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Using Sentinel-1A DInSAR interferometry and Landsat 8 data for monitoring water level changes in two lakes in Crete, Greece / D.D. Alexakis in Geocarto international, vol 34 n° 7 ([01/06/2019])  

Public [article]  inGeocarto international > vol 34 n° 7 [01/06/2019] . - pp 703 - 721 Titre : Using Sentinel-1A DInSAR interferometry and Landsat 8 data for monitoring water level changes in two lakes in Crete, Greece Type de document : Article/Communication Auteurs : D.D. Alexakis, Auteur ; E.G. Stavroulaki, Auteur ; I.K. Tsanis, Auteur Année de publication : 2019 Article en page(s) : pp 703 - 721 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] bande C [Termes IGN] Crète (île) [Termes IGN] données polarimétriques [Termes IGN] image Landsat-8 [Termes IGN] image multitemporelle [Termes IGN] image radar moirée [Termes IGN] image Sentinel-SAR [Termes IGN] lac [Termes IGN] niveau de l'eau [Termes IGN] Normalized Difference Vegetation Index [Termes IGN] surveillance hydrologique Résumé : (auteur) Differential Interferometric Synthetic Aperture Radar (DInSAR) methodology has been successfully employed to detect water level changes and produce corresponding water level variation maps. In this study, Agia and Kournas lakes, located in Western Crete, Greece, were used as pilot areas to monitor water level change with means of SAR interferometry and auxiliary Earth Observation (EO) data. The water level variation was monitored for the period 2015–2016, using Sentinel-1A imageries and corresponding stage water level data. Landsat 8 data were additionally used to study vegetation regime and surface water extent and how these parameters affect interferograms performance. The results highlighted the fact that the combination of SAR backscattering intensity and unwrapped phase can provide additional insight into hydrological studies. The overall analysis of both interferometric characteristics and backscattering mechanism denoted their potential in enhancing the reliability of the water-level retrieval scheme and optimizing the capture of hydrological patterns spatial distribution. Numéro de notice : A2019-512 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2018.1434685 Date de publication en ligne : 11/02/2018 En ligne : https://doi.org/10.1080/10106049.2018.1434685 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93821 [article]

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Long-term soil moisture content estimation using satellite and climate data in agricultural area of Mongolia / Enkhjargal Natsagdorj in Geocarto international, vol 34 n° 7 ([01/06/2019])  

Public [article]  inGeocarto international > vol 34 n° 7 [01/06/2019] . - pp 722 - 734 Titre : Long-term soil moisture content estimation using satellite and climate data in agricultural area of Mongolia Type de document : Article/Communication Auteurs : Enkhjargal Natsagdorj, Auteur ; Tsolmon Renchin, Auteur ; Philippe De Maeyer, Auteur ; et al., Auteur Année de publication : 2019 Article en page(s) : pp 722 - 734 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] données météorologiques [Termes IGN] image Aqua-MODIS [Termes IGN] image SPOT-Végétation [Termes IGN] image Terra-MODIS [Termes IGN] Mongolie [Termes IGN] Normalized Difference Vegetation Index [Termes IGN] surface cultivée [Termes IGN] teneur en eau de la végétation [Termes IGN] variation temporelle Résumé : (auteur) The purpose of this study is to estimate long-term SMC and find its relation with soil moisture (SM) of climate station in different depths and NDVI for the growing season. The study area is located in agricultural regions in the North of Mongolia. The Pearson’s correlation methodology was used in this study. We used MODIS and SPOT satellite data and 14 years data for precipitation, temperature and SMC of 38 climate stations. The estimated SMC from this methodology were compared with SM from climate data and NDVI. The estimated SMC was compared with SM of climate stations at a 10-cm depth (r2 = 0.58) and at a 50-cm depth (r2 = 0.38), respectively. From the analysis, it can be seen that the previous month’s SMC affects vegetation growth of the following month, especially from May to August. The methodology can be an advantageous indicator for taking further environmental analysis in the region. Numéro de notice : A2019-513 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2018.1434686 Date de publication en ligne : 08/03/2018 En ligne : https://doi.org/10.1080/10106049.2018.1434686 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93822 [article]

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