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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]Improved atmospheric correction and chlorophyll-a remote sensing models for turbid waters in a dusty environment / Maryam R. Al Shehhi in ISPRS Journal of photogrammetry and remote sensing, vol 133 (November 2017)
[article]
Titre : Improved atmospheric correction and chlorophyll-a remote sensing models for turbid waters in a dusty environment Type de document : Article/Communication Auteurs : Maryam R. Al Shehhi, Auteur ; Imen Gherboidj, Auteur ; Hosni Gherida, Auteur Année de publication : 2017 Article en page(s) : pp 46 - 60 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse comparative
[Termes IGN] Arabie
[Termes IGN] chlorophylle
[Termes IGN] correction atmosphérique
[Termes IGN] couleur de l'océan
[Termes IGN] eau de mer
[Termes IGN] image Aqua-MODIS
[Termes IGN] image Terra-MODIS
[Termes IGN] test de performance
[Termes IGN] turbidité océaniqueRésumé : (Auteur) This study presents a comprehensive assessment of the performance of the commonly used atmospheric correction models (NIR, SWIR, NIR-SWIR and FM) and ocean color products (OC3 and OC2) derived from MODIS images over the Arabian Gulf, Sea of Oman, and Arabian Sea. The considered atmospheric correction models have been used to derive MODIS normalized water-leaving radiances (nLw), which are compared to in situ water nLw(λ) data collected at different locations by Masdar Institute, United Arab of Emirates, and from AERONET-OC (the ocean color component of the Aerosol Robotic Network) database. From this comparison, the NIR model has been found to be the best performing model among the considered atmospheric correction models, which in turn shows disparity, especially at short wavelengths (400–500 nm) under high aerosol optical depth conditions (AOT (869) > 0.3) and over turbid waters. To reduce the error induced by these factors, a modified model taking into consideration the atmospheric and water turbidity conditions has been proposed. A turbidity index was used to identify the turbid water and a threshold of AOT (869) = 0.3 was used to identify the dusty atmosphere. Despite improved results in the MODIS nLw(λ) using the proposed approach, Chl-a models (OC3 and OC2) show low performance when compared to the in situ Chl-a measurements collected during several field campaigns organized by local, regional and international organizations. This discrepancy might be caused by the improper parametrization of these models or/and the improper selection of bands. Thus, an adaptive power fit algorithm (R2 = 0.95) has been proposed to improve the estimation of Chl-a concentration from 0.07 to 10 mg/m3 by using a new blue/red MODIS band ratio of (443,488)/645 instead of the default band ratio used for OC3(443,488)/547. The selection of this new band ratio (443,488)/645 has been based on using band 645 nm which has been found to represent both water turbidity and algal absorption. Numéro de notice : A2017-721 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2017.09.011 En ligne : https://doi.org/10.1016/j.isprsjprs.2017.09.011 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=88406
in ISPRS Journal of photogrammetry and remote sensing > vol 133 (November 2017) . - pp 46 - 60[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2017111 RAB Revue Centre de documentation En réserve L003 Disponible 081-2017112 DEP-EAF Revue Nancy Dépôt en unité Exclu du prêt 081-2017113 DEP-EXM Revue Saint-Mandé Dépôt en unité Exclu du prêt Atmospheric correction over coastal waters using multilayer neural networks / Yongzhen Fan in Remote sensing of environment, vol 199 (15 September 2017)
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Titre : Atmospheric correction over coastal waters using multilayer neural networks Type de document : Article/Communication Auteurs : Yongzhen Fan, Auteur ; Wei Li, Auteur ; Charles K. Gatebe, Auteur ; Cédric Jamet, Auteur ; et al., Auteur Année de publication : 2017 Article en page(s) : pp 218 - 240 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] correction atmosphérique
[Termes IGN] couleur de l'océan
[Termes IGN] eaux côtières
[Termes IGN] image Aqua-MODIS
[Termes IGN] Perceptron multicouche
[Termes IGN] transfert radiatifRésumé : (auteur) Standard atmospheric correction (AC) algorithms work well in open ocean areas where the water inherent optical properties (IOPs) are correlated with pigmented particles. However, the IOPs of turbid coastal waters may independently vary with pigmented particles, suspended inorganic particles, and colored dissolved organic matter (CDOM). In turbid coastal waters standard AC algorithms often exhibit large inaccuracies that may lead to negative water-leaving radiances (Lw) or remote sensing reflectance (Rrs). We introduce a new atmospheric correction algorithm for coastal waters based on a multilayer neural network (MLNN) method. We use a coupled atmosphere-ocean radiative transfer model to simulate the Rayleigh-corrected