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Improving the accuracy of spring phenology detection by optimally smoothing satellite vegetation index time series based on local cloud frequency / Jiaqi Tian in ISPRS Journal of photogrammetry and remote sensing, vol 180 (October 2021)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 180 (October 2021) . - pp 29 - 44 Titre : Improving the accuracy of spring phenology detection by optimally smoothing satellite vegetation index time series based on local cloud frequency Type de document : Article/Communication Auteurs : Jiaqi Tian, Auteur ; Xiaolin Zhu, Auteur ; Jin Chen, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 29 - 44 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] Enhanced vegetation index [Termes IGN] filtrage du bruit [Termes IGN] image Terra-MODIS [Termes IGN] indice de végétation [Termes IGN] lissage de données [Termes IGN] nébulosité [Termes IGN] phénologie [Termes IGN] série temporelle Résumé : (auteur) Vegetation phenology can be extracted from vegetation index (VI) time series of satellite data. The maximum value composite (MVC) procedure and smoothing filters have been conventionally used as standard methods to exclude noises in the VI time series before extracting the vegetation phenology [e.g., National Aeronautics and Space Administration (NASA) VNP22Q2 and United States Geological Survey (USGS) MCD12Q2 phenology products]. However, it is unclear how to optimize the MVC and smoothing filters to produce the most accurate phenology metrics given that cloud frequency varies spatially. This study designed two simulation experiments, namely (1) using only the MVC and (2) using the MVC and smoothing filters together to smooth the enhanced vegetation index (EVI) time series for detecting spring phenology, i.e., start of season (SOS), over the northern hemisphere (north of 30°N) on a 5° × 5° grid cell basis by the inflection point and relative threshold algorithms. The results revealed that (1) the inappropriate selection of MVC periods (e.g., too short or too long) affected the accuracy of the SOS extracted by both phenology detection algorithms; (2) a filtering process with optimal parameters can reduce the effects of the MVC period on SOS extraction to a considerable extent, i.e., 65% and 61% for iterative Savitzky–Golay (SG) and penalized cubic splines (SP) filters, respectively; (3) optimal parameters for both the MVC and smoothing filters showed significant spatial heterogeneity; and (4) validation with ground PhenoCam data indicated that optimal parameters of the MVC and smoothing filters can produce more accurate results than official vegetation phenology products that use uniform parameters. Specifically, the R2 values of the NASA product and the USGS product were 0.58 and 0.67, which were increased to 0.70 and 0.81, respectively, by the optimal smoothing process. Optimal parameters of the MVC and smoothing filters provided by this study in each 5° × 5° sub-region may help future studies to improve the accuracy of phenology detection from satellite VI time series. Numéro de notice : A2021-653 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2021.08.003 date de publication en ligne : 14/08/2021 En ligne : https://doi.org/10.1016/j.isprsjprs.2021.08.003 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98383 [article]

Recurrent-based regression of Sentinel time series for continuous vegetation monitoring / Anatol Garioud in Remote sensing of environment, vol 263 (15 September 2021)  

