Combining a SVAT model with landsat imagery for a ten year simulation of grassland carbon and water budget
This study investigates the use of high-spatial, low-temporal scale visible remote sensing data for calibration of a Soil-Vegetation-Atmosphere-Transfer (SVAT) model for semi-arid perennial grasslands. The SVAT model is driven by meteorological data and simulates plant growth and water budget on a daily time step. The model was combined with a canopy reflectance model to simulate shortwave radiometric temporal profiles. Landsat Thematic Mapper (TM) images obtained during a series of ten consecutive years were used to refine the model to work on a spatially-distributed basis over a semi-arid grassland watershed. Continuous simulations were used to estimate two spatially-variable initial conditions and model parameters through a calibration procedure which minimized the difference between the surface reflectance simulated by the model and measured by the TM sensor. Accuracy of model products such as daily above-ground biomass and soil moisture was assessed by comparison with field measurements. The promising results suggest that this approach could provide spatially-distributed information about vegetation and soil conditions for day-to-day grassland management.
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Subjects: | U30 - Méthodes de recherche, U10 - Informatique, mathématiques et statistiques, F62 - Physiologie végétale - Croissance et développement, prairie, télédétection, modèle de simulation, zone semi-aride, taux de croissance, bilan hydrique, bilan radiatif, biomasse, Réflectance, modélisation, http://aims.fao.org/aos/agrovoc/c_6154, http://aims.fao.org/aos/agrovoc/c_6498, http://aims.fao.org/aos/agrovoc/c_24242, http://aims.fao.org/aos/agrovoc/c_6963, http://aims.fao.org/aos/agrovoc/c_16130, http://aims.fao.org/aos/agrovoc/c_8311, http://aims.fao.org/aos/agrovoc/c_6420, http://aims.fao.org/aos/agrovoc/c_926, http://aims.fao.org/aos/agrovoc/c_28538, http://aims.fao.org/aos/agrovoc/c_230ab86c, http://aims.fao.org/aos/agrovoc/c_615, |
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dig-cirad-fr-4797382024-01-28T09:07:17Z http://agritrop.cirad.fr/479738/ http://agritrop.cirad.fr/479738/ Combining a SVAT model with landsat imagery for a ten year simulation of grassland carbon and water budget. Nouvellon Yann, Moran M. Susan, Bryant Ross, Ni Wanmei, Heilman Phil, Emmerich B., Lo Seen Danny, Bégué Agnès, Rambal Serge, Qi J.. 2000. In : Second International Conference on Geospatial Information in Agriculture and Forestry, Lake Buena Vista, Florida, 10-12 January 2000. s.l. : s.n., 257-264. International Conference on Geospatial Information in Agriculture and Forestry. 2, Lake Buena Vista, États-Unis, 10 Janvier 2000/12 Janvier 2000. Combining a SVAT model with landsat imagery for a ten year simulation of grassland carbon and water budget Nouvellon, Yann Moran, M. Susan Bryant, Ross Ni, Wanmei Heilman, Phil Emmerich, B. Lo Seen, Danny Bégué, Agnès Rambal, Serge Qi, J. eng 2000 s.n. Second International Conference on Geospatial Information in Agriculture and Forestry, Lake Buena Vista, Florida, 10-12 January 2000 U30 - Méthodes de recherche U10 - Informatique, mathématiques et statistiques F62 - Physiologie végétale - Croissance et développement prairie télédétection modèle de simulation zone semi-aride taux de croissance bilan hydrique bilan radiatif biomasse Réflectance modélisation http://aims.fao.org/aos/agrovoc/c_6154 http://aims.fao.org/aos/agrovoc/c_6498 http://aims.fao.org/aos/agrovoc/c_24242 http://aims.fao.org/aos/agrovoc/c_6963 http://aims.fao.org/aos/agrovoc/c_16130 http://aims.fao.org/aos/agrovoc/c_8311 http://aims.fao.org/aos/agrovoc/c_6420 http://aims.fao.org/aos/agrovoc/c_926 http://aims.fao.org/aos/agrovoc/c_28538 http://aims.fao.org/aos/agrovoc/c_230ab86c Arizona