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Spatiotemporal fusion modelling using STARFM: Examples of Landsat 8 and Sentinel-2 NDVI in Bavaria / Maninder Singh Dhillon in Remote sensing, vol 14 n° 3 (February-1 2022)
[article]
Titre : Spatiotemporal fusion modelling using STARFM: Examples of Landsat 8 and Sentinel-2 NDVI in Bavaria Type de document : Article/Communication Auteurs : Maninder Singh Dhillon, Auteur ; Thorsten Dahms, Auteur ; Carina Kübert-Flock, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 677 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] Bavière (Allemagne)
[Termes IGN] carte d'occupation du sol
[Termes IGN] fusion de données
[Termes IGN] image Landsat-8
[Termes IGN] image Sentinel-MSI
[Termes IGN] image Terra-MODIS
[Termes IGN] Normalized Difference Vegetation Index
[Termes IGN] pouvoir de résolution géométrique
[Termes IGN] réflectance
[Termes IGN] surveillance de la végétation
[Termes IGN] utilisation du solRésumé : (auteur) The increasing availability and variety of global satellite products provide a new level of data with different spatial, temporal, and spectral resolutions; however, identifying the most suited resolution for a specific application consumes increasingly more time and computation effort. The region’s cloud coverage additionally influences the choice of the best trade-off between spatial and temporal resolution, and different pixel sizes of remote sensing (RS) data may hinder the accurate monitoring of different land cover (LC) classes such as agriculture, forest, grassland, water, urban, and natural-seminatural. To investigate the importance of RS data for these LC classes, the present study fuses NDVIs of two high spatial resolution data (high pair) (Landsat (30 m, 16 days; L) and Sentinel-2 (10 m, 5–6 days; S), with four low spatial resolution data (low pair) (MOD13Q1 (250 m, 16 days), MCD43A4 (500 m, one day), MOD09GQ (250 m, one-day), and MOD09Q1 (250 m, eight day)) using the spatial and temporal adaptive reflectance fusion model (STARFM), which fills regions’ cloud or shadow gaps without losing spatial information. These eight synthetic NDVI STARFM products (2: high pair multiply 4: low pair) offer a spatial resolution of 10 or 30 m and temporal resolution of 1, 8, or 16 days for the entire state of Bavaria (Germany) in 2019. Due to their higher revisit frequency and more cloud and shadow-free scenes (S = 13, L = 9), Sentinel-2 (overall R2 = 0.71, and RMSE = 0.11) synthetic NDVI products provide more accurate results than Landsat (overall R2 = 0.61, and RMSE = 0.13). Likewise, for the agriculture class, synthetic products obtained using Sentinel-2 resulted in higher accuracy than Landsat except for L-MOD13Q1 (R2 = 0.62, RMSE = 0.11), resulting in similar accuracy preciseness as S-MOD13Q1 (R2 = 0.68, RMSE = 0.13). Similarly, comparing L-MOD13Q1 (R2 = 0.60, RMSE = 0.05) and S-MOD13Q1 (R2 = 0.52, RMSE = 0.09) for the forest class, the former resulted in higher accuracy and precision than the latter. Conclusively, both L-MOD13Q1 and S-MOD13Q1 are suitable for agricultural and forest monitoring; however, the spatial resolution of 30 m and low storage capacity makes L-MOD13Q1 more prominent and faster than that of S-MOD13Q1 with the 10-m spatial resolution. Numéro de notice : A2022-124 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.3390/rs14030677 Date de publication en ligne : 31/01/2022 En ligne : https://doi.org/10.3390/rs14030677 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99687
in Remote sensing > vol 14 n° 3 (February-1 2022) . - n° 677[article]Apport de la télédétection et des variables auxiliaires dans l'étude de l'évolution des périodes de sécheresse / Nesrine Farhani (2022)
Titre : Apport de la télédétection et des variables auxiliaires dans l'étude de l'évolution des périodes de sécheresse Type de document : Thèse/HDR Auteurs : Nesrine Farhani, Auteur ; Gilles Boulet, Directeur de thèse ; Zohra Lili-Chabaane, Directeur de thèse Editeur : Toulouse : Université de Toulouse 3 Paul Sabatier Année de publication : 2022 Importance : 194 p. Format : 21 x 30 cm Note générale : Bibliographie
Thèse en vue de l'obtention du Doctorat de l'Université Toulouse délivré par l'Université Toulouse 3 - Paul Sabatier, spécialité Surfaces et Interfaces Continentales, HydrologieLangues : Français (fre) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] albedo
[Termes IGN] capteur actif
[Termes IGN] capteur passif
[Termes IGN] données météorologiques
[Termes IGN] évapotranspiration
[Termes IGN] gestion de l'eau
[Termes IGN] image Landsat-ETM+
[Termes IGN] image Landsat-TM
[Termes IGN] image Terra-MODIS
[Termes IGN] image thermique
[Termes IGN] indice de stress