Public [article]  inRemote sensing of environment > vol 263 (15 September 2021) . - n° 112419 Titre : Recurrent-based regression of Sentinel time series for continuous vegetation monitoring Type de document : Article/Communication Auteurs : Anatol Garioud , Auteur ; Silvia Valero, Auteur ; Sébastien Giordano , Auteur ; Clément Mallet , Auteur Année de publication : 2021 Projets : 3-projet - voir note / Article en page(s) : n° 112419 Note générale : bibliographie This work is funded by the Agence de la transition écologique (ADEME) and the Centre National d'Études Spatiales (CNES). Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] classification par réseau neuronal [Termes IGN] classification par réseau neuronal récurrent [Termes IGN] fusion d'images [Termes IGN] image Sentinel-MSI [Termes IGN] image Sentinel-SAR [Termes IGN] Normalized Difference Vegetation Index [Termes IGN] régression [Termes IGN] série temporelle [Termes IGN] surveillance de la végétation Résumé : (auteur) Dense time series of optical satellite imagery describing vegetation activity provide essential information for the efficient and regular monitoring of vegetation. Nevertheless, the temporal resolution of optical sensors is strongly affected by cloud cover, resulting in significant missing information. The use of complementary acquisitions, such as Synthetic Aperture Radar (SAR) data, opens the door to the development of new multi-sensor methodologies aiming at the reconstruction of missing information. However, the joint exploitation of new radar and optical missions, such as the Sentinel, raises new challenges given the different nature and response of the two data sources. In this work, the SenRVM methodology is proposed as a new multi-sensor approach to regress SAR time series towards Normalized Difference Vegetation Index (NDVI). A deep Recurrent Neural Network architecture which integrates SAR acquisitions and ancillary data is adopted. The regression task permits a continuous optical temporal resolution of 6 days. Multiple experiments are carried out to assess the SenRVM framework by studying two large-scale areas in France. Through an extensive interpretation of the results, SenRVM is evaluated on three main vegetation types (grasslands, crops, and forests). High accurate results (R2 > 0.83 and MAE Numéro de notice : A2021-499 Affiliation des auteurs : UGE-LaSTIG+Ext (2020- ) Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1016/j.rse.2021.112419 date de publication en ligne : 25/06/2021 En ligne : https://doi.org/10.1016/j.rse.2021.112419 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98004 [article]

Geoglam, l'agriculture par satellite / Laurent Polidori in Géomètre, n° 2194 (septembre 2021)

Public [article]  inGéomètre > n° 2194 (septembre 2021) . - pp 17 - 17 Titre : Geoglam, l'agriculture par satellite Type de document : Article/Communication Auteurs : Laurent Polidori, Auteur Année de publication : 2021 Article en page(s) : pp 17 - 17 Langues : Français (fre) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] blé (céréale) [Termes IGN] carte d'occupation du sol [Termes IGN] Glycine max [Termes IGN] image à haute résolution [Termes IGN] Leaf Area Index [Termes IGN] maïs (céréale) [Termes IGN] modèle dynamique [Termes IGN] Normalized Difference Vegetation Index [Termes IGN] observation de la Terre [Termes IGN] Oryza (genre) [Termes IGN] série temporelle [Termes IGN] surface cultivée Résumé : (Auteur) Des satellites pour la sécurité alimentaire et la transparence du marché agricole Numéro de notice : A2021-579 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtSansCL DOI : sans date de publication en ligne : 13/09/2021 Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98400 [article]

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063-2021081	SL	Revue	Centre de documentation	Revues en salle	En circulation Exclu du prêt

Les journées de la Recherche IGN 2021 / Anonyme in Géomatique expert, n° 135 (septembre 2021)

Public [article]  inGéomatique expert > n° 135 (septembre 2021) . - pp 36 - 47 Titre : Les journées de la Recherche IGN 2021 Type de document : Article/Communication Auteurs : Anonyme, Auteur Année de publication : 2021 Article en page(s) : pp 36 - 47 Langues : Français (fre) Descripteur : [Vedettes matières IGN] Information géographique [Termes IGN] BD Topo [Termes IGN] chronométrie [Termes IGN] Copernicus (programme européen) [Termes IGN] correction radiométrique [Termes IGN] corrélation d'images [Termes IGN] déformation de surface [Termes IGN] données GRACE [Termes IGN] éclairement lumineux [Termes IGN] extraction de données [Termes IGN] fonte des glaces [Termes IGN] horloge atomique [Termes IGN] ilot thermique urbain [Termes IGN] image aérienne [Termes IGN] intelligence artificielle [Termes IGN] MicMac [Termes IGN] modèle numérique de surface [Termes IGN] recherche scientifique [Termes IGN] série temporelle [Termes IGN] surveillance sanitaire [Termes IGN] visualisation de données Résumé : (Auteur) L’édition 2021 des Journées de la Recherche à l’IGN s’est déroulée, cette année encore, « en ligne ». C’était, malgré tout, l’occasion de marquer la trentième édition de l’événement. Numéro de notice : A2021-661 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/IMAGERIE/POSITIONNEMENT Nature : Article nature-HAL : ArtSansCL DOI : sans date de publication en ligne : 01/09/2021 Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98481 [article]

A new approach for the development of grid models calculating tropospheric key parameters over China / Ge Zhu in Remote sensing, vol 13 n° 17 (September-1 2021)  

