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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]

The fractional vegetation cover (FVC) and associated driving factors of modeling in mining areas / Jun Li in Photogrammetric Engineering & Remote Sensing, PERS, vol 88 n° 10 (October 2022)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 88 n° 10 (October 2022) . - pp 665 - 671 Titre : The fractional vegetation cover (FVC) and associated driving factors of modeling in mining areas Type de document : Article/Communication Auteurs : Jun Li, Auteur ; Tianyu Guo, Auteur ; Chengye Zhang, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 665 - 671 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] analyse comparative [Termes IGN] Chine [Termes IGN] couvert végétal [Termes IGN] Google Earth Engine [Termes IGN] hétérogénéité spatiale [Termes IGN] image Landsat-ETM+ [Termes IGN] image Landsat-OLI [Termes IGN] industrie minière [Termes IGN] mine [Termes IGN] Normalized Difference Vegetation Index [Termes IGN] régression géographiquement pondérée [Termes IGN] régression multiple [Termes IGN] réseau neuronal artificiel Résumé : (auteur) To determine the fractional vegetation cover (FVC) and associated driving factors of modeling in mining areas, six types of data were used as driving factors and three methods —multi-linear regression (MLR), geographically weighted regression (GWR), and geographically weighted artificial neural network (GWANN)— were adopted in the modeling. The experiments, conducted in Shengli mining areas located in Xilinhot city, China, show that the MLR model without consideration of spatial heterogeneity and spatial non-stationarity performs the worst and that the GWR model presents obvious location differences, since it predefines a linear relationship which is unable to describe FVC for some locations. The GWANN model, improving on these defects, is the most suitable model for the FVC driving process in mining areas; it outperforms the other two models, with root-mean-square error (RMSE) and mean absolute percentage error (MAPE) reaching 0.16 and 0.20. It has improvements of approximately 24% in RMSE and 33% in MAPE compared to the MLR model, and those values grow to 59% and 71% when compared with the GWR model. Numéro de notice : A2022-813 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.21-00070R3 Date de publication en ligne : 01/10/2022 En ligne : https://doi.org/10.14358/PERS.21-00070R3 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101973 [article]

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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]

Large-area high spatial resolution albedo retrievals from remote sensing for use in assessing the impact of wildfire soot deposition on high mountain snow and ice melt / André Bertoncini in Remote sensing of environment, vol 278 (September 2022)  

Public [article]  inRemote sensing of environment > vol 278 (September 2022) . - n° 113101 Titre : Large-area high spatial resolution albedo retrievals from remote sensing for use in assessing the impact of wildfire soot deposition on high mountain snow and ice melt Type de document : Article/Communication Auteurs : André Bertoncini, Auteur ; Caroline Aubry-Wake, Auteur ; John W. Pomeroy, Auteur Année de publication : 2022 Article en page(s) : n° 113101 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] albedo [Termes IGN] Colombie-Britannique (Canada) [Termes IGN] distribution du coefficient de réflexion bidirectionnelle BRDF [Termes IGN] fonte des glaces [Termes IGN] glacier [Termes IGN] Google Earth Engine [Termes IGN] image Sentinel-MSI [Termes IGN] image SRTM [Termes IGN] image Terra-MODIS [Termes IGN] incendie de forêt [Termes IGN] montagne [Termes IGN] neige [Termes IGN] pouvoir de résolution radiométrique Résumé : (auteur) Soot deposition from wildfires decreases snow and ice albedo and increases the absorption of shortwave radiation, which advances and accelerates melt. Soot deposition also induces algal growth, which further decreases snow and ice albedo. In recent years, increasingly severe and widespread wildfire activity has occurred in western Canada in association with climate change. In the summers of 2017 and 2018, westerly winds transported smoke from extensive record-breaking wildfires in British Columbia eastward to the Canadian Rockies, where substantial amounts of soot were deposited on high mountain glaciers, snowfields, and icefields. Several studies have addressed the problem of soot deposition on snow and ice, but the spatiotemporal resolution applied has not been compatible with studying mountain icefields that are extensive but contain substantial internal variability and have dynamical albedos. This study evaluates spatial patterns in the albedo decrease and net shortwave radiation (K*) increase caused by soot from intense wildfires in Western Canada deposited on the Columbia Icefield (151 km2), Canadian Rockies, during 2017 and 2018. Twelve Sentinel-2 images were used to generate high spatial resolution albedo retrievals during four summers (2017 to 2020) using a MODIS bidirectional reflectance distribution function (BRDF) model, which was employed to model the snow and ice reflectance anisotropy. Remote sensing estimates were evaluated using site-measured albedo on the icefield's Athabasca Glacier tongue, resulting in a R2, mean bias, and root mean square error (RMSE) of 0.68, 0.019, and 0.026, respectively. The biggest inter-annual spatially averaged soot-induced albedo declines were of 0.148 and 0.050 (2018 to 2020) for southeast-facing glaciers and the snow plateau, respectively. The highest inter-annual spatially-averaged soot-induced shortwave radiative forcing was 203 W/m2 for southeast-facing glaciers (2018 to 2020) and 106 W/m2 for the snow plateau (2017 to 2020). These findings indicate that snow albedo responded rapidly to and recovered rapidly from soot deposition. However, ice albedo remained low the year after fire, and this was likely related to a bio-albedo feedback involving microorganisms. Snow and ice K* were highest during low albedo years, especially for south-facing glaciers. These large-scale effects accelerated melt of the Columbia Icefield. The findings highlight the importance of using large-area high spatial resolution albedo estimates to analyze the effect of wildfire soot deposition on snow and ice albedo and K* on icefields, which is not possible using other approaches. Numéro de notice : A2022-466 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.rse.2022.113101 Date de publication en ligne : 30/05/2022 En ligne : https://doi.org/10.1016/j.rse.2022.113101 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100800 [article]

