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A compilation of snow cover datasets for Svalbard: A multi-sensor, multi-model study / Hannah Vickers in Remote sensing, vol 13 n°10 (May-2 2021)
[article]
Titre : A compilation of snow cover datasets for Svalbard: A multi-sensor, multi-model study Type de document : Article/Communication Auteurs : Hannah Vickers, Auteur ; Eirik Malnes, Auteur ; Ward van Pelt, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 2002 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] données multicapteurs
[Termes IGN] image à haute résolution
[Termes IGN] image Sentinel-MSI
[Termes IGN] image Terra-MODIS
[Termes IGN] manteau neigeux
[Termes IGN] modélisation
[Termes IGN] Normalized Difference Snow Index
[Termes IGN] série temporelle
[Termes IGN] surveillance hydrologique
[Termes IGN] SvalbardRésumé : (auteur) Reliable and accurate mapping of snow cover are essential in applications such as water resource management, hazard forecasting, calibration and validation of hydrological models and climate impact assessments. Optical remote sensing has been utilized as a tool for snow cover monitoring over the last several decades. However, consistent long-term monitoring of snow cover can be challenging due to differences in spatial resolution and retrieval algorithms of the different generations of satellite-based sensors. Snow models represent a complementary tool to remote sensing for snow cover monitoring, being able to fill in temporal and spatial data gaps where a lack of observations exist. This study utilized three optical remote sensing datasets and two snow models with overlapping periods of data coverage to investigate the similarities and discrepancies in snow cover estimates over Nordenskiöld Land in central Svalbard. High-resolution Sentinel-2 observations were utilized to calibrate a 20-year MODIS snow cover dataset that was subsequently used to correct snow cover fraction estimates made by the lower resolution AVHRR instrument and snow model datasets. A consistent overestimation of snow cover fraction by the lower resolution datasets was found, as well as estimates of the first snow-free day (FSFD) that were, on average, 10–15 days later when compared with the baseline MODIS estimates. Correction of the AVHRR time series produced a significantly slower decadal change in the land-averaged FSFD, indicating that caution should be exercised when interpreting climate-related trends from earlier lower resolution observations. Substantial differences in the dynamic characteristics of snow cover in early autumn were also present between the remote sensing and snow model datasets, which need to be investigated separately. This work demonstrates that the consistency of earlier low spatial resolution snow cover datasets can be improved by using current-day higher resolution datasets. Numéro de notice : A2021-438 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.3390/rs13102002 Date de publication en ligne : 20/05/2021 En ligne : https://doi.org/10.3390/rs13102002 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97822
in Remote sensing > vol 13 n°10 (May-2 2021) . - n° 2002[article]Detection of rainstorm pattern in arid regions using MODIS NDVI time series analysis / Mohamed E. Hereher in Geocarto international, vol 36 n° 8 ([01/05/2021])
[article]
Titre : Detection of rainstorm pattern in arid regions using MODIS NDVI time series analysis Type de document : Article/Communication Auteurs : Mohamed E. Hereher, Auteur Année de publication : 2021 Article en page(s) : pp 861 - 873 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] Arabie
[Termes IGN] bassin hydrographique
[Termes IGN] gestion de l'eau
[Termes IGN] image Aqua-MODIS
[Termes IGN] image Terra-MODIS
[Termes IGN] Normalized Difference Vegetation Index
[Termes IGN] orage
[Termes IGN] pluie
[Termes IGN] précipitation
[Termes IGN] ressources en eau
[Termes IGN] série temporelle
[Termes IGN] zone arideRésumé : (auteur) The normalized difference vegetation index (NDVI) was used to delineate potential water suppliers west of the Arabian Peninsula. Time series NDVI data extracted from the moderate resolution imaging spectroradiometer NDVI product were used to develop a robust estimate of rainstorm frequency and intensity. A total of 216 NDVI images were acquired between February 2000 and January 2018 to carry out this investigation. As NDVI values of negative records correspond to water, it was possible to address and delineate the occurrence and duration of temporal ponded water. Results showed that at least 7 locations are potential to harvest water from flashfloods. Some locations witnessed 10, 11 and 13 rainstorms and ponding of water ranged from 1 to 20 months. These locations, if properly managed, could sustain a fresh water resource for local uses. The study demonstrates that NDVI time series curves could help identify the time/duration of previous rainstorms. Numéro de notice : A2021-482 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2019.1629643 Date de publication en ligne : 19/06/2019 En ligne : https://doi.org/10.1080/10106049.2019.1629643 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97433
in Geocarto international > vol 36 n° 8 [01/05/2021] . - pp 861 - 873[article]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)PermalinkShoreline 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)PermalinkUrban 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)Permalink