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MODIS-based land surface temperature for climate variability and change research: the tale of a typical semi-arid to arid environment / Salahuddin M. Jaber in European journal of remote sensing, vol 53 n°1 (2020)  

Public [article]  inEuropean journal of remote sensing > vol 53 n°1 (2020) . - pp 81 - 90 Titre : MODIS-based land surface temperature for climate variability and change research: the tale of a typical semi-arid to arid environment Type de document : Article/Communication Auteurs : Salahuddin M. Jaber, Auteur ; Mahmoud M. Abu-Allaban, Auteur Année de publication : 2020 Article en page(s) : pp 81 - 90 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] action anthropique [Termes descripteurs IGN] changement climatique [Termes descripteurs IGN] climat aride [Termes descripteurs IGN] image MODIS [Termes descripteurs IGN] image thermique [Termes descripteurs IGN] Jordanie [Termes descripteurs IGN] MNS ASTER [Termes descripteurs IGN] nuit [Termes descripteurs IGN] régression linéaire [Termes descripteurs IGN] série temporelle [Termes descripteurs IGN] température au sol [Termes descripteurs IGN] variation diurne [Termes descripteurs IGN] zone semi-aride Résumé : (auteur) This study aims to (1) determine the seasonalities and spatial and temporal rates of change of MODIS-based daytime and nighttime land surface temperature (LST) for the last 19 years from 2000 to 2018 and (2) investigate whether these rates are induced by natural (represented by elevation) or anthropogenic (represented by population counts) forcing. The study area is Jordan – a typical Middle Eastern semi-arid to arid country. Time-series additive seasonal decomposition and simple linear regression produced the following results. (1) For both daytime and nighttime the highest LST values were observed in June while the lowest LST values were observed in December. (2) No significant linear rates of change of LST were noticed in daytime, while significant linear rates of increase of LST, which varied from 0.041°C/year to 0.119°C/year, were observed in nighttime in about one-third of the area of the country mainly in the western parts. (3) The significant linear rates of increase of nighttime LST increased significantly by 0.005°C/year for every 1,000 m increase in elevation and by 0.003°C/year for every 1,000 people increase in population counts. (4) Both natural and anthropogenic factors affected LST in nighttime; however, anthropogenic factors seemed to be more important than natural factors. Numéro de notice : A2020-166 Thématique : IMAGERIE Nature : Article DOI : 10.1080/22797254.2020.1735264 date de publication en ligne : 06/03/2020 En ligne : https://doi.org/10.1080/22797254.2020.1735264 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94834 [article]

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)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 150 (April 2019) . - pp 11 - 26 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 descripteurs IGN] désagrégation [Termes descripteurs IGN] humidité du sol [Termes descripteurs IGN] image à haute résolution [Termes descripteurs IGN] image Landsat [Termes descripteurs IGN] image Landsat-8 [Termes descripteurs IGN] image Sentinel-SAR [Termes descripteurs IGN] image Terra-MODIS [Termes descripteurs IGN] Maroc [Termes descripteurs IGN] modèle de transfert radiatif [Termes descripteurs IGN] réflectance spectrale [Termes descripteurs IGN] régression multiple [Termes descripteurs IGN] température au sol [Termes descripteurs IGN] zone semi-aride Résumé : (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 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 [article]

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Monitoring crops water needs at high spatio-temporal resolution by synergy of optical / thermal and radar observations / Abdelhakim Amazirh (2019)  

