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Fusing microwave and optical satellite observations to simultaneously retrieve surface soil moisture, vegetation water content, and surface soil roughness / Yohei Sawada in IEEE Transactions on geoscience and remote sensing, vol 55 n° 11 (November 2017)
[article]
Titre : Fusing microwave and optical satellite observations to simultaneously retrieve surface soil moisture, vegetation water content, and surface soil roughness Type de document : Article/Communication Auteurs : Yohei Sawada, Auteur ; Toshio Koike, Auteur ; Kentaro Aida, Auteur ; et al., Auteur Année de publication : 2017 Article en page(s) : pp 6195 - 6206 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] fusion d'images
[Termes IGN] humidité du sol
[Termes IGN] image Aqua-AMSR
[Termes IGN] image Aqua-MODIS
[Termes IGN] image optique
[Termes IGN] image radar
[Termes IGN] rugosité du sol
[Termes IGN] teneur en eau de la végétationRésumé : (Auteur) Uncertainty in surface soil roughness strongly degrades the performance of surface soil moisture (SSM) and vegetation water content (VWC) retrieval from passive microwave observations. This paper proposes an algorithm to objectively determine the surface soil roughness parameter of the radiative transfer model by fusing microwave and optical satellite observations. It is then demonstrated in a semiarid in situ observation site. The roughness correction of this new algorithm positively impacted the performance of SSM (root-mean-square error reduced from 0.088 to 0.070) and VWC retrieval from the Advanced Microwave Scanning Radiometer 2 and Moderate Resolution Imaging Spectroradiometer. Since this surface soil roughness correction may be transferrable to other microwave satellite retrieval algorithms such as those for the Soil Moisture and Ocean Salinity and Soil Moisture Active Passive satellites, this new algorithm can contribute to many microwave earth surface observation satellite missions. Numéro de notice : A2017-746 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2017.2722468 En ligne : https://doi.org/10.1109/TGRS.2017.2722468 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=88781
in IEEE Transactions on geoscience and remote sensing > vol 55 n° 11 (November 2017) . - pp 6195 - 6206[article]Applicability of generalized additive model in groundwater potential modelling and comparison its performance by bivariate statistical methods / Fatemeh Falah in Geocarto international, vol 32 n° 10 (October 2017)
[article]
Titre : Applicability of generalized additive model in groundwater potential modelling and comparison its performance by bivariate statistical methods Type de document : Article/Communication Auteurs : Fatemeh Falah, Auteur ; Samira Ghorbani Nejad, Auteur ; Omid Rahmati, Auteur ; et al., Auteur Année de publication : 2017 Article en page(s) : pp 1069 - 1089 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications SIG
[Termes IGN] analyse bivariée
[Termes IGN] ArcGIS
[Termes IGN] eau souterraine
[Termes IGN] géostatistique
[Termes IGN] Iran
[Termes IGN] modèle de simulation
[Termes IGN] ressources en eau
[Termes IGN] système d'information géographiqueRésumé : (Auteur) Groundwater is the most valuable natural resource in arid areas. Therefore, any attempt to investigate potential zones of groundwater for further management of water supply is necessary. Hence, many researchers have worked on this subject all around the world. On the other hand, the Generalized Additive Model (GAM) has been applied to environmental and ecological modelling, but its applicability to other kinds of predictive modelling such as groundwater potential mapping has not yet been investigated. Therefore, the main purpose of this study is to evaluate the performance of GAM model and then its comparison with three popular GIS-based bivariate statistical methods, namely Frequency Ratio (FR), Statistical Index (SI) and Weight-of-Evidence (WOE) for producing groundwater spring potential map (GSPM) in Lorestan Province Iran. To achieve this, out of 6439 existed springs, 4291 spring locations were selected for training phase and the remaining 2147 springs for model evaluation. Next, the thematic layers of 12 effective spring parameters including altitude, plan curvature, slope angle, slope aspect, drainage density, distance from rivers, topographic wetness index, fault density, distance from fault, lithology, soil and land use/land cover were mapped and integrated using the ArcGIS 10.2 software to generate a groundwater prospect map using mentioned approaches. The produced GSPMs were then classified into four distinct groundwater potential zones, namely low, moderate, high and very high classes. The results of the analysis were finally validated using the receiver operating characteristic (ROC) curve technique. The results indicated that out of four models, SI is superior (prediction accuracy of 85.4%) following by FR, GAM and WOE, respectively (prediction accuracy of 83.7, 77 and 76.3%). The result of groundwater spring potential map is helpful as a guide for engineers in water resources management and land use planning in order to select suitable areas to implement development schemes and also government entities. Numéro de notice : A2017-669 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.201 Date de publication en ligne : 07/06/2016 En ligne : https://doi.org/10.1080/10106049.2016.1188166 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=87144
