Towards the Improvement of Blue Water Evapotranspiration Estimates by Combining Remote Sensing and Model Simulation
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 v...
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doaj-6d33676d89384382b5b5cf504cbc3cc22020-11-25T00:02:49ZengMDPI AGRemote Sensing2072-42922014-07-01687026704910.3390/rs6087026rs6087026Towards the Improvement of Blue Water Evapotranspiration Estimates by Combining Remote Sensing and Model SimulationMireia Romaguera0Mhd. Suhyb Salama1Maarten S. Krol2Arjen Y. Hoekstra3Zhongbo Su4Faculty of Geo-Information Science and Earth Observation, University of Twente, 7500 AE Enschede, The NetherlandsFaculty of Geo-Information Science and Earth Observation, University of Twente, 7500 AE Enschede, The NetherlandsTwente Water Centre, University of Twente, 7500 AE Enschede, The NetherlandsTwente Water Centre, University of Twente, 7500 AE Enschede, The NetherlandsFaculty of Geo-Information Science and Earth Observation, University of Twente, 7500 AE Enschede, The NetherlandsThe 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.http://www.mdpi.com/2072-4292/6/8/7026evapotranspirationblue waterirrigationclassificationremote sensingland surface model |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Mireia Romaguera Mhd. Suhyb Salama Maarten S. Krol Arjen Y. Hoekstra Zhongbo Su |
spellingShingle |
Mireia Romaguera Mhd. Suhyb Salama Maarten S. Krol Arjen Y. Hoekstra Zhongbo Su Towards the Improvement of Blue Water Evapotranspiration Estimates by Combining Remote Sensing and Model Simulation Remote Sensing evapotranspiration blue water irrigation classification remote sensing land surface model |
author_facet |
Mireia Romaguera Mhd. Suhyb Salama Maarten S. Krol Arjen Y. Hoekstra Zhongbo Su |
author_sort |
Mireia Romaguera |
title |
Towards the Improvement of Blue Water Evapotranspiration Estimates by Combining Remote Sensing and Model Simulation |
title_short |
Towards the Improvement of Blue Water Evapotranspiration Estimates by Combining Remote Sensing and Model Simulation |
title_full |
Towards the Improvement of Blue Water Evapotranspiration Estimates by Combining Remote Sensing and Model Simulation |
title_fullStr |
Towards the Improvement of Blue Water Evapotranspiration Estimates by Combining Remote Sensing and Model Simulation |
title_full_unstemmed |
Towards the Improvement of Blue Water Evapotranspiration Estimates by Combining Remote Sensing and Model Simulation |
title_sort |
towards the improvement of blue water evapotranspiration estimates by combining remote sensing and model simulation |
publisher |
MDPI AG |
series |
Remote Sensing |
issn |
2072-4292 |
publishDate |
2014-07-01 |
description |
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. |
topic |
evapotranspiration blue water irrigation classification remote sensing land surface model |
url |
http://www.mdpi.com/2072-4292/6/8/7026 |
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