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Optimization of multi-ecosystem model ensembles to simulate vegetation growth at the global scale / Linling Tang in IEEE Transactions on geoscience and remote sensing, vol 59 n° 2 (February 2021)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 59 n° 2 (February 2021) . - pp 962 - 978 Titre : Optimization of multi-ecosystem model ensembles to simulate vegetation growth at the global scale Type de document : Article/Communication Auteurs : Linling Tang, Auteur ; Qian Lei, Auteur ; Weizhe Liu, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 962 - 978 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] croissance végétale [Termes descripteurs IGN] écosystème [Termes descripteurs IGN] estimation bayesienne [Termes descripteurs IGN] Leaf Area Index [Termes descripteurs IGN] modèle de simulation [Termes descripteurs IGN] optimisation (mathématiques) [Termes descripteurs IGN] optimisation par essaim de particules Résumé : (auteur) Process-based ecosystem models are increasingly used to simulate the effects of a changing environment on vegetation growth in the past, present, and future. To improve the simulation, the multimodel ensemble mean (MME) and ensemble Bayesian model averaging (EBMA) methods are often used in optimizing the integration of ecosystem model ensemble. These two methods were compared with four other optimization techniques, including genetic algorithm (GA), particle swarm optimization (PSO), cuckoo search (CS), and interior-point method (IPM), to evaluate their efficiency in this article. Here, we focused on eight commonly used ecosystem models to simulate vegetation growth, represented by the growing season leaf area index (LAIgs), collected globally from 2000 to 2014. The performances of the multimodel ensembles and individual models were compared using the satellite-observed LAI products as the reference. Generally, ensemble simulations provide more accurate estimates than individual models. There were significant performance differences among the six tested methods. The IPM ensemble model simulated LAIgs more accurately than the other tested models, as the reduction in the root-mean-square error was 84.99% higher than the MME results and 61.50% higher than the EBMA results. Thus, IPM optimization can reproduce LAIgs trends accurately for 91.62% of the global vegetated area, which is double the area of the results from MME. Furthermore, the contributions and uncertainties of the individual models in the final simulated IPM LAIgs changes indicated that the best individual model (CABLE) showed the greatest area fraction for the maximum IPM weight (32.49%), especially in the low-lalitude to midlatitude areas. Numéro de notice : A2021-111 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.12.014 date de publication en ligne : 03/06/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.12.014 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96913 [article]

Polarization of light reflected by grass: modeling using visible-sunlit areas / Bin Yang in Photogrammetric Engineering & Remote Sensing, PERS, vol 86 n° 12 (December 2020)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 86 n° 12 (December 2020) . - pp 745 - 752 Titre : Polarization of light reflected by grass: modeling using visible-sunlit areas Type de document : Article/Communication Auteurs : Bin Yang, Auteur ; Lei Yan, Auteur ; Siyuan Liu, Auteur Année de publication : 2020 Article en page(s) : pp 745 - 752 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] aérosol [Termes descripteurs IGN] canopée [Termes descripteurs IGN] distribution du coefficient de réflexion bidirectionnelle BRDF [Termes descripteurs IGN] ensoleillement [Termes descripteurs IGN] image POLDER [Termes descripteurs IGN] image Terra-MODIS [Termes descripteurs IGN] Leaf Area Index [Termes descripteurs IGN] polarisation [Termes descripteurs IGN] réflectance de surface [Termes descripteurs IGN] réflectance végétale Résumé : (Auteur) The Bidirectional polarization distribution function (BPDF) of land surfaces is important for studies of land surfaces and aerosol. With the availability of a huge number of polarization measurements, several semi-empirical BPDF models have been proposed. However, these models do not pay much attention to canopy structure, which is fundamental for generation of polarization. In this article, we propose a new BPDF model using canopy structure information, which is parameterized by visible-sunlit areas. It is evaluated over grassland using POLDER BPDF and MODIS leaf area index data sets. Experiments suggest that compared to Nadal–Bréon and Litvinov models, the new BPDF model reduces root-mean-square error by 7% and 10%, respectively. The new BPDF model also provides better performance when it is fitted using observations clustered by sun zenith angle. The new BPDF model thus provides an effective tool for the study of land surface polarization. Numéro de notice : A2020-763 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.86.12.745 date de publication en ligne : 01/12/2020 En ligne : https://doi.org/10.14358/PERS.86.12.745 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96552 [article]

