Assimilation of LAI and dry biomass data from optical and SAR images into an agro-meteorological model to estimate soybean yield

Crop monitoring at a fine scale and crop yield estimation are critical from an environmental perspective because they provide essential information to combine increased food production and sustainable management of agricultural landscapes. The aim of this article is to estimate soybean yield using an agro-meteorological model controlled by optical and/or synthetic aperture radar (SAR) multipolarized satellite images. Satellite and ground data were collected over seven working farms. Optical and SAR images were acquired by Formosat-2, Spot-4, Spot-5, and Radarsat-2 satellites during the soybean vegetation cycle. A vegetation index (NDVI) was derived from the optical images, and backscattering coefficients and polarimetric indicators were computed from full quad-pol Radarsat-2 images. An angular normalization of SAR data was performed to minimize the incidence angle effects on SAR signals by using the complementarities provided by SAR and optical data. The best results are obtained when the model is controlled by both the leaf area index (LAI) derived from the optical vegetation index modified triangular vegetation index (MTVI2) or from the SAR backscattering coefficient σ °VV (LAI MTVI2 or (LAI σ ° VV ) and the dry biomass (DB) derived from the SAR Pauli matrix T33 (DB T33 )(r2 > 0.83), demonstrating the complementary of optical and SAR data.

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Main Authors: Betbeder, Julie, Fieuzal, Remy, Baup, Frederic
Format: article biblioteca
Language:eng
Subjects:F01 - Culture des plantes, U30 - Méthodes de recherche, P40 - Météorologie et climatologie, soja, rendement des cultures, indice de végétation, agrométéorologie, modèle de simulation, imagerie par satellite, radar, télédétection, image spot, polarimétrie, http://aims.fao.org/aos/agrovoc/c_14477, http://aims.fao.org/aos/agrovoc/c_10176, http://aims.fao.org/aos/agrovoc/c_9000171, http://aims.fao.org/aos/agrovoc/c_8689, http://aims.fao.org/aos/agrovoc/c_24242, http://aims.fao.org/aos/agrovoc/c_36761, http://aims.fao.org/aos/agrovoc/c_24071, http://aims.fao.org/aos/agrovoc/c_6498, http://aims.fao.org/aos/agrovoc/c_16343, http://aims.fao.org/aos/agrovoc/c_28504, http://aims.fao.org/aos/agrovoc/c_4819, http://aims.fao.org/aos/agrovoc/c_3081,
Online Access:http://agritrop.cirad.fr/595145/
http://agritrop.cirad.fr/595145/1/Betbederetal_JSTARS_2016%281%29.pdf
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spelling dig-cirad-fr-5951452024-01-29T02:38:02Z http://agritrop.cirad.fr/595145/ http://agritrop.cirad.fr/595145/ Assimilation of LAI and dry biomass data from optical and SAR images into an agro-meteorological model to estimate soybean yield. Betbeder Julie, Fieuzal Remy, Baup Frederic. 2016. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 9 (6) : 2540-2553.https://doi.org/10.1109/JSTARS.2016.2541169 <https://doi.org/10.1109/JSTARS.2016.2541169> Assimilation of LAI and dry biomass data from optical and SAR images into an agro-meteorological model to estimate soybean yield Betbeder, Julie Fieuzal, Remy Baup, Frederic eng 2016 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing F01 - Culture des plantes U30 - Méthodes de recherche P40 - Météorologie et climatologie soja rendement des cultures indice de végétation agrométéorologie modèle de simulation imagerie par satellite radar télédétection image spot polarimétrie http://aims.fao.org/aos/agrovoc/c_14477 http://aims.fao.org/aos/agrovoc/c_10176 http://aims.fao.org/aos/agrovoc/c_9000171 http://aims.fao.org/aos/agrovoc/c_8689 http://aims.fao.org/aos/agrovoc/c_24242 http://aims.fao.org/aos/agrovoc/c_36761 http://aims.fao.org/aos/agrovoc/c_24071 http://aims.fao.org/aos/agrovoc/c_6498 http://aims.fao.org/aos/agrovoc/c_16343 http://aims.fao.org/aos/agrovoc/c_28504 Midi-Pyrénées France http://aims.fao.org/aos/agrovoc/c_4819 http://aims.fao.org/aos/agrovoc/c_3081 Crop monitoring at a fine scale and crop yield estimation are critical from an environmental perspective because they provide essential information to combine increased food production and sustainable management of agricultural landscapes. The