SSEB (Simplified Surface Energy Balance model)

The SSEB approach estimates ETa using the relative ET fractions scaled from thermal imagery in combination with a spatially explicit maximum reference ET.

Evapotranspirationthermal imagery
  277

Contributor

contributed at 2019-12-29

Authorship

Affiliation:  
SAIC/US Geological Survey EROS/Geographic Information Science Center of Excellence, South, Dakota State University, USA
Affiliation:  
SAIC/US Geological Survey EROS, USA
Affiliation:  
US Geological Survey EROS, USA
Affiliation:  
Department of Environmental Studies, Florida International University, USA
Is authorship not correct? Feed back

Classification(s)

Application-focused categoriesNatural-perspectiveLand regions

Model Description

English {{currentDetailLanguage}} English

Quoted from: A Review of Surface Energy Balance Models for Estimating Actual Evapotranspiration with Remote Sensing at High Spatiotemporal Resolution over Large Extents, Scientific Investigations Report 2017–5087, U.S. Department of the Interior and U.S. Geological Survey. https://www.fs.fed.us/rm/pubs_journals/2017/rmrs_2017_mcshane_r001.pdf 

      The SSEB model functions similarly to METRIC (Allen and others, 2007b). The METRIC model assumes that variation in land surface temperature (LST) is linearly related to the temperature difference between the land surface and air. This relation is defined through the selection of two reference pixels: a hot pixel that represents bare, dry fields; and a cold pixel that represents vegetated, wet fields. The temperature gradient is used in equation 2 to estimate H (sensible heat flux), which is assumed to vary linearly between the hot and cold pixels. This assumption holds for SSEB, where it is further assumed that LE in equation 2 (energy consumed through ETa) also varies linearly between the hot and cold pixels. Senay and others (2007) remark that this assumption is supported by research showing that the temperature difference between the land surface and air is linearly related to soil moisture. They additionally assume that ETa can be inferred from the temperature gradient, which can be estimated from land surface temperatures of the hot and cold pixels. At the hot pixel, ETa is assumed to be zero, and ETa at the cold pixel is assumed to be maximal—that is, to equal ETr. At all other pixels in a study area, ETa is scaled proportionately to the surface temperature of each pixel in relation to that of the hot and cold pixels. With this assumption, fractional evapotranspiration (ETf ) is calculated for each pixel:

(14)

      where

      Th is LST for the hot pixel, in kelvins;

      Ts is LST of each pixel, in kelvins; and

      Tc is LST for the cold pixel, in kelvins.

      To calculate the parameters in equation 14, SSEB uses satellite imagery for LST and a vegetation index (NDVI) to assist in choosing the hot and cold reference pixels. From the study area, regions of high temperature and low NDVI (hot, bare fields) and low temperature and high NDVI (cold, wellvegetated fields) are identified from which the hot and cold reference pixels are chosen. The ETa is calculated from ETf for each pixel in the study area:

(15)

      where

      ETf is fractional ET (dimensionless); and

      ET0 is reference ET, in millimeters.

      Available gridded data such as those from the Global Data Assimilation System (GDAS) model are used to calculate ET0, which results in a 1-degree grid of global daily data (Senay and others, 2008). The GDAS model uses the standardized Penman-Monteith equation (American Society of Civil Engineers, 2005) to compute ET0 for a shortgrass crop (Allen and others, 1998). Senay and others (2007) disaggregate the 1-degree data from this model onto a 10-km grid. However, if ET0 is available from a weather station, ETa estimates will likely be more accurate using the local values of ET0.

 

 

Model Metadata

Name {{metadata.overview.name}}
Version {{metadata.overview.version}}
Model Type {{metadata.overview.modelType}}
Model Domain
{{domain}}
Sacle {{metadata.overview.scale}}

There is no overview about this model. You can click to add overview.

Purpose {{metadata.design.purpose}}
Principles
{{principle}}
Incorporated Models
{{incorporatedModel}}
Model part of larger framework: {{metadata.design.framework}}
Incorporated Models
{{process}}

There is no design info about this model. You can click to add overview.

Information {{metadata.usage.information}}
Initialization {{metadata.usage.initialization}}
Hardware Requirements {{metadata.usage.hardware}}
Software Requirements {{metadata.usage.software}}
Inputs
{{input}}
Outputs
{{output}}

There is no usage info about this model. You can click to add overview.

How to Cite

Gabriel B. Senay, Michael Budde, James P. Verdin, Assefa M. Melesse (2019). SSEB (Simplified Surface Energy Balance model), Model Item, OpenGMS, https://geomodeling.njnu.edu.cn/modelItem/38f8b58e-26a8-4033-8432-2e8a6941ca95
Copy

QR Code

Contributor(s)

Initial contribute: 2019-12-29

Authorship

Affiliation:  
SAIC/US Geological Survey EROS/Geographic Information Science Center of Excellence, South, Dakota State University, USA
Affiliation:  
SAIC/US Geological Survey EROS, USA
Affiliation:  
US Geological Survey EROS, USA
Affiliation:  
Department of Environmental Studies, Florida International University, USA
Is authorship not correct? Feedback

QR Code

×

{{curRelation.overview}}
{{curRelation.author.join('; ')}}
{{curRelation.journal}}









You can link related {{typeName}} from repository to this model item, or you can create a new {{typeName.toLowerCase()}}.

Related Items
Related Items

You can link resource from repository to this model item, or you can create a new {{typeName.toLowerCase()}}.

Drop the file here, orclick to upload.
Select From My Space
+ add

These authorship information will be submitted to the contributor to review.

Cancel Submit
Model Classifications
Cancel Submit
Localizations + Add
{{ item.label }} {{ item.value }}
Model Name :
Cancel Submit
Name:
Version:
Model Type:
Model Domain:
Scale:
Purpose:
Principles:
Incorporated models:

Model part of

larger framework

Process:
Information:
Initialization:
Hardware Requirements:
Software Requirements:
Inputs:
Outputs:
Cancel Submit
Title Author Date Journal Volume(Issue) Pages Links Doi Operation
Cancel Submit
Add Cancel

{{articleUploading.title}}

Authors:  {{articleUploading.authors[0]}}, {{articleUploading.authors[1]}}, {{articleUploading.authors[2]}}, et al.

Journal:   {{articleUploading.journal}}

Date:   {{articleUploading.date}}

Page range:   {{articleUploading.pageRange}}

Link:   {{articleUploading.link}}

DOI:   {{articleUploading.doi}}

Yes, this is it Cancel

The article {{articleUploading.title}} has been uploaded yet.

OK
Cancel Confirm