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Including Sentinel-1 radar data to improve the disaggregation of MODIS land surface temperature data / Abdelhakim Amazirh in ISPRS Journal of photogrammetry and remote sensing, vol 150 (April 2019)
[article]
Titre : Including Sentinel-1 radar data to improve the disaggregation of MODIS land surface temperature data Type de document : Article/Communication Auteurs : Abdelhakim Amazirh, Auteur ; Olivier Merlin, Auteur ; Salah Er-Raki, Auteur Année de publication : 2019 Article en page(s) : pp 11 - 26 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] désagrégation
[Termes IGN] humidité du sol
[Termes IGN] image à haute résolution
[Termes IGN] image Landsat
[Termes IGN] image Landsat-8
[Termes IGN] image Sentinel-SAR
[Termes IGN] image Terra-MODIS
[Termes IGN] Maroc
[Termes IGN] modèle de transfert radiatif
[Termes IGN] réflectance spectrale
[Termes IGN] régression multiple
[Termes IGN] température au sol
[Termes IGN] zone semi-arideRésumé : (Auteur) The use of land surface temperature (LST) for monitoring the consumption and water status of crops requires data at fine spatial and temporal resolutions. Unfortunately, the current spaceborne thermal sensors provide data at either high temporal (e.g. MODIS: Moderate Resolution Imaging Spectro-radiometer) or high spatial (e.g. Landsat) resolution separately. Disaggregating low spatial resolution (LR) LST data using ancillary data available at high spatio-temporal resolution could compensate for the lack of high spatial resolution (HR) LST observations. Existing LST downscaling approaches generally rely on the fractional green vegetation cover (fgv) derived from HR reflectances but they do not take into account the soil water availability to explain the spatial variability in LST at HR. In this context, a new method is developed to disaggregate kilometric MODIS LST at 100 m resolution by including the Sentinel-1 (S-1) backscatter, which is indirectly linked to surface soil moisture, in addition to the Landsat-7 and Landsat-8 (L-7 & L-8) reflectances. The approach is tested over two different sites – an 8 km by 8 km irrigated crop area named “R3” and a 12 km by 12 km rainfed area named “Sidi Rahal” in central Morocco (Marrakech) – on the seven dates when S-1, and L-7 or L-8 acquisitions coincide with a one-day precision during the 2015–2016 growing season. The downscaling methods are applied to the 1 km resolution MODIS-Terra LST data, and their performance is assessed by comparing the 100 m disaggregated LST to Landsat LST in three cases: no disaggregation, disaggregation using Landsat fgv only, disaggregation using both Landsat fgv and S-1 backscatter. When including fgv only in the disaggregation procedure, the mean root mean square error in LST decreases from 4.20 to 3.60 °C and the mean correlation coefficient (R) increases from 0.45 to 0.69 compared to the non-disaggregated case within R3. The new methodology including the S-1 backscatter as input to the disaggregation is found to be systematically more accurate on the available dates with a disaggregation mean error decreasing to 3.35 °C and a mean R increasing to 0.75. Numéro de notice : A2019-136 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2019.02.004 Date de publication en ligne : 15/02/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.02.004 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92467
in ISPRS Journal of photogrammetry and remote sensing > vol 150 (April 2019) . - pp 11 - 26[article]Exemplaires(3)
Code-barres Cote Support Localisation Section Disponibilité 081-2019041 RAB Revue Centre de documentation En réserve L003 Disponible 081-2019043 DEP-RECP Revue LASTIG Dépôt en unité Exclu du prêt 081-2019042 DEP-RECF Revue Nancy Dépôt en unité Exclu du prêt Assessment of different vegetation parameters for parameterizing the coupled water cloud model and advanced integral equation model for soil moisture retrieval using time series Sentinel-1A data / Long Wang in Photogrammetric Engineering & Remote Sensing, PERS, vol 85 n° 1 (January 2019)
[article]
