A Machine Learning Based Downscaling Approach to Produce High Spatio-Temporal Resolution Land Surface Temperature of the Antarctic Dry Valleys from MODIS Data

Lezama Valdes, Lilian-Maite; Katurji, Marwan; Meyer, Hanna

doi:10.3390/rs13224673

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A Machine Learning Based Downscaling Approach to Produce High Spatio-Temporal Resolution Land Surface Temperature of the Antarctic Dry Valleys from MODIS Data

Lilian-Maite Lezama Valdes

^1,*

Marwan Katurji

and

Hanna Meyer

Institute of Landscape Ecology, Westfälische Wilhelms-Universität Münster, Heisenbergstr. 2, 48149 Münster, Germany

Centre for Atmospheric Research, School of Earth and Environment, University of Canterbury, Arts Road, Ilam, Christchurch 8140, New Zealand

Author to whom correspondence should be addressed.

Academic Editors: Andreas J. Dietz, Sebastian Roessler and Celia Amélie Baumhoer

Remote Sens. 2021, 13(22), 4673; https://doi.org/10.3390/rs13224673

Received: 8 October 2021 / Revised: 10 November 2021 / Accepted: 15 November 2021 / Published: 19 November 2021

(This article belongs to the Special Issue Remote Sensing of Polar Regions)

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Abstract

To monitor environmental and biological processes, Land Surface Temperature (LST) is a central variable, which is highly variable in space and time. This particularly applies to the Antarctic Dry Valleys, which host an ecosystem highly adapted to the extreme conditions in this cold desert. To predict possible climate induced changes on the Dry Valley ecosystem, high spatial and temporal resolution environmental variables are needed. Thus we enhanced the spatial resolution of the MODIS satellite LST product that is sensed sub-daily at a 1 km spatial resolution to a 30 m spatial resolution. We employed machine learning models that are trained using Landsat 8 thermal infrared data from 2013 to 2019 as a reference to predict LST at 30 m resolution. For the downscaling procedure, terrain derived variables and information on the soil type as well as the solar insolation were used as potential predictors in addition to MODIS LST. The trained model can be applied to all available MODIS scenes from 1999 onward to develop a 30 m resolution LST product of the Antarctic Dry Valleys. A spatio-temporal validation revealed an R

^{2}

of 0.78 and a RMSE of 3.32

^{\circ}

C. The downscaled LST will provide a valuable surface climate data set for various research applications, such as species distribution modeling, climate model evaluation, and the basis for the development of further relevant environmental information such as the surface moisture distribution. View Full-Text

Keywords: downscaling; Land Surface Temperature; Antarctica; McMurdo Dry Valleys; MODIS; machine learning

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Lezama Valdes, L.-M.; Katurji, M.; Meyer, H. A Machine Learning Based Downscaling Approach to Produce High Spatio-Temporal Resolution Land Surface Temperature of the Antarctic Dry Valleys from MODIS Data. Remote Sens. 2021, 13, 4673. https://doi.org/10.3390/rs13224673

AMA Style

Lezama Valdes L-M, Katurji M, Meyer H. A Machine Learning Based Downscaling Approach to Produce High Spatio-Temporal Resolution Land Surface Temperature of the Antarctic Dry Valleys from MODIS Data. Remote Sensing. 2021; 13(22):4673. https://doi.org/10.3390/rs13224673

Chicago/Turabian Style

Lezama Valdes, Lilian-Maite, Marwan Katurji, and Hanna Meyer. 2021. "A Machine Learning Based Downscaling Approach to Produce High Spatio-Temporal Resolution Land Surface Temperature of the Antarctic Dry Valleys from MODIS Data" Remote Sensing 13, no. 22: 4673. https://doi.org/10.3390/rs13224673

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A Machine Learning Based Downscaling Approach to Produce High Spatio-Temporal Resolution Land Surface Temperature of the Antarctic Dry Valleys from MODIS Data

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