Evapotranspiration estimation using Landsat-8 data with a two-layer framework

Jian Yin; Hailong Wang; Chesheng Zhan; Yang Lu

doi:10.1117/1.JRS.11.016034

1 March 2017 Evapotranspiration estimation using Landsat-8 data with a two-layer framework

Jian Yin, Hailong Wang, Chesheng Zhan, Yang Lu

Author Affiliations +

Journal of Applied Remote Sensing, Vol. 11, Issue 1, 016034 (March 2017). https://doi.org/10.1117/1.JRS.11.016034

Abstract

Evapotranspiration (ET) plays an important role in hydrological cycle by linking land surface and atmosphere through water and energy transfers. Based on the data from the Landsat-8 satellite for typical days with clear sky condition from 2013 to 2016, a two-layer daily ET remote sensing framework was built, which includes four compartments: surface feature parameter estimation, evaporative fraction estimation, daily net radiation estimation, and daily ET extension. Based on the model, evaporation, transpiration, and daily ET in Shahe River Basin were estimated. The estimated daily ET showed a mean absolute percentage error of 8.7% in the plain areas, and 12.1% in the mountainous areas, compared to observations using large aperture scintillometer and eddy covariance system. The method gave higher accuracy than other remote sensing models applied in the same area previously, including the surface energy balance system and the ETWatch. By analyzing the relationship between land use types and surface water/heat fluxes, it was found that the surface energy balance components in the basin have prominent spatial-temporal features, and the soil component’s features are more obvious. It indicated that the proposed two-layer approach is superior to others in terms of simulation accuracy, and applicable to daily scale ET estimations on complex terrains.

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Jian Yin, Hailong Wang, Chesheng Zhan, and Yang Lu "Evapotranspiration estimation using Landsat-8 data with a two-layer framework," Journal of Applied Remote Sensing 11(1), 016034 (1 March 2017). https://doi.org/10.1117/1.JRS.11.016034

Received: 11 November 2016; Accepted: 13 February 2017; Published: 1 March 2017

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KEYWORDS

Vegetation

Earth observing sensors

Landsat

Remote sensing

Heat flux

Data modeling

Data analysis

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