[Termes IGN] Leaf Area Index
[Termes IGN] Normalized Difference Vegetation Index
[Termes IGN] sécheresse
[Termes IGN] stress hydrique
[Termes IGN] température de surface
[Termes IGN] Tunisie
[Termes IGN] zone arideIndex. décimale : THESE Thèses et HDR Résumé : (auteur) La surveillance des sécheresses dans les régions arides et semi-arides est cruciale car ses conséquences pour l'agriculture peuvent être dramatiques. Afin d'aider les décideurs à établir de bonnes pratiques de gestion de la ressource en eau et d'atténuation du risque des sécheresses, nous nous intéressons à l'analyse des indices de stress hydriques. À cette fin, un modèle de bilan d'énergie à double source permet, en combinant de l'information satellitaire (température de surface, NDVI, albédo et LAI) et de l'information météorologique (température de l'air, humidité relative de l'air, vitesse du vent et rayonnement global), de simuler l'évapotranspiration ainsi que le stress hydrique. Ces deux variables doivent être fournies d'une façon continue et sur une longue période temporelle pour une analyse adéquate des périodes de sécheresses. Or, les réseaux d'observations météorologiques sont parfois insuffisants (faible densité des sites instrumentés et périodes d'observation courtes et souvent non-concomitantes). Notre premier objectif est alors de simuler des scénarios de différentes variables climatiques afin de les prolonger. Nous avons adapté un générateur de conditions météorologiques "MetGen" qui permet de combler les lacunes présentes sur une période d'observation et de projeter des scénarios sur une période distincte de la période d'observation. MetGen exploite parmi ses co-variables, les données de réanalyses qui fournissent des variables à faible résolution spatiale (environ 31 km), comme source d'information importante. Nous comparons cette méthode avec des méthodes de correction de biais (univariée et multivariée) qui exploitent également les données de réanalyses. Cette approche statistique est validée selon deux volets : l'évaluation de la capacité (1) à bien reproduire les variables météorologiques et (2) à bien restituer les variables de bilan d'énergie. Les analyses, menées avec les données des stations météorologiques du système d'observations, ont permis de valider MetGen sur une période de validation (2011-2016). Nous avons utilisé alors cette méthode afin de simuler des données climatiques sur toute la période d'étude (2000-2019). Cette série ainsi que celle provenant des réanalyses brutes sont utilisées comme forçages climatiques du modèle d'énergie à double source SPARSE, afin de simuler deux indices de stress thermiques SI(SWG) et SI(ERA5) issus du générateur et des réanalyses ERA5 respectivement, à une échelle kilométrique. Ces deux indices sensibles aux anomalies de température de surface, sont comparés avec d'autres indices standardisés issus de différentes longueurs d'onde : le NDVI issu du visible/proche infrarouge, SWI du micro-onde et un indice standardisé de précipitations UPI qui est utilisé comme une référence pour notre analyse. Cette analyse est effectuée en termes de pertinence, de cohérence et de précocité pour la détection d'une sécheresse agronomique. Les deux indices thermiques ont montré des bonnes performances pour la détection du stress, notamment SI(SWG) qui a montré plus de précision et de capacité à détecter le stress hydrique d'une façon précoce. Ces analyses et tous ces approches statistiques sont effectuées au niveau du bassin versant de Merguellil situé au centre de la Tunisie et qui présente un modèle typique des régions semi-arides. Note de contenu : 1- Introduction
2- Partie A
2.1 Etat de l'art
2.2 Matériel et méthodes
2.3 Conclusion partielle et synthèse
3- Partie B
3.1 Introduction
3.2 Développement de la méthode statistique
3.3 Résultats complémentaires
3.4 Conclusion partielle et synthèse
4- Partie C
4.1 Introduction
4.2 Interpolation journalière de l’évapotranspiration
4.3 Indice de sécheresse
4.4 Utilisation de l’indice thermique pour le stress de la végétation
4.5 Comparaison SI et ESI
4.6 Conclusion partielle et synthèse
Conclusion et perspectivesNuméro de notice : 28880 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Thèse française Note de thèse : Thèse de Doctorat : Surfaces et Interfaces Continentales, Hydrologie : Toulouse 3 : 2022 Organisme de stage : CESBIO DOI : sans En ligne : http://www.theses.fr/2022TOU30022 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101529 Attributs de texture extraits d'images multispectrales acquises en conditions d'éclairage non contrôlées : application à l'agriculture de précision / Anis Amziane (2022)
