On Parameterizing Soil Evaporation in a Direct Remote Sensing Model of ET: PT-JPL
- Autores
- Marshall, Michael; Tu, Kevin; Andreo, Verónica Carolina
- Año de publicación
- 2020
- Idioma
- inglés
- Tipo de recurso
- artículo
- Estado
- versión publicada
- Descripción
- Remote sensing models that measure evapotranspiration directly from the Penman-Monteith or Priestley-Taylor equations typically estimate the soil evaporation component over large areas using coarse spatial resolution relative humidity (RH) from geospatial climate datasets. As a result, the models tend to underperform in dry areas at local scales where moisture status is not well represented by surrounding areas. Earth observation sensors that monitor large-scale global dynamics (e.g., MODIS) afford comparable spatial coverage and temporal frequency, but at a higher spatial resolution than geospatial climate datasets. In this study, we compared soil evaporation parameterized with optical and thermal indices derived from MODIS to RH-based soil evaporation as implemented in the Priestley Taylor-Jet Propulsion Laboratory (PT-JPL) model. We evaluated the parameterizations by subtracting PT-JPL transpiration from observation-based flux tower evapotranspiration in agricultural fields across the contiguous United States. We compared the apparent thermal inertia (ATI) index, land surface water index (LSWI), normalized difference water index (NDWI), and a new index derived from red and shortwave infrared bands (soil moisture divergence index [SMDI]). Relationships were significant at the 95% confidence band. LSWI and SMDI explained 18–33% of variance in 8-day soil evaporation. This led to a 3–11% increase in explained ET variance. LSWI and SMDI tended to perform better at the irrigated sites than RH. LSWI and SMDI led to markedly better performance over other indices at a seasonal time step. L-band microwave backscatter can penetrate clouds and can distinguish soil from canopy moisture content. We are presently fusing red-SWIR-RADAR to improve soil evaporation estimation.
Fil: Marshall, Michael. University of Twente. Faculty of Geo‐information Science and Earth Observation. Department of Natural Resources; Países Bajos
Fil: Tu, Kevin. Corteva Agriscience; Estados Unidos
Fil: Andreo, Verónica Carolina. Comision Nacional de Actividades Espaciales. Instituto de Altos Estudios Espaciales "Mario Gulich"; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba; Argentina - Materia
-
EVAPOTRANSPIRATION
LAND SURFACE TEMPERATURE
LATENT HEAT
MODIS
PRIESTLEY-TAYLOR
SHORTWAVE INFRARED - Nivel de accesibilidad
- acceso abierto
- Condiciones de uso
- https://creativecommons.org/licenses/by/2.5/ar/
- Repositorio
- Institución
- Consejo Nacional de Investigaciones Científicas y Técnicas
- OAI Identificador
- oai:ri.conicet.gov.ar:11336/143384
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On Parameterizing Soil Evaporation in a Direct Remote Sensing Model of ET: PT-JPLMarshall, MichaelTu, KevinAndreo, Verónica CarolinaEVAPOTRANSPIRATIONLAND SURFACE TEMPERATURELATENT HEATMODISPRIESTLEY-TAYLORSHORTWAVE INFRAREDhttps://purl.org/becyt/ford/1.5https://purl.org/becyt/ford/1Remote sensing models that measure evapotranspiration directly from the Penman-Monteith or Priestley-Taylor equations typically estimate the soil evaporation component over large areas using coarse spatial resolution relative humidity (RH) from geospatial climate datasets. As a result, the models tend to underperform in dry areas at local scales where moisture status is not well represented by surrounding areas. Earth observation sensors that monitor large-scale global dynamics (e.g., MODIS) afford comparable spatial coverage and temporal frequency, but at a higher spatial resolution than geospatial climate datasets. In this study, we compared soil evaporation parameterized with optical and thermal indices derived from MODIS to RH-based soil evaporation as implemented in the Priestley Taylor-Jet Propulsion Laboratory (PT-JPL) model. We evaluated the parameterizations by subtracting PT-JPL transpiration from observation-based flux tower evapotranspiration in agricultural fields across the contiguous United States. We compared the apparent thermal inertia (ATI) index, land surface water index (LSWI), normalized difference water index (NDWI), and a new index derived from red and shortwave infrared bands (soil moisture divergence index [SMDI]). Relationships were significant at the 95% confidence band. LSWI and SMDI explained 18–33% of variance in 8-day soil evaporation. This