Version 1
: Received: 31 May 2019 / Approved: 3 June 2019 / Online: 3 June 2019 (08:48:32 CEST)
How to cite:
Ruiz-Álvarez, M.; Alonso-Sarría, F.; Gomariz-Castillo, F. Interpolation of Instantaneous Air Temperature Using Geographical and MODIS Derived Variables with Machine Learning Techniques. Preprints2019, 2019060008. https://doi.org/10.20944/preprints201906.0008.v1
Ruiz-Álvarez, M.; Alonso-Sarría, F.; Gomariz-Castillo, F. Interpolation of Instantaneous Air Temperature Using Geographical and MODIS Derived Variables with Machine Learning Techniques. Preprints 2019, 2019060008. https://doi.org/10.20944/preprints201906.0008.v1
Ruiz-Álvarez, M.; Alonso-Sarría, F.; Gomariz-Castillo, F. Interpolation of Instantaneous Air Temperature Using Geographical and MODIS Derived Variables with Machine Learning Techniques. Preprints2019, 2019060008. https://doi.org/10.20944/preprints201906.0008.v1
APA Style
Ruiz-Álvarez, M., Alonso-Sarría, F., & Gomariz-Castillo, F. (2019). Interpolation of Instantaneous Air Temperature Using Geographical and MODIS Derived Variables with Machine Learning Techniques. Preprints. https://doi.org/10.20944/preprints201906.0008.v1
Chicago/Turabian Style
Ruiz-Álvarez, M., Francisco Alonso-Sarría and Francisco Gomariz-Castillo. 2019 "Interpolation of Instantaneous Air Temperature Using Geographical and MODIS Derived Variables with Machine Learning Techniques" Preprints. https://doi.org/10.20944/preprints201906.0008.v1
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 Machine and Random Forest, are compared with Multivariate Linear Regression, TVX and Ordinary kriging. Several geographic, remote sensing and time variables are used as predictors. The validation is carried out using four different statistics on a daily basis allowing the use of ANOVA to compare the results. The main conclusion is that Random Forest with residual kriging produces the best results (R$^2$=0.612 $\pm$ 0.019, NSE=0.578 $\pm$ 0.025, RMSE=1.068 $\pm$ 0.027, PBIAS=-0.172 $\pm$ 0.046), whereas TVX produces the least accurate results. The environmental conditions in the study area are not really suited to TVX, moreover this method only takes into account satellite data. On the other hand, regression methods (Support Vector Machine, Random Forest and Multivariate Linear Regression) use several parameters that are easily calculated from a Digital Elevation Model, adding very little difficulty to the use of satellite data alone. The most important variables in the Random Forest Model were satellite temperature, potential irradiation and cdayt, a cosine transformation of the julian day.
Keywords
Air temperature; MODIS; machine learning; interpolation
Subject
Environmental and Earth Sciences, Remote Sensing
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.