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Developing a subswath-based wind speed retrieval model for sentinel-1 VH-Polarized SAR data over the ocean surface / Kangyu Zhang in IEEE Transactions on geoscience and remote sensing, vol 57 n° 3 (March 2019)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 57 n° 3 (March 2019) . - pp 1561 - 1572 Titre : Developing a subswath-based wind speed retrieval model for sentinel-1 VH-Polarized SAR data over the ocean surface Type de document : Article/Communication Auteurs : Kangyu Zhang, Auteur ; Jingfeng Huang, Auteur ; Lamin R. Mansaray, Auteur ; Qiaoying Guo, Auteur ; et al., Auteur Année de publication : 2019 Article en page(s) : pp 1561 - 1572 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes descripteurs IGN] bande C [Termes descripteurs IGN] données polarimétriques [Termes descripteurs IGN] image Sentinel-SAR [Termes descripteurs IGN] polarimétrie radar [Termes descripteurs IGN] surface de la mer [Termes descripteurs IGN] vent [Termes descripteurs IGN] vitesse Résumé : (Auteur) This paper evaluates the capability of Sentinel-1 VH-polarized synthetic aperture radar signals, involving 738 scenes in the interferometric wide swath (IW) mode, for ocean surface wind speed retrieval using a novel subswath-based C-band cross-polarized ocean model. When compared with in situ measurements, it is observed that wind speed retrieval accuracy varies progressively along swath, with the most accurate wind speed retrievals being derived from subswath 3 [root-mean-square error (RMSE) of 1.82 m · s -1 ], followed by subswath 2 (RMSE of 1.92 m · s -1 ), while subswath 1 showed the lowest retrieval accuracy (RMSE of 2.37 m · s -1 ). The average RMSE of wind speeds retrieved from all the three subswaths is 2.08 m · s -1 under low-to-high wind speed regimes (wind speeds Numéro de notice : A2019-130 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2018.2867438 date de publication en ligne : 20/09/2018 En ligne : https://doi.org/10.1109/TGRS.2018.2867438 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92459 [article]

A modeling-based approach for soil frost detection in the northern boreal forest region with C-Band SAR / Juval Cohen in IEEE Transactions on geoscience and remote sensing, vol 57 n° 2 (February 2019)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 57 n° 2 (February 2019) . - pp 1069 - 1083 Titre : A modeling-based approach for soil frost detection in the northern boreal forest region with C-Band SAR Type de document : Article/Communication Auteurs : Juval Cohen, Auteur ; Kimmo Rautinainen, Auteur ; Jaakko Ikonen, Auteur Année de publication : 2019 Article en page(s) : pp 1069 - 1083 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes descripteurs IGN] bande C [Termes descripteurs IGN] Betula (genre) [Termes descripteurs IGN] état du sol [Termes descripteurs IGN] Finlande [Termes descripteurs IGN] forêt boréale [Termes descripteurs IGN] Picea abies [Termes descripteurs IGN] Pinus sylvestris [Termes descripteurs IGN] podzosol Résumé : (Auteur) This paper presents a new approach for monitoring soil frost in the northern boreal forest region using co-polarized C-band synthetic aperture radar (SAR) data. Due to the high sensitivity of the C-band signal to vegetation, estimating the soil freeze/thaw (F/T) state directly from the measured backscatter is not feasible over dense vegetation, such as boreal forests. The presented method is based on applying a simple zeroth-order model to estimate the contribution of the ground and the forest canopy on the observed total backscatter. The retrieved ground and canopy backscatter values were compared with in situ information on soil F/T state. By using a linear least sum of square errors classification algorithm, the retrieved ground and canopy backscatter values representing frozen and thawed ground were successfully separated. The method was tested for various soil types and incidence angles. For soil types with higher water holding capacities and lower infiltration rates such as fine Haplic Podzol and Umbric Gleysol, the estimation accuracy of the F/T state was over 97%, whereas for drier, well-drained soil types such as Haplic Arenosol and Coarse Haplic Podzol it was over 94%. Estimation accuracy slightly increased with higher incidence angle. The method is not feasible in rocky terrain due to very low water content, or in wet snow conditions due to lack of penetration of the C-band SAR signal through wet snow. With low ancillary data and computational requirements, the proposed method is applicable for continuous near real-time monitoring of soil F/T state. Numéro de notice : A2019-111 Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2018.2864635 date de publication en ligne : 17/09/2018 En ligne : https://doi.org/10.1109/TGRS.2018.2864635 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92450 [article]

Evaluating SAR-optical sensor fusion for aboveground biomass estimation in a Brazilian tropical forest / Aline Bernarda Debastiani in Annals of forest research, vol 62 n° 1 (January - June 2019)  

