Centre de documentation
scientifique

A simple approach to enhance the TROPOMI solar-induced chlorophyll fluorescence product by combining with canopy reflected radiation at near-infrared band / Xinjie Liu in Remote sensing of environment, vol 284 (January 2023)  

Public [article]  inRemote sensing of environment > vol 284 (January 2023) . - n° 113341 Titre : A simple approach to enhance the TROPOMI solar-induced chlorophyll fluorescence product by combining with canopy reflected radiation at near-infrared band Type de document : Article/Communication Auteurs : Xinjie Liu, Auteur ; Liangyun Liu, Auteur ; Cédric Bacour, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : n° 113341 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] canopée [Termes IGN] chlorophylle [Termes IGN] fluorescence [Termes IGN] image Sentinel-5P-TROPOMI [Termes IGN] image Terra-MODIS [Termes IGN] production primaire brute [Termes IGN] rayonnement proche infrarouge [Termes IGN] réflectance de surface [Termes IGN] réflectance végétale Résumé : (auteur) Satellite-based data of solar-induced chlorophyll fluorescence (SIF) and the near-infrared radiation reflected by vegetation (NIRvP) are being increasingly used for the estimation of vegetation gross primary product (GPP) at the global scale. Although SIF contains more physiological information than NIRvP, NIRvP can have higher data quality and spatio-temporal resolution. Therefore, the two variables can be considered complementary for GPP monitoring. Here, we propose a simple framework to combine SIF and NIRvP data from different data sources to generate an enhanced SIF product (eSIF). The original SIF data comes from the TROPOMI instrument onboard the Sentinel-5P mission, whereas NIRvP data are derived from MODIS spectral reflectance and ERA5 reanalysis data. The resulting eSIF product has a spatial resolution of 0.05° and a temporal resolution of 8 days, as well as a higher signal-to-noise ratio and a lower angular dependency than the original TROPOMI SIF data. Our results demonstrate that eSIF has similar spatial patterns to the original SIF but is more spatially continuous and less noisy. Comparisons with the FLUXCOM global GPP product show that eSIF has a more universal relationship with GPP than NIRvP for different grass/crop plant functional types (the coefficients of variation are 18.9% for slopes of GPP to eSIF and 27.3% for slopes of GPP to NIRvP), but NIRvP outperforms eSIF for tracking GPP for forest PFTs exclude BoENF. Moreover, eSIF is able to better track the seasonal variations in GPP related to environmental stresses. This study highlights that our methodology based on the combination of SIF and NIRvP is a promising approach for better monitoring of GPP. Numéro de notice : A2023-017 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1016/j.rse.2022.113341 Date de publication en ligne : 07/11/2022 En ligne : https://doi.org/10.1016/j.rse.2022.113341 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102151 [article]

Attributs de texture extraits d'images multispectrales acquises en conditions d'éclairage non contrôlées : application à l'agriculture de précision / Anis Amziane (2022)  

Public 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 Images Langues : 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 spectrale Index. 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 Conclusion Numéro 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

Downscaling MODIS spectral bands using deep learning / Rohit Mukherjee in GIScience and remote sensing, vol 58 n° 8 (2021)  

Public [article]  inGIScience and remote sensing > vol 58 n° 8 (2021) . - pp 1300 - 1315 Titre : Downscaling MODIS spectral bands using deep learning Type de document : Article/Communication Auteurs : Rohit Mukherjee, Auteur ; Desheng Liu, Auteur Année de publication : 2021 Article en page(s) : pp 1300 - 1315 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] apprentissage profond [Termes IGN] bande spectrale [Termes IGN] image à basse résolution [Termes IGN] image Terra-MODIS [Termes IGN] image thermique [Termes IGN] rayonnement proche infrarouge [Termes IGN] réduction d'échelle [Termes IGN] résolution multiple Résumé : (auteur) MODIS sensors are widely used in a broad range of environmental studies, many of which involve joint analysis of multiple MODIS spectral bands acquired at disparate spatial resolutions. To extract land surface information from multi-resolution MODIS spectral bands, existing studies often downscale lower resolution (LR) bands to match the higher resolution (HR) bands based on simple interpolation or more advanced statistical modeling. Statistical downscaling methods rely on the functional relationship between the LR spectral bands and HR spatial information, which may vary across different land surface types, making statistical downscaling methods less robust. In this paper, we propose an alternative approach based on deep learning to downscale 500 m and 1000 m spectral bands of MODIS to 250 m without additional spatial information. We employ a superresolution architecture based on an encoder decoder network. This deep learning-based method uses a custom loss function and a self-attention layer to preserve local and global spatial relationships of the predictions. We compare our approach with a statistical method specifically developed for downscaling MODIS spectral bands, an interpolation method widely used for downscaling multi-resolution spectral bands, and a deep learning superresolution architecture previously used for downscaling satellite imagery. Results show that our deep learning method outperforms on almost all spectral bands both quantitatively and qualitatively. In particular, our deep learning-based method performs very well on the thermal bands due to the larger scale difference between the input and target resolution. This study demonstrates that our proposed deep learning-based downscaling method can maintain the spatial and spectral fidelity of satellite images and contribute to the integration and enhancement of multi-resolution satellite imagery. Numéro de notice : A2021-124 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1080/15481603.2021.1984129 Date de publication en ligne : 26/10/2021 En ligne : https://doi.org/10.1080/15481603.2021.1984129 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99309 [article]

