Providing a comprehensive understanding of missing data imputation processes in evapotranspiration-related research: A systematic literature review

Abstract

ABSTRACT This study aimed to review the existing research focalizing on the missing data imputation techniques for the systems enabling actual evapotranspiration calculation (such as eddy covariance, Bowen ratio, and lysimeters) and divergent evapotranspiration related variables, i.e. temperature, wind speed, humidity, and solar radiation. Thus, the Scopus engine was utilized to scan the entire literature and 62 articles were diligently investigated. Results show classical approaches have been widely used by researchers due to their ease of implementation. However, the applicability and validity of these methods heavily rely on assumptions made about the distribution and characteristics of missing data. Hence, advanced imputation techniques produce more accurate outcomes as they handle complex and non-linear problems. Also, current trends embraced by the research community revealed that employing deep learning techniques and incorporating explainable artificial intelligence into imputations have significant potential to make insightful contributions to the body of knowledge.