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Monitoring and analysis of crop irrigation dynamics in Central Italy through the use of MODIS NDVI data / Marta Chiesi in European journal of remote sensing, vol 55 n° 1 (January 2022)  

Public [article]  inEuropean journal of remote sensing > vol 55 n° 1 (January 2022) . - pp 23 - 36 Titre : Monitoring and analysis of crop irrigation dynamics in Central Italy through the use of MODIS NDVI data Type de document : Article/Communication Auteurs : Marta Chiesi, Auteur ; Luca Angeli, Auteur ; Piero Battista, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 23 - 36 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] bilan hydrique [Termes IGN] carte agricole [Termes IGN] cultures irriguées [Termes IGN] image Aqua-MODIS [Termes IGN] image Sentinel-MSI [Termes IGN] image Terra-MODIS [Termes IGN] Normalized Difference Vegetation Index [Termes IGN] Toscane (Italie) Résumé : (auteur) A recent study has proposed and tested a semi-empirical method to estimate crop irrigation based on a water balance logic and Sentinel-2 Multi Spectral Instrument (MSI) NDVI imagery. The current paper aims at extending the same approach to the analysis of the main irrigation patterns occurred in Tuscany (Central Italy) during the 2000–2019 period. This operation was made possible by feeding the irrigation water (IW) estimation method with 250-m spatial resolution Terra/Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI images. The results of this operation were first assessed versus various reference datasets available for the region; next, the annual maps of IW estimated for the 20 study years were analyzed at province scale in conjunction with relevant agricultural statistics. The use of MODIS in place of MSI images reduces the IW estimation accuracy irregularly at local scale, depending on the size and spatial arrangement of irrigated and non-irrigated fields; the reduction in accuracy is, however, marginal over relatively large areas. Irrigated crops are decreasing throughout most Tuscany provinces, while they are increasing in the most southern and driest province. The possible reasons and implications of these findings are finally discussed in relation to the main environmental issues affecting the region. Numéro de notice : A2022-099 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1080/22797254.2021.2013735 Date de publication en ligne : 05/01/2022 En ligne : https://doi.org/10.1080/22797254.2021.2013735 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99549 [article]

Multi-temporal remote sensing data to monitor terrestrial ecosystem responses to climate variations in Ghana / Ram Avtar in Geocarto international, vol 37 n° 2 ([06/01/2022])  

Public [article]  inGeocarto international > vol 37 n° 2 [06/01/2022] . - pp 396 - 412 Titre : Multi-temporal remote sensing data to monitor terrestrial ecosystem responses to climate variations in Ghana Type de document : Article/Communication Auteurs : Ram Avtar, Auteur ; Ali P. Yunus, Auteur ; Osamu Saito, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 396 - 412 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] changement climatique [Termes IGN] données multitemporelles [Termes IGN] écosystème [Termes IGN] Ghana [Termes IGN] image Landsat [Termes IGN] image SPOT [Termes IGN] image Terra-MODIS [Termes IGN] Normalized Difference Vegetation Index [Termes IGN] phénologie [Termes IGN] variation temporelle Résumé : (auteur) Operational monitoring of vegetation and its response to climate change involves the use of vegetation indices (VIs) in relation to relevant climatic data. This study analyses the temporal variations of vegetation indices in response to climatic data (temperature and precipitation) to better understand the phenological changes in the Wa-West and Tolon districts of Ghana during 1999–2011. This study also examines the inter-annual variation of vegetation indices and lag effects of climate variables (temperature and precipitation) using simple regression and correlation approaches. Results indicate that the mean Normalized Difference Vegetation Index (NDVI) and Normalized Difference Soil Index (NDSI) were significantly correlated with the mean temperature, whereby the value of NDVI increases with a decrease in temperature and value of NDSI increases with an increase in temperature. On examining seasonal variations, our findings indicated that the months of August and September have the highest mean NDVI values. This study confirms that consistently rising temperature and altered precipitation patterns have exerted a strong influence on temporal distributions and productivities of the terrestrial ecosystems of the Tolon and Wa-West districts of Ghana. Furthermore, this research demonstrates how vegetation indices can be used as an indicator to monitor phenological changes in the terrestrial ecosystem. Numéro de notice : A2022-050 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2020.1723716 Date de publication en ligne : 11/02/2020 En ligne : https://doi.org/10.1080/10106049.2020.1723716 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99442 [article]