Spectral 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)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 87 n° 10 (October 2021) . - pp 735 - 746 Titre : Spectral reflectance estimation of UAS multispectral imagery using satellite cross-calibration method Type de document : Article/Communication Auteurs : Saket Gowravaram, Auteur ; Haiyang Chao, Auteur ; Andrew Molthan, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 735 - 746 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] aéronef [Termes IGN] distribution du coefficient de réflexion bidirectionnelle BRDF [Termes IGN] étalonnage croisé [Termes IGN] forêt [Termes IGN] image captée par drone [Termes IGN] image Landsat-8 [Termes IGN] image multibande [Termes IGN] image proche infrarouge [Termes IGN] Kansas (Etats-Unis ; état) [Termes IGN] orthoimage [Termes IGN] orthorectification [Termes IGN] prairie [Termes IGN] rayonnement proche infrarouge [Termes IGN] réflectance spectrale Résumé : (Auteur) This paper introduces a satellite-based cross-calibration (SCC) method for spectral reflectance estimation of unmanned aircraft system (UAS) multispectral imagery. The SCC method provides a low-cost and feasible solution to convert high-resolution UAS images in digital numbers (DN) to reflectance when satellite data is available. The proposed method is evaluated using a multispectral data set, including orthorectified KHawk UAS DN imagery and Landsat 8 Operational Land Imager Level-2 surface reflectance (SR) data over a forest/grassland area. The estimated UAS reflectance images are compared with the National Ecological Observatory Network's imaging spectrometer (NIS) SR data for validation. The UAS reflectance showed high similarities with the NIS data for the near-infrared and red bands with Pearson's r values being 97 and 95.74, and root-mean-square errors being 0.0239 and 0.0096 over a 32-subplot hayfield. Numéro de notice : A2021-676 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.20-00091R2 En ligne : https://doi.org/10.14358/PERS.20-00091R2 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98863 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
105-2021101	SL	Revue	Centre de documentation	Revues en salle	Disponible

Atmospheric correction of Sentinel-3/OLCI data for mapping of suspended particulate matter and chlorophyll-a concentration in Belgian turbid coastal waters / Quinten Vanhellemont in Remote sensing of environment, Vol 256 (April 2020)  

