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Refining MODIS NIR atmospheric water vapor retrieval algorithm using GPS-derived water vapor data / Jia He in IEEE Transactions on geoscience and remote sensing, vol 59 n° 5 (May 2021)
[article]
Titre : Refining MODIS NIR atmospheric water vapor retrieval algorithm using GPS-derived water vapor data Type de document : Article/Communication Auteurs : Jia He, Auteur ; Zhizhao Liu, Auteur Année de publication : 2021 Article en page(s) : pp 3682 - 3694 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] Amérique du nord
[Termes IGN] données météorologiques
[Termes IGN] données spatiotemporelles
[Termes IGN] image Terra-MODIS
[Termes IGN] modèle de régression
[Termes IGN] modèle de transfert radiatif
[Termes IGN] précision des données
[Termes IGN] station GPS
[Termes IGN] vapeur d'eauRésumé : (Auteur) A new algorithm of retrieving atmospheric water vapor from MODIS near-infrared (IR) (NIR) data by using a regression fitting method based on Global Positioning System (GPS)-derived water vapor is developed in this work. The algorithm has been used to retrieve total column water vapor from Moderate Resolution Imaging Spectroradiometer (MODIS) satellites both Terra and Aqua under cloud-free conditions from solar radiation in the NIR channels. Water vapor data estimated from GPS observations recorded from 2003 to 2017 by the SuomiNet GPS network over the western North America are used as ground truth references. The GPS stations were classified into six subsets based on the surface types adopted from MCD12Q1 IGBP legend. The differences in surface types are considered in the regression fitting procedure, thus different regression functions are trained for different surface types. Thus, the wet bias in the operational MODIS water vapor products has been significantly reduced. Water vapor retrieved from each of the three absorption channels and the weighted water vapor of combined three absorption channels are analyzed. Validation shows that the weighted water vapor performs better than the single-channel results. Compared to the MODIS/Terra water vapor products, the RMSE has been reduced by 50.78% to 2.229 mm using the two-channel ratio transmittance method and has been reduced by 53.06% to 2.126 mm using the three-channel ratio transmittance method. Compared to the MODIS/Aqua water vapor products, the RMSE has been reduced by 45.54% to 2.423 mm using the two-channel ratio transmittance method and has been reduced by 45.34% to 2.432 mm using the three-channel ratio transmittance method. Numéro de notice : A2021-338 Affiliation des auteurs : non IGN Thématique : IMAGERIE/POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.3016655 Date de publication en ligne : 24/08/2020 En ligne : https://doi.org/10.1109/TGRS.2020.3016655 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97569
in IEEE Transactions on geoscience and remote sensing > vol 59 n° 5 (May 2021) . - pp 3682 - 3694[article]Validation and analysis of Terra and Aqua MODIS, and SNPP VIIRS vegetation indices under zero vegetation conditions: A case study using Railroad Valley Playa / Tomoaki Miura in Remote sensing of environment, vol 257 (May 2021)
[article]
Titre : Validation and analysis of Terra and Aqua MODIS, and SNPP VIIRS vegetation indices under zero vegetation conditions: A case study using Railroad Valley Playa Type de document : Article/Communication Auteurs : Tomoaki Miura, Auteur ; Charlotte Z. Smith, Auteur ; Hiroki Yoshioka, Auteur Année de publication : 2021 Article en page(s) : n° 112344 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] Enhanced vegetation index
[Termes IGN] image Aqua-MODIS
[Termes IGN] image proche infrarouge
[Termes IGN] image Terra-MODIS
[Termes IGN] indice de végétation
[Termes IGN] Nevada (Etats-Unis)
[Termes IGN] Normalized Difference Vegetation Index
[Termes IGN] réflectance du solRésumé : (auteur) Spectral vegetation index (VI) time series data from coarse resolution satellite sensors, such as the Moderate Resolution Imaging Spectroradiometer (MODIS), have been utilized in studying vegetation dynamics. Numerous studies have evaluated how well VI products capture variations in vegetation biophysical or physiological conditions. Equally important is to evaluate VI products over “zero vegetation” surfaces consisting of soils, litters, and/or rocks, as they define the lower bound for vegetation detection. VIs, however, vary over zero vegetation surfaces as a function of soil moisture content and surface roughness. In this study, we evaluated the behavior of VIs from Terra MODIS (T-MODIS), Aqua MODIS (A-MODIS), and Suomi-National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite (S-VIIRS) at Railroad Valley Playa, Nevada for a period from April 2013 to September 2019. The