Public Titre : Monitoring crops water needs at high spatio-temporal resolution by synergy of optical / thermal and radar observations Type de document : Thèse/HDR Auteurs : Abdelhakim Amazirh, Auteur ; A. Chehbouni, Directeur de thèse ; Salah Er-Raki, Auteur Editeur : Toulouse : Université de Toulouse 3 Paul Sabatier Année de publication : 2019 Importance : 240 p. Format : 21 x 30 cm Note générale : bibliographie Thèse en vue de l’obtention du Doctorat de l'Université de Toulouse, Spécialité : Surfaces et Interfaces Continentales, Hydrologie Langues : Français (fre) Descripteur : [Vedettes matières IGN] Traitement d'image mixte [Termes descripteurs IGN] évapotranspiration [Termes descripteurs IGN] gestion de l'eau [Termes descripteurs IGN] humidité du sol [Termes descripteurs IGN] image Landsat [Termes descripteurs IGN] image Landsat-8 [Termes descripteurs IGN] image MODIS [Termes descripteurs IGN] image multibande [Termes descripteurs IGN] image Sentinel-SAR [Termes descripteurs IGN] image thermique [Termes descripteurs IGN] indice de végétation [Termes descripteurs IGN] MARRAKECH [Termes descripteurs IGN] micro-onde [Termes descripteurs IGN] modèle de transfert radiatif [Termes descripteurs IGN] Normalized Difference Vegetation Index [Termes descripteurs IGN] parcelle agricole [Termes descripteurs IGN] ressources en eau [Termes descripteurs IGN] stress hydrique [Termes descripteurs IGN] température de surface [Termes descripteurs IGN] zone semi-aride Résumé : (auteur) Optimizing water management in agriculture is essential over semi-arid areas in order to preserve water resources which are already low and erratic due to human actions and climate change. This thesis aims to use the synergy of multispectral remote sensing observations (radar, optical and thermal data) for high spatio-temporal resolution monitoring of crops water needs. In this context, different approaches using various sensors (Landsat-7/8, Sentinel-1 and MODIS) have been developed to provide information on the crop Soil Moisture (SM) and water stress at a spatio-temporal scale relevant to irrigation management. This work fits well the REC "Root zone soil moisture Estimates at the daily and agricultural parcel scales for Crop irrigation management and water use impact: a multi-sensor remote sensing approach" (http://rec.isardsat.com/) project objectives, which aim to estimate the Root Zone Soil Moisture (RZSM) for optimizing the management of irrigation water. Innovative and promising approaches are set up to estimate evapotranspiration (ET), RZSM, land surface temperature (LST) and vegetation water stress through SM indices derived from multispectral observations with high spatio-temporal resolution. The proposed methodologies rely on image-based methods, radiative transfer modelling and water and energy balance modelling and are applied in a semi-arid climate region (central Morocco). In the frame of my PhD thesis, three axes have been investigated. In the first axis, a Landsat LST-derived RZSM index is used to estimate the ET over wheat parcels and bare soil. The ET modelling estimation is explored using a modified Penman-Monteith equation obtained by introducing a simple empirical relationship between surface resistance (rc) and a RZSM index. The later is estimated from Landsat-derived land surface temperature (LST) combined with the LST endmembers (in wet and dry conditions) simulated by a surface energy balance model driven by meteorological forcing and Landsat-derived fractional vegetation cover. The investigated method is calibrated and validated over two wheat parcels located in the same area near Marrakech City in Morocco. In the next axis, a method to retrieve near surface (0-5 cm) SM at high spatial and temporal resolution is developed from a synergy between radar (Sentinel-1) and thermal (Landsat) data and by using a soil energy balance model. The developed approach is validated over bare soil agricultural fields and gives an accurate estimates of near surface SM with a root mean square difference compared to in situ SM equal to 0.03 m3 m-3. In the final axis a new method is developed to disaggregate the 1 km resolution MODIS LST at 100 m resolution by integrating the near surface SM derived from Sentinel-1 radar data and the optical-vegetation index derived from Landsat observations. The new algorithm including the S-1 backscatter as input to the disaggregation, produces more stable and robust results during the selected year. Where, 3.35 °C and 0.75 were the lowest RMSE and the highest correlation coefficient assessed using the new algorithm. Note de contenu : General Introduction 1- Bibliographic synthesis 2- Data & study sites description 3- Models & methods 4- Results & discussions Conclusions and perspectives Numéro de notice : 25694 Thématique : IMAGERIE Nature : Thèse française Note de thèse : Thèse de Doctorat : Spécialité : Surfaces et Interfaces Continentales, Hydrologie : Toulouse : 2019 Organisme de stage : CESBIO - Centre d'Etudes Spatiales de la Biosphère En ligne : http://thesesups.ups-tlse.fr/4412/ Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94759

Polarimetric radar vegetation index for biomass estimation in desert fringe ecosystems / Jisung Geba Chang in IEEE Transactions on geoscience and remote sensing, vol 56 n° 12 (December 2018)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 56 n° 12 (December 2018) . - pp 7102 - 7108 Titre : Polarimetric radar vegetation index for biomass estimation in desert fringe ecosystems Type de document : Article/Communication Auteurs : Jisung Geba Chang, Auteur ; Maxim Shoshany, Auteur ; Yisok Oh, Auteur Année de publication : 2018 Article en page(s) : pp 7102 - 7108 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes descripteurs IGN] allométrie [Termes descripteurs IGN] bande C [Termes descripteurs IGN] bande L [Termes descripteurs IGN] bassin méditerranéen [Termes descripteurs IGN] biomasse [Termes descripteurs IGN] carte de la végétation [Termes descripteurs IGN] coefficient de rétrodiffusion [Termes descripteurs IGN] données de terrain [Termes descripteurs IGN] image ALOS-PALSAR [Termes descripteurs IGN] image Sentinel-SAR [Termes descripteurs IGN] indice de végétation [Termes descripteurs IGN] polarimétrie radar [Termes descripteurs IGN] zone aride [Termes descripteurs IGN] zone semi-aride Résumé : (auteur) Biomass estimation of eastern Mediterranean shrublands was investigated using PALSAR full- and dual-polarization L-band and Sentinel-1 dual-polarization C-band data. First, we conducted an empirical assessment of single and multiple regressions between polarized backscattering coefficients and shrubland biomass distribution along the climatic gradient between semiarid and arid regions. We then found that the PALSAR L-band HV-polarized backscattering coefficient has higher biomass information content than Sentinel-1 C-band data. Based on a theoretical volume scattering model and a semiempirical model, we propose a new polarimetric radar vegetation index (PRVI) that utilizes the degree of polarization and the cross-polarized backscattering coefficient. The relationship between the new index and the biomass was assessed with reference to normalized difference vegetation index-based biomass estimates calculated using Landsat imagery. The PRVI was found to have higher correlation with biomass compared with other radar polarization parameters, in general, and an existing radar vegetation index (RVI), in particular. Assessment of PRVI-based biomass predictions compared with allometric data extracted from air photographs, Lidar, and field data for 67 sites across the desert fringe zone indicated moderate performance with an RMSE of 0.329 kg/m 2 , while an RVI-based biomass estimation had an RMSE of 0.439 kg/m². Numéro de notice : A2018-553 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2018.2848285 date de publication en ligne : 03/07/2018 En ligne : http://dx.doi.org/10.1109/TGRS.2018.2848285 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91659 [article]