Public [article]  inAnnals of forest research > vol 62 n° 1 (January - June 2019) . - pp 109 - 122 Titre : Evaluating SAR-optical sensor fusion for aboveground biomass estimation in a Brazilian tropical forest Type de document : Article/Communication Auteurs : Aline Bernarda Debastiani, Auteur ; Carlos Roberto Sanquetta, Auteur ; Ana Paula Dalla Corte, Auteur ; et al., Auteur Année de publication : 2019 Article en page(s) : pp 109 - 122 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte [Termes descripteurs IGN] Amazonie [Termes descripteurs IGN] apprentissage automatique [Termes descripteurs IGN] arbre aléatoire [Termes descripteurs IGN] bande C [Termes descripteurs IGN] biomasse aérienne [Termes descripteurs IGN] Brésil [Termes descripteurs IGN] forêt tropicale [Termes descripteurs IGN] fusion d'images [Termes descripteurs IGN] image radar moirée [Termes descripteurs IGN] image Sentinel-MSI [Termes descripteurs IGN] image Sentinel-SAR [Termes descripteurs IGN] réseau neuronal convolutif Résumé : (auteur) The aim of the present study is to evaluate the potential of C-band SAR data from the Sentinel-1/2 instruments and machine learning algorithms for the estimation of forest above ground forest biomass (AGB) in a high-biomass tropical ecosystem. This study was carried out in Jamari National Forest, located in the Brazilian Amazon. The response variable was AGB (Mg/ha) estimated from airborne laser surveys. The following treatments were considered as model predictors: 1) Sentinel-1 Sigma 0 at VV and VH polarizations; 2) (1) plus Sentinel-1 textural metrics; 3) (2) plus Sentinel-2 bands and derived vegetation indices (LAI, RVI, SAVI, NDVI).Our modeling design estimated the relative importance of SAR vs. optical variables in explaining AGB. The modeling was performed with twelve machine-learning algorithms including, neural network and regression tree. The addition of texture and optical data provided a noticeable improvement (3%) over models with SAR backscatter only. The best model performance was achieved with the Random Tree algorithm. Our results demonstrate the potential of freely-available SAR data and machine learning for mapping AGB in tropical ecosystems. Numéro de notice : A2019-335 Thématique : IMAGERIE Nature : Article DOI : 10.15287/afr.2018.1267 date de publication en ligne : 30/07/2019 En ligne : http://dx.doi.org/10.15287%2Fafr.2018.1267 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93349 [article]

Toward global soil moisture monitoring with sentinel-1 : harnessing assets and overcoming obstacles / Bernhard Bauer-Marschallinger in IEEE Transactions on geoscience and remote sensing, vol 57 n° 1 (January 2019)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 57 n° 1 (January 2019) . - pp 520 - 539 Titre : Toward global soil moisture monitoring with sentinel-1 : harnessing assets and overcoming obstacles Type de document : Article/Communication Auteurs : Bernhard Bauer-Marschallinger, Auteur ; Vahid Freeman, Auteur ; Senmao Cao, Auteur ; et al., Auteur Année de publication : 2019 Article en page(s) : pp 520 - 539 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes descripteurs IGN] bande C [Termes descripteurs IGN] bilan hydrique [Termes descripteurs IGN] humidité du sol [Termes descripteurs IGN] image radar moirée [Termes descripteurs IGN] image Sentinel-SAR [Termes descripteurs IGN] Italie [Termes descripteurs IGN] Ombrie (Italie) [Termes descripteurs IGN] surveillance agricole [Termes descripteurs IGN] surveillance météorologique Résumé : (Auteur) Soil moisture is a key environmental variable, important to, e.g., farmers, meteorologists, and disaster management units. Here, we present a method to retrieve surface soil moisture (SSM) from the Sentinel-1 (S-1) satellites, which carry C-band Synthetic Aperture Radar (CSAR) sensors that provide the richest freely available SAR data source so far, unprecedented in accuracy and coverage. Our SSM retrieval method, adapting well-established change detection algorithms, builds the first globally deployable soil moisture observation data set with 1-km resolution. This paper provides an algorithm formulation to be operated in data cube architectures and high-performance computing environments. It includes the novel dynamic Gaussian upscaling method for spatial upscaling of SAR imagery, harnessing its field-scale information and successfully mitigating effects from the SAR's high signal complexity. Also, a new regression-based approach for estimating the radar slope is defined, coping with Sentinel-1's inhomogeneity in spatial coverage. We employ the S-1 SSM algorithm on a 3-year S-1 data cube over Italy, obtaining a consistent set of model parameters and product masks, unperturbed by coverage discontinuities. An evaluation of therefrom generated S-1 SSM data, involving a 1-km soil water balance model over Umbria, yields high agreement over plains and agricultural areas, with low agreement over forests and strong topography. While positive biases during the growing season are detected, the excellent capability to capture small-scale soil moisture changes as from rainfall or irrigation is evident. The S-1 SSM is currently in preparation toward operational product dissemination in the Copernicus Global Land Service. Numéro de notice : A2019-108 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2018.2858004 date de publication en ligne : 22/08/2018 En ligne : https://doi.org/10.1109/TGRS.2018.2858004 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92425 [article]

