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

Abstract

Abstract: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.

Irrigation assessment via remote sensing and land surface model data

“…The method to estimate ET b used in this chapter is described in Romaguera et al (2012aRomaguera et al ( , 2014a. It is based on the calculation of the differences in actual evapotranspiration (ET) given by the remotely sensed ET data (RS-ET in the following) and the GLDAS ET model simulations (GLDAS-ET in the following).…”

“…The classification scheme was improved in recent literature (Romaguera et al, 2014a) by testing different classification approaches and proposing a new set of input parameters. This allowed to obtain a better differentiation of the bias curves, reduced the standard deviation of the data and captured the expected variability of the maximum bias.…”

“…Therefore, following Romaguera et al (2014a), in the present work a yearly classification of every study area was carried out with the k-means algorithm and using the following parameters as inputs: a yearly climatic indicator (CI) based on net radiation and precipitation, the maximum value of monthly ET along the year (ETm max ), the month where the ET mmax occurs (t_ET mmax ) and the maximum NDVI (NDVI max ) in the year of interest. The optimal number of classes was calculated using a scattering distance (SD) quality index (Rezaee et al, 1998).…”

“…The objective of this chapter is to apply the remote sensing method by Romaguera et al (2012aRomaguera et al ( , 2014a) and obtain ET b values at relevant time scales for water management purposes, that is at monthly and country/province scale, as well as to show preliminary results and the potential of exploiting these data when combined with the source of irrigation water, from the aforementioned GIAM map. The regions and period of study are the Horn of Africa (period 2010Africa (period -2012 and the Chinese province of Sichuan (period 2001Sichuan (period -2010, both affected by a drought event during the period of study, but with differences in terms of water control and storage infrastructure.…”

“…This allows characterizing the physical processes and monitoring crops in appropriate space and time scales. In this context, Romaguera et al (2012aRomaguera et al ( , 2014a included the use of remote sensing data and proposed a methodology to estimate blue water evapotranspiration (ET b ) that could benefit from the remote sensing advantages. This method allows estimating ET b at different time scales, i.e.…”