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A GIS-based method for modeling methane emissions from paddy fields by fusing multiple sources of data / Linhua Ma in Science of the total environment, vol 859 n° 1 (February 2023)  

Public [article]  inScience of the total environment > vol 859 n° 1 (February 2023) . - n° 159917 Titre : A GIS-based method for modeling methane emissions from paddy fields by fusing multiple sources of data Type de document : Article/Communication Auteurs : Linhua Ma, Auteur ; Yuanlai Cui, Auteur ; Bo Liu, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : n° 159917 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] analyse spatio-temporelle [Termes IGN] Chine [Termes IGN] Corée [Termes IGN] données multisources [Termes IGN] Etats-Unis [Termes IGN] humidité du sol [Termes IGN] image à haute résolution [Termes IGN] image infrarouge [Termes IGN] Italie [Termes IGN] méthane [Termes IGN] modélisation [Termes IGN] réflectance du sol [Termes IGN] rizière [Termes IGN] système d'information géographique [Termes IGN] variation saisonnière Résumé : (auteur) Quantification of regional methane (CH4) gas emission in the paddy fields is critical under climate warming. Mechanism models generally require numerous parameters while empirical models are too coarse. Based on the mechanism and structure of the widely used model CH4MOD, a GIS-based Regional CH4 Emission Calculation (GRMC) method was put forward by introducing multiple sources of remote sensing images, including MOD09A1, MOD11A2, MOD15A2H as well as local water management standards. The stress of soil moisture condition (f(water)) on CH4 emissions was quantified by calculating the redox potential (Eh) from days after flooding or falling dry. The f(water)-t curve was calculated under different exogenous organic matter addition. Combining the f(water)-t curve with local water management standards, the seasonal variation of f(water) was obtained. It was proven that f(water) was effective in reflecting the regulation role of soil moisture condition. The GRMC was tested at four Eddy Covariance (EC) sites: Nanchang (NC) in China, Twitchell (TWT) in the USA, Castellaro (CAS) in Italy and Cheorwon (CRK) in Korea and has been proven to well track the seasonal dynamics of CH4 emissions with R2 ranges of 0.738–0.848, RMSE ranges of 31.94–149.22 mg C/m2d and MBE ranges of −66.42- -14.79 mg C/m2d. The parameters obtained in Nanchang (NC) site in China were then applied to the Ganfu Plain Irrigation System (GFPIS), a typical rice planting area of China, to analyse the spatial-temporal variations of CH4 emissions. The total CH4 emissions of late rice in the GFPIS from 2001 to 2013 was in the range of 14.47–20.48 (103 t CH4-C). Ts caused spatial variation of CH4 production capacity, resulting in the spatial variability of CH4 emissions. Overall, the GRMC is effective in obtaining CH4 emissions from rice fields on a regional scale. Numéro de notice : A2023-015 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/IMAGERIE Nature : Article DOI : 10.1016/j.scitotenv.2022.159917 Date de publication en ligne : 04/11/2022 En ligne : https://doi.org/10.1016/j.scitotenv.2022.159917 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102133 [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]

An advanced bidirectional reflectance factor (BRF) spectral approach for estimating flavonoid content in leaves of Ginkgo plantations / Kai Zhou in ISPRS Journal of photogrammetry and remote sensing, vol 193 (November 2022)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 193 (November 2022) . - pp 1 - 16 Titre : An advanced bidirectional reflectance factor (BRF) spectral approach for estimating flavonoid content in leaves of Ginkgo plantations Type de document : Article/Communication Auteurs : Kai Zhou, Auteur ; Lin Cao, Auteur ; Shiyun Yin, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 1 - 16 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] bande spectrale [Termes IGN] coefficient de corrélation [Termes IGN] distribution du coefficient de réflexion bidirectionnelle BRDF [Termes IGN] feuille (végétation) [Termes IGN] Ginkgo biloba [Termes IGN] image à haute résolution [Termes IGN] indice foliaire [Termes IGN] Kiangsou (Chine) [Termes IGN] réflectance végétale Résumé : (auteur) As a key phenolic pigment concentrated in the surface tissues of leaves, flavonoids (Flav) are the major bioactive ingredients in Ginkgo leaf extracts. Flav are also marked natural antioxidants and significant indicators of biotic and abiotic stresses, critical for determining cultivation quality and enhancing Flav yield. In particular, area-based Flav (Flavarea) is related to the shortwave-blue light interaction within leaves per unit leaf area, whereas mass-based Flav (Flavmass) is useful for the quantitative assessment of Flav yield. In order to accurately estimate the contents of Flavarea and Flavmass in leaves of Ginkgo plantations, in this study, we developed an advanced bidirectional reflectance factor (BRF) spectra-based approach by reducing the effects of specular reflection and enhancing the absorption signals of Flav (in the shortwave-blue region of spectrum), using a suite of new spectral indices (SIs) (i.e., flavonoid index (FI), modified flavonoid index (mFI) and double difference index (DD)) calculated from the leaf clip equipped spectrometers-collected data. The results demonstrated that most of the SIs derived from the developed BRF spectra-based approach obtained relatively high performance for Flav estimation by alleviating adverse effects of specular reflection to different extents (CV-R2 = 0.60–0.76). In specific, DDnir434,421 selected from DD-type indices performed (CV-R2 = 0.76 for Flavarea; CV-R2 = 0.69 for Flavmass) better than other indices. These findings represent marked potentials of the developed BRF spectra-based approach for non-destructively estimating leaf Flav content, as well as improving the understanding of the mechanisms of specular effects on Flav estimations in leaves of Ginkgo plantations. Numéro de notice : A2022-744 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2022.08.020 Date de publication en ligne : 09/09/2022 En ligne : https://doi.org/10.1016/j.isprsjprs.2022.08.020 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101727 [article]