Simulation of the meltwater under different climate change scenarios in a poorly gauged snow and glacier-fed Chitral River catchment (Hindukush region) / Huma Hayat in Geocarto international, vol 37 n° 1 ([03/01/2022])  

Public [article]  inGeocarto international > vol 37 n° 1 [03/01/2022] . - pp 103 - 119 Titre : Simulation of the meltwater under different climate change scenarios in a poorly gauged snow and glacier-fed Chitral River catchment (Hindukush region) Type de document : Article/Communication Auteurs : Huma Hayat, Auteur ; Adnan Ahmad Tahir, Auteur ; sara Wajid, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 103 - 119 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] bassin hydrographique [Termes IGN] changement climatique [Termes IGN] données météorologiques [Termes IGN] eau de fonte [Termes IGN] estimation statistique [Termes IGN] fonte des glaces [Termes IGN] image Terra-MODIS [Termes IGN] inondation [Termes IGN] modèle de simulation [Termes IGN] modèle numérique de surface [Termes IGN] Pakistan [Termes IGN] prévention des risques [Termes IGN] ressources en eau [Termes IGN] ruissellement [Termes IGN] série temporelle [Termes IGN] variation saisonnière Résumé : (auteur) Seasonal and annual water supplies of the rivers originating in the Hindukush-Karakoram-Himalaya (HKH) region of Pakistan are important to manage the Indus basin irrigation system for better agricultural production and its dependent agrarian economy. In this study, we simulated the current and future snowmelt runoff in a poorly gauged river basin of the Hindukush region under Representative Concentration Pathways (RCP) climate change scenarios. Snowmelt Runoff Model (SRM) furnished with satellite snow cover maps and hydro-meteorological data were used to simulate the daily river discharge for the period 2000‒2005. The results indicated that SRM has effectually simulated the runoff in Chitral River with Nash-Sutcliffe model efficiency coefficient of 0.85 (0.84) and 0.88 (0.83) in the basin-wide (zone-wise) application during the calibration and validation periods, respectively. The results obtained under future climate change scenario showed ∼14‒19% increase in mean summer discharge under three mid-21st century RCP (2.6, 4.5 and 8.5) scenarios. While an increase of ∼13‒37% is expected under late-21st century RCP scenarios. This study can help water resource managers to plan and manage peak discharges from the Chitral River Basin in the future and can thus prevent major losses due to floods in the area. Numéro de notice : A2022-047 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2019.1700557 Date de publication en ligne : 12/12/2019 En ligne : https://doi.org/10.1080/10106049.2019.1700557 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99421 [article]

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

Public [article]  inGIScience and remote sensing > vol 58 n° 8 (November 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]

Snow cover change assessment in the upper Bhagirathi basin using an enhanced cloud removal algorithm / Mritunjay Kumar Singh in Geocarto international, vol 36 n° 20 ([10/11/2021])  