playa is a dried lakebed devoid of vegetation throughout the year. Long-term in situ reflectance measurements acquired over the 1 km-by−1 km Radiometric Calibration Test Site (RadCaTS) located on the playa were obtained from the Radiometric Calibration Network (RadCalNet) portal and used as a reference. Three VIs were analyzed, including the normalized difference VI (NDVI), enhanced VI (EVI), and two-band EVI (EVI2). RadCaTS NDVI, EVI, and EVI2 of the playa surface increased and decreased occasionally for the time period examined in this study, and the satellite NDVIs, EVIs, and EVI2s had comparable temporal signatures to the RadCaTS counterparts. T-MODIS and A-MODIS NDVI and EVI2 values were comparable to the RadCaTS counterparts, whereas T-MODIS and A-MODIS EVI values were lower than the RadCaTS counterparts by ~0.006 and ~ 0.01 EVI units, respectively. All the three VIs of S-VIIRS were consistently higher than their RadCaTS counterparts by ~0.008 VI units, due to the higher near-infrared (NIR) reflectances of S-VIIRS than the RadCaTS NIR reflectance. The red and NIR, and red and blue reflectances each formed linear relationships (i.e., soil lines) for each of the three sensors. Variations in reflectance due to surface conditions and observation geometries all appeared as variations along these soil lines. The satellite red-NIR soil lines were comparable to the RadCaTS counterparts, whereas the satellite red-blue soil lines had steeper slopes than the RadCaTS counterparts due to a negative bias in the satellite blue reflectances. This translated into the T-MODIS and A-MODIS EVI behaviors different from those depicted by RadCaTS EVI, and the satellite NDVI and EVI2 behaving more comparably with the RadCaTS counterparts and across the three sensors than the satellite EVI. Numéro de notice : A2021-277 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1016/j.rse.2021.112344 Date de publication en ligne : 19/02/2021 En ligne : https://doi.org/10.1016/j.rse.2021.112344 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97370
in Remote sensing of environment > vol 257 (May 2021) . - n° 112344[article]Assessing forest phenology: A multi-scale comparison of near-surface (UAV, spectral reflectance sensor, PhenoCam) and satellite (MODIS, Sentinel-2) remote sensing / Shangharsha Thapa in Remote sensing, vol 13 n° 8 (April-2 2021)
[article]
Titre : Assessing forest phenology: A multi-scale comparison of near-surface (UAV, spectral reflectance sensor, PhenoCam) and satellite (MODIS, Sentinel-2) remote sensing Type de document : Article/Communication Auteurs : Shangharsha Thapa, Auteur ; Virginia Garcia Millan, Auteur ; Lars Eklundh, Auteur Année de publication : 2021 Article en page(s) : n° 1597 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] analyse multiéchelle
[Termes IGN] capteur multibande
[Termes IGN] image captée par drone
[Termes IGN] image RVB
[Termes IGN] image Sentinel-MSI
[Termes IGN] image Terra-MODIS
[Termes IGN] Normalized Difference Vegetation Index
[Termes IGN] phénologie
[Termes IGN] réflectance spectrale
[Termes IGN] série temporelle
[Termes IGN] Suède
[Termes IGN] surveillance forestière
[Termes IGN] variation saisonnièreRésumé : (auteur) The monitoring of forest phenology based on observations from near-surface sensors such as Unmanned Aerial Vehicles (UAVs), PhenoCams, and Spectral Reflectance Sensors (SRS) over satellite sensors has recently gained significant attention in the field of remote sensing and vegetation phenology. However, exploring different aspects of forest phenology based on observations from these sensors and drawing comparatives from the time series of vegetation indices (VIs) still remains a challenge. Accordingly, this research explores the potential of near-surface sensors to track the temporal dynamics of phenology, cross-compare their results against satellite observations (MODIS, Sentinel-2), and validate satellite-derived phenology. A time series of Normalized Difference Vegetation Index (NDVI), Green Chromatic Coordinate (GCC), and Normalized Difference of Green & Red (VIgreen) indices were extracted from both near-surface and satellite sensor platforms. The regression analysis between time series of NDVI data from different sensors shows the high Pearson’s correlation coefficients (r > 0.75). Despite the good correlations, there was a remarkable offset and significant differences in slope during green-up and senescence periods. SRS showed the most distinctive NDVI profile and was different to other sensors. PhenoCamGCC tracked green-up of the canopy better than the other indices, with a well-defined start, end, and peak of the season, and was most closely correlated (r > 0.93) with the satellites, while SRS-based VIgreen accounted for the least correlation (r = 0.58) against Sentinel-2. Phenophase transition dates were estimated and validated against visual inspection of the PhenoCam data. The Start of Spring (SOS) and End of Spring (EOS) could be predicted with an accuracy of Numéro de notice : A2021-382 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/rs13081597 Date de publication en ligne : 20/04/2021 En ligne : https://doi.org/10.3390/rs13081597 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97633
