Towards the Improvement of Blue Water Evapotranspiration Estimates by Combining Remote Sensing and Model Simulation

Abstract

The estimation of evapotranspiration of blue water (ETb) from farmlands, due to irrigation, is crucial to improve water management, especially in regions where water resources are scarce. Large scale ETb was previously obtained, based on the differences between remote sensing derived actual ET and values simulated from the Global Land Data Assimilation System (GLDAS). In this paper, we improve on the previous approach by enhancing the classification scheme employed so that it represents regions with common hydrometeorological conditions. Bias between the two data sets for reference areas (non-irrigated croplands) were identified per class, and used to adjust the remote sensing products. Different classifiers were compared and evaluated based on the generated bias curves per class and their variability. The results in Europe show that the k-means classifier was better suited to identify the bias curves per class, capturing the dynamic range of these curves and minimizing their variability within each corresponding class. The method was applied in Africa and the classification and bias results were consistent with the findings in Europe. The ETb results were compared with existing literature and provided differences up to 50 mm/year in Europe, while the comparison in Africa was found to be highly influenced by the assigned cover type and the heterogeneity of the pixel. Although further research is needed to fully understand the ETb values found, this paper shows a more robust approach to classify and characterize the bias between the two sets of ET data.

Keywords

• evapotranspiration
• blue water
• irrigation
• classification
• remote sensing
• land surface model