Location: Southwest Watershed Research CenterTitle: Evapotranspiration estimates derived using multi-platform remote sensing in a semiarid region Author
|Knipper, D.r. - Colorado School Of Mines|
|Hogue, T.s. - Colorado School Of Mines|
|Scott, Russell - Russ|
|Franz, K.j. - Colorado School Of Mines|
Submitted to: Remote Sensing
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 2/16/2017
Publication Date: 2/23/2017
Citation: Knipper, D., Hogue, T., Scott, R.L., Franz, K. 2017. Evapotranspiration estimates derived using multi-platform remote sensing in a semiarid region. Remote Sensing. 9:184. https://doi.org/10.3390/rs9030184.
DOI: https://doi.org/10.3390/rs9030184 Interpretive Summary: Evapotranspiration (ET) is a key component of the water cycle, especially in arid and semiarid regions where much of the rainfall returns to the atmosphere. The current study takes advantage of satellite remote sensing to develop an estimate of ET across a large region. The ET estimates are then compared against measurements available at four sites in southern Arizona, and they are also compared with two different computer model simulations. The developed approach shows that the proposed method is an effective alternative to more complex ET models for estimating actual ET. The proposed methodology also requires limited ground-based data, site specific modifications, or subjective specifications, allowing it to be transferable to other water-limited regions.
Technical Abstract: Evapotranspiration (ET) is a key component of the water balance, especially in arid and semiarid regions. The current study takes advantage of spatially-distributed, near real-time information provided by satellite remote sensing to develop a regional scale ET product derived from remotely-sensed observations. ET is calculated by scaling PET estimated from Moderate Resolution Imaging Spectroradiometer (MODIS) products with downscaled soil moisture derived using the Soil Moisture Ocean Salinity (SMOS) satellite and a second order polynomial regression formula. The MODis-Soil Moisture ET (MOD-SMET) estimates are validated using four flux tower sites in southern Arizona USA, a calibrated empirical ET model, and model output from Version 2 of the North American Land Data Assimilation System (NLDAS-2). Validation against daily eddy covariance ET indicates correlations between 0.63 and 0.83 and root mean square errors (RMSE) between 40 and 96 W/m2. MOD-SMET estimates compare well to the calibrated empirical ET model, with a -0.14 difference in correlation between sites, on average. By comparison, NLDAS-2 models underestimate daily ET compared to both flux towers and MOD-SMET estimates. Our analysis shows the MOD-SMET approach to be effective for estimating ET. Because it requires limited ancillary ground-based data and no site-specific calibration, the method is applicable to regions where ground-based measurements are not available.