in Remote sensing > vol 13 n° 8 (April-2 2021) . - n° 1597[article]Precipitable water vapor fusion based on a generalized regression neural network / Bao Zhang in Journal of geodesy, vol 95 n° 4 (April 2021)
[article]
Titre : Precipitable water vapor fusion based on a generalized regression neural network Type de document : Article/Communication Auteurs : Bao Zhang, Auteur ; Yibing Yao, Auteur Année de publication : 2021 Article en page(s) : n° 36 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de géodésie spatiale
[Termes IGN] Amérique du nord
[Termes IGN] coefficient d'étalonnage
[Termes IGN] coefficient de corrélation
[Termes IGN] données GNSS
[Termes IGN] données météorologiques
[Termes IGN] erreur systématique
[Termes IGN] fusion de données
[Termes IGN] image Aqua-MODIS
[Termes IGN] image Terra-MODIS
[Termes IGN] précipitation
[Termes IGN] prévision météorologique
[Termes IGN] régression
[Termes IGN] réseau neuronal artificiel
[Termes IGN] vapeur d'eau
[Termes IGN] variation temporelleRésumé : (auteur) Water vapor plays an important role in Earth’s weather and climate processes and energy transfer. Plenty of techniques have developed to monitor precipitable water vapor (PWV), but joint use of different techniques has some problems, including systematic biases, different spatiotemporal coverages and resolutions among different datasets. To address the above problems and improve the data utilization, we propose to use a generalized regression neural network (GRNN) to fuse PWVs from Global Navigation Satellite System (GNSS), Moderate-Resolution Imaging Spectroradiometer (MODIS), and European Centre for Medium‐Range Weather Forecasts Reanalysis 5 (ERA5). The core idea of this method is to use the high-quality GNSS PWV to calibrate and optimize the relatively low-quality MODIS and ERA5 PWV through the constructed GRNNs. Using the proposed method, we generated more than 400 PWV maps that combine GNSS, MODIS, and ERA5 PWVs in North America in 2018. Results show that the overall bias, standard deviation (STD), and root-mean-square (RMS) error are 0.0 mm, 2.1 mm, and 2.2 mm for the improved MODIS PWV, and 0.0 mm, 1.6 mm, and 1.6 mm for the improved ERA5 PWV. Compared to the original MODIS and ERA5 PWV, the total improvements are 37.1% and 15.8% in terms of RMS. The RMS improvements are mainly contributed from the calibration of bias for the MODIS PWV and optimization for the ERA5 PWV. It also demonstrates that the original MODIS PWV tends to be greater than the GNSS PWV while the ERA5 PWV has very small biases. After calibration and optimization, the correlation coefficients between the modified PWV and the GNSS PWV are 0.96 for the MODIS PWV and 0.98 for the ERA5 PWV. The proposed method also diminishes the temporal and spatial variations in accuracy, generating homogeneous PWV products. Since the biases among the three datasets are well removed and data accuracies are improved to the same level, they are thus easily fused and jointly used. Numéro de notice : A2021-259 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s00190-021-01482-z Date de publication en ligne : 01/03/2021 En ligne : https://doi.org/10.1007/s00190-021-01482-z Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97299
in Journal of geodesy > vol 95 n° 4 (April 2021) . - n° 36[article]Shoreline changes along Northern Ibaraki Coast after the great East Japan earthquake of 2011 / Quang Nguyen Hao in Remote sensing, vol 13 n° 7 (April-1 2021)
[article]
Titre : Shoreline changes along Northern Ibaraki Coast after the great East Japan earthquake of 2011 Type de document : Article/Communication Auteurs : Quang Nguyen Hao, Auteur ; Satoshi Takewaka, Auteur Année de publication : 2021 Article en page(s) : n° 1399 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] détection de changement
[Termes IGN] effondrement de terrain
[Termes IGN] image ALOS-AVNIR2
[Termes IGN] image proche infrarouge
[Termes IGN] image Sentinel-MSI
[Termes IGN] image Terra-ASTER
[Termes IGN] Japon
[Termes IGN] Normalized Difference Water Index
[Termes IGN] séisme
[Termes IGN] surveillance du littoral
