Improvement of Two Evapotranspiration Estimation Models Using a Linear Spectral Mixture Model over a Small Agricultural Watershed

Li, Gen; Jing, Yuanshu; Wu, Yihua; Zhang, Fangmin

doi:10.3390/w10040474

i

Improvement of Two Evapotranspiration Estimation Models Using a Linear Spectral Mixture Model over a Small Agricultural Watershed

2018
By Li, Gen ; Jing, Yuanshu ; Wu, Yihua ; ...

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Journal Title:

Water
Personal Author:

Li, Gen ; Jing, Yuanshu ; Wu, Yihua ; Zhang, Fangmin
NOAA Program & Office:

NWS (National Weather Service) ; EMC (Environmental Modelling Center) ; NCEP (National Centers for Environmental Prediction)
Description:

Accurately measuring regional evapotranspiration (ET) is of great significance for studying global climate change, regional hydrological cycles, and surface energy balance. However, estimating regional ET from mixed vegetation types is still challenging. In this study, the Surface Energy Balance Algorithm for Land (SEBAL) and the Surface Energy Balance System (SEBS) models were applied to estimate surface ET in a small agricultural watershed. Landsat8 satellite images were used as input data to the single-source models. The two models were validated at single point and ecosystem scales. The results showed that both models overestimated ET observations in paddy fields and orange groves but underestimated them in dry farmland. The error was mainly caused by the heterogeneity of the mixed pixels. The linear spectral mixture model and a set of equations were introduced to reduce the simulation error. The revised results showed that the relative precision of SEBAL was improved by 9.87% and 10.06%, respectively. This research is expected to provide new ideas for future development of accurate remote-sensing ET estimations on heterogeneous surfaces.
Source:

Water, 10(4), 474
DOI:

https://doi.org/10.3390/w10040474
ISSN:

2073-4441
Format:

PDF
Publisher:

MDPI AG
Document Type:

Journal Article
License:

https://creativecommons.org/licenses/by/4.0/
Rights Information:

CC BY
Compliance:

Library
Main Document Checksum:

urn:sha-512:0dd8f10408e6f6458003b33e6b55a09aa39a129450387375ba798fd00ba1c55f2299f8ad17275d63b27ec73eb6965b45b2fcfcefa99bfe64b064827394ae81eb
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https://repository.library.noaa.gov/view/noaa/65529/noaa_65529_DS1.pdf
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[PDF - 20.86 MB ]

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