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Quantification of cotton water consumption by remote sensing / Jefferson Vieira José in Geocarto international, vol 35 n° 16 ([01/12/2020])  

Public [article]  inGeocarto international > vol 35 n° 16 [01/12/2020] . - pp 1800 - 1813 Titre : Quantification of cotton water consumption by remote sensing Type de document : Article/Communication Auteurs : Jefferson Vieira José, Auteur ; Niclene Ponce Rodrigues de Oliveira, Auteur ; Tonny José de Araújo da Silva, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 1800 - 1813 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes descripteurs IGN] biome [Termes descripteurs IGN] cultures irriguées [Termes descripteurs IGN] évapotranspiration [Termes descripteurs IGN] gestion de l'eau [Termes descripteurs IGN] Gossypium (genre) [Termes descripteurs IGN] image thermique [Termes descripteurs IGN] irrigation [Termes descripteurs IGN] Leaf Area Index [Termes descripteurs IGN] Mato Grosso [Termes descripteurs IGN] SEBAL (algorithme) Résumé : (auteur) Quantifying crop water consumption is essential for water resource management. The objective was to estimate the current evapotranspiration (ETa) of the cotton crop (Gossypium hirsutum L.) in the rainfed system, as well as the components of the radiation and energy balance in the Cerrado biome conditions using orbital images and the SEBAL algorithm and validate the estimates of evapotranspiration (ET) using FAO guidelines for crop coefficient (K c) of the cotton crop. Research was carried out in the State of Mato Grosso, Brazil, in areas with three cotton cultivars. Images of the Operational Land Imager and Thermal Infrared Sensor sensors were used and ET estimation was made based on the SEBAL algorithm. Mean ETa in the cotton cycle was 3.5 mm dia−1 and the K c values ranged from 0.22 and 1.12, on average, in the smaller and larger leaf area, respectively. Numéro de notice : A2020-726 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2019.1583777 date de publication en ligne : 18/03/2019 En ligne : https://doi.org/10.1080/10106049.2019.1583777 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96329 [article]

Bistatic specular scattering measurements for the estimation of rice crop growth variables using fuzzy inference system at X-, C-, and L-bands / Ajeet Kumar Vishwakarma in Geocarto international, vol 35 n° 13 ([01/10/2020])  

Public [article]  inGeocarto international > vol 35 n° 13 [01/10/2020] . - pp 1433 - 1449 Titre : Bistatic specular scattering measurements for the estimation of rice crop growth variables using fuzzy inference system at X-, C-, and L-bands Type de document : Article/Communication Auteurs : Ajeet Kumar Vishwakarma, Auteur ; Rajendra Prasad, Auteur Année de publication : 2020 Article en page(s) : pp 1433 - 1449 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] bande C [Termes descripteurs IGN] bande L [Termes descripteurs IGN] bande X [Termes descripteurs IGN] biomasse [Termes descripteurs IGN] indice foliaire [Termes descripteurs IGN] Inférence floue [Termes descripteurs IGN] Leaf Area Index [Termes descripteurs IGN] Oryza (genre) [Termes descripteurs IGN] polarisation [Termes descripteurs IGN] radar bistatique [Termes descripteurs IGN] teneur en eau de la végétation Résumé : (auteur) Bistatic scatterometer measurements were performed on the rice crop-bed in the angular range of 20° to 60° for specular direction (ϕ=0) at X-, C- and L-bands for HH-, VV-, and HV-polarizations. The dominant scattering contribution to bistatic specular scattering coefficients (σ0) was analysed with the crop growth stages at various angle of incidence. The regression analysis showed high correlation between σ0 and crop growth variables at 40° angle of incidence for HH-polarization at X-band and for VV-polarization at C- and L-bands. The estimation of rice crop growth variables using subtractive clustering based fuzzy inference system (S-FIS) was done at 40° angle of incidence. The lower values of computed root mean square error (RMSE) between the observed and estimated values showed high potential of developed S-FIS model for the estimation of leaf area index for HH-polarisation at X-band, vegetation water content and fresh biomass for VV-polarization at C- and L-bands, respectively. Numéro de notice : A2020-608 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2019.1576777 date de publication en ligne : 18/03/2019 En ligne : https://doi.org/10.1080/10106049.2019.1576777 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95969 [article]

