Uncertainties in latent heat flux measurement and estimation: implications for using a simplified approach with remote sensing data

2004 ◽  
Vol 30 (5) ◽  
pp. 769-787 ◽  
Author(s):  
Le Jiang ◽  
Shafiqul Islam ◽  
Toby N Carlson
Keyword(s):  
Remote Sensing ◽  
Heat Flux ◽  
Latent Heat ◽  
Latent Heat Flux ◽  
Remote Sensing Data ◽  
Flux Measurement ◽  
Simplified Approach ◽  
Sensing Data ◽  
Heat Flux Measurement

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 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

scholarly journals Assessment of actual evapotranspiration over a semiarid heterogeneous land surface by means of coupled low-resolution remote sensing data with an energy balance model: comparison to extra-large aperture scintillometer measurements

2018 ◽  
Vol 22 (4) ◽  
pp. 2187-2209 ◽  
Author(s):  
Sameh Saadi ◽  
Gilles Boulet ◽  
Malik Bahir ◽  
Aurore Brut ◽  
Émilie Delogu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Heat Flux ◽  
Water Stress ◽  
Latent Heat ◽  
Latent Heat Flux ◽  
Water Availability ◽  
Remote Sensing Data ◽  
Sparse Model ◽  
Sensing Data ◽  
Large Aperture

Abstract. In semiarid areas, agricultural production is restricted by water availability; hence, efficient agricultural water management is a major issue. The design of tools providing regional estimates of evapotranspiration (ET), one of the most relevant water balance fluxes, may help the sustainable management of water resources. Remote sensing provides periodic data about actual vegetation temporal dynamics (through the normalized difference vegetation index, NDVI) and water availability under water stress (through the surface temperature Tsurf), which are crucial factors controlling ET. In this study, spatially distributed estimates of ET (or its energy equivalent, the latent heat flux LE) in the Kairouan plain (central Tunisia) were computed by applying the Soil Plant Atmosphere and Remote Sensing Evapotranspiration (SPARSE) model fed by low-resolution remote sensing data (Terra and Aqua MODIS). The work's goal was to assess the operational use of the SPARSE model and the accuracy of the modeled (i) sensible heat flux (H) and (ii) daily ET over a heterogeneous semiarid landscape with complex land cover (i.e., trees, winter cereals, summer vegetables). SPARSE was run to compute instantaneous estimates of H and LE fluxes at the satellite overpass times. The good correspondence (R2 = 0.60 and 0.63 and RMSE = 57.89 and 53.85 W m−2 for Terra and Aqua, respectively) between instantaneous H estimates and large aperture scintillometer (XLAS) H measurements along a path length of 4 km over the study area showed that the SPARSE model presents satisfactory accuracy. Results showed that, despite the fairly large scatter, the instantaneous LE can be suitably estimated at large scales (RMSE = 47.20 and 43.20 W m−2 for Terra and Aqua, respectively, and R2 = 0.55 for both satellites). Additionally, water stress was investigated by comparing modeled (SPARSE) and observed (XLAS) water stress values; we found that most points were located within a 0.2 confidence interval, thus the general tendencies are well reproduced. Even though extrapolation of instantaneous latent heat flux values to daily totals was less obvious, daily ET estimates are deemed acceptable.

scholarly journals Assessment of actual evapotranspiration over a semi-arid heterogeneous land surface by means of coupled low resolution remote sensing data with energy balance model: comparison to extra Large Aperture Scintillometer measurements

2017 ◽  
Author(s):  
Sameh Saadi ◽  
Gilles Boulet ◽  
Malik Bahir ◽  
Aurore Brut ◽  
Bernard Mougenot ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Heat Flux ◽  
Water Stress ◽  
Latent Heat ◽  
Water Availability ◽  
Land Surface ◽  
Remote Sensing Data ◽  
Sparse Model ◽  
Sensing Data ◽  
Semi Arid

Abstract. In semi-arid areas, agricultural production is restricted by water availability; hence efficient agricultural water management is a major issue. The design of tools providing regional estimates of evapotranspiration (ET), one of the most relevant water balance fluxes, may help the sustainable management of water resources. Remote sensing provides periodic data about actual vegetation temporal dynamics (through the Normalized Difference Vegetation Index NDVI) and water availability under water stress (through the land surface temperature LST) which are crucial factors controlling ET. In this study, spatially distributed estimates of ET (or its energy equivalent, the latent heat fluxes LE) in the Kairouan plain (Central Tunisia) were computed by applying the Soil Plant Atmosphere and Remote Sensing Evapotraspiration (SPARSE) model fed by low resolution remote sensing data (Terra and Aqua MODIS). The work goal was to assess the operational use of the SPARSE model and the accuracy of the modelled i) sensible heat flux (H) and ii) daily ET over a heterogeneous semi-arid landscape with a complex land cover (i.e. trees, winter cereals, summer vegetables). The SPARSE's layer approach was run to compute instantaneous estimates of H and LE fluxes at the satellite overpass time. The good correspondence (R2 = 0.60 and 0.63 and RMSE = 57.89 W/m-2 and 53.85 W/m-2; for Terra and Aqua, respectively) between instantaneous H estimates and large aperture scintillometer (XLAS)'s H measurements along a pathlength of 4 km over the study area showed that the SPARSE model presents satisfactory accuracy. Results showed that, despite the fairly large scatter, the instantaneous LE can be suitably estimated at large scale (RMSE = 47.20 W/m-2 and 43.20 W/m-2; for Terra and Aqua, respectively and R2 = 0.55 for both satellites). Additionally, water stress was investigated by comparing modelled (SPARSE derived) to observed (XLAS derived) water stress values; we found that most points were located within a 0.2 confidence interval, thus the general tendencies are well reproduced. Even though extrapolation of instantaneous latent heat flux values to daily totals was less obvious, daily ET estimates are deemed acceptable.

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