aim of this article is to estimate soybean yield using an agro-meteorological model controlled by optical and/or synthetic aperture radar (SAR) multipolarized satellite images. Satellite and ground data were collected over seven working farms. Optical and SAR images were acquired by Formosat-2, Spot-4, Spot-5, and Radarsat-2 satellites during the soybean vegetation cycle. A vegetation index (NDVI) was derived from the optical images, and backscattering coefficients and polarimetric indicators were computed from full quad-pol Radarsat-2 images. An angular normalization of SAR data was performed to minimize the incidence angle effects on SAR signals by using the complementarities provided by SAR and optical data. The best results are obtained when the model is controlled by both the leaf area index (LAI) derived from the optical vegetation index modified triangular vegetation index (MTVI2) or from the SAR backscattering coefficient σ °VV (LAI MTVI2 or (LAI σ ° VV ) and the dry biomass (DB) derived from the SAR Pauli matrix T33 (DB T33 )(r2 > 0.83), demonstrating the complementary of optical and SAR data. article info:eu-repo/semantics/article Journal Article info:eu-repo/semantics/publishedVersion http://agritrop.cirad.fr/595145/1/Betbederetal_JSTARS_2016%281%29.pdf text Cirad license info:eu-repo/semantics/restrictedAccess https://agritrop.cirad.fr/mention_legale.html https://doi.org/10.1109/JSTARS.2016.2541169 10.1109/JSTARS.2016.2541169 info:eu-repo/semantics/altIdentifier/doi/10.1109/JSTARS.2016.2541169 info:eu-repo/semantics/altIdentifier/purl/https://doi.org/10.1109/JSTARS.2016.2541169
institution CIRAD FR
collection DSpace
country Francia
countrycode FR
component Bibliográfico
access En linea
databasecode dig-cirad-fr
tag biblioteca
region Europa del Oeste
libraryname Biblioteca del CIRAD Francia
language eng
topic F01 - Culture des plantes
U30 - Méthodes de recherche
P40 - Météorologie et climatologie
soja
rendement des cultures
indice de végétation
agrométéorologie
modèle de simulation
imagerie par satellite
radar
télédétection
image spot
polarimétrie
http://aims.fao.org/aos/agrovoc/c_14477
http://aims.fao.org/aos/agrovoc/c_10176
http://aims.fao.org/aos/agrovoc/c_9000171
http://aims.fao.org/aos/agrovoc/c_8689
http://aims.fao.org/aos/agrovoc/c_24242
http://aims.fao.org/aos/agrovoc/c_36761
http://aims.fao.org/aos/agrovoc/c_24071
http://aims.fao.org/aos/agrovoc/c_6498
http://aims.fao.org/aos/agrovoc/c_16343
http://aims.fao.org/aos/agrovoc/c_28504
http://aims.fao.org/aos/agrovoc/c_4819
http://aims.fao.org/aos/agrovoc/c_3081
F01 - Culture des plantes
U30 - Méthodes de recherche
P40 - Météorologie et climatologie
soja
rendement des cultures
indice de végétation
agrométéorologie
modèle de simulation
imagerie par satellite
radar
télédétection
image spot
polarimétrie
http://aims.fao.org/aos/agrovoc/c_14477
http://aims.fao.org/aos/agrovoc/c_10176
http://aims.fao.org/aos/agrovoc/c_9000171
http://aims.fao.org/aos/agrovoc/c_8689
http://aims.fao.org/aos/agrovoc/c_24242
http://aims.fao.org/aos/agrovoc/c_36761
http://aims.fao.org/aos/agrovoc/c_24071
http://aims.fao.org/aos/agrovoc/c_6498
http://aims.fao.org/aos/agrovoc/c_16343
http://aims.fao.org/aos/agrovoc/c_28504
http://aims.fao.org/aos/agrovoc/c_4819
http://aims.fao.org/aos/agrovoc/c_3081
spellingShingle F01 - Culture des plantes
U30 - Méthodes de recherche
P40 - Météorologie et climatologie
soja
rendement des cultures
indice de végétation
agrométéorologie
modèle de simulation
imagerie par satellite
radar
télédétection
image spot
polarimétrie
http://aims.fao.org/aos/agrovoc/c_14477
http://aims.fao.org/aos/agrovoc/c_10176
http://aims.fao.org/aos/agrovoc/c_9000171
http://aims.fao.org/aos/agrovoc/c_8689
http://aims.fao.org/aos/agrovoc/c_24242
http://aims.fao.org/aos/agrovoc/c_36761
http://aims.fao.org/aos/agrovoc/c_24071
http://aims.fao.org/aos/agrovoc/c_6498
http://aims.fao.org/aos/agrovoc/c_16343
http://aims.fao.org/aos/agrovoc/c_28504
http://aims.fao.org/aos/agrovoc/c_4819
http://aims.fao.org/aos/agrovoc/c_3081
F01 - Culture des plantes
U30 - Méthodes de recherche
P40 - Météorologie et climatologie
soja
rendement des cultures
indice de végétation
agrométéorologie
modèle de simulation
imagerie par satellite
radar
télédétection