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Comparative analysis of index and chemometric techniques-based assessment of leaf area index (LAI) in wheat through field spectroradiometer, Landsat-8, Sentinel-2 and Hyperion bands / Bappa Das in Geocarto international, vol 35 n° 13 ([01/10/2020])  

Public [article]  inGeocarto international > vol 35 n° 13 [01/10/2020] . - pp 1415 - 1432 Titre : Comparative analysis of index and chemometric techniques-based assessment of leaf area index (LAI) in wheat through field spectroradiometer, Landsat-8, Sentinel-2 and Hyperion bands Type de document : Article/Communication Auteurs : Bappa Das, Auteur ; Rabi N. Sahoo, Auteur ; Sourabh Pargal, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 1415 - 1432 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] analyse comparative [Termes descripteurs IGN] blé (céréale) [Termes descripteurs IGN] canopée [Termes descripteurs IGN] image EO1-Hyperion [Termes descripteurs IGN] image hyperspectrale [Termes descripteurs IGN] image Landsat-8 [Termes descripteurs IGN] image Sentinel-MSI [Termes descripteurs IGN] indice de végétation [Termes descripteurs IGN] Leaf Area Index [Termes descripteurs IGN] modèle de régression [Termes descripteurs IGN] réflectance spectrale [Termes descripteurs IGN] régression des moindres carrés partiels [Termes descripteurs IGN] séparateur à vaste marge [Termes descripteurs IGN] spectroradiomètre Résumé : (auteur) Successful retrieval of leaf area index (LAI) from hyperspectral remote sensing relies on the proper selection of indices or multivariate models. The objectives of the research work were to identify best vegetation index and multivariate model based on canopy reflectance and LAI measured at different growth stages of wheat. Comparison of existing indices revealed optimized soil-adjusted vegetation index (OSAVI) as the best index based on R2 of calibration, validation and root mean square error of validation. Proposed ratio index (RI; R670, R845) and normalized difference index (NDI; R670, R845) provided comparable performance with the existing vegetation indices (R2 = 0.65 and 0.62 for RI and NDI, respectively, during validation). Among the multivariate models, partial least squares regression (PLSR) model with Hyperion band configuration performed the best during validation (R2 = 0.80 and RMSE = 0.58 m2 m−2). Our results manifested the opportunities for developing biophysical products based on satellite sensors. Numéro de notice : A2020-607 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2019.1581271 date de publication en ligne : 28/03/2019 En ligne : https://doi.org/10.1080/10106049.2019.1581271 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95967 [article]

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Ground-based remote sensing of forests exploiting GNSS signals / Leila Guerriero in IEEE Transactions on geoscience and remote sensing, vol 58 n° 10 (October 2020)  

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Towards a semi-automated mapping of Australia native invasive alien Acacia trees using Sentinel-2 and radiative transfer models in South Africa / Cecilia Masemola in ISPRS Journal of photogrammetry and remote sensing, vol 166 (August 2020)  

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Path length correction for improving leaf area index measurements over sloping terrains: A deep analysis through computer simulation / Gaofei Yin in IEEE Transactions on geoscience and remote sensing, vol 58 n° 7 (July 2020)  

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Unsupervised semantic and instance segmentation of forest point clouds / Di Wang in ISPRS Journal of photogrammetry and remote sensing, vol 165 (July 2020)  

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Wheat leaf area index retrieval using RISAT-1 hybrid polarized SAR data / Thota Sivasankar in Geocarto international, Vol 35 n° 8 ([01/06/2020])  

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Year-to-year crown condition poorly contributes to ring width variations of beech trees in French ICP level I network / Clara Tallieu in Forest ecology and management, Vol 465 (1st June 2020)  

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Modeling strawberry biomass and leaf area using object-based analysis of high-resolution images / Zhen Guan in ISPRS Journal of photogrammetry and remote sensing, vol 163 (May 2020)  

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Temporal Validation of Four LAI Products over Grasslands in the Northeastern Tibetan Plateau / Gaofei Yin in Photogrammetric Engineering & Remote Sensing, PERS, vol 86 n° 4 (April 2020)  

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Red-edge band vegetation indices for leaf area index estimation from Sentinel-2/MSI imagery / Yuanheng Sun in IEEE Transactions on geoscience and remote sensing, vol 58 n° 2 (February 2020)  

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The effects of different combinations of simulated climate change-related stressors on juveniles of seven forest tree species grown as mono-species and mixed cultures / Alfas Pliüra in Baltic forestry, vol 26 n° 1 (2020)  

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