Thu Apr 10, F. Flechtner
• Sorted by Date • Sorted by Last Name of First Author •
Liu, Yuan, Yue, Qimeng, Wang, Qianyang, Yu, Jingshan, Zheng, Yuexin, Yao, Xiaolei, and Xu, Shugao, 2021. A Framework for Actual Evapotranspiration Assessment and Projection Based on Meteorological, Vegetation and Hydrological Remote Sensing Products. Remote Sensing, 13(18):3643, doi:10.3390/rs13183643.
• from the NASA Astrophysics Data System • by the DOI System •
@ARTICLE{2021RemS...13.3643L,
author = {{Liu}, Yuan and {Yue}, Qimeng and {Wang}, Qianyang and {Yu}, Jingshan and {Zheng}, Yuexin and {Yao}, Xiaolei and {Xu}, Shugao},
title = "{A Framework for Actual Evapotranspiration Assessment and Projection Based on Meteorological, Vegetation and Hydrological Remote Sensing Products}",
journal = {Remote Sensing},
year = 2021,
month = sep,
volume = {13},
number = {18},
pages = {3643},
abstract = "{As the most direct indicator of drought, the dynamic assessment and
prediction of actual evapotranspiration (AET) is crucial to
regional water resources management. This research aims to
develop a framework for the regional AET evaluation and
prediction based on multiple machine learning methods and multi-
source remote sensing data, which combines Boruta algorithm,
Random Forest (RF), and Support Vector Regression (SVR) models,
employing datasets from CRU, GLDAS, MODIS, GRACE (-FO), and
CMIP6, covering meteorological, vegetation, and hydrological
variables. To verify the framework, it is applied to grids of
South America (SA) as a case. The results meticulously
demonstrate the tendency of AET and identify the decisive role
of T, P, and NDVI on AET in SA. Regarding the projection, RF has
better performance in different input strategies in SA.
According to the accuracy of RF and SVR on the pixel scale, the
AET prediction dataset is generated by integrating the optimal
results of the two models. By using multiple parameter inputs
and two models to jointly obtain the optimal output, the results
become more reasonable and accurate. The framework can
systematically and comprehensively evaluate and forecast AET;
although prediction products generated in SA cannot calibrate
relevant parameters, it provides a quite valuable reference for
regional drought warning and water allocating.}",
doi = {10.3390/rs13183643},
adsurl = {https://ui.adsabs.harvard.edu/abs/2021RemS...13.3643L},
adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}
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