Evaluation de variables limnologiques grâce à des images Landsat / Danielle Teixeira Alves Da Silva in Géomatique expert, n° 118 (septembre - octobre 2017)

Public [article]  inGéomatique expert > n° 118 (septembre - octobre 2017) . - pp 30 - 39 Titre : Evaluation de variables limnologiques grâce à des images Landsat Type de document : Article/Communication Auteurs : Danielle Teixeira Alves Da Silva, Auteur ; Aziz Serradj, Auteur ; Aline do Vale Figueiredo, Auteur ; Vanessa Becker, Auteur Année de publication : 2017 Article en page(s) : pp 30 - 39 Note générale : bibliographie Langues : Français (fre) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] analyse spatiale [Termes descripteurs IGN] Brésil [Termes descripteurs IGN] carte thématique [Termes descripteurs IGN] chlorophylle [Termes descripteurs IGN] eaux continentales [Termes descripteurs IGN] écologie [Termes descripteurs IGN] image Landsat [Termes descripteurs IGN] limnologie [Termes descripteurs IGN] ressources aquatiques [Termes descripteurs IGN] teneur en chlorophylle des feuilles [Termes descripteurs IGN] zone semi-aride Résumé : (auteur) Utilisation des images Landsat pour estimer la concentration de la chlorophylle-a et de la transparence de l'eau sur un territoire semi-aride du Nord-est brésilien. Numéro de notice : A2017-586 Thématique : IMAGERIE Nature : Article DOI : sans Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=86728 [article]

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TM-Based SOC models augmented by auxiliary data for carbon crediting programs in semi-arid environments / Salahuddin M. Jaber in Photogrammetric Engineering & Remote Sensing, PERS, vol 83 n° 6 (June 2017)  

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L'approche détection des changements pour estimer l'humidité du sol en milieu semi-aride à partir d'images ASAR, cas des hautes plaines de l'Est de l'Algérie / Mokhtar Guerfi in Revue Française de Photogrammétrie et de Télédétection, n° 210 (Avril 2015)

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Télédétection, SIG et modélisation de l'érosion hydrique dans le bassin versant de l'Oued Amzaz, Rif central / Jamal Chaaouan in Revue Française de Photogrammétrie et de Télédétection, n° 203 (Juillet 2013)

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Analysis of desertification in the Upper East Region (UER) of Ghana using remote sensing, field study, and local knowledge / Alex B. Owusu in Cartographica, vol 48 n° 1 (March 2013)  

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Utilisation d'image satellitaire et d'un modèle numérique d'altitude pour la cartographie des zones à risque d'inondation sur le littoral méditerranéen de Saïdia (Nord-Est du Maroc) / M. Mouzouri in Revue Française de Photogrammétrie et de Télédétection, n° 201 (Janvier 2013)

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Estimating irrigated agricultural water use through Landsat TM and a simplified surface energy balance modeling in the semi-arid environments of Arizona / S. Kaplan in Photogrammetric Engineering & Remote Sensing, PERS, vol 78 n° 8 (August 2012)  

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Analysis of C-band scatterometer moisture estimations derived over a semiarid region / R. Amri in IEEE Transactions on geoscience and remote sensing, vol 50 n° 7 Tome 1 (July 2012)  

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Atmospheric correction to IRS-P6 AWiFS data and its validation with ground measurements: a study over the semi-arid region / Ashu Sharma in Geocarto international, vol 25 n° 7 (November 2010)  

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Discrimination of agricultural crops in a tropical semi-arid region of Brazil based on L-band polarimetric airborne SAR data / W. Silva in ISPRS Journal of photogrammetry and remote sensing, vol 64 n° 5 (September - October 2009)

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Optimizing Support Vector Machine learning for semi-arid vegetation mapping by using clustering analysis / L. Su in ISPRS Journal of photogrammetry and remote sensing, vol 64 n° 4 (July - August 2009)

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