radiance (Lrc) at the top of the atmosphere (TOA) and the Rrs just above the surface simultaneously, and train a MLNN to derive the aerosol optical depth (AOD) and Rrs directly from the TOA Lrc. The method is validated using both a synthetic dataset and Aerosol Robotic Network – Ocean Color (AERONET–OC) measurements. The SeaDAS NIR algorithm, the SeaDAS NIR/SWIR algorithm, and the MODIS version of the Case 2 regional water - CoastColour (C2RCC) algorithm are also included in the comparison with AERONET–OC measurements. The performance of the AC algorithms is evaluated with four statistical metrics: the Pearson correlation coefficient (R), the average percentage difference (APD), the mean percentage bias, and the root mean square difference (RMSD). The comparison with AERONET–OC measurements shows that the MLNN algorithm significantly improves retrieval of normalized Lw in blue bands (412 nm and 443 nm) and yields minor improvements in green and red bands compared with the other three algorithms. On a global scale, the MLNN algorithm reduces APD in normalized Lw by up to 13% in blue bands and by 2–7% in green and red bands when compared with the standard SeaDAS NIR algorithm. In highly absorbing coastal waters, such as the Baltic Sea, the MLNN algorithm reduces APD in normalized Lw by more than 60% in blue bands compared to the standard SeaDAS NIR algorithm, while in highly scattering coastal waters, such as the Black Sea, the MLNN algorithm reduces APD by more than 25%. These results indicate that the MLNN algorithm is suitable for application in turbid coastal waters. Application of the MLNN algorithm to MODIS Aqua images in several coastal areas also shows that it is robust and resilient to contamination due to sunglint or adjacency effects of land and cloud edges. The MLNN algorithm is very fast once the neural network has been properly trained and is therefore suitable for operational use. A significant advantage of the MLNN algorithm is that it does not need SWIR bands. Numéro de notice : A2017-417 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.rse.2017.07.016 En ligne : https://doi.org/10.1016/j.rse.2017.07.016 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=86310
in Remote sensing of environment > vol 199 (15 September 2017) . - pp 218 - 240[article]An adaptive weighted tensor completion method for the recovery of remote sensing images with missing data / Michael Kwok-Po Ng in IEEE Transactions on geoscience and remote sensing, vol 55 n° 6 (June 2017)
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Titre : An adaptive weighted tensor completion method for the recovery of remote sensing images with missing data Type de document : Article/Communication Auteurs : Michael Kwok-Po Ng, Auteur ; Qiangqiang Yuan, Auteur ; Li Yan, Auteur ; Jing Sun, Auteur Année de publication : 2017 Article en page(s) : pp 3367 - 3381 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] bande spectrale
[Termes IGN] détection de partie cachée
[Termes IGN] données spatiotemporelles
[Termes IGN] image Aqua-MODIS
[Termes IGN] spectroradiométrie
[Termes IGN] tenseurRésumé : (Auteur) Missing information, such as dead pixel values and cloud effects, is very common image quality degradation problems in remote sensing. Missing information can reduce the accuracy of the subsequent image processing, in applications such as classification, unmixing, and target detection, and even the quantitative retrieval process. The main aim of this paper is to study an adaptive weighted tensor completion (AWTC) method for the recovery of remote sensing images with missing data. Our idea is to collectively make use of the spatial, spectral, and temporal information to build a new weighted tensor low-rank regularization model for recovering the missing data. In the model, the weights are determined adaptively by considering the contribution of the spatial, spectral, and temporal information in each dimension. Experimental results based on both simulated and real data sets are presented to verify that the proposed method can recover missing data, and its performance is found to be better than the other tested methods. In the simulated experiments, the peak signal-to-noise ratio is improved by more than 3 dB, compared with the original tensor completion model. In the real data experiments, the proposed AWTC model can better recover the dead line problem in Aqua Moderate Resolution Imaging Spectroradiometer band 6 and the scan-line corrector-off problem in enhanced thematic mapper plus images, with the smallest spectral distortion. Numéro de notice : A2017-476 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2017.2670021 En ligne : http://dx.doi.org/10.1109/TGRS.2017.2670021 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=86401
in IEEE Transactions on geoscience and remote sensing > vol 55 n° 6 (June 2017) . - pp 3367 - 3381[article]A comparison of two downscaling procedures to increase the spatial resolution of mapping actual evapotranspiration / Milad Mahour in ISPRS Journal of photogrammetry and remote sensing, vol 126 (April 2017)