Titre : Attributs de texture extraits d'images multispectrales acquises en conditions d'éclairage non contrôlées : application à l'agriculture de précision Type de document : Thèse/HDR Auteurs : Anis Amziane, Auteur ; Ludovic Macaire, Directeur de thèse Editeur : Lille : Université de Lille Année de publication : 2022 Importance : 214 p. Format : 21 x 30 cm Note générale : Bibliographie
Thèse pour obtenir le grade de Docteur de l'Université de Lille, spécialité Automatique, Génie Informatique, Traitement du Signal et des ImagesLangues : Français (fre) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] agriculture de précision
[Termes IGN] bande spectrale
[Termes IGN] classification dirigée
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] détection automatique
[Termes IGN] éclairage
[Termes IGN] exitance spectrale
[Termes IGN] extraction de la végétation
[Termes IGN] rayonnement proche infrarouge
[Termes IGN] reconnaissance d'objets
[Termes IGN] réflectance végétale
[Termes IGN] signature spectraleIndex. décimale : THESE Thèses et HDR Résumé : (auteur) The main objective of this work is to develop an automatic recognition system of crop and weed plants in field conditions. In Chapter 2 we describe the formation of multispectral radiance images under the Lambertian surface assumption and the different devices that can be used to acquire such images. We then provide a detailed description of the multispectral camera used in this study. Because radiance multispectral images are acquired under varying illumination, we propose an original multispectral image formation model that takes the variation of illumination conditions into account. In chapter 3, we estimate the reflectance as an illumination-invariant spectral signature. First, we present state-of-the-art methods that can be used to estimate the reflectance from multispectral images. We then introduce the reference state-of-the-art method for reflectance estimation and de- scribe our proposed method for reflectance estimation under varying illumination. Chapter 4 focuses on estimated reflectance assessment. The quality of reflectance estimated by our method is evaluated against state-of-the-art methods, and its contribution to supervised crop/weed recognition is demonstrated. Chapter 5 addresses the dimension reduction issue. The acquired multispectral images are composed of a high number of spectral channels, whose analysis is memory and time consuming. Moreover, spectral bands associated to these channels may be redundant or contain highly correlated spectral information. Therefore, we select the best spectral bands for crop/weed classification and use them to specify a camera suited for crop/weed recognition.Chapter 6 deals with the problem of spatio-spectral feature extraction from multispectral images. We propose an approach that extracts both spatial and spectral information at reduced computation costs based on a CNN. Its contribution to crop/weed recognition is demonstrated. Note de contenu : 1- Introduction
2- Multispectral imaging
3- Reflectance estimation
4- Reflectance estimation assessment
5- dimension reduction
6- Raw textures features for crop/weed recognition
ConclusionNuméro de notice : 24102 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Thèse française Organisme de stage : Laboratoire Cristal (Lille) DOI : sans En ligne : https://www.theses.fr/2022ULILB020 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102577 Characteristics of taiga and tundra snowpack in development and validation of remote sensing of snow / Henna-Reetta Hannula (2022)
Titre : Characteristics of taiga and tundra snowpack in development and validation of remote sensing of snow Type de document : Thèse/HDR Auteurs : Henna-Reetta Hannula, Auteur Editeur : Helsinki [Finland] : University of Helsinki Année de publication : 2022 Importance : 79 p. Format : 21 x 30 cm ISBN/ISSN/EAN : 978-952-336-153-9 Note générale : Bibliographie
Academic dissertation, Faculty of Science, University of HelsinkiLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] analyse des mélanges spectraux
[Termes IGN] carte thématique
[Termes IGN] changement climatique
[Termes IGN] distribution du coefficient de réflexion bidirectionnelle BRDF
[Termes IGN] distribution spatiale
[Termes IGN] données spatiotemporelles
[Termes IGN] échantillonnage de données
[Termes IGN] Finlande
[Termes IGN] forêt boréale
[Termes IGN] image infrarouge
[Termes IGN] manteau neigeux
[Termes IGN] problème inverse
[Termes IGN] réflectance spectrale
[Termes IGN] taïga