[Termes IGN] trait de côteRésumé : (auteur) In this study, we analyze the influence of the Great East Japan Earthquake, which occurred on 11 March 2011, on the shoreline of the northern Ibaraki Coast. After the earthquake, the area experienced subsidence of approximately 0.4 m. Shoreline changes at eight sandy beaches along the coast are estimated using various satellite images, including the ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer), ALOS AVNIR-2 (Advanced Land Observing Satellite, Advanced Visible and Near-infrared Radiometer type 2), and Sentinel-2 (a multispectral sensor). Before the earthquake (for the period March 2001–January 2011), even though fluctuations in the shoreline position were observed, shorelines were quite stable, with the averaged change rates in the range of ±1.5 m/year. The shoreline suddenly retreated due to the earthquake by 20–40 m. Generally, the amount of retreat shows a strong correlation with the amount of land subsidence caused by the earthquake, and a moderate correlation with tsunami run-up height. The ground started to uplift gradually after the sudden subsidence, and shoreline positions advanced accordingly. The recovery speed of the beaches varied from +2.6 m/year to +6.6 m/year, depending on the beach conditions. Numéro de notice : A2021-351 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.3390/rs13071399 Date de publication en ligne : 05/04/2021 En ligne : https://doi.org/10.3390/rs13071399 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97601
in Remote sensing > vol 13 n° 7 (April-1 2021) . - n° 1399[article]Urban heat island formation in greater Cairo: Spatio-temporal analysis of daytime and nighttime land surface temperatures along the urban–rural gradient / Darshana Athukorala in Remote sensing, vol 13 n° 7 (April-1 2021)PermalinkDétection des zones de dégradation et de régénération de la couverture végétale dans le sud du Sénégal à travers l'analyse des tendances de séries temporelles MODIS NDVI et des changements d'occupation des sols à partir d'images LANDSAT / Boubacar Solly in Revue Française de Photogrammétrie et de Télédétection, n° 223 (mars - décembre 2021)PermalinkRépartitions spatiale et temporelle des feux à Madagascar / Solofo Rakotondraompiana in Revue Française de Photogrammétrie et de Télédétection, n° 223 (mars - décembre 2021)PermalinkAssessing the accuracy of remotely sensed fire datasets across the southwestern Mediterranean Basin / Luis Felipe Galizia in Natural Hazards and Earth System Sciences, vol 21 n° 1 (January 2021)PermalinkPermalinkÉvaluation de l'évapotranspiration des zones irriguées en piémont du Haut Atlas, Maroc / Jamal Elfarkh (2021)PermalinkGeospatial analysis of September, 2019 floods in the lower gangetic plains of Bihar using multi-temporal satellites and river gauge data / C.M. Bhatt in Geomatics, Natural Hazards and Risk, vol 12 n° 1 (2021)PermalinkMonitoring tree-crown scale autumn leaf phenology in a temperate forest with an integration of PlanetScope and drone remote sensing observations / Shengbiao Wu in ISPRS Journal of photogrammetry and remote sensing, vol 171 (January 2021)PermalinkPermalinkPermalinkPermalinkSemantic segmentation of sea ice type on Sentinel-1 SAR data using convolutional neural networks / Alissa Kouraeva (2021)PermalinkCharacterizing the spatial and temporal variation of the land surface temperature hotspots in Wuhan from a local scale / Chen Yang in Geo-spatial Information Science, vol 23 n° 4 (December 2020)PermalinkPolarization of light reflected by grass: modeling using visible-sunlit areas / Bin Yang in Photogrammetric Engineering & Remote Sensing, PERS, vol 86 n° 12 (December 2020)PermalinkA spatially explicit surface urban heat island database for the United States: Characterization, uncertainties, and possible applications / T. Chakraborty in ISPRS Journal of photogrammetry and remote sensing, vol 168 (October 2020)PermalinkOn-Orbit Calibration of Terra MODIS VIS Bands Using Polarization-Corrected Desert Observations / Amit Angal in IEEE Transactions on geoscience and remote sensing, vol 58 n° 8 (August 2020)PermalinkCross-calibration of MODIS reflective solar bands with Sentinel 2A/2B MSI instruments / Amit Angal in IEEE Transactions on geoscience and remote sensing, vol 58 n° 7 (July 2020)PermalinkAn integrated approach for detection and prediction of greening situation in a typical desert area in China and its human and climatic factors analysis / Lei Zhou in ISPRS International journal of geo-information, vol 9 n° 6 (June 2020)PermalinkEstimating spatio-temporal air temperature in London (UK) using machine learning and earth observation satellite data / Rochelle Schneider dos Santos in International journal of applied Earth observation and geoinformation, vol 88 (June 2020)PermalinkIncorporating Sentinel-1 SAR imagery with the MODIS MCD64A1 burned area product to improve burn date estimates and reduce burn date uncertainty in wildland fire mapping / Kristofer Lasko in Geocarto international, vol 35 n° 6 ([01/05/2020])Permalink