Centre dedocumentation
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]

Estimating 10-m land surface albedo from Sentinel-2 satellite observations using a direct estimation approach with Google Earth Engine / Xingwen Lin in ISPRS Journal of photogrammetry and remote sensing, vol 194 (December 2022)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 194 (December 2022) . - pp 1 - 20 Titre : Estimating 10-m land surface albedo from Sentinel-2 satellite observations using a direct estimation approach with Google Earth Engine Type de document : Article/Communication Auteurs : Xingwen Lin, Auteur ; Shengbiao Wu, Auteur ; Bin Chen, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 1 - 20 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] albedo [Termes IGN] bande spectrale [Termes IGN] distribution du coefficient de réflexion bidirectionnelle BRDF [Termes IGN] Google Earth Engine [Termes IGN] hétérogénéité spatiale [Termes IGN] image Sentinel-MSI [Termes IGN] image Terra-MODIS [Termes IGN] Leaf Area Index [Termes IGN] modèle de transfert radiatif [Termes IGN] phénologie [Termes IGN] réflectance de surface Résumé : (auteur) Land surface albedo plays an important role in controlling the surface energy budget and regulating the biophysical processes of natural dynamics and anthropogenic activities. Satellite remote sensing is the only practical approach to estimate surface albedo at regional and global scales. It nevertheless remains challenging for current satellites to capture fine-scale albedo variations due to their coarse spatial resolutions from tens to hundreds of meters. The emerging Sentinel-2 satellites, with a high spatial resolution of 10 m and an approximate 5-day revisiting cycle, provide a promising solution to address these observational limitations, yet their potentials remain underexplored. In this study, we integrated the Sentinel-2 observations with an updated direct estimation approach to improve the estimation and monitoring of fine-scale surface albedo. To enable the capability of the direct estimation approach at a 10-m scale, we combined the 10-m resolution European Space Agency (ESA) WorldCover land cover data and the 500-m resolution Moderate-Resolution Imaging Spectroradiometer (MODIS) Bidirectional Reflectance Distribution Function (BRDF)/albedo product to build a high-quality and representative BRDF training database. To evaluate our approach, we proposed an integrated evaluation framework leveraging 3-D physical model simulations, ground measurements, and satellite observations. Specifically, we first simulated a comprehensive dataset of Sentinel-2-like surface reflectance and broadband albedo across a variety of geometric configurations using the MODIS BRDF training samples. With this dataset, we built the Look-Up-Tables (LUTs) that connect surface broadband albedo and Sentinel-2 reflectance through a direct angular bin-based linear regression approach, and further coupled these LUTs with the Google Earth Engine (GEE) cloud-computing platform. We next evaluated the proposed algorithm at two spatial levels: (1) 10-m scale for absolute accuracy assessment using the references from the Discrete Anisotropic Radiative Transfer (DART) simulations and flux-site observations, and (2) 500-m scale for large-scale mapping assessment by comparing the estimated albedo with the MODIS albedo product. Lastly, we presented four examples to show the capability of Sentinel-2 albedo in detecting fine-scale characteristics of vegetation and urban covers. Results show that: (1) the proposed algorithm accurately estimates surface albedo from Sentinel-2-like reflectance across different landscape configurations (overall root-mean-square-error (RMSE) = 0.018, bias = 0.005, and coefficient of determination (R2) = 0.88); (2) the Sentinel-2-derived surface albedo agrees well with ground measurements (overall RMSE = 0.030, bias = -0.004, and R2 = 0.94) and MODIS products (overall RMSE = 0.030, bias = 0.021, and R2 = 0.97); and (3) Sentinel-2-derived albedo accurately captures seasonal leaf phenology and rapid snow events, and detects the interspecific (or interclass) variations of tree species and colored urban rooftops. These results demonstrate the capability of the proposed approach to map high-resolution surface albedo from Sentinel-2 satellites over large spatial and temporal contexts, suggesting the potential of using such fine-scale datasets to improve our understanding of albedo-related biophysical processes in the coupled human-environment system. Numéro de notice : A2022-823 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2022.09.016 Date de publication en ligne : 14/10/2022 En ligne : https://doi.org/10.1016/j.isprsjprs.2022.09.016 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101999 [article]

