scholarly journals Understanding the Effects of Fires on Surface Evapotranspiration Patterns Using Satellite Remote Sensing in Combination with an Energy Balance Model

10.5772/14243 ◽  
2011 ◽  
Author(s):  
Juan M. ◽  
Vicente Caselles ◽  
Eva Rubio
Keyword(s):  
Remote Sensing ◽  
Energy Balance ◽  
Satellite Remote Sensing ◽  
Energy Balance Model ◽  
Balance Model

Estimating evapotranspiration using remote sensing and the METRIC energy balance model: case study of Sidi Benour region (Morocco)

2021 ◽  
Author(s):  
Abdelmejid Rahimi ◽  
Abdelkrim Bouasria ◽  
Mohamed Bounif ◽  
Fatna Zaakour ◽  
Ikram El Mjiri
Keyword(s):  
Remote Sensing ◽  
Energy Balance ◽  
Energy Balance Model ◽  
Balance Model ◽  
Model Case

scholarly journals Estimation of surface energy fluxes in the Arctic tundra using the remote sensing thermal-based Two-Source Energy Balance model

2017 ◽  
Vol 21 (3) ◽  
pp. 1339-1358 ◽  
Author(s):  
Jordi Cristóbal ◽  
Anupma Prakash ◽  
Martha C. Anderson ◽  
William P. Kustas ◽  
Eugénie S. Euskirchen ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Heat Flux ◽  
Energy Balance ◽  
Surface Energy ◽  
Arctic Tundra ◽  
Soil Heat Flux ◽  
Energy Balance Model ◽  
The Arctic ◽  
Balance Model ◽  
Climate Conditions

Abstract. The Arctic has become generally a warmer place over the past decades leading to earlier snow melt, permafrost degradation and changing plant communities. Increases in precipitation and local evaporation in the Arctic, known as the acceleration components of the hydrologic cycle, coupled with land cover changes, have resulted in significant changes in the regional surface energy budget. Quantifying spatiotemporal trends in surface energy flux partitioning is key to forecasting ecological responses to changing climate conditions in the Arctic. An extensive local evaluation of the Two-Source Energy Balance model (TSEB) – a remote-sensing-based model using thermal infrared retrievals of land surface temperature – was performed using tower measurements collected over different tundra types in Alaska in all sky conditions over the full growing season from 2008 to 2012. Based on comparisons with flux tower observations, refinements in the original TSEB net radiation, soil heat flux and canopy transpiration parameterizations were identified for Arctic tundra. In particular, a revised method for estimating soil heat flux based on relationships with soil temperature was developed, resulting in significantly improved performance. These refinements result in mean turbulent flux errors generally less than 50 W m−2 at half-hourly time steps, similar to errors typically reported in surface energy balance modeling studies conducted in more temperate climatic regimes. The MODIS leaf area index (LAI) remote sensing product proved to be useful for estimating energy fluxes in Arctic tundra in the absence of field data on the local biomass amount. Model refinements found in this work at the local scale build toward a regional implementation of the TSEB model over Arctic tundra ecosystems, using thermal satellite remote sensing to assess response of surface fluxes to changing vegetation and climate conditions.

scholarly journals Estimation of surface energy fluxes in the Arctic tundra using the remote sensing thermal-based Two-Source Energy Balance model

2016 ◽  
Author(s):  
Jordi Cristóbal ◽  
Anupma Prakash ◽  
Martha C. Anderson ◽  
William P. Kustas ◽  
Eugénie S. Euskirchen ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Heat Flux ◽  
Energy Balance ◽  
Surface Energy ◽  
Arctic Tundra ◽  
Soil Heat Flux ◽  
Energy Balance Model ◽  
The Arctic ◽  
Balance Model ◽  
Climate Conditions

Abstract. The Arctic has become generally a warmer place over the past decades leading to earlier snow melt, permafrost degradation and changing plant communities. Increases in precipitation and local evaporation in the Arctic, known as one of the acceleration components of the hydrologic cycle, coupled with land cover changes, have resulted in significant changes in the regional surface energy budget. Quantifying spatiotemporal trends in surface energy flux partitioning is a key to forecasting ecological responses to changing climate conditions in the Arctic regions. An extensive evaluation of the two-source energy balance model (TSEB) – a remote sensing-based model using thermal infrared retrievals of land–surface temperature – was performed using tower measurements collected over different tundra types in Alaska in all sky conditions over the full growing season from 2008 to 2012. Based on comparisons with flux tower observations, refinements in the original TSEB net radiation, soil heat flux and canopy transpiration parameterizations were identified for the unique Arctic tundra conditions. In particular, a revised method for estimating soil heat flux based on relationships with soil temperature was developed, resulting in significantly improved performance. These refinements result in mean flux errors around 50 W m−2 at half-hourly timesteps similar to errors typically reported in surface energy balance modeling studies conducted in more temperate climatic regimes. MODIS LAI remote sensing product proved to be useful for estimating energy fluxes in Arctic tundra in the absence of field data. This work builds toward a regional implementation of the TSEB model over Arctic tundra ecosystems, using thermal satellite remote sensing to assess response of surface fluxes to changing vegetation and climate conditions.

