{"type": "FeatureCollection", "features": [{"id": "10.1016/j.rse.2016.11.010", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-21T16:16:03Z", "type": "Journal Article", "created": "2016-11-26", "title": "Normalizing land surface temperature data for elevation and illumination effects in mountainous areas: A case study using ASTER data over a steep-sided valley in Morocco", "description": "Abstract   The remotely sensed land surface temperature (LST) is a key parameter to monitor surface energy and water fluxes but the strong impact of topography on LST has limited its use to mostly flat areas. To fill the gap, this study proposes a physically-based method to normalize LST data for topographic - namely illumination and elevation - effects over mountainous areas. Both topographic effects are first quantified by inverting a dual-source soil/vegetation energy balance (EB) model forced by 1) the instantaneous solar radiation simulated by a 3D radiative transfer model named DART (Discrete Anisotropic Radiative Transfer) that uses a digital elevation model (DEM), 2) a satellite-derived vegetation index, and 3) local meteorological (air temperature, air relative humidity and wind speed) data available at a given location. The satellite LST is then normalized for topography by simulating the LST using both pixel- and image-scale DART solar radiation and elevation data. The approach is tested on three ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) overpass dates over a steep-sided 6\u00a0km by 6\u00a0km area in the Atlas Mountain in Morocco. The mean correlation coefficient and root mean square difference (RMSD) between EB-simulated and ASTER LST is 0.80 and 3\u00a0\u00b0C, respectively. Moreover, the EB-based method is found to be more accurate than a more classical approach based on a multi-linear regression with DART solar radiation and elevation data. The EB-simulated LST is also evaluated against an extensive ground dataset of 135 autonomous 1-cm depth temperature sensors deployed over the study area. While the mean RMSD between 90\u00a0m resolution ASTER LST and localized ibutton measurements is 6.1\u00a0\u00b0C, the RMSD between EB-simulated LST and ibutton soil temperature is 5.4 and 5.3\u00a0\u00b0C for a DEM at 90\u00a0m and 8\u00a0m resolution, respectively. The proposed topographic normalization is self-calibrated from (LST, DEM, vegetation index and in situ meteorological data) data available over large extents. As a significant perspective this approach opens the path to using normalized LST as input to evapotranspiration retrieval methods based on LST.", "keywords": ["[SDE] Environmental Sciences", "550", "Topographic normalization", "DEM", "0207 environmental engineering", "Energy balance", "02 engineering and technology", "01 natural sciences", "ASTER", "13. 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To fill the gap, this study proposes a physically-based method to normalize LST data for topographic - namely illumination and elevation - effects over mountainous areas. Both topographic effects are first quantified by inverting a dual-source soil/vegetation energy balance (EB) model forced by 1) the instantaneous solar radiation simulated by a 3D radiative transfer model named DART (Discrete Anisotropic Radiative Transfer) that uses a digital elevation model (DEM), 2) a satellite-derived vegetation index, and 3) local meteorological (air temperature, air relative humidity and wind speed) data available at a given location. The satellite LST is then normalized for topography by simulating the LST using both pixel- and image-scale DART solar radiation and elevation data. The approach is tested on three ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) overpass dates over a steep-sided 6\u00a0km by 6\u00a0km area in the Atlas Mountain in Morocco. The mean correlation coefficient and root mean square difference (RMSD) between EB-simulated and ASTER LST is 0.80 and 3\u00a0\u00b0C, respectively. Moreover, the EB-based method is found to be more accurate than a more classical approach based on a multi-linear regression with DART solar radiation and elevation data. The EB-simulated LST is also evaluated against an extensive ground dataset of 135 autonomous 1-cm depth temperature sensors deployed over the study area. While the mean RMSD between 90\u00a0m resolution ASTER LST and localized ibutton measurements is 6.1\u00a0\u00b0C, the RMSD between EB-simulated LST and ibutton soil temperature is 5.4 and 5.3\u00a0\u00b0C for a DEM at 90\u00a0m and 8\u00a0m resolution, respectively. The proposed topographic normalization is self-calibrated from (LST, DEM, vegetation index and in situ meteorological data) data available over large extents. As a significant perspective this approach opens the path to using normalized LST as input to evapotranspiration retrieval methods based on LST.", "keywords": ["[SDE] Environmental Sciences", "550", "Topographic normalization", "DEM", "0207 environmental engineering", "Energy balance", "02 engineering and technology", "01 natural sciences", "ASTER", "13. 