Titre : Assessment of different vegetation parameters for parameterizing the coupled water cloud model and advanced integral equation model for soil moisture retrieval using time series Sentinel-1A data Type de document : Article/Communication Auteurs : Long Wang, Auteur ; Binbin He, Auteur ; Xiaojing Bai, Auteur ; Minfeng Xing, Auteur Année de publication : 2019 Article en page(s) : pp 43 - 54 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] Enhanced vegetation index
[Termes IGN] étalonnage de modèle
[Termes IGN] humidité du sol
[Termes IGN] image Sentinel-SAR
[Termes IGN] image Terra-MODIS
[Termes IGN] indice foliaire
[Termes IGN] Iowa (Etats-Unis)
[Termes IGN] Leaf Area Index
[Termes IGN] modèle de rétrodiffusion
[Termes IGN] Normalized Difference Vegetation Index
[Termes IGN] série temporelleRésumé : (auteur) Soil moisture is an important state variable of the land surface ecosystem. In this paper, the water cloud model (WCM) and advanced integral equation model (AIEM) are coupled to retrieve soil moisture using time series Sentinel-1A data and moderate resolution imaging spectroradiometer (MODIS) data. Normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), leaf area index (LAI) and fraction of photosynthetically active radiation (FPAR), are cross-combined to initialize the calibrated model. The calibration results show the following: (1) Vegetation parameters have a great influence on model calibration; and (2) The combination of (NDVI, LAI) is recommended to calibrate the coupled model, the RMSE, R2 is 0.739 dB, and 0.716 for the observed and estimated backscattering coefficients. The soil moisture inversion results show that: (1) the accuracy of model calibration and soil moisture inversion are inconsistent; and (2) The normalized vegetation parameters, such as NDVI, EVI and FPAR, are suitable for WCM to describe vegetation characteristics, and NDVI is the optimum. When V2 is the NDVI, the average bias, MAE, RMSE, ubRMSE and R2 are –0.007 m3/m3, 0.074 m3/m3, 0.087 m³/m³, 0.087 m3/m3 and 0.750, respectively. Numéro de notice : A2019-029 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.85.1.43 Date de publication en ligne : 01/01/2019 En ligne : https://doi.org/10.14358/PERS.85.1.43 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91965
in Photogrammetric Engineering & Remote Sensing, PERS > vol 85 n° 1 (January 2019) . - pp 43 - 54[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 105-2019011 SL Revue Centre de documentation Revues en salle Disponible Biomass ratio varies along soil water availability : An analysis based on wood density data collected by the French NFI / Baptiste Kerfriden (2019)
Titre : Biomass ratio varies along soil water availability : An analysis based on wood density data collected by the French NFI Type de document : Article/Communication Auteurs : Baptiste Kerfriden , Auteur ; Jean-Daniel Bontemps , Auteur ; Jean-Michel Leban , Auteur Editeur : Paris [France] : Institut national de recherche pour l’agriculture, l’alimentation et l’environnement INRAE (2020-) Année de publication : 2019 Conférence : Conference 2019, A century of national forest inventories – informing past, present and future decisions 19/05/2019 21/05/2019 Oslo Norvège programme sans actes Langues : Anglais (eng) Descripteur : [Termes IGN] biomasse forestière
[Termes IGN] changement climatique
[Termes IGN] composition d'un peuplement forestier
[Termes IGN] densité du bois
[Termes IGN] humidité du sol
[Termes IGN] inventaire forestier national (données France)
[Vedettes matières IGN] Végétation et changement climatiqueRésumé : (auteur) Biomass ratio is a state variable allowing the conversion of the forest growing stock into biomass (Kauppi et al., 2006). This variable is most often considered constant per tree species despite the huge range of intraspecific variability of basic density. Indeed, the factors that influence wood density are numerous: tree growth and age, competition (e.g. forest structure), genetics as well as intra and inter annual climatic variations. In light of such diversity, scientific research into forest biomass characteristics is required to better incorporate the heterogeneity of forests in the forest carbon accounting methods (Bowen et al., 2011).
