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

Remote Sensing

Mireia Romaguera²  Mhd. Suhyb Salama²  Maarten S. Krol¹  Arjen Y. Hoekstra¹ 

[1] Twente Water Centre, University of Twente, 7500 AE Enschede, The Netherlands; E-Mails:;Faculty of Geo-Information Science and Earth Observation, University of Twente, 7500 AE Enschede, The Netherlands; E-Mails:

关键词: evapotranspiration;    blue water;    irrigation;    classification;    remote sensing;    land surface model;   

DOI  :  10.3390/rs6087026

来源: mdpi

【 摘 要 】

The estimation of evapotranspiration of blue water (ET_b) from farmlands, due to irrigation, is crucial to improve water management, especially in regions where water resources are scarce. Large scale ET_b 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 ET_b 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 ET_b values found, this paper shows a more robust approach to classify and characterize the bias between the two sets of ET data.

【 授权许可】

CC BY   
© 2014 by the authors; licensee MDPI, Basel, Switzerland