[Termes IGN] toundraRésumé : (auteur) Remote sensing of snow is a method to measure snow cover characteristics without direct physical contact with the target from airborne or space-borne platforms. Reliable estimates of snow cover extent and snow properties are vital for several applications including climate change research and weather and hydrological forecasting. Optical remote sensing methods detect the extent of snow cover based on its high reflectivity compared to other natural surfaces. A universal challenge for snow cover mapping is the high spatiotemporal variability of snow properties and heterogeneous landscapes such as the boreal forest biome. The optical satellite sensor’s footprint may extend from tens of meters to a kilometer; the signal measured by the sensor can simultaneously emerge from several target categories within individual satellite pixels. By use of spectral unmixing or inverse model-based methods, the fractional snow cover (FSC) within the satellite image pixel can be resolved from the recorded electromagnetic signal. However, these algorithms require knowledge of the spectral reflectance properties of the targets present within the satellite scene and the accuracy of snow cover maps is dependent on the feasibility of these spectral model parameters. On the other hand, abrupt changes in land cover types with large differences in their snow properties may be located within a single satellite image pixel and complicate the interpretation of the observations. Ground-based in-situ observations can be used to validate the snow parameters derived by indirect methods, but these data are affected by the chosen sampling. This doctoral thesis analyses laboratory-based spectral reflectance information on several boreal snow types for the purpose of the more accurate reflectance representation of snow in mapping method used for the detection of fractional snow cover. Multi-scale reflectance observations representing boreal spectral endmembers typically used in optical mapping of snow cover, are exploited in the thesis. In addition, to support the interpretation of remote sensing observations in boreal and tundra environments, extensive in-situ dataset of snow depth, snow water equivalent and snow density are exploited to characterize the snow variability and to assess the uncertainty and representativeness of these point-wise snow measurements applied for the validation of remote sensing observations. The overall goal is to advance knowledge about the spectral endmembers present in boreal landscape to improve the accuracy of the FSC estimates derived from the remote sensing observations and support better interpretation and validation of remote sensing observations over these heterogeneous landscapes. The main outcome from the work is that laboratory-controlled experiments that exclude disturbing factors present in field circumstances may provide more accurate representation of wet (melting) snow endmember reflectance for the FSC mapping method. The behavior of snow band reflectance is found to be insensitive to width and location differences between visible satellite sensor bands utilized in optical snow cover mapping which facilitates the use of various sensors for the construction of historical data records. The results also reveal the high deviation of snow reflectance due to heterogeneity in snow macro- and microstructural properties. The quantitative statistics of bulk snow properties show that areal averages derived from in-situ measurements and used to validate remote sensing observations are dependent on the measurement spacing and sample size especially over land covers with high absolute snow depth variability, such as barren lands in tundra. Applying similar sampling protocol (sample spacing and sample size) over boreal and tundra land cover types that represent very different snow characteristics will yield to non-equal representativeness of the areal mean values. The extensive datasets collected for this work demonstrate that observations measured at various scales can provide different view angle to the same challenge but at the same time any dataset individually cannot provide a full understanding of the target complexity. This work and the collected datasets directly facilitate further investigation of uncertainty in fractional snow cover maps retrieved by optical remote sensing and the interpretation of satellite observations in boreal and tundra landscapes. Note de contenu : 1. Introduction
2. Snow and its properties
3. Multispectral optical remote sensing of snow
4. Study site, datasets and methods
5. Results and discussion
6. Conclusions and future workNuméro de notice : 24060 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Thèse étrangère Note de thèse : PhD Thesis : Sciences : University of Helsinki : 2022 DOI : 10.35614/isbn.9789523361522 En ligne : https://doi.org/10.35614/isbn.9789523361522 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101997 DART: An efficient 3D Monte Carlo vector radiative transfer model for remote sensing applications / Yingjie Wang (2022)
