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Open AccessArticle

Interpolation of Instantaneous Air Temperature Using Geographical and MODIS Derived Variables with Machine Learning Techniques

1
Instituto Universitario de Agua y Medio Ambiente, Campus de Espinardo, Universidad de Murcia, 30001 Murcia, Spain
2
Instituto Euromediterráneo del Agua, Campus de Espinardo, 30001 Murcia, Spain
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Academic Editor: Wolfgang Kainz
ISPRS Int. J. Geo-Inf. 2019, 8(9), 382; https://doi.org/10.3390/ijgi8090382
Received: 5 August 2019 / Revised: 25 August 2019 / Accepted: 29 August 2019 / Published: 31 August 2019
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. View Full-Text
Keywords: air temperature; MODIS; machine learning; interpolation air temperature; MODIS; machine learning; interpolation
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MDPI and ACS Style

Ruiz-Álvarez, M.; Alonso-Sarria, F.; Gomariz-Castillo, F. Interpolation of Instantaneous Air Temperature Using Geographical and MODIS Derived Variables with Machine Learning Techniques. ISPRS Int. J. Geo-Inf. 2019, 8, 382.

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