Developing machine learning models for air temperature estimation using MODIS data
Article Sidebar
Main Article Content
Abstract
Air temperature is a key variable in a wide range of environmental applications, including land–atmosphere interaction, climate change research and hydrology and crop growth models, among others. The objective of this study was to estimate daily maximum (Tmax) and minimum (Tmin) temperatures, based on MODIS AQUA/TERRA land surface temperature (LST), NDVI, extraterrestrial solar radiation and precipitation data. Artificial neural networks (ANN) and random forests (RF) models were developed to predict these temperatures covering weather stations in Córdoba (Argentina) for 2018-2020. The results show that RF and ANN machine learning algorithms are capable of modeling non-linear relationships between registered temperatures and LST MODIS data, in a very robust way. The validation of the models confirms that Tmax and Tmin can be accurately estimated using, jointly or separately, AQUA and TERRA LST. The best models present determination coefficients equal to 0.81/0.91 and root mean square error of 2.7/2.1 ºC for Tmax/Tmin, when using AQUA LST day/night satellite overpass time data, respectively. The robustness and confidence of the models developed, and the ease and free accessibility of input data at a global scale, suggest that these methodologies have the potential to be applied to other regions.
Article Details
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
How to Cite
References
Aher, P. D., Adinarayana, J., and Gorantiwar, S. D. (2011). Remote Sensing and Artificial Neural Network in Spatial Assessment of Air Temperature in a Semi-arid Watershed. International Journal of Earth Sciences and Engineering, 4(6), 351-354. https://d1wqtxts1xzle7.cloudfront.net/30680999/020410229-with-cover-page-v2.pdf?Expires=1654361918&Signature=egT15fyoyGBtbHxgeRoCZ~s5fqWPV-IJcmIhwV1fDZAOgWLM26nL2rUG~ZRiBLAeDOdZL6ID62X29lASrktIX0I-maORsYqtzgwGivHgNBAaKa63aXTEFog14zM9h8oOPUbjvwyda4PQDqUzGT2ajV76dgMyRYok4G-8EqaQZznFfgO0LiooiacFe9Z-ZMbqem8tpUx4~Qut5H5GOnk4BOk-st5~BzlYArzvscmmGnsa52ReeieHsrFj5chh~6YHy5z5Vj9ALU80ShlRuHZLAj5zFqgdBS9ZU8sfyb9BXsI5aUlsQNklEcAEdDM~MN5BgSLTf6lYTEK58u4l4DzwTg__&Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA
Alademomi, A. S., Okolie, C. J., Daramola, O. E., Agboola, R. O., and Salami, T. J. (2020). Assessing the Relationship of LST, NDVI and EVI with Land Cover Changes in the Lagos Lagoon Environment. QuaestionesGeographicae, 39(3), 87-109. https://doi.org/10.2478/quageo-2020-0025
Alfieri, S. M., De Lorenzi, F., Bonfante, A., Basile, A., and Menenti, M. (2012). Mapping Air Temperature by Fourier Analysis of Land Surface