led to a 3–11% increase in explained ET variance. LSWI and SMDI tended to perform better at the irrigated sites than RH. LSWI and SMDI led to markedly better performance over other indices at a seasonal time step. L-band microwave backscatter can penetrate clouds and can distinguish soil from canopy moisture content. We are presently fusing red-SWIR-RADAR to improve soil evaporation estimation.Fil: Marshall, Michael. University of Twente. Faculty of Geo‐information Science and Earth Observation. Department of Natural Resources; Países BajosFil: Tu, Kevin. Corteva Agriscience; Estados UnidosFil: Andreo, Verónica Carolina. Comision Nacional de Actividades Espaciales. Instituto de Altos Estudios Espaciales "Mario Gulich"; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba; ArgentinaAmerican Geophysical Union2020-05info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/143384Marshall, Michael; Tu, Kevin; Andreo, Verónica Carolina; On Parameterizing Soil Evaporation in a Direct Remote Sensing Model of ET: PT-JPL; American Geophysical Union; Water Resources Research; 56; 5; 5-2020; 1-180043-13971944-7973CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2019WR026290info:eu-repo/semantics/altIdentifier/doi/10.1029/2019WR026290info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T10:19:23Zoai:ri.conicet.gov.ar:11336/143384instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-29 10:19:24.205CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse |
dc.title.none.fl_str_mv |
On Parameterizing Soil Evaporation in a Direct Remote Sensing Model of ET: PT-JPL |
title |
On Parameterizing Soil Evaporation in a Direct Remote Sensing Model of ET: PT-JPL |
spellingShingle |
On Parameterizing Soil Evaporation in a Direct Remote Sensing Model of ET: PT-JPL Marshall, Michael EVAPOTRANSPIRATION LAND SURFACE TEMPERATURE LATENT HEAT MODIS PRIESTLEY-TAYLOR SHORTWAVE INFRARED |
title_short |
On Parameterizing Soil Evaporation in a Direct Remote Sensing Model of ET: PT-JPL |
title_full |
On Parameterizing Soil Evaporation in a Direct Remote Sensing Model of ET: PT-JPL |
title_fullStr |
On Parameterizing Soil Evaporation in a Direct Remote Sensing Model of ET: PT-JPL |
title_full_unstemmed |
On Parameterizing Soil Evaporation in a Direct Remote Sensing Model of ET: PT-JPL |
title_sort |
On Parameterizing Soil Evaporation in a Direct Remote Sensing Model of ET: PT-JPL |
dc.creator.none.fl_str_mv |
Marshall, Michael Tu, Kevin Andreo, Verónica Carolina |
author |
Marshall, Michael |
author_facet |
Marshall, Michael Tu, Kevin Andreo, Verónica Carolina |
author_role |
author |
author2 |
Tu, Kevin Andreo, Verónica Carolina |
author2_role |
author author |
dc.subject.none.fl_str_mv |
EVAPOTRANSPIRATION LAND SURFACE TEMPERATURE LATENT HEAT MODIS PRIESTLEY-TAYLOR SHORTWAVE INFRARED |
topic |
EVAPOTRANSPIRATION LAND SURFACE TEMPERATURE LATENT HEAT MODIS PRIESTLEY-TAYLOR SHORTWAVE INFRARED |
purl_subject.fl_str_mv |
https://purl.org/becyt/ford/1.5 https://purl.org/becyt/ford/1 |
dc.description.none.fl_txt_mv |
Remote sensing models that measure evapotranspiration directly from the Penman-Monteith or Priestley-Taylor equations typically estimate the soil evaporation component over large areas using coarse spatial resolution relative humidity (RH) from geospatial climate datasets. As a result, the models tend to underperform in dry areas at local scales where moisture status is not well represented by surrounding areas. Earth observation sensors that monitor large-scale global dynamics (e.g., MODIS) afford comparable spatial coverage and temporal frequency, but at a higher spatial resolution than geospatial climate datasets. In this study, we compared soil evaporation parameterized with optical and thermal indices derived from MODIS to RH-based soil evaporation as implemented in the Priestley Taylor-Jet Propulsion Laboratory (PT-JPL) model. We evaluated the parameterizations by subtracting PT-JPL transpiration from observation-based flux tower evapotranspiration in agricultural fields across the contiguous United States. We compared the apparent thermal inertia (ATI) index, land surface water index (LSWI), normalized difference water index (NDWI), and a new index derived from red and shortwave infrared bands (soil moisture divergence index [SMDI]). Relationships were significant at the 95% confidence band. LSWI and SMDI explained 18–33% of variance in 8-day soil evaporation. This led to a 3–11% increase in explained ET variance. LSWI and SMDI tended to perform better at the irrigated sites than RH. LSWI and SMDI led to markedly better performance over other indices at a seasonal time step. L-band microwave backscatter can penetrate clouds and can distinguish soil from canopy moisture content. We are presently fusing red-SWIR-RADAR to improve soil evaporation estimation. Fil: Marshall, Michael. University of Twente. Faculty of Geo‐information Science and Earth Observation. Department of Natural Resources; Países Bajos Fil: Tu, Kevin. Corteva Agriscience; Estados Unidos Fil: Andreo, Verónica Carolina. Comision Nacional de Actividades Espaciales. Instituto de Altos Estudios Espaciales "Mario Gulich"; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba; Argentina |