Titre : DART: An efficient 3D Monte Carlo vector radiative transfer model for remote sensing applications Titre original : Modélisation 3D du transfert radiatif avec polarisation pour l'étude des surfaces terrestres par télédétection Type de document : Thèse/HDR Auteurs : Yingjie Wang, Auteur ; Jean-Philippe Gastellu-Etchegorry, Directeur de thèse ; A. Deschamps, Directeur de thèse Editeur : Toulouse : Université de Toulouse Année de publication : 2022 Importance : 248 p. Format : 21 x 30 cm Note générale : Bibliographie
Thèse en vue de l'obtention du Doctorat de l'Université de Toulouse, spécialité Surfaces et interfaces continentales, hydrologieLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] distribution du coefficient de réflexion bidirectionnelle BRDF
[Termes IGN] méthode de Monte-Carlo
[Termes IGN] modèle de transfert radiatif
[Termes IGN] modélisation 3D
[Termes IGN] polarisation
[Termes IGN] radianceIndex. décimale : THESE Thèses et HDR Résumé : (auteur) Accurate understanding of the land surface functioning, such as the energy budget, carbon and water cycles, and ecosystem dynamics, is essential to better interpret, predict and mitigate the impact of the expected global changes. It thus requires observing our planet at different spatial and temporal scales that only the remote sensing (RS) can achieve because of its ability to provides systematic and synoptic radiometric observations. These observations can be transformed to surface parameters (e.g., temperature, vegetation biomass, etc.) used as input in process models (e.g., evapotranspiration) or be assimilated in the latter. Understanding the radiation interactions in the land surface and atmosphere is essential in two aspects: interpret RS signals as information about the observed land surfaces, and model the processes of functioning of land surfaces where the radiation participates. This explains the development of radiative transfer models (RTMs) that simulate the radiative budget and RS observations. The initial 3D RTMs in the 1980s simulated basic radiation mechanisms in very schematic representations of land surfaces (e.g., turbid medium, geometric primitive). Since then, their accuracy and performance have been greatly improved to address the increasing need of accurate information about land surfaces as well as the advances of RS instruments. So far, two types of improvements are still needed: 1. More accurate and efficient radiative transfer (RT) modelling (e.g., polarization, specular reflection, atmospheric scattering and emission, etc.) 2. Representation of land surfaces at different realism degrees and spatial scales. DART is one of the most accurate and comprehensive 3D RTMs (dart.omp.eu). It simulates the radiative budget and RS observations of urban and natural landscapes, with topography and atmosphere, from the ultraviolet to the thermal infrared domains. Its initial version, DART-FT, in 1992, used the discrete ordinates method to iteratively track the radiation along finite number of discrete directions in voxelized representations of the landscapes. It has been validated with other RTMs, and also RS and field measurements. However, it cannot simulate RS observations with the presently needed precision because of its voxelized representation of landscapes, and absence of some physical mechanisms (e.g., polarization). During this thesis, in collaboration with the DART team, I developed in DART a new Monte Carlo vector RT mode called DART-Lux that takes full advantage of the latest advances in RT modelling, especially in computer graphics. The central idea is to transfer the radiation transfer problem as a multi-dimensional integral problem and solve it with the Monte Carlo method that is considerably efficient and accurate in computing multi-dimensional integral such as the complex mechanisms (e.g., polarization) in realistic representations of 3D landscapes. For that, I implemented the bidirectional path tracing algorithm that generates a group of "source-sensor" paths by connecting two sub-paths, one is generated starting from the light source and another one is generated starting from the sensor. Then, the contribution of these paths to the integral is estimated by the multiple importance sampling. This method allows to accurately and efficiently simulate polarimetric RS observations of kilometre-scale realistic landscapes coupled with plane-parallel atmosphere, with consideration of the anisotropic scattering, the thermal emission, and the solar induced fluorescence. Compared to DART-FT, DART-Lux improves the computer efficiency (i.e., computer time and memory) usually by a factor of more than 100 for large-scale and complex landscapes. It provides new perspectives for studying the land surface functioning and also for preparing Earth observation satellite missions such as the missions TRISHNA (CNES and ISRO), LSTM and next generation Sentinel-2 (ESA), and CHANGE (NASA). Note de contenu : General introduction