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Titre : A comparison of two downscaling procedures to increase the spatial resolution of mapping actual evapotranspiration Type de document : Article/Communication Auteurs : Milad Mahour, Auteur ; Valentyn Tolpekin, Auteur ; Alfred Stein, Auteur ; Ali Sharifi, Auteur Année de publication : 2017 Article en page(s) : pp 56 – 67 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] évapotranspiration
[Termes IGN] image à moyenne résolution
[Termes IGN] image Aqua-MODIS
[Termes IGN] image Landsat-8
[Termes IGN] image Terra-MODIS
[Termes IGN] Iran
[Termes IGN] irrigation
[Termes IGN] krigeage
[Termes IGN] mise à l'échelle
[Termes IGN] Normalized Difference Vegetation Index
[Termes IGN] pouvoir de résolution géométrique
[Termes IGN] température au solRésumé : (auteur) This research addressed the effects of downscaling cokriging Land Surface Temperature (LST) on estimation of Actual Evapotranspiration (AET) from remote sensing images. Two procedures were followed. We first applied downscaling cokriging to a coarse resolution LST product of MODIS at 1000 m. With its outcome, daily AET of a medium spatial resolution (250 m) was obtained using the Surface Energy Balance System (SEBS). Second, we downscaled a coarse AET map to medium spatial resolution (250 m). For both procedures, the 250 m resolution MODIS NDVI product was used as a co-variable. Validation was carried out using Landsat 8 images, from which LST was derived from the thermal bands. The two procedures were applied to an agricultural area with a traditional irrigation network in Iran. We obtained an average LST value of 305.8 K as compared to a downscaled LST value of 307.0 K. Reference AET estimated with SEBS using Landsat 8 data was equal to 5.756 mm day−1, as compared with a downscaled AET value of 5.571 mm day−1. The RMSE between reference AET and downscaled AET was equal to 1.26 mm day−1 (r = 0.49) and between reference and downscaled LST to 3.67 K (r = 0.48). The study showed that AET values obtained with the two downscaling procedures were similar to each other, but that AET showed a higher spatial variability if obtained with downscaled LST. We concluded that LST had a large effect on producing AET maps from Remote Sensing (RS) images, and that downscaling cokriging was helpful to provide daily AET maps at medium spatial resolution. Numéro de notice : A2017-113 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2017.02.004 En ligne : http://dx.doi.org/10.1016/j.isprsjprs.2017.02.004 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=84508
in ISPRS Journal of photogrammetry and remote sensing > vol 126 (April 2017) . - pp 56 – 67[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2017041 RAB Revue Centre de documentation En réserve L003 Disponible 081-2017043 DEP-EXM Revue LASTIG Dépôt en unité Exclu du prêt 081-2017042 DEP-EAF Revue Nancy Dépôt en unité Exclu du prêt Learning-based spatial-temporal superresolution mapping of forest cover with MODIS images / Yihang Zhang in IEEE Transactions on geoscience and remote sensing, vol 55 n° 1 (January 2017)PermalinkThe MODIS cloud optical and microphysical products : collection 6 updates and examples from Terra and Aqua / Steven Platnick in IEEE Transactions on geoscience and remote sensing, vol 55 n° 1 (January 2017)PermalinkA global study of NDVI difference among moderate-resolution satellite sensors / Xingwang Fan in ISPRS Journal of photogrammetry and remote sensing, vol 121 (November 2016)PermalinkSpatio-temporal change detection from multidimensional arrays: Detecting deforestation from MODIS time series / Meng Lu in ISPRS Journal of photogrammetry and remote sensing, vol 117 (July 2016)PermalinkA simple method for detecting phenological change from time series of vegetation index / Jin Chen in IEEE Transactions on geoscience and remote sensing, vol 54 n° 6 (June 2016)PermalinkExtraction des zones cohérentes par l’analyse spatio-temporelle d’images de télédétection / Thomas Guyet in Revue internationale de géomatique, vol 25 n° 4 (octobre - décembre 2015)PermalinkA robust fixed rank kriging method for improving the spatial completeness and accuracy of satellite SST products / Yuxin Zhu in IEEE Transactions on geoscience and remote sensing, vol 53 n° 9 (September 2015)PermalinkMeasuring the directional variations of land surface reflectance from MODIS / François-Marie Bréon in IEEE Transactions on geoscience and remote sensing, vol 53 n° 8 (August 2015)PermalinkLand cover dynamics monitoring with Landsat data in Kunming, China: a cost-effective sampling and modelling scheme using Google Earth imagery and random forests / Ning Lu in Geocarto international, vol 30 n° 1 - 2 (January - February 2015)PermalinkMODIS-based vegetation index has sufficient sensitivity to indicate stand-level intra-seasonal climatic stress in oak and beech forests / Tomáš Hlásny in Annals of Forest Science, vol 72 n° 1 (January 2015)Permalink