensing-based Evapotranspiration Estimates: A Systematic Review of Methods and Gaps

Abstract. Satellite remote sensing (RS) data are increasingly being used to estimate total evaporation or evapotranspiration (ET) over large regions. Since RS-based ET (RS-ET) estimation inherits uncertainties from several sources, many available studies have assessed these uncertainties using different methods and reference data. However, the suitability of methods and reference data subsequently affects the validity of these evaluations. This study summarizes the status of the various methods applied for uncertainty assessment of RS-ET estimates, discusses the advances and caveats of these methods, identifies assessment gaps, and provides recommendations for future studies. We systematically reviewed 601 research papers published from 2011 to 2021 that assessed the uncertainty or accuracy of RS-ET estimates. We categorized and classified them based on (i) the methods used to assess uncertainties, (ii) the context where uncertainties were evaluated, and (iii) the metrics used to report uncertainties. Our quantitative synthesis shows that the uncertainty assessments of RS-ET estimates are not consistent and comparable in terms of methodology, reference data, geographical distribution, and uncertainty presentation. Most studies used validation methods using Eddy Covariance (EC) based ET estimates as reference. However, in many regions such as Africa and the Middle East, other references are often used due to the lack of EC stations. The accuracy and uncertainty of RS-ET estimates are most often described by Root-Mean-Squared Error (RMSE). When validating against EC-based estimates, the RMSE of daily RS-ET varies greatly among different locations and levels of temporal support, ranging from 0.01 to 6.65 mm/day with a mean of 1.12 mm/day. We conclude that future studies need to report the context of validation, the uncertainty of the reference datasets, the mismatch in temporal and spatial scales of reference datasets to that of the RS-ET estimates, and multiple performance metrics with their variation in different conditions and statistical significance to provide a comprehensive interpretation to assist potential users. We provide specific recommendations in this regard. Furthermore, extending the application of RS-ET to regions that lack validation will require obtaining additional ground-based data and combining different methods for uncertainty assessment.