Public [article]  inRemote sensing of environment > Vol 256 (April 2020) . - n° 112284 Titre : Atmospheric correction of Sentinel-3/OLCI data for mapping of suspended particulate matter and chlorophyll-a concentration in Belgian turbid coastal waters Type de document : Article/Communication Auteurs : Quinten Vanhellemont, Auteur ; Kevin Ruddick, Auteur Année de publication : 2021 Article en page(s) : n° 112284 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] Belgique [Termes IGN] chlorophylle [Termes IGN] correction atmosphérique [Termes IGN] eaux côtières [Termes IGN] image Sentinel-OLCI [Termes IGN] particule [Termes IGN] rayonnement infrarouge [Termes IGN] réflectance [Termes IGN] turbidité des eaux Résumé : (auteur) The performance of different atmospheric correction algorithms for the Ocean and Land Colour Instrument (OLCI) on board of Sentinel-3 (S3) is evaluated for retrieval of water-leaving radiance reflectance, and derived parameters chlorophyll-a concentration and turbidity in turbid coastal waters in the Belgian Coastal Zone (BCZ). This is performed using in situ measurements from an autonomous pan-and-tilt hyperspectral radiometer system (PANTHYR). The PANTHYR provides validation data for any satellite band between 400 and 900 nm, with the deployment in the BCZ of particular interest due to the wide range of observed Near-InfraRed (NIR) reflectance. The Dark Spectrum Fitting (DSF) atmospheric correction algorithm is adapted for S3/OLCI processing in ACOLITE, and its performance and that of 5 other processing algorithms (L2-WFR, POLYMER, C2RCC, SeaDAS, and SeaDAS-ALT) is compared to the in situ measured reflectances. Water turbidities across the matchups in the Belgian Coastal Zone are about 20–100 FNU, and the overall performance is best for ACOLITE and L2-WFR, with the former providing lowest relative (Mean Absolute Relative Difference, MARD 7–27%) and absolute errors (Mean Average Difference, MAD -0.002, Root Mean Squared Difference, RMSD 0.01–0.016) in the bands between 442 and 681 nm. L2-WFR provides the lowest errors at longer NIR wavelengths (754–885 nm). The algorithms that assume a water reflectance model, i.e. POLYMER and C2RCC, are at present not very suitable for processing imagery over the turbid Belgian coastal waters, with especially the latter introducing problems in the 665 and 709 nm bands, and hence the chlorophyll-a and turbidity retrievals. This may be caused by their internal model and/or training dataset not being well adapted to the waters encountered in the BCZ. The 1020 nm band is used most frequently by ACOLITE/DSF for the estimation of the atmospheric path reflectance (67% of matchups), indicating its usefulness for turbid water atmospheric correction. Turbidity retrieval using a single band algorithm showed good performance for L2-WFR and ACOLITE compared to PANTHYR for e.g. the 709 nm band (MARD 15 and 17%), where their reflectances were also very close to the in situ observations (MARD 11%). For the retrieval of chlorophyll-a, all methods except C2RCC gave similar performance, due to the RedEdge band-ratio algorithm being robust to typical spectrally flat atmospheric correction errors. C2RCC does not retain the spectral relationship in the Red and RedEdge bands, and hence its chlorophyll-a concentration retrieval is not at all reliable in Belgian coastal waters. L2-WFR and ACOLITE show similar performance compared to in situ radiometry, but due to the assumption of spatially consistent aerosols, ACOLITE provides less noisy products. With the superior performance of ACOLITE in the 490–681 nm wavelength range, and smoother output products, it can be recommended for processing of S3/OLCI data in turbid waters similar to those encountered in the BCZ. The ACOLITE processor for OLCI and the in situ matchup dataset used here are made available under an open source license. Numéro de notice : A2021-476 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.rse.2021.112284 Date de publication en ligne : 12/02/2021 En ligne : https://doi.org/10.1016/j.rse.2021.112284 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97116 [article]

Local color and morphological image feature based vegetation identification and its application to human environment street view vegetation mapping, or how green is our county? / Istvan G. Lauko in Geo-spatial Information Science, vol 23 n° 3 (September 2020)  

Permalink

Complete and accurate data correction for seamless mosaicking of airborne hyperspectral images: A case study at a mining site in Inner Mongolia, China / Kun Tan in ISPRS Journal of photogrammetry and remote sensing, vol 165 (July 2020)  

Permalink

Tephra mass eruption rate from ground-based X-band and L-band microwave radars during the November 23, 2013, Etna Paroxysm / Frank S. Marzano in IEEE Transactions on geoscience and remote sensing, vol 58 n° 5 (May 2020)  

Permalink

Estimation of soil surface water contents for intertidal mudflats using a near-infrared long-range terrestrial laser scanner / Kai Tan in ISPRS Journal of photogrammetry and remote sensing, vol 159 (January 2020)  

Permalink

Uncertainty analysis of remotely-acquired thermal infrared data to extract the thermal Properties of active lava surfaces / James A. Thompson in Remote sensing, vol 12 n° 1 (January 2020)  

Permalink

Evaluating metrics derived from Landsat 8 OLI imagery to map crop cover / Rei Sonobe in Geocarto international, vol 34 n° 8 ([15/06/2019])  

Permalink

Application of Landsat-8 and ASTER satellite remote sensing data for porphyry copper exploration: a case study from Shahr-e-Babak, Kerman, south of Iran / Morteza Safari in Geocarto international, vol 33 n° 11 (November 2018)  

Permalink

Visible + Near Infrared spectroscopy as taxonomic tool for identifying birch species / Mulualem Tigabu in Silva fennica, vol 52 n° 4 (September 2018)  

Permalink

Egalisation radiométrique d'orthoimages thermiques / Jonathan Lisein (2018)    

Permalink

Sense City, mini ville sensible expérimentale / Marielle Mayo in Géomètre, n° 2153 (décembre 2017)

Permalink