Polarimetric radar vegetation index for biomass estimation in desert fringe ecosystems / Jisung Geba Chang in IEEE Transactions on geoscience and remote sensing, vol 56 n° 12 (December 2018)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 56 n° 12 (December 2018) . - pp 7102 - 7108 Titre : Polarimetric radar vegetation index for biomass estimation in desert fringe ecosystems Type de document : Article/Communication Auteurs : Jisung Geba Chang, Auteur ; Maxim Shoshany, Auteur ; Yisok Oh, Auteur Année de publication : 2018 Article en page(s) : pp 7102 - 7108 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes descripteurs IGN] allométrie [Termes descripteurs IGN] bande C [Termes descripteurs IGN] bande L [Termes descripteurs IGN] bassin méditerranéen [Termes descripteurs IGN] biomasse [Termes descripteurs IGN] carte de la végétation [Termes descripteurs IGN] coefficient de rétrodiffusion [Termes descripteurs IGN] données de terrain [Termes descripteurs IGN] image ALOS-PALSAR [Termes descripteurs IGN] image Sentinel-SAR [Termes descripteurs IGN] indice de végétation [Termes descripteurs IGN] polarimétrie radar [Termes descripteurs IGN] zone aride [Termes descripteurs IGN] zone semi-aride Résumé : (auteur) Biomass estimation of eastern Mediterranean shrublands was investigated using PALSAR full- and dual-polarization L-band and Sentinel-1 dual-polarization C-band data. First, we conducted an empirical assessment of single and multiple regressions between polarized backscattering coefficients and shrubland biomass distribution along the climatic gradient between semiarid and arid regions. We then found that the PALSAR L-band HV-polarized backscattering coefficient has higher biomass information content than Sentinel-1 C-band data. Based on a theoretical volume scattering model and a semiempirical model, we propose a new polarimetric radar vegetation index (PRVI) that utilizes the degree of polarization and the cross-polarized backscattering coefficient. The relationship between the new index and the biomass was assessed with reference to normalized difference vegetation index-based biomass estimates calculated using Landsat imagery. The PRVI was found to have higher correlation with biomass compared with other radar polarization parameters, in general, and an existing radar vegetation index (RVI), in particular. Assessment of PRVI-based biomass predictions compared with allometric data extracted from air photographs, Lidar, and field data for 67 sites across the desert fringe zone indicated moderate performance with an RMSE of 0.329 kg/m 2 , while an RVI-based biomass estimation had an RMSE of 0.439 kg/m². Numéro de notice : A2018-553 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2018.2848285 date de publication en ligne : 03/07/2018 En ligne : http://dx.doi.org/10.1109/TGRS.2018.2848285 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91659 [article]

Polarization orientation angle and polarimetric SAR scattering characteristics of steep terrain / Jong-Sen Lee in IEEE Transactions on geoscience and remote sensing, vol 56 n° 12 (December 2018)  

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Estimation of winter wheat crop growth parameters using time series Sentinel-1A SAR data / P. Kumar in Geocarto international, vol 33 n° 9 (September 2018)  

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A statistical approach to preprocess and enhance C-band SAR images in order to detect automatically marine oil slicks / Zhour Najoui in IEEE Transactions on geoscience and remote sensing, vol 56 n° 5 (May 2018)  

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InSAR to support sustainable urbanization over compacting aquifers: The case of Toluca Valley, Mexico / Pascal Castellazzi in International journal of applied Earth observation and geoinformation, vol 63 (December 2017)  

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The potential of multifrequency SAR images for estimating forest biomass in Mediterranean areas / Emanuele Santi in Remote sensing of environment, vol 200 (October 2017)  

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Displacement monitoring and modelling of a high-speed railway bridge using C-band Sentinel-1 data / Qihuan Huang in ISPRS Journal of photogrammetry and remote sensing, vol 128 (June 2017)  

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Satellite-based probabilistic assessment of soil moisture using C-band quad-polarized RISAT1 data / Manali Pal in IEEE Transactions on geoscience and remote sensing, vol 55 n° 3 (March 2017)  

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Apport de la télédétection radar satellitaire pour la cartographie de la forêt des Landes / Yousra Hamrouni (2016)

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Radar based classification prior to biomass retrieval from P-Band SAR data / Pierre-Louis Frison (2016)  

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InSAR assessment of surface deformations in urban coastal terrains associated with groundwater dynamics / Jonathan C. L. Normand in IEEE Transactions on geoscience and remote sensing, vol 53 n° 12 (December 2015)

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