description |
Remote sensing models that measure evapotranspiration directly from the Penman-Monteith or Priestley-Taylor equations typically estimate the soil evaporation component over large areas using coarse spatial resolution relative humidity (RH) from geospatial climate datasets. As a result, the models tend to underperform in dry areas at local scales where moisture status is not well represented by surrounding areas. Earth observation sensors that monitor large-scale global dynamics (e.g., MODIS) afford comparable spatial coverage and temporal frequency, but at a higher spatial resolution than geospatial climate datasets. In this study, we compared soil evaporation parameterized with optical and thermal indices derived from MODIS to RH-based soil evaporation as implemented in the Priestley Taylor-Jet Propulsion Laboratory (PT-JPL) model. We evaluated the parameterizations by subtracting PT-JPL transpiration from observation-based flux tower evapotranspiration in agricultural fields across the contiguous United States. We compared the apparent thermal inertia (ATI) index, land surface water index (LSWI), normalized difference water index (NDWI), and a new index derived from red and shortwave infrared bands (soil moisture divergence index [SMDI]). Relationships were significant at the 95% confidence band. LSWI and SMDI explained 18–33% of variance in 8-day soil evaporation. This led to a 3–11% increase in explained ET variance. LSWI and SMDI tended to perform better at the irrigated sites than RH. LSWI and SMDI led to markedly better performance over other indices at a seasonal time step. L-band microwave backscatter can penetrate clouds and can distinguish soil from canopy moisture content. We are presently fusing red-SWIR-RADAR to improve soil evaporation estimation. |
publishDate |
2020 |
dc.date.none.fl_str_mv |
2020-05 |
dc.type.none.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion http://purl.org/coar/resource_type/c_6501 info:ar-repo/semantics/articulo |
format |
article |
status_str |
publishedVersion |
dc.identifier.none.fl_str_mv |
http://hdl.handle.net/11336/143384 Marshall, Michael; Tu, Kevin; Andreo, Verónica Carolina; On Parameterizing Soil Evaporation in a Direct Remote Sensing Model of ET: PT-JPL; American Geophysical Union; Water Resources Research; 56; 5; 5-2020; 1-18 0043-1397 1944-7973 CONICET Digital CONICET |
url |
http://hdl.handle.net/11336/143384 |
identifier_str_mv |
Marshall, Michael; Tu, Kevin; Andreo, Verónica Carolina; On Parameterizing Soil Evaporation in a Direct Remote Sensing Model of ET: PT-JPL; American Geophysical Union; Water Resources Research; 56; 5; 5-2020; 1-18 0043-1397 1944-7973 CONICET Digital CONICET |
dc.language.none.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
info:eu-repo/semantics/altIdentifier/url/https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2019WR026290 info:eu-repo/semantics/altIdentifier/doi/10.1029/2019WR026290 |
dc.rights.none.fl_str_mv |
info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/2.5/ar/ |
eu_rights_str_mv |
openAccess |
rights_invalid_str_mv |
https://creativecommons.org/licenses/by/2.5/ar/ |
dc.format.none.fl_str_mv |
application/pdf application/pdf |
dc.publisher.none.fl_str_mv |
American Geophysical Union |
publisher.none.fl_str_mv |
American Geophysical Union |
dc.source.none.fl_str_mv |
reponame:CONICET Digital (CONICET) instname:Consejo Nacional de Investigaciones Científicas y Técnicas |
reponame_str |
CONICET Digital (CONICET) |
collection |
CONICET Digital (CONICET) |
instname_str |
Consejo Nacional de Investigaciones Científicas y Técnicas |
repository.name.fl_str_mv |
CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas |
repository.mail.fl_str_mv |
dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar |
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1844614165631598592 |
score |
13.070432 |