Upscaling latent heat flux for thermal remote sensing studies: Comparison of alternative approaches and correction of bias

2012 ◽  
Vol 468-469 ◽  
pp. 35-46 ◽  
Author(s):  
Thomas G. Van Niel ◽  
Tim R. McVicar ◽  
Michael L. Roderick ◽  
Albert I.J.M. van Dijk ◽  
Jason Beringer ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Heat Flux ◽  
Latent Heat ◽  
Latent Heat Flux ◽  
Thermal Remote Sensing ◽  
Alternative Approaches

Latent heat flux estimation in clear sky days over Indian agroecosystems using noontime satellite remote sensing data

2009 ◽  
Vol 149 (10) ◽  
pp. 1646-1665 ◽  
Author(s):  
Kaniska Mallick ◽  
Bimal K. Bhattacharya ◽  
V.U.M. Rao ◽  
D. Raji Reddy ◽  
Saon Banerjee ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Heat Flux ◽  
Latent Heat ◽  
Latent Heat Flux ◽  
Satellite Remote Sensing ◽  
Remote Sensing Data ◽  
Sensing Data ◽  
Clear Sky ◽  
Heat Flux Estimation ◽  
Flux Estimation

scholarly journals Quantifying the uncertainty in estimates of surface–atmosphere fluxes through joint evaluation of the SEBS and SCOPE models

2013 ◽  
Vol 17 (4) ◽  
pp. 1561-1573 ◽  
Author(s):  
J. Timmermans ◽  
Z. Su ◽  
C. van der Tol ◽  
A. Verhoef ◽  
W. Verhoef
Keyword(s):  
Remote Sensing ◽  
Heat Flux ◽  
Energy Balance ◽  
Latent Heat ◽  
Latent Heat Flux ◽  
Estimation Error ◽  
Accurate Estimation ◽  
Area Index ◽  
Ground Heat Flux ◽  
Scope Model

Abstract. Accurate estimation of global evapotranspiration is considered to be of great importance due to its key role in the terrestrial and atmospheric water budget. Global estimation of evapotranspiration on the basis of observational data can only be achieved by using remote sensing. Several algorithms have been developed that are capable of estimating the daily evapotranspiration from remote sensing data. Evaluation of remote sensing algorithms in general is problematic because of differences in spatial and temporal resolutions between remote sensing observations and field measurements. This problem can be solved in part by using soil-vegetation-atmosphere transfer (SVAT) models, because on the one hand these models provide evapotranspiration estimations also under cloudy conditions and on the other hand can scale between different temporal resolutions. In this paper, the Soil Canopy Observation, Photochemistry and Energy fluxes (SCOPE) model is used for the evaluation of the Surface Energy Balance System (SEBS) model. The calibrated SCOPE model was employed to simulate remote sensing observations and to act as a validation tool. The advantages of the SCOPE model in this validation are (a) the temporal continuity of the data, and (b) the possibility of comparing different components of the energy balance. The SCOPE model was run using data from a whole growth season of a maize crop. It is shown that the original SEBS algorithm produces large uncertainties in the turbulent flux estimations caused by parameterizations of the ground heat flux and sensible heat flux. In the original SEBS formulation the fractional vegetation cover is used to calculate the ground heat flux. As this variable saturates very fast for increasing leaf area index (LAI), the ground heat flux is underestimated. It is shown that a parameterization based on LAI reduces the estimation error over the season from RMSE = 25 W m−2 to RMSE = 18 W m−2. In the original SEBS formulation the roughness height for heat is only valid for short vegetation. An improved parameterization was implemented in the SEBS algorithm for tall vegetation. This improved the correlation between the latent heat flux predicted by the SEBS and the SCOPE algorithm from −0.05 to 0.69, and led to a decrease in difference from 123 to 94 W m−2 for the latent heat flux, with SEBS latent heat being consistently lower than the SCOPE reference. Lastly the diurnal stability of the evaporative fraction was investigated.

scholarly journals Are Soil Moisture and Latent Heat Overcoupled in Land Models?

Eos ◽  
2019 ◽  
Vol 100 ◽  
Author(s):  
Aaron Sidder
Keyword(s):  
Remote Sensing ◽  
Heat Flux ◽  
Soil Moisture ◽  
Latent Heat ◽  
Latent Heat Flux ◽  
Coupling Strength ◽  
Statistical Approach ◽  
Flux Coupling ◽  
The Relationship

A novel statistical approach demonstrates how to reduce bias in remote sensing estimates of soil moisture and latent heat flux coupling strength and clarifies the relationship between the variables.

scholarly journals Estimation of Terrestrial Latent Heat Flux Based on Chinese GaoFen-1 Remote Sensing Data

2021 ◽  
Author(s):  
Ruiyang Yu ◽  
Yunjun Yao ◽  
Ke Shang ◽  
Junming Yang ◽  
Xiaozheng Guo ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Heat Flux ◽  
Latent Heat ◽  
Latent Heat Flux ◽  
Remote Sensing Data ◽  
Sensing Data

scholarly journals Mapping Latent Heat Flux in the Western Forest Covered Regions of Algeria Using Remote Sensing Data and a Spatialized Model

2009 ◽  
Vol 1 (4) ◽  
pp. 795-817 ◽  
Author(s):  
Souidi Zahira ◽  
Hamimed Abderrahmane ◽  
Khalladi Mederbal ◽  
Donze Frederic
Keyword(s):  
Remote Sensing ◽  
Heat Flux ◽  
Latent Heat ◽  
Latent Heat Flux ◽  
Remote Sensing Data ◽  
Sensing Data
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