Water and surface energy balance model with a multilayer canopy representation for remote sensing purposes

1989 ◽  
Vol 25 (5) ◽  
pp. 949-971 ◽  
Author(s):  
A. A. van de Griend ◽  
J. H. van Boxel
Keyword(s):  
Remote Sensing ◽  
Energy Balance ◽  
Surface Energy ◽  
Surface Energy Balance ◽  
Energy Balance Model ◽  
Balance Model

Assessment of olive transpiration derived from a remote sensing energy balance model compared with sap flow measurements

2018 ◽  
pp. 189-194
Author(s):  
C. Riveros-Burgos ◽  
S. Ortega-Farías ◽  
L. Ahumada-Orellana
Keyword(s):  
Remote Sensing ◽  
Energy Balance ◽  
Sap Flow ◽  
Energy Balance Model ◽  
Balance Model ◽  
Flow Measurements ◽  
Sap Flow Measurements

Disentangling ecosystem transpiration from evapotranspiration observations employing simplified vegetation-substrate energy balance model

2020 ◽  
Author(s):  
Kaniska Mallick ◽  
Dennis Baldocchi ◽  
Andrew Jarvis ◽  
Ivonne Trebs ◽  
Mauro Sulis ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Energy Balance ◽  
Land Surface ◽  
Sensible Heat Flux ◽  
Energy Balance Model ◽  
Arid Ecosystems ◽  
Balance Model ◽  
Area Index ◽  
Availability Constraints

<p>Evapotranspiration (E<sub>ET</sub>) observed by eddy covariance (EC) towers is composed of physical evaporation (E<sub>E</sub>) from wet surfaces and biological transpiration (E<sub>T</sub>), that involves soil moisture uptake by roots and water vapor transfer regulated through the canopy-stomatal conductance (g<sub>C</sub>) during photosynthesis. E<sub>T</sub> plays a dominant role in the global water cycle and represents 80% of the total terrestrial E<sub>ET</sub>. Understanding the magnitude and variability of E<sub>T</sub> are critical to assess the ecophysiological responses of vegetation to drought. While separating E<sub>T</sub> signals from lumped E<sub>ET</sub> observations and/or simulating E<sub>T</sub> by terrestrial systems models is insufficiently constrained owing to the large uncertainties in disentangling g<sub>C</sub> from the aggregated canopy-substrate conductance (g<sub>cS</sub>), evaluating ecosystem E<sub>T</sub> derived through partitioning E<sub>ET</sub> observations (or model simulation) is also challenging due to the absence of any ecosystem-scale measurements of this biotic flux and g<sub>C</sub>. To date, the main methods for partitioning EC-E<sub>ET</sub> observations are largely based on regressing E<sub>ET</sub> with gross photosynthesis (P<sub>g</sub>) and atmospheric vapor pressure deficit (D<sub>A</sub>) observations. However, such methods ignore the essential feedback of the surface energy balance (SEB) and canopy temperature (T<sub>C</sub>) on g<sub>C</sub> and E<sub>T</sub>.</p><p>This study demonstrates partitioning E<sub>ET</sub> observations into E<sub>T</sub> and E<sub>E</sub> [soil evaporation (E<sub>Es</sub>) and interception evaporation (E<sub>Ei</sub>)] through an ‘analytical solution’ of g<sub>C</sub>, T<sub>C</sub> and canopy vapor pressures by employing a Shuttleworth-Gurney vegetation-substrate energy balance model with minimal complexity. The model is called TRANSPIRE (Top-down partitioning evapotRANSPIRation modEl), which ingests remote sensing land surface temperature (LST) and leaf area index (L<sub>ai</sub>) information in conjunction with meteorological, sensible heat flux (H) and E<sub>ET</sub> observations from EC tower into the SEB equations for retrieving canopy and soil temperatures (T<sub>S</sub>, T<sub>C</sub>), g<sub>C</sub>, and E<sub>T</sub>.</p><p>E<sub>T</sub> estimates from TRANSPIRE were tested and evaluated with a remote sensing based E<sub>T</sub> estimate from an analytical model (STIC1.2), where lumped E<sub>ET</sub> was partitioned by employing a moisture availability constraints across an aridity gradient in the North Australian Tropical Transect (NATT) by using time-series of 8-day MODIS Terra LST and LAI products in conjunction with EC measurements from 2011 to 2018. Both methods captured the seasonal pattern of E<sub>T</sub>/E<sub>ET</sub> ratio in a very similar way. While E<sub>T</sub> accounted for 60±10% of the annual E<sub>ET</sub> in the tropical savanna, E<sub>T</sub> in the arid mulga contributed 75±12% of the annual E<sub>ET</sub>. Seasonal variation of E<sub>T</sub> was higher in the arid, semi-arid ecosystems (50 - 90%), as compared to the humid tropical ecosystem (10 - 50%). The TRANSPIRE model reasonably captured E<sub>T</sub> variations along with soil moisture and precipitation dynamics in both sparse and homogeneous vegetation and showed the potential of partitioning E<sub>ET</sub> observations for cross-site comparison with a variety of models.</p>

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