Materials and methods: In 2016 and 2017 over fifty five thousands increment cores were collected in the field by the French NFI. Wood density measurements were performed at INRA (Leban et al., 2019, this conference) and combined with the French NFI calculation system, biomass ratio can now be computed as a standard. This quantity can now be depicted over stratification variables such as soil water availability. An exploratory and ANOVA-assisted analysis of this gradient was performed.
Results: Biomass ratio was found strongly related to soil water availability, with a decrease of biomass ratio at the hardwood and softwood scale, but no correlation were found at the specie level. Explanations come from the species repartition along the gradients from high wood density to low density driven by soil water availability.
Conclusion: Wood density data acquired on the French NFI allow major steps forward toward the proper estimate of forest biomass resources and of carbon sequestration. Biomass ratio was found correlated with climatic contexts, suggesting that biomass sequestration at constant volume may increase in a warmer climate. But this change will be made through change of composition of the forest rather than an adaptation of the species.Numéro de notice : C2019-049 Affiliation des auteurs : LIF+Ext (2012-2019) Thématique : FORET Nature : Poster nature-HAL : Poster-avec-CL DOI : sans Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95768 Improving the spatial bias correction algorithm in SMOS image reconstruction processor : validation of soil moisture retrievals with in situ data / Ali Khazaal in IEEE Transactions on geoscience and remote sensing, vol 57 n° 1 (January 2019)
[article]
Titre : Improving the spatial bias correction algorithm in SMOS image reconstruction processor : validation of soil moisture retrievals with in situ data Type de document : Article/Communication Auteurs : Ali Khazaal, Auteur ; Philippe Richaume, Auteur ; François Cabot, Auteur ; Eric Anterrieu, Auteur ; Arnaud Mialon, Auteur ; Yann H. Kerr, Auteur Année de publication : 2019 Article en page(s) : pp 277 - 290 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] correction d'image
[Termes IGN] erreur systématique
[Termes IGN] humidité du sol
[Termes IGN] image SMOS
[Termes IGN] résidu
[Termes IGN] télédétection en hyperfréquence
[Termes IGN] température de luminanceRésumé : (Auteur) SMOS is a space mission led by the European Space Agency and designed to provide global maps of Soil Moisture and Ocean salinity, two important geophysical parameters for understanding the water cycle variations and climate change. The SMOS payload is a 2-D interferometer operating at L-band that consists of 69 elementary antennas located along a Y-shaped structure. Important spatial biases persist in the retrieved brightness temperature (BT) images mainly due to the phenomenon of aliasing inside the field of view of SMOS but also due to the Gibbs oscillations near land/ocean transitions. To minimize these biases, a differential image reconstruction algorithm is used in the operational processor that reduces the contrast of the image to be retrieved. To do that, the contribution of a constant artificial temperature map is removed from the measurements prior to reconstruction and then added back after the reconstruction. In this paper, we show that strong residual biases are still present in the retrieved images. To reduce them, we propose to improve the bias correction algorithm by using a more realistic artificial temperature scene based on separating the land and ocean regions and assigning a constant temperature over land and a Fresnel BT model over the ocean. The artificial scene is also improved by means of representing each pixel by its water fraction percentage to smooth the land/ocean transitions. The improved algorithm is validated over the ocean by comparing the retrieved temperatures to a forward geophysical model but also over land by comparing the retrieved soil moisture to in situ measurements. Numéro de notice : A2019-106 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2018.2853619 Date de publication en ligne : 09/08/2018 En ligne : https://doi.org/10.1109/TGRS.2018.2853619 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92412
in IEEE Transactions on geoscience and remote sensing > vol 57 n° 1 (January 2019) . - pp 277 - 290[article]Microwave indices from active and passive sensors for remote sensing applications / Emanuele Santi (2019)