Spatio-temporal patterns of wildfires in Siberia during 2001–2020 / Oleg Tomshin in Geocarto international, vol 37 n° 25 ([01/12/2022])  

Public [article]  inGeocarto international > vol 37 n° 25 [01/12/2022] . - pp 7339 - 7357 Titre : Spatio-temporal patterns of wildfires in Siberia during 2001–2020 Type de document : Article/Communication Auteurs : Oleg Tomshin, Auteur ; Vladimir Solovyev, Auteur Année de publication : 2022 Article en page(s) : pp 7339 - 7357 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] changement climatique [Termes IGN] image radar moirée [Termes IGN] image Terra-MODIS [Termes IGN] incendie de forêt [Termes IGN] modélisation spatio-temporelle [Termes IGN] occupation du sol [Termes IGN] précipitation [Termes IGN] réflectance de surface [Termes IGN] Sibérie [Termes IGN] température de l'air Résumé : (auteur) Siberia is one of the most fire-prone regions of northern Eurasia and also the region with the greatest warming in the Eastern Hemisphere over the last decades. In this study, spatiotemporal features of wildfires in Siberia and their recent trends and relationship with air temperature and precipitation during 2001–2020 were investigated. The main results show that the annual burned area (BA) in Siberia during the study period is 6.5 Mha with a non-significant positive trend (58 kha year−1, p = 0.49), but analysis of the spatial patterns revealed regions with significant trends in BA: negative in the south of Western Siberia (−17 kha year−1, p  Numéro de notice : A2022-926 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1080/10106049.2021.1973581 Date de publication en ligne : 06/09/2021 En ligne : https://doi.org/10.1080/10106049.2021.1973581 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102659 [article]

Comparing Landsat-8 and Sentinel-2 top of atmosphere and surface reflectance in high latitude regions: case study in Alaska / Jiang Chen in Geocarto international, vol 37 n° 20 ([20/09/2022])  

Public [article]  inGeocarto international > vol 37 n° 20 [20/09/2022] . - pp 6052 - 6071 Titre : Comparing Landsat-8 and Sentinel-2 top of atmosphere and surface reflectance in high latitude regions: case study in Alaska Type de document : Article/Communication Auteurs : Jiang Chen, Auteur ; Weining Zhu, Auteur Année de publication : 2022 Article en page(s) : pp 6052 - 6071 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] Alaska (Etats-Unis) [Termes IGN] analyse comparative [Termes IGN] Google Earth Engine [Termes IGN] image Landsat-8 [Termes IGN] image proche infrarouge [Termes IGN] image Sentinel-MSI [Termes IGN] latitude [Termes IGN] Normalized Difference Vegetation Index [Termes IGN] observation de la Terre [Termes IGN] réflectance de surface Résumé : (auteur) Combining Landsat-8 and Sentinel-2 images is an effective approach to obtain high spatiotemporal resolution data for Earth observation and remote sensing modeling. The differences between Landsat-8 and Sentinel-2 products, such as the reflectance at the top of atmosphere (TOA) and land surface, should be compared and evaluated to make sure they are spectrally consistent. Their consistency has been evaluated and the differences have been empirically corrected at mid-low latitudes, but in high latitude areas with a higher solar zenith angle (SZA), the similar work has not been explored. In this study, Landsat-8 and Sentinel-2 TOA and surface reflectance in Alaska as well as some surface parameters, such as the normalized difference vegetation index (NDVI) and normalized difference snow index (NDSI), were compared using the massive data distributed on Google earth engine (GEE) online platform, and their consistency was evaluated and the uncertainty was analyzed. Some empirical models were suggested to convert Sentinel-2 products to be consistent with Landsat-8 products at all bands. The results show that TOA reflectance is more consistent than surface reflectance in Alaska. This study suggests that the consistency between Landsat-8 and Sentinel-2 at high latitudes should be paid more attention because their consistency is lower than that at mid-low latitudes. Numéro de notice : A2022-717 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : https://doi.org/10.1080/10106049.2021.1924295 Date de publication en ligne : 17/05/2021 En ligne : https://doi.org/10.1080/10106049.2021.1924295 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101642 [article]