1- Radiometry and radiative transfer
2- Numerical models for radiative transfer
3- DART-Lux: theory and implementation
4- Modelling of atmospheric effects
5- Modelling of polarization
Conclusion and perspectivesNuméro de notice : 24106 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Thèse française Organisme de stage : CESBIO DOI : sans En ligne : https://www.theses.fr/2022TOU30173 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=103060 Detecting and visualizing observation hot-spots in massive volunteer-contributed geographic data across spatial scales using GPU-accelerated kernel density estimation / Guiming Zhang in ISPRS International journal of geo-information, vol 11 n° 1 (January 2022)PermalinkDetection and biomass estimation of phaeocystis globosa blooms off Southern China from UAV-based hyperspectral measurements / Xue Li in IEEE Transactions on geoscience and remote sensing, vol 60 n° 1 (January 2022)PermalinkEarly detection of spruce vitality loss with hyperspectral data: Results of an experimental study in Bavaria, Germany / Kathrin Einzmann in Remote sensing of environment, vol 266 (December 2021)PermalinkSpatial variability of suspended sediments in San Francisco Bay, California / Niky C. Taylor in Remote sensing, vol 13 n° 22 (November-2 2021)PermalinkA novel cotton mapping index combining Sentinel-1 SAR and Sentinel-2 multispectral imagery / Lan Xun in ISPRS Journal of photogrammetry and remote sensing, Vol 181 (November 2021)PermalinkDeep-learning-based burned area mapping using the synergy of Sentinel-1&2 data / Qi Zhang in Remote sensing of environment, vol 264 (October 2021)PermalinkEarly detection of pine wilt disease using deep learning algorithms and UAV-based multispectral imagery / Run Yu in Forest ecology and management, vol 497 (October-1 2021)PermalinkIntegrating spatio-temporal-spectral information for downscaling Sentinel-3 OLCI images / Yijie Tang in ISPRS Journal of photogrammetry and remote sensing, vol 180 (October 2021)PermalinkSpectral reflectance estimation of UAS multispectral imagery using satellite cross-calibration method / Saket Gowravaram in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 10 (October 2021)PermalinkUncertainties in measurements of leaf optical properties are small compared to the biological variation within and between individuals of European beech / Fanny Petibon in Remote sensing of environment, vol 264 (October 2021)PermalinkBinary space partitioning visibility tree for polygonal and environment light rendering / Hiroki Okuno in The Visual Computer, vol 37 n° 9 - 11 (September 2021)PermalinkEstimating regional soil moisture with synergistic use of AMSR2 and MODIS images / Majid Rahimzadegan in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 9 (September 2021)PermalinkUnsupervised band selection of hyperspectral data based on mutual information derived from weighted cluster entropy for snow classification / Divyesh Varade in Geocarto international, vol 36 n° 15 ([15/08/2021])PermalinkBackground segmentation in multicolored illumination environments / Nikolas Ladas in The Visual Computer, vol 37 n° 8 (August 2021)PermalinkLeaf and wood separation for individual trees using the intensity and density data of terrestrial laser scanners / Kai Tan in IEEE Transactions on geoscience and remote sensing, vol 59 n° 8 (August 2021)PermalinkDetecting high-temperature anomalies from Sentinel-2 MSI images / Yongxue Liu in ISPRS Journal of photogrammetry and remote sensing, vol 177 (July 2021)PermalinkFluvial gravel bar mapping with spectral signal mixture analysis / Liza Stančič in European journal of remote sensing, vol 54 sup 1 (2021)PermalinkEvaluating the performance of hyperspectral leaf reflectance to detect water stress and estimation of photosynthetic capacities / Jingjing Zhou in Remote sensing, vol 13 n° 11 (June-1 2021)PermalinkFractional vegetation cover estimation algorithm for FY-3B reflectance data based on random forest regression method / Duanyang Liu in Remote sensing, vol 13 n° 11 (June-1 2021)PermalinkMapping and quantification of the dwarf eelgrass Zostera noltii using a random forest algorithm on a SPOT 7 satellite image / Salma Benmokhtar in ISPRS International journal of geo-information, vol 10 n° 5 (May 2021)Permalink