The datasets are supplementary to the article by Gök et al. (2024), in which Landsat-derived land surface temperature (LST) trends of the Swiss Alps are mapped and analyzed. The LST trends were obtained through the regression of a harmonic model, which includes a linear trend component, within Google Earth Engine. These Landsat-derived LST trends are subject to bias due to changes in Landsat acquisition times. The LST trend bias was estimated using modelled incoming shortwave radiation and further calibrated with LST data from high alpine weather stations.
The associated Jupyter notebook (Landsat_LSTtimeseries_gee.ipynb) to reproduce the Landsat LST products requires the Google Earth Engine (GEE) Python API and uses Landsat TM, ETM+, and OLI/TIRS - Surface temperature data.
In “Investigating Mesozoic Climate Trends and Sensitivities with a Large Ensemble of Climate Model Simulations” we study global trends in the climatic evolution through the Mesozoic era (252-66 Ma). The data presented here is the model output on which the results of this manuscript are based. Also included are different boundary condition model input files and scripts to generate the included figures (using the Python programming language in a Jupyter Notebook).
The model output is provided in different netcdf files. The data is generated using the coupled ocean-atmosphere model CLIMBER3alpha (Montoya et al. 2005) which models climate globally on a 3.75° x 3.75° (ocean, lon.x lat.) and 22.5° x 7.5° (atmosphere) grid. Please note that data from other research that is shown in the figures in Landwehrs et al. (2020a) is not included in this data publication to avoid copyright issues.
High resolution debris thickness mapping using land surface temperature maps (LST) and surface energy balance modelling (SEBM). LST data was produced by a radiometric thermal infrared measurements from an uncrewed aerial vehicle (UAV). The SEBM considers the rate of change of heat storage as an energy balance component derived from diurnal temperature variablity.
The dataset is composed of Neo HySpex (VNIR & SWIR) and Telops Hyper-Cam (LWIR) hyperspectral imagery acquired during the MOSES GFZ/FUB/UFZ airborne campaign on August 8th, 2020 over the test area Oschersleben covering parts of the Bode catchment in the northern foreland of the Harz Mountain, Central Germany. The study area covers an ecological transect including three TERENO climate stations/flux towers ranging from forest sites (Hohes Holz) to lowland meadows (Grosses Bruch) to intensively used agricultural land (Hordorf). The survey was conducted within the frame of the Helmholtz program MOSES (Modular Observation Solutions for Earth Systems) heatwave event chain, which overall objective is to monitor heat extremes and drought events. In particular, the 2020 MOSES heatwave campaign over the Oschersleben test site aimed at an GFZ/UFZ intercalibration comparison measurements between different hyperspectral instruments flown on same day with different platforms and altitude, and test impact of different workflows on resulting data. This publication contains the GFZ VNIR-SWIR-LWIR hyperspectral dataset. It includes 1) 17 HySpex cloud-free flight lines already mosaicked in orthorectified reflectance, covering the VNIR to SWIR wavelength regions (0.4-2.5 µm) with 408 spectral bands, and 2) a composite of Hyper-Cam 1956 frames processed to surface temperature and spectral emissivity covering the LWIR (7.7 – 11.7 µm) in 125 bands. The dataset also includes Level 2A EnMAP-like reflectance imagery simulated using the end-to-end Simulation tool (EeteS). Associated field data and UFZ hyperspectral data are included in related publications of this campaign.