http://aims.fao.org/aos/agrovoc/c_615 This study investigates the use of high-spatial, low-temporal scale visible remote sensing data for calibration of a Soil-Vegetation-Atmosphere-Transfer (SVAT) model for semi-arid perennial grasslands. The SVAT model is driven by meteorological data and simulates plant growth and water budget on a daily time step. The model was combined with a canopy reflectance model to simulate shortwave radiometric temporal profiles. Landsat Thematic Mapper (TM) images obtained during a series of ten consecutive years were used to refine the model to work on a spatially-distributed basis over a semi-arid grassland watershed. Continuous simulations were used to estimate two spatially-variable initial conditions and model parameters through a calibration procedure which minimized the difference between the surface reflectance simulated by the model and measured by the TM sensor. Accuracy of model products such as daily above-ground biomass and soil moisture was assessed by comparison with field measurements. The promising results suggest that this approach could provide spatially-distributed information about vegetation and soil conditions for day-to-day grassland management. conference_item info:eu-repo/semantics/conferenceObject Conference info:eu-repo/semantics/closedAccess http://catalogue-bibliotheques.cirad.fr/cgi-bin/koha/opac-detail.pl?biblionumber=164665 |
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U30 - Méthodes de recherche U10 - Informatique, mathématiques et statistiques F62 - Physiologie végétale - Croissance et développement prairie télédétection modèle de simulation zone semi-aride taux de croissance bilan hydrique bilan radiatif biomasse Réflectance modélisation http://aims.fao.org/aos/agrovoc/c_6154 http://aims.fao.org/aos/agrovoc/c_6498 http://aims.fao.org/aos/agrovoc/c_24242 http://aims.fao.org/aos/agrovoc/c_6963 http://aims.fao.org/aos/agrovoc/c_16130 http://aims.fao.org/aos/agrovoc/c_8311 http://aims.fao.org/aos/agrovoc/c_6420 http://aims.fao.org/aos/agrovoc/c_926 http://aims.fao.org/aos/agrovoc/c_28538 http://aims.fao.org/aos/agrovoc/c_230ab86c http://aims.fao.org/aos/agrovoc/c_615 U30 - Méthodes de recherche U10 - Informatique, mathématiques et statistiques F62 - Physiologie végétale - Croissance et développement prairie télédétection modèle de simulation zone semi-aride taux de croissance bilan hydrique bilan radiatif biomasse Réflectance modélisation http://aims.fao.org/aos/agrovoc/c_6154 http://aims.fao.org/aos/agrovoc/c_6498 http://aims.fao.org/aos/agrovoc/c_24242 http://aims.fao.org/aos/agrovoc/c_6963 http://aims.fao.org/aos/agrovoc/c_16130 http://aims.fao.org/aos/agrovoc/c_8311 http://aims.fao.org/aos/agrovoc/c_6420 http://aims.fao.org/aos/agrovoc/c_926 http://aims.fao.org/aos/agrovoc/c_28538 http://aims.fao.org/aos/agrovoc/c_230ab86c http://aims.fao.org/aos/agrovoc/c_615 |
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U30 - Méthodes de recherche U10 - Informatique, mathématiques et statistiques F62 - Physiologie végétale - Croissance et développement prairie télédétection modèle de simulation zone semi-aride taux de croissance bilan hydrique bilan radiatif biomasse Réflectance modélisation http://aims.fao.org/aos/agrovoc/c_6154 http://aims.fao.org/aos/agrovoc/c_6498 http://aims.fao.org/aos/agrovoc/c_24242 http://aims.fao.org/aos/agrovoc/c_6963 http://aims.fao.org/aos/agrovoc/c_16130 http://aims.fao.org/aos/agrovoc/c_8311 http://aims.fao.org/aos/agrovoc/c_6420 http://aims.fao.org/aos/agrovoc/c_926 http://aims.fao.org/aos/agrovoc/c_28538 http://aims.fao.org/aos/agrovoc/c_230ab86c http://aims.fao.org/aos/agrovoc/c_615 U30 - Méthodes de recherche U10 - Informatique, mathématiques et statistiques F62 - Physiologie végétale - Croissance et développement prairie télédétection modèle de simulation zone semi-aride taux de croissance bilan hydrique