in Geocarto international > vol 32 n° 10 (October 2017) . - pp 1069 - 1089[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 059-2017101 RAB Revue Centre de documentation En réserve L003 Disponible An information fusion approach for PALSAR data to retrieve soil moisture / Ankita Jain in Geocarto international, vol 32 n° 9 (September 2017)
[article]
Titre : An information fusion approach for PALSAR data to retrieve soil moisture Type de document : Article/Communication Auteurs : Ankita Jain, Auteur ; Dharmendra Singh, Auteur Année de publication : 2017 Article en page(s) : pp 1017 - 1033 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] bande spectrale
[Termes IGN] couvert végétal
[Termes IGN] données polarimétriques
[Termes IGN] fusion de données
[Termes IGN] humidité du sol
[Termes IGN] image ALOS-PALSAR
[Termes IGN] polarimétrie radarRésumé : (Auteur) Estimation of vegetation covered soil moisture with satellite images is still a challenging task. Several models are available for soil moisture retrieval in which water cloud model (WCM) is most common. But, it requires an estimation of accurate vegetation parameterization. Thus, there is a need to develop such an approach for soil moisture retrieval which minimize these limitations. Therefore, this paper deals with the soil moisture retrieval using fully polarimetric SAR data by fusing the information from different bands. Various polarimetric indices and observables were critically analysed, and found that the index; SPAN (total scattered power) gives better information of vegetation cover as compared to other indices/observables. Based on this, WCM model has been modified using SPAN as parameter and soil moisture content were retrieved. Numéro de notice : A2017-459 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2016.1188163 Date de publication en ligne : 10/06/2016 En ligne : http://dx.doi.org/10.1080/10106049.2016.1188163 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=86384
in Geocarto international > vol 32 n° 9 (September 2017) . - pp 1017 - 1033[article]A time-series approach to estimating soil moisture from vegetated surfaces using L-band radar backscatter / Jeffrey D. Ouellette in IEEE Transactions on geoscience and remote sensing, vol 55 n° 6 (June 2017)
[article]
Titre : A time-series approach to estimating soil moisture from vegetated surfaces using L-band radar backscatter Type de document : Article/Communication Auteurs : Jeffrey D. Ouellette, Auteur ; Joel T. Johnson, Auteur ; Anna Balenzano, Auteur ; et al., Auteur Année de publication : 2017 Article en page(s) : pp 3186 - 3193 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] couvert végétal
[Termes IGN] détection de changement
[Termes IGN] humidité du sol
[Termes IGN] image radar
[Termes IGN] radiométrie
[Termes IGN] rétrodiffusion
[Termes IGN] série temporelle
[Termes IGN] traitement d'image radarRésumé : (Auteur) Many previous studies have shown the sensitivity of radar backscatter to surface soil moisture content, particularly at L-band. Moreover, the estimation of soil moisture from radar for bare soil surfaces is well-documented, but estimation underneath a vegetation canopy remains unsolved. Vegetation significantly increases the complexity of modeling the electromagnetic scattering in the observed scene, and can even obstruct the contributions from the underlying soil surface. Existing approaches to estimating soil moisture under vegetation using radar typically rely on a forward model to describe the backscattered signal and often require that the vegetation characteristics of the observed scene be provided by an ancillary data source. However, such information may not be reliable or available during the radar overpass of the observed scene (e.g., due to cloud coverage if derived from an optical sensor). Thus, the approach described herein is an extension of a change-detection method for soil moisture estimation, which does not require ancillary vegetation information, nor does it make use of a complicated forward scattering model. Novel modifications to the original algorithm include extension to multiple polarizations and a new technique for bounding the radar-derived soil moisture product using radiometer-based soil moisture estimates. Soil moisture estimates are generated using data from the Soil Moisture Active/Passive (SMAP) satellite-borne radar and radiometer data, and are compared with up-scaled data from a selection of in situ networks used in SMAP validation activities. These results show that the new algorithm can consistently achieve rms errors less than 0.07 m3/m3 over a variety land cover types. Numéro de notice : A2017-475 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2017.2663768 En ligne : https://doi.org/10.1109/TGRS.2017.2663768 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=86400