Exploring the influencing factors in identifying soil texture classes using multitemporal Landsat-8 and Sentinel-2 data / Yanan Zhou in Remote sensing, vol 14 n° 21 (November-1 2022)  

Public [article]  inRemote sensing > vol 14 n° 21 (November-1 2022) . - n° 5571 Titre : Exploring the influencing factors in identifying soil texture classes using multitemporal Landsat-8 and Sentinel-2 data Type de document : Article/Communication Auteurs : Yanan Zhou, Auteur ; Wei Wu, Auteur ; Hongbin Liu, Auteur Année de publication : 2022 Article en page(s) : n° 5571 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] carte d'occupation du sol [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] classification par séparateurs à vaste marge [Termes IGN] composition des sols [Termes IGN] données multitemporelles [Termes IGN] Extreme Gradient Machine [Termes IGN] Fleuve bleu (Chine) [Termes IGN] image Landsat-8 [Termes IGN] image Sentinel-MSI [Termes IGN] limon [Termes IGN] qualité du sol [Termes IGN] réflectance spectrale [Termes IGN] texture du sol Résumé : (auteur) Soil texture is a key soil property driving physical, chemical, biological, and hydrological processes in soils. The rapid development of remote sensing techniques shows great potential for mapping soil properties. This study highlights the effectiveness of multitemporal remote sensing data in identifying soil textural class by using retrieved vegetation properties as proxies of soil properties. The impacts of sensors, modeling resolutions, and modeling techniques on the accuracy of soil texture classification were explored. Multitemporal Landsat-8 and Sentinel-2 images were individually acquired at the same time periods. Three satellite-based experiments with different inputs, i.e., Landsat-8 data, Sentinel-2 data (excluding red-edge parameters), and Sentinel-2 data (including red-edge parameters) were conducted. Modeling was carried out at three spatial resolutions (10, 30, 60 m) using five machine-learning (ML) methods: random forest, support vector machine, gradient-boosting decision tree, categorical boosting, and super learner that combined the four former classifiers based on the stacking concept. In addition, a novel SHapley Addictive Explanation (SHAP) technique was introduced to explain the outputs of the ML model. The results showed that the sensors, modeling resolutions, and modeling techniques significantly affected the prediction accuracy. The models using Sentinel-2 data with red-edge parameters performed consistently best. The models usually gave better results at fine (10 m) and medium (30 m) modeling resolutions than at a coarse (60 m) resolution. The super learner provided higher accuracies than other modeling techniques and gave the highest values of overall accuracy (0.8429), kappa (0.7611), precision (0.8378), recall rate (0.8393), and F1-score (0.8398) at 30 m with Sentinel-2 data involving red-edge parameters. The SHAP technique quantified the contribution of each variable for different soil textural classes, revealing the critical roles of red-edge parameters in separating loamy soils. This study provides comprehensive insights into the effective modeling of soil properties on various scales using multitemporal optical images. Numéro de notice : A2022-856 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.3390/rs14215571 Date de publication en ligne : 04/11/2022 En ligne : https://doi.org/10.3390/rs14215571 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102104 [article]

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

Public [article]  inRemote sensing of environment > vol 279 (15 September 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]

Analytical method for high-precision seabed surface modelling combining B-spline functions and Fourier series / Tyler Susa in Marine geodesy, vol 45 n° 5 (September 2022)  

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Forest tree species classification based on Sentinel-2 images and auxiliary data / Haotian You in Forests, vol 13 n° 9 (september 2022)  

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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)  

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DART-Lux: An unbiased and rapid Monte Carlo radiative transfer method for simulating remote sensing images / Yingjie Wang in Remote sensing of environment, vol 274 (June 2022)  

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Variance based fusion of VCI and TCI for efficient classification of agriculture drought using MODIS data / Anjana N.J. Kukunuri in Geocarto international, vol 37 n° 10 ([01/06/2022])  

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Detection and mapping of snow avalanche debris from Western Himalaya, India using remote sensing satellite images / Kamal Kant Singh in Geocarto international, vol 37 n° 9 ([15/05/2022])  

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Unmixing-based spatiotemporal image fusion accounting for complex land cover changes / Xiaolu Jiang in IEEE Transactions on geoscience and remote sensing, vol 60 n° 5 (May 2022)  

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A convolution neural network for forest leaf chlorophyll and carotenoid estimation using hyperspectral reflectance / Shuo Shi in International journal of applied Earth observation and geoinformation, vol 108 (April 2022)  

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Are northern German Scots pine plantations climate smart? The impact of large-scale conifer planting on climate, soil and the water cycle / Christoph Leuschner in Forest ecology and management, vol 507 (1 March 2022)  

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Land surface phenology retrieval through spectral and angular harmonization of Landsat-8, Sentinel-2 and Gaofen-1 data / Jun Lu in Remote sensing, vol 14 n° 5 (March-1 2022)  

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