image spot
polarimétrie
http://aims.fao.org/aos/agrovoc/c_14477
http://aims.fao.org/aos/agrovoc/c_10176
http://aims.fao.org/aos/agrovoc/c_9000171
http://aims.fao.org/aos/agrovoc/c_8689
http://aims.fao.org/aos/agrovoc/c_24242
http://aims.fao.org/aos/agrovoc/c_36761
http://aims.fao.org/aos/agrovoc/c_24071
http://aims.fao.org/aos/agrovoc/c_6498
http://aims.fao.org/aos/agrovoc/c_16343
http://aims.fao.org/aos/agrovoc/c_28504
http://aims.fao.org/aos/agrovoc/c_4819
http://aims.fao.org/aos/agrovoc/c_3081
Betbeder, Julie
Fieuzal, Remy
Baup, Frederic
Assimilation of LAI and dry biomass data from optical and SAR images into an agro-meteorological model to estimate soybean yield
description Crop monitoring at a fine scale and crop yield estimation are critical from an environmental perspective because they provide essential information to combine increased food production and sustainable management of agricultural landscapes. The aim of this article is to estimate soybean yield using an agro-meteorological model controlled by optical and/or synthetic aperture radar (SAR) multipolarized satellite images. Satellite and ground data were collected over seven working farms. Optical and SAR images were acquired by Formosat-2, Spot-4, Spot-5, and Radarsat-2 satellites during the soybean vegetation cycle. A vegetation index (NDVI) was derived from the optical images, and backscattering coefficients and polarimetric indicators were computed from full quad-pol Radarsat-2 images. An angular normalization of SAR data was performed to minimize the incidence angle effects on SAR signals by using the complementarities provided by SAR and optical data. The best results are obtained when the model is controlled by both the leaf area index (LAI) derived from the optical vegetation index modified triangular vegetation index (MTVI2) or from the SAR backscattering coefficient σ °VV (LAI MTVI2 or (LAI σ ° VV ) and the dry biomass (DB) derived from the SAR Pauli matrix T33 (DB T33 )(r2 > 0.83), demonstrating the complementary of optical and SAR data.
format article
topic_facet F01 - Culture des plantes
U30 - Méthodes de recherche
P40 - Météorologie et climatologie
soja
rendement des cultures
indice de végétation
agrométéorologie
modèle de simulation
imagerie par satellite
radar
télédétection
image spot
polarimétrie
http://aims.fao.org/aos/agrovoc/c_14477
http://aims.fao.org/aos/agrovoc/c_10176
http://aims.fao.org/aos/agrovoc/c_9000171
http://aims.fao.org/aos/agrovoc/c_8689
http://aims.fao.org/aos/agrovoc/c_24242
http://aims.fao.org/aos/agrovoc/c_36761
http://aims.fao.org/aos/agrovoc/c_24071
http://aims.fao.org/aos/agrovoc/c_6498
http://aims.fao.org/aos/agrovoc/c_16343
http://aims.fao.org/aos/agrovoc/c_28504
http://aims.fao.org/aos/agrovoc/c_4819
http://aims.fao.org/aos/agrovoc/c_3081
author Betbeder, Julie
Fieuzal, Remy
Baup, Frederic
author_facet Betbeder, Julie
Fieuzal, Remy
Baup, Frederic
author_sort Betbeder, Julie
title Assimilation of LAI and dry biomass data from optical and SAR images into an agro-meteorological model to estimate soybean yield
title_short Assimilation of LAI and dry biomass data from optical and SAR images into an agro-meteorological model to estimate soybean yield
title_full Assimilation of LAI and dry biomass data from optical and SAR images into an agro-meteorological model to estimate soybean yield
title_fullStr Assimilation of LAI and dry biomass data from optical and SAR images into an agro-meteorological model to estimate soybean yield
title_full_unstemmed Assimilation of LAI and dry biomass data from optical and SAR images into an agro-meteorological model to estimate soybean yield
title_sort assimilation of lai and dry biomass data from optical and sar images into an agro-meteorological model to estimate soybean yield
url http://agritrop.cirad.fr/595145/
http://agritrop.cirad.fr/595145/1/Betbederetal_JSTARS_2016%281%29.pdf
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AT fieuzalremy assimilationoflaianddrybiomassdatafromopticalandsarimagesintoanagrometeorologicalmodeltoestimatesoybeanyield
AT baupfrederic assimilationoflaianddrybiomassdatafromopticalandsarimagesintoanagrometeorologicalmodeltoestimatesoybeanyield
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