F
Frank-M. Göttsche
Researcher at Karlsruhe Institute of Technology
Publications - 45
Citations - 2695
Frank-M. Göttsche is an academic researcher from Karlsruhe Institute of Technology. The author has contributed to research in topics: Emissivity & Moderate-resolution imaging spectroradiometer. The author has an hindex of 23, co-authored 38 publications receiving 1943 citations. Previous affiliations of Frank-M. Göttsche include United Arab Emirates University.
Papers
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Land surface temperature and emissivity estimation from passive sensor data: Theory and practice-current trends
TL;DR: In this article, the authors provide an overview of the current state of the art in the field of satellite-based land surface temperature and emissivity estimation. But, they do not consider the use of the satellite data for point measurements on the ground.
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Validation of Collection 6 MODIS land surface temperature product using in situ measurements
Si-Bo Duan,Zhao-Liang Li,Hua Li,Frank-M. Göttsche,Hua Wu,Wei Zhao,Pei Leng,Xia Zhang,César Coll +8 more
TL;DR: In this paper, the C6 MODIS LST product was validated using in situ measurements from the selected homogeneous sites during daytime and nighttime: except for the GBB site, large RMSE values (>2 K) were obtained during daytime.
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Google Earth Engine Open-Source Code for Land Surface Temperature Estimation from the Landsat Series
TL;DR: This work provides a code repository that allows computing LSTs from Landsat 4, 5, 7, and 8 within GEE, an online platform created to allow remote sensing users to easily perform big data analyses without increasing the demand for local computing resources.
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Modelling of diurnal cycles of brightness temperature extracted from METEOSAT data
Frank-M. Göttsche,Folke Olesen +1 more
TL;DR: In this article, the authors proposed that cloud cover usually denies the generation of land surface temperature (LST) time series over large areas from thermal infrared (TIR) data sensed by satellites.
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Quantifying the Uncertainty of Land Surface Temperature Retrievals From SEVIRI/Meteosat
TL;DR: A quantification of the uncertainty of LST estimations is presented, taking into account error statistics of the GSW under a globally representative collection of atmospheric profiles, and a careful characterization ofThe uncertainty of input data, particularly the surface emissivity and forecasts of the total water vapor content is characterization.