in IEEE Transactions on geoscience and remote sensing > vol 55 n° 6 (June 2017) . - pp 3186 - 3193[article]Surface soil moisture retrieval using the L-band synthetic aperture radar onboard the Soil Moisture Active–Passive Satellite and evaluation at core validation sites / Seung-Bum Kim in IEEE Transactions on geoscience and remote sensing, vol 55 n° 4 (April 2017)
[article]
Titre : Surface soil moisture retrieval using the L-band synthetic aperture radar onboard the Soil Moisture Active–Passive Satellite and evaluation at core validation sites Type de document : Article/Communication Auteurs : Seung-Bum Kim, Auteur ; Joel T. Johnson, Auteur ; Mahta Moghaddam, Auteur ; et al., Auteur Année de publication : 2017 Article en page(s) : pp 1897 - 1914 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] bande L
[Termes IGN] croissance végétale
[Termes IGN] données hétérogènes
[Termes IGN] humidité du sol
[Termes IGN] image radar moirée
[Termes IGN] mission SMAP
[Termes IGN] pente
[Termes IGN] polarisation
[Termes IGN] problème inverse
[Termes IGN] série temporelleRésumé : (Auteur) This paper evaluates the retrieval of soil moisture in the top 5-cm layer at 3-km spatial resolution using L-band dual-copolarized Soil Moisture Active-Passive (SMAP) synthetic aperture radar (SAR) data that mapped the globe every three days from mid-April to early July, 2015. Surface soil moisture retrievals using radar observations have been challenging in the past due to complicating factors of surface roughness and vegetation scattering. Here, physically based forward models of radar scattering for individual vegetation types are inverted using a time-series approach to retrieve soil moisture while correcting for the effects of static roughness and dynamic vegetation. Compared with the past studies in homogeneous field scales, this paper performs a stringent test with the satellite data in the presence of terrain slope, subpixel heterogeneity, and vegetation growth. The retrieval process also addresses any deficiencies in the forward model by removing any time-averaged bias between model and observations and by adjusting the strength of vegetation contributions. The retrievals are assessed at 14 core validation sites representing a wide range of global soil and vegetation conditions over grass, pasture, shrub, woody savanna, corn, wheat, and soybean fields. The predictions of the forward models used agree with SMAP measurements to within 0.5 dB unbiased-root-mean-square error (ubRMSE) and -0.05 dB (bias) for both copolarizations. Soil moisture retrievals have an accuracy of 0.052 m3/m3 ubRMSE, -0.015 m3/m3 bias, and a correlation of 0.50, compared to in situ measurements, thus meeting the accuracy target of 0.06 m3/m3 ubRMSE. The successful retrieval demonstrates the feasibility of a physically based time series retrieval with L-band SAR data for characterizing soil moisture over diverse conditions of soil moisture, surface roughness, and vegetation. Numéro de notice : A2017-169 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2016.2631126 En ligne : http://dx.doi.org/10.1109/TGRS.2016.2631126 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=84713
in IEEE Transactions on geoscience and remote sensing > vol 55 n° 4 (April 2017) . - pp 1897 - 1914[article]Derivation and validation of the high resolution satellite soil moisture products: a case study of the Biebrza Sentinel-1 validation sites / Jan Musiał in Geoinformation issues, Vol 8 n° 1 (2016)PermalinkSatellite-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)PermalinkDelineation of groundwater potential zones using remote sensing and GIS-based data-driven models / Samira Ghorbani Nejad in Geocarto international, vol 32 n° 2 (February 2017)PermalinkLand Surface Remote Sensing in Continental Hydrology, ch. 3. Using satellite scatterometers to monitor continental surfaces / Pierre-Louis Frison (2017)PermalinkTélédétection pour l'observation des surfaces continentales, ch. 3. Utilisation des diffusiomètres satellitaires pour le suivi des surfaces continentales / Pierre-Louis Frison (2017)PermalinkTélédétection pour l'observation des surfaces continentales, Volume 4. Observation des surfaces continentales par télédétection 2 / Nicolas Baghdadi (2017)PermalinkOptimizing the bioindication of forest soil acidity, nitrogen and mineral nutrition using plant species / Paulina E. Pinto in Ecological indicators, vol 71 (December 2016)PermalinkAssimilation of SMOS retrievals in the land information system / Clay B. Blankenship in IEEE Transactions on geoscience and remote sensing, vol 54 n° 11 (November 2016)PermalinkDirect measurement of evapotranspiration from a forest using a superconducting gravimeter / Michel Van Camp in Geophysical research letters, vol 43 n° 19 (15 October 2016)PermalinkLong-term soil moisture dynamics derived from GNSS interferometric reflectometry: a case study for Sutherland, South Africa / Sibylle Vey in GPS solutions, vol 20 n° 4 (October 2016)Permalink