Public [article]  inGeocarto international > vol 36 n° 20 [10/11/2021] . - pp 2279 - 2302 Titre : Snow cover change assessment in the upper Bhagirathi basin using an enhanced cloud removal algorithm Type de document : Article/Communication Auteurs : Mritunjay Kumar Singh, Auteur ; Renoj J. Thayyen, Auteur ; Sanjay K. Jain, Auteur Année de publication : 2021 Article en page(s) : pp 2279 - 2302 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] analyse diachronique [Termes IGN] analyse spatio-temporelle [Termes IGN] bassin hydrographique [Termes IGN] bilan de masse [Termes IGN] changement climatique [Termes IGN] eau de fonte [Termes IGN] filtrage spatiotemporel [Termes IGN] glacier [Termes IGN] Himalaya [Termes IGN] image Aqua-MODIS [Termes IGN] image Terra-MODIS [Termes IGN] Inde [Termes IGN] manteau neigeux [Termes IGN] MNS ASTER [Termes IGN] nébulosité [Termes IGN] nuage [Termes IGN] variation saisonnière Résumé : (auteur) This research paper proposes a new five-step protocol to enhance the result of existing cloud removal algorithms using Moderate Resolution Imaging Spectroradiometer (MODIS) daily snow cover products (SCPs). The study has been carried out for the upper Bhagirathi basin (up to Maneri Hydropower Project) located in the Western Himalaya. Gafurov and Bárdossy test employed to validate the performance of the proposed method, followed by comparing with the field observed snow cover duration (SCD) data. The result shows that the mean overall accuracy of the proposed method for cloud removal is about ∼95%. However, the cloud removal method by Gafurov and Bardossy also achieved similar mean overall accuracy but with the higher variability within the individual images as compared with the variability within the results obtained by the proposed method. SCD computed from cloud removed SCPs matched significantly with the field observed SCD for a point location, supporting the accuracy achieved by the cloud removal method. This study also examines the spatiotemporal variability of the snow cover in the study area during the past 18 years (2000–2018). During the observation period, no specific trend was observed for annual maximum snow cover, while yearly minimum snow cover in the basin showed an increasing trend since 2010. Seasonally, December and June month witnessed significant changes. December experienced a declining trend in snow cover between 3000–6000 m a.s.l. covering 88% of the basin area, whereas, June showed an increasing trend between 4500 to 6000 m (a.s.l.). This elevation range covers 61% of the basin area, including core 86% of the glacier area within the basin. September and October experienced the highest inter-annual snow cover variability. Maximum snow cover month of February and minimum snow cover month of August experienced the least variability. The present study suggests significant elevation-dependent increasing as well as the decreasing trend in the snow cover with seasonal contrast, which may affect the glaciers as well as the hydrological behavior of the basin. Numéro de notice : A2021-832 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2019.1704069 Date de publication en ligne : 19/12/2021 En ligne : https://doi.org/10.1080/10106049.2019.1704069 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99005 [article]

Identifying surface urban heat island drivers and their spatial heterogeneity in China’s 281 cities: An empirical study based on multiscale geographically weighted regression / Lu Niu in Remote sensing, vol 13 n° 21 (November-1 2021)  

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Improving the accuracy of spring phenology detection by optimally smoothing satellite vegetation index time series based on local cloud frequency / Jiaqi Tian in ISPRS Journal of photogrammetry and remote sensing, vol 180 (October 2021)  

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Estimating 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)  

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Monitoring forest disturbance using time-series MODIS NDVI in Michoacán, Mexico / Yao Gao in Geocarto international, vol 36 n° 15 ([10/08/2021])  

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Surface modelling of forest aboveground biomass based on remote sensing and forest inventory data / Xiaofang Sun in Geocarto international, vol 36 n° 14 ([01/08/2021])  

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Atmospheric correction to passive microwave brightness temperature in snow cover mapping over china / Yubao Qiu in IEEE Transactions on geoscience and remote sensing, vol 59 n° 8 (August 2021)  

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An integrated methodology for surface soil moisture estimating using remote sensing data approach / Rida Khellouk in Geocarto international, vol 36 n° 13 ([15/07/2021])  

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Evaluation of sum-NDVI values to estimate wheat grain yields using multi-temporal Landsat OLI data / Asadollah Mirasi in Geocarto international, vol 36 n° 12 ([01/07/2021])  

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Feux de forêts et technologies spatiales / Laurent Polidori in Géomètre, n° 2193 (juillet-août 2021)

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A combined drought monitoring index based on multi-sensor remote sensing data and machine learning / Hongzhu Han in Geocarto international, vol 36 n° 10 ([01/06/2021])  

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