Titre : Microwave indices from active and passive sensors for remote sensing applications Type de document : Monographie Auteurs : Emanuele Santi, Éditeur scientifique ; Simonetta Paloscia, Éditeur scientifique Editeur : Bâle [Suisse] : Multidisciplinary Digital Publishing Institute MDPI Année de publication : 2019 Importance : 224 p. Format : 18 x 26 cm ISBN/ISSN/EAN : 978-3-03897-820-6 Note générale : bibliographie Langues : Français (fre) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] bande C
[Termes IGN] bande Ku
[Termes IGN] bande X
[Termes IGN] diffusométrie
[Termes IGN] filtrage spatiotemporel
[Termes IGN] glace de mer
[Termes IGN] humidité du sol
[Termes IGN] image radar moirée
[Termes IGN] phénologie
[Termes IGN] prairie
[Termes IGN] série temporelleRésumé : (éditeur) Past research has comprehensively assessed the capabilities of satellite sensors operating at microwave frequencies, both active (SAR, scatterometers) and passive (radiometers), for the remote sensing of Earth’s surface. Besides brightness temperature and backscattering coefficient, microwave indices, defined as a combination of data collected at different frequencies and polarizations, revealed a good sensitivity to hydrological cycle parameters such as surface soil moisture, vegetation water content, and snow depth and its water equivalent. The differences between microwave backscattering and emission at more frequencies and polarizations have been well established in relation to these parameters, enabling operational retrieval algorithms based on microwave indices to be developed. This Special Issue aims at providing an overview of microwave signal capabilities in estimating the main land parameters of the hydrological cycle, e.g., soil moisture, vegetation water content, and snow water equivalent, on both local and global scales, with a particular focus on the applications of microwave indices. Note de contenu : Editorial
1- Ku-, X- and C-Band microwave backscatter indices from saline snow covers on Arctic first-year sea ice
2- Retrieval of effective correlation length and snow water equivalent from radar and passive microwave measurements
3- Soil moisture from fusion of scatterometer and SAR: closing the scale gap with temporal filtering
4- Using SAR-derived vegetation descriptors in a water cloud model to improve soil
moisture retrieval
5- Sensitivity of Sentinel-1 backscatter to vegetation dynamics: An Austrian case study
6- AMSR2 soil moisture downscaling using temperature and vegetation data
7- Analysis of the Radar Vegetation Index and potential improvements
8- Radiometric microwave indices for remote sensing of land surfaces
9- Soil moisture in the Biebrza wetlands retrieved from Sentinel-1 imagery
10- Exploiting time series of Sentinel-1 and Sentinel-2 imagery to detect meadow phenology in mountain regionsNuméro de notice : 25941 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Monographie DOI : 10.3390/books978-3-03897-821-3 En ligne : https://doi.org/10.3390/books978-3-03897-821-3 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96313 Monitoring crops water needs at high spatio-temporal resolution by synergy of optical / thermal and radar observations / Abdelhakim Amazirh (2019)PermalinkPermalinkToward global soil moisture monitoring with sentinel-1 : harnessing assets and overcoming obstacles / Bernhard Bauer-Marschallinger in IEEE Transactions on geoscience and remote sensing, vol 57 n° 1 (January 2019)PermalinkPolarization orientation angle and polarimetric SAR scattering characteristics of steep terrain / Jong-Sen Lee in IEEE Transactions on geoscience and remote sensing, vol 56 n° 12 (December 2018)PermalinkSeparating the influence of vegetation changes in polarimetric differential SAR interferometry / Virginia Brancato in IEEE Transactions on geoscience and remote sensing, vol 56 n° 12 (December 2018)PermalinkSoil moisture estimation in Ferlo region (Senegal) using radar (ENVISAT/ASAR) and optical (SPOT/VEGETATION) data / Gayane Faye in The Egyptian Journal of Remote Sensing and Space Science, Vol. 21 suppl.1 (juillet 2018)PermalinkEstimation of surface roughness over bare agricultural soil from Sentinel-1 data / Mohammad Choker (2018)PermalinkPermalinkIdentification of rainwater harvesting sites using SCS-CN methodology, remote sensing and Geographical Information System techniques / Tarun Kumar in Geocarto international, vol 32 n° 12 (December 2017)PermalinkInSAR to support sustainable urbanization over compacting aquifers: The case of Toluca Valley, Mexico / Pascal Castellazzi in International journal of applied Earth observation and geoinformation, vol 63 (December 2017)Permalink