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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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 |
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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 |
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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 |
work_keys_str_mv |
AT betbederjulie assimilationoflaianddrybiomassdatafromopticalandsarimagesintoanagrometeorologicalmodeltoestimatesoybeanyield AT fieuzalremy assimilationoflaianddrybiomassdatafromopticalandsarimagesintoanagrometeorologicalmodeltoestimatesoybeanyield AT baupfrederic assimilationoflaianddrybiomassdatafromopticalandsarimagesintoanagrometeorologicalmodeltoestimatesoybeanyield |
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1792499912344076288 |