Simulation of land use/land cover changes and urban expansion in Estonia by a hybrid ANN-CA-MCA model and utilizing spectral-textural indices / Najmeh Mozaffaree Pour in Environmental Monitoring and Assessment, vol 194 n° 9 (September 2022)  

Public [article]  inEnvironmental Monitoring and Assessment > vol 194 n° 9 (September 2022) . - n° 584 Titre : Simulation of land use/land cover changes and urban expansion in Estonia by a hybrid ANN-CA-MCA model and utilizing spectral-textural indices Type de document : Article/Communication Auteurs : Najmeh Mozaffaree Pour, Auteur ; Oleksandr Karasov, Auteur ; Iuliia Burdun, Auteur ; Tõnu Oja, Auteur Année de publication : 2022 Article en page(s) : n° 584 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] automate cellulaire [Termes IGN] chaîne de Markov [Termes IGN] croissance urbaine [Termes IGN] Estonie [Termes IGN] Google Earth Engine [Termes IGN] image Landsat-8 [Termes IGN] modèle de simulation [Termes IGN] occupation du sol [Termes IGN] réseau neuronal artificiel Résumé : (auteur) Over the recent two decades, land use/land cover (LULC) drastically changed in Estonia. Even though the population decreased by 11%, noticeable agricultural and forest land areas were turned into urban land. In this work, we analyzed those LULC changes by mapping the spatial characteristics of LULC and urban expansion in the years 2000–2019 in Estonia. Moreover, using the revealed spatiotemporal transitions of LULC, we simulated LULC and urban expansion for 2030. Landsat 5 and 8 data were used to estimate 147 spectral-textural indices in the Google Earth Engine cloud computing platform. After that, 19 selected indices were used to model LULC changes by applying the hybrid artificial neural network, cellular automata, and Markov chain analysis (ANN-CA-MCA). While determining spectral-textural indices is quite common for LULC classifications, utilization of these continues indices in LULC change detection and examining these indices at the landscape scale is still in infancy. This country-wide modeling approach provided the first comprehensive projection of future LULC utilizing spectral-textural indices. In this work, we utilized the hybrid ANN-CA-MCA model for predicting LULC in Estonia for 2030; we revealed that the predicted changes in LULC from 2019 to 2030 were similar to the observed changes from 2011 to 2019. The predicted change in the area of artificial surfaces was an increased rate of 1.33% to reach 787.04 km2 in total by 2030. Between 2019 and 2030, the other significant changes were the decrease of 34.57 km2 of forest lands and the increase of agricultural lands by 14.90 km2 and wetlands by 9.31 km2. These findings can develop a proper course of action for long-term spatial planning in Estonia. Therefore, a key policy priority should be to plan for the stable care of forest lands to maintain biodiversity. Numéro de notice : A2022-458 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/INFORMATIQUE/URBANISME Nature : Article DOI : 10.1007/s10661-022-10266-7 Date de publication en ligne : 13/07/2022 En ligne : http://dx.doi.org/10.1007/s10661-022-10266-7 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101258 [article]

The influence of data density and integration on forest canopy cover mapping using Sentinel-1 and Sentinel-2 time series in Mediterranean oak forests / Vahid Nasiri in ISPRS International journal of geo-information, vol 11 n° 8 (August 2022)  

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About tree height measurement: Theoretical and practical issues for uncertainty quantification and mapping / Samuele De petris in Forests, vol 13 n° 7 (July 2022)  

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The role of blue green infrastructure in the urban thermal environment across seasons and local climate zones in East Africa / Xueqin Li in Sustainable Cities and Society, vol 80 (May 2022)  

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An open science and open data approach for the statistically robust estimation of forest disturbance areas / Saverio Francini in International journal of applied Earth observation and geoinformation, vol 106 (February 2022)  

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Mapping active paddy rice area over monsoon asia using time-series Sentinel-2 images in Google earth engine : a case study over lower gangetic plain / Arabinda Maiti in Geocarto international, vol 37 n° inconnu ([30/01/2022])  

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Using Google Earth Engine to classify unique forest and agroforest classes using a mix of Sentinel 2a spectral data and topographical features: a Sri Lanka case study / W.D.K.V. Nandasena in Geocarto international, vol 37 n° inconnu ([30/01/2022])  

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Forest fire susceptibility assessment using google earth engine in Gangwon-do, Republic of Korea / Yong Piao in Geomatics, Natural Hazards and Risk, vol 13 n° 1 (2022)  

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Monitoring forest-savanna dynamics in the Guineo-Congolian transition area of the centre region of Cameroon / Le Bienfaiteur Sagang Takougoum (2022)  

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Efficient measurement of large-scale decadal shoreline change with increased accuracy in tide-dominated coastal environments with Google Earth Engine / Yongjing Mao in ISPRS Journal of photogrammetry and remote sensing, Vol 181 (November 2021)  

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Phenology-based delineation of irrigated and rain-fed paddy fields with Sentinel-2 imagery in Google Earth Engine / Daniel Marc G. dela Torre in Geo-spatial Information Science, vol 24 n° 4 (October 2021)  

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