Publication: Interpolation of Instantaneous Air Temperature Using Geographical and MODIS Derived Variables with Machine Learning Techniques
Authors
Ruiz Álvarez, Marcos ; Alonso-Sarria, Francisco ; Gomariz Castillo, Francisco
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Publisher
MDPI
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DOI
https://doi.org/10.3390/ijgi8090382
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info:eu-repo/semantics/article
Description
© 2019 by the authors. This manuscript version is made available under the CC-BY 4.0 license http://creativecommons.org/licenses/byd/4.0/
This document is the Published Manuscript version of a Published Work that appeared in final form in ISPRS International Journal of Geo-Information. To access the final edited and published work see https://doi.org/10.3390/ijgi8090382
Abstract
Several methods have been tried to estimate air temperature using satellite imagery. In this paper, the results of two machine learning algorithms, Support Vector Machines and Random Forest, are compared with Multiple Linear Regression and Ordinary kriging. Several geographic, remote sensing and time variables are used as predictors. The validation is carried out using two different approaches, a leave-one-out cross validation in the spatial domain and a spatio-temporal k-block cross-validation, and four different statistics on a daily basis, allowing the use of ANOVA to compare the results. The main conclusion is that Random Forest produces the best results (R2 = 0.888 ± 0.026, Root mean square error = 3.01 ± 0.325 using k-block cross-validation). Regression methods (Support Vector Machine, Random Forest and Multiple Linear Regression) are calibrated with MODIS data and several predictors easily calculated from a Digital Elevation Model. The most important variables in the Random Forest model were satellite temperature, potential irradiation and cdayt, a cosine transformation of the julian day.
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Citation
ISPRS International Journal of Geo-Information, 2019, Vol. 8 (9): 382
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Este ítem está sujeto a una licencia Creative Commons. http://creativecommons.org/licenses/by/4.0/