bilan radiatif biomasse Réflectance modélisation http://aims.fao.org/aos/agrovoc/c_6154 http://aims.fao.org/aos/agrovoc/c_6498 http://aims.fao.org/aos/agrovoc/c_24242 http://aims.fao.org/aos/agrovoc/c_6963 http://aims.fao.org/aos/agrovoc/c_16130 http://aims.fao.org/aos/agrovoc/c_8311 http://aims.fao.org/aos/agrovoc/c_6420 http://aims.fao.org/aos/agrovoc/c_926 http://aims.fao.org/aos/agrovoc/c_28538 http://aims.fao.org/aos/agrovoc/c_230ab86c http://aims.fao.org/aos/agrovoc/c_615 Nouvellon, Yann Moran, M. Susan Bryant, Ross Ni, Wanmei Heilman, Phil Emmerich, B. Lo Seen, Danny Bégué, Agnès Rambal, Serge Qi, J. Combining a SVAT model with landsat imagery for a ten year simulation of grassland carbon and water budget |
description |
This study investigates the use of high-spatial, low-temporal scale visible remote sensing data for calibration of a Soil-Vegetation-Atmosphere-Transfer (SVAT) model for semi-arid perennial grasslands. The SVAT model is driven by meteorological data and simulates plant growth and water budget on a daily time step. The model was combined with a canopy reflectance model to simulate shortwave radiometric temporal profiles. Landsat Thematic Mapper (TM) images obtained during a series of ten consecutive years were used to refine the model to work on a spatially-distributed basis over a semi-arid grassland watershed. Continuous simulations were used to estimate two spatially-variable initial conditions and model parameters through a calibration procedure which minimized the difference between the surface reflectance simulated by the model and measured by the TM sensor. Accuracy of model products such as daily above-ground biomass and soil moisture was assessed by comparison with field measurements. The promising results suggest that this approach could provide spatially-distributed information about vegetation and soil conditions for day-to-day grassland management. |
format |
conference_item |
topic_facet |
U30 - Méthodes de recherche U10 - Informatique, mathématiques et statistiques F62 - Physiologie végétale - Croissance et développement prairie télédétection modèle de simulation zone semi-aride taux de croissance bilan hydrique bilan radiatif biomasse Réflectance modélisation http://aims.fao.org/aos/agrovoc/c_6154 http://aims.fao.org/aos/agrovoc/c_6498 http://aims.fao.org/aos/agrovoc/c_24242 http://aims.fao.org/aos/agrovoc/c_6963 http://aims.fao.org/aos/agrovoc/c_16130 http://aims.fao.org/aos/agrovoc/c_8311 http://aims.fao.org/aos/agrovoc/c_6420 http://aims.fao.org/aos/agrovoc/c_926 http://aims.fao.org/aos/agrovoc/c_28538 http://aims.fao.org/aos/agrovoc/c_230ab86c http://aims.fao.org/aos/agrovoc/c_615 |
author |
Nouvellon, Yann Moran, M. Susan Bryant, Ross Ni, Wanmei Heilman, Phil Emmerich, B. Lo Seen, Danny Bégué, Agnès Rambal, Serge Qi, J. |
author_facet |
Nouvellon, Yann Moran, M. Susan Bryant, Ross Ni, Wanmei Heilman, Phil Emmerich, B. Lo Seen, Danny Bégué, Agnès Rambal, Serge Qi, J. |
author_sort |
Nouvellon, Yann |
title |
Combining a SVAT model with landsat imagery for a ten year simulation of grassland carbon and water budget |
title_short |
Combining a SVAT model with landsat imagery for a ten year simulation of grassland carbon and water budget |
title_full |
Combining a SVAT model with landsat imagery for a ten year simulation of grassland carbon and water budget |
title_fullStr |
Combining a SVAT model with landsat imagery for a ten year simulation of grassland carbon and water budget |
title_full_unstemmed |
Combining a SVAT model with landsat imagery for a ten year simulation of grassland carbon and water budget |
title_sort |
combining a svat model with landsat imagery for a ten year simulation of grassland carbon and water budget |
publisher |
s.n. |
url |
http://agritrop.cirad.fr/479738/ |
work_keys_str_mv |
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