Public [article]  inRemote sensing > vol 13 n° 17 (September-1 2021) . - n° 3546 Titre : A new approach for the development of grid models calculating tropospheric key parameters over China Type de document : Article/Communication Auteurs : Ge Zhu, Auteur ; Liangke Huang, Auteur ; Lilong Liu, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 3546 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie spatiale [Termes IGN] Chine [Termes IGN] données maillées [Termes IGN] MERRA [Termes IGN] météorologie [Termes IGN] positionnement par GNSS [Termes IGN] retard troposphérique [Termes IGN] série temporelle [Termes IGN] teneur en vapeur d'eau [Termes IGN] troposphère Résumé : (auteur) Pressure, water vapor pressure, temperature, and weighted mean temperature (Tm) are tropospheric parameters that play an important role in high-precision global navigation satellite system navigation (GNSS). As accurate tropospheric parameters are obligatory in GNSS navigation and GNSS water vapor detection, high-precision modeling of tropospheric parameters has gained widespread attention in recent years. A new approach is introduced to develop an empirical tropospheric delay model named the China Tropospheric (CTrop) model, providing meteorological parameters based on the sliding window algorithm. The radiosonde data in 2017 are treated as reference values to validate the performance of the CTrop model, which is compared to the canonical Global Pressure and Temperature 3 (GPT3) model. The accuracy of the CTrop model in regards to pressure, water vapor pressure, temperature, and weighted mean temperature are 5.51 hPa, 2.60 hPa, 3.09 K, and 3.35 K, respectively, achieving an improvement of 6%, 9%, 10%, and 13%, respectively, when compared to the GPT3 model. Moreover, three different resolutions of the CTrop model based on the sliding window algorithm are also developed to reduce the amount of gridded data provided to the users, as well as to speed up the troposphere delay computation process, for which users can access model parameters of different resolutions for their requirements. With better accuracy of estimating the tropospheric parameters than that of the GPT3 model, the CTrop model is recommended to improve the performance of GNSS positioning and navigation. Numéro de notice : A2021-688 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article DOI : 10.3390/rs13173546 date de publication en ligne : 06/09/2021 En ligne : https://doi.org/10.3390/rs13173546 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98423 [article]

Monitoring forest disturbance using time-series MODIS NDVI in Michoacán, Mexico / Yao Gao in Geocarto international, Vol 36 n° 15 ([10/08/2021])  

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Estimation of surface deformation due to Pasni earthquake using RADAR interferometry / Muhammad Ali in Geocarto international, vol 36 n° 14 ([01/08/2021])  

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Spatiotemporal analysis of urban heat island intensification in the city of Minneapolis-St. Paul and Chicago metropolitan areas using Landsat data from 1984 to 2016 / Mbongowo J. Mbuh in Geocarto international, vol 36 n° 14 ([01/08/2021])  

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Atmospheric correction to passive microwave brightness temperature in snow cover mapping over china / Yubao Qiu in IEEE Transactions on geoscience and remote sensing, vol 59 n° 8 (August 2021)  

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Ten years of Lake Taupō surface height estimates using the GNSS interferometric reflectometry / Lucas D. Holden in Journal of geodesy, vol 95 n° 7 (July 2021)  

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Detecting high-temperature anomalies from Sentinel-2 MSI images / Yongxue Liu in ISPRS Journal of photogrammetry and remote sensing, vol 177 (July 2021)  

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Ionospheric irregularity layer height and thickness estimation with a GNSS receiver array / Seebany Datta-Barua in IEEE Transactions on geoscience and remote sensing, Vol 59 n° 7 (July 2021)  

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JUST: MATLAB and python software for change detection and time series analysis / Ebrahim Ghaderpour in GPS solutions, vol 25 n° 3 (July 2021)  

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Marrying deep learning and data fusion for accurate semantic labeling of Sentinel-2 images / Guillemette Fonteix in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, V-2-2021 (July 2021)  

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Comparison and evaluation of high-resolution marine gravity recovery via sea surface heights or sea surface slopes / Shengjun Zhang in Journal of geodesy, vol 95 n° 6 (June 2021)  

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