The FIRST model: Spatiotemporal fusion incorrporting spectral autocorrelation / Shuaijun Liu in Remote sensing of environment, vol 279 (September-15 2022)  

Public [article]  inRemote sensing of environment > vol 279 (September-15 2022) . - n° 113111 Titre : The FIRST model: Spatiotemporal fusion incorrporting spectral autocorrelation Type de document : Article/Communication Auteurs : Shuaijun Liu, Auteur ; Junxiong Zhou, Auteur ; Yuean Qiu, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 113111 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse comparative [Termes IGN] autocorrélation [Termes IGN] bande spectrale [Termes IGN] détection de changement [Termes IGN] données spatiotemporelles [Termes IGN] fusion de données [Termes IGN] image Landsat-OLI [Termes IGN] image Terra-MODIS [Termes IGN] réflectance de surface [Termes IGN] réflectance spectrale [Termes IGN] régression des moindres carrés partiels [Termes IGN] régression multiple Résumé : (auteur) Over the past decade, spatiotemporal fusion has become an indispensable tool for monitoring land surface dynamics due to its promising ability to produce surface reflectance products with both high spatial and temporal resolutions. However, existing fusion methods usually generate multispectral band products by predicting each spectral band separately, so the useful information of spectral autocorrelation within the spectrum has been ignored and waits to be exploited. To address this issue, we propose a novel spatiotemporal fusion method, the spatiotemporal Fusion Incorrporting Spectral autocorrelaTion (FIRST) model, to fully utilize the multiple spectral bands of surface reflectance products. Compared with other fusion methods, the model has three distinct advantages: (1) it utilizes spectral autocorrelation in a many-to-many regression framework that simultaneously inputs and predicts multispectral bands without the collinearity effect; (2) it maintains high fusion accuracy when the spatiotemporal variation is large with acceptable computational efficiency; and (3) it can produce robust results even with input images contaminated by haze and thin clouds. We tested the FIRST model at several experimental sites and compared it with four typical methods, the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM), Flexible Spatiotemporal DAta Fusion (FSDAF) model, the regression model Fitting, spatial Filtering and residual Compensation (Fit-FC) model and the enhanced STARFM (ESTARFM). The results demonstrate that FIRST yields better overall performance for its simple and effective technical principles. FIRST is thus expected to provide high-quality remotely sensed data with high spatial resolution and frequent observations for various applications. Numéro de notice : A2022-554 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.rse.2022.113111 Date de publication en ligne : 16/06/2022 En ligne : https://doi.org/10.1016/j.rse.2022.113111 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101166 [article]

Historical mapping of rice fields in Japan using phenology and temporally aggregated Landsat images in Google Earth Engine / Luis Carrasco in ISPRS Journal of photogrammetry and remote sensing, vol 191 (September 2022)  

Permalink

A second-order attention network for glacial lake segmentation from remotely sensed imagery / Shidong Wang in ISPRS Journal of photogrammetry and remote sensing, vol 189 (July 2022)  

Permalink

A phenology-based vegetation index classification (PVC) algorithm for coastal salt marshes using Landsat 8 images / Jing Zeng in International journal of applied Earth observation and geoinformation, vol 110 (June 2022)  

Permalink

A continuous change tracker model for remote sensing time series reconstruction / Yangjian Zhang in Remote sensing, vol 14 n° 9 (May-1 2022)  

Permalink

Development of the GLASS 250-m leaf area index product (version 6) from MODIS data using the bidirectional LSTM deep learning model / Han Ma in Remote sensing of environment, vol 273 (May 2022)  

Permalink

Assessment of sky diffuse irradiance and building reflected irradiance in cast shadows / Manchun Lei (2021)  

Permalink

Learning harmonised Pleiades and Sentinel-2 surface reflectances / J. Michel (2021)  

Permalink

Polarization of light reflected by grass: modeling using visible-sunlit areas / Bin Yang in Photogrammetric Engineering & Remote Sensing, PERS, vol 86 n° 12 (December 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

Application of geographic Information system and remote sensing in multiple criteria analysis to identify priority areas for biodiversity conservation in Vietnam / Xuan Dinh Vu (2020)  

Permalink