Utility of remote sensing based two-source energy balance model to estimate land surface fluxes during SMACEX

2004 ◽  
Author(s):  
Fuqin Li ◽  
W.P. Kustas ◽  
T.J. Jackson ◽  
J.H. Prueger
Keyword(s):  
Remote Sensing ◽  
Energy Balance ◽  
Land Surface ◽  
Surface Fluxes ◽  
Energy Balance Model ◽  
Balance Model

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>

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 REMOTE SENSING OF GLOBAL MONTHLY EVAPOTRANSPIRATION WITH AN ENERGY BALANCE (EB) MODEL

2019 ◽  
Vol XLII-2/W13 ◽  
pp. 1729-1733
Author(s):  
X. Chen ◽  
Z. Su ◽  
Y. Ma
Keyword(s):  
Remote Sensing ◽  
Energy Balance ◽  
Land Surface ◽  
Roughness Length ◽  
Meteorological Data ◽  
Global Scale ◽  
Balance Model ◽  
Time Step ◽  
Available Energy ◽  
Hydrological Cycles

<p><strong>Abstract.</strong> A global monthly evapotranspiration (ET) product without spatial-temporal gaps for 2000&amp;ndash;2017 is delivered by using an energy balance (EB) algorithm and MODIS satellite data. It provides us with a moderate resolution estimate of ET without spatial-temporal gaps on a global scale. The model is driven by monthly remote sensing land surface temperature and ERA-Interim meteorological data. A global turbulent exchange parameterization scheme was developed for global momentum and heat roughness length calculation with remote sensing information. The global roughness length was used in the energy balance model, which uses monthly land-air temperature gradient to estimate the turbulent sensible heat, and take the latent heat flux as a residual of the available energy. This study produced an ET product for global landmass, at a monthly time step and 0.05-degree spatial resolution. The performance of ET data has been evaluated in comparison to hundreds flux sites measurements representing a broad range of land covers and climates. The ET product has a mean bias of 3.3&amp;thinsp;mm/month, RMSE value of 36.9&amp;thinsp;mm/month. The monthly ET product can be used to study the global energy and hydrological cycles at either seasonal or inter-annual temporal resolution.</p>

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.

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