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The map corresponds to the Quaternary Geological Map on land. The data set series consists of three data sets: Bottom sediments (formation), overview, Bunn sediments (formation), regional and Bunnsedimenter (formation), detailed, which are digitised at different scales and have different coverage areas.", "formats": [{"name": "GDB"}], "keywords": ["avsetning", "barentshavet", "bunnforhold", "dannelse", "fellesdatakatalog", "fjord", "geologi", "geology", "havbunn", "jordart", "kvart\u00e6r", "l\u00f8smasser", "mareano", "marin", "marine-grunnkart", "marinegrunnkart", "morene", "national", "ngu", "no", "nordsj\u00f8en", "norge-digitalt", "norskehavet", "sea-regions", "sediment", "sj\u00f8", "soil", "tokningsdata"], "contacts": [{"organization": "https://register.geonorge.no/organisasjoner/norges-geologiske-unders\u00f8kelse/aave_lepland", "roles": ["publisher"]}]}, "links": [{"href": "https://geo.ngu.no/mapserver/MarinBunnsedimenterWMS/?request=getcapabilities&service=wms&version=1.3.0"}, {"href": "https://kartkatalog.geonorge.no/Metadata/uuid/f85e9b47-cd03-4de1-b037-c0fc0cfb2fed"}, {"href": "https://www.ngu.no/emne/karttjenester?field_temagruppe_tid=2362&visning=liste"}, {"href": "http://data.europa.eu/88u/dataset/f85e9b47-cd03-4de1-b037-c0fc0cfb2fed"}, {"rel": "self", "type": "application/geo+json", "title": "f85e9b47-cd03-4de1-b037-c0fc0cfb2fed", "name": "item", "description": "f85e9b47-cd03-4de1-b037-c0fc0cfb2fed", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/f85e9b47-cd03-4de1-b037-c0fc0cfb2fed"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"null": "date"}}, {"id": "222cc8fe9d3162269cce41ce21b1759d", "type": "Feature", "geometry": null, "properties": {"updated": "2024-08-23T13:52:42.987960Z", "type": "Dataset", "language": "en", "title": "Data on Spectral decomposition of high-frequency CO\u2082 concentration", "description": "Data was collected at different locations along the hillslope-riparian-stream transect in the V\u00e4strab\u00e4cken Catchment during 2015\u20132016. 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At both riparian and hillslope locations, three groundwater tubes were installed, with the first two tubes used for groundwater sampling at 0- to 0.5-m and 0.5- to 1-m depth and the third groundwater tube, perforated along its entire 1-m length, was used for water table depth measurements (pressure transducer 1400, MJK Automation, Sweden) and groundwater temperature measurements (temperature sensors TO3R, TOJO Skogsteknik, Sweden). All sensors were connected to a data logger (CR1000, Campbell Scientific, USA) that recorded measurements at 60-min intervals. The system ran for 2 years (2015\u20132016) from snowmelt in early May until November. PAR was measured as part of the research infrastructure Integrated Carbon Observations System approximately 150 m from the soil-stream transect. The measurements were made at 1.1-m height using a SQ-110 Sun Calibration Quantum Sensor (Apogee Instruments Inc., USA).  Due to the need for simultaneous observations that are evenly distributed in time, the collected 2-year time series were screened for gaps caused by environmental constraints (mainly due to freezing of the stream water) and occasional instrumental malfunctions. By selection of gap-free periods, two time series consisting of 2,966 and 3,877 hourly observations were extracted covering the period of 10 June 2015 20:00 to 12 October 2015 09:00 and 16 May 2016 00:00 to 24 October 2016 12:00, respectively. These time series (hereafter denoted as 2015 and 2016) were used to calculate the PSDs as well as the wavelet power and coherence spectrum. In addition, several subsets of the time series (hereafter denoted time windows), with the length of 480 observations (i.e., 20 days), were extracted from the two time series. These time windows were obtained by sequentially shifting the first observation in the window by 120 observations (i.e., 5 days). This sliding window approach resulted in a set of partly overlapping time windows, 21 and 29 time windows for the Years 2015 and 2016, respectively, which subsequently were used to calculate the PSDs.   For further information, please see manuscript \"Spectral Decomposition Reveals New Perspectives on CO2 Concentration Patterns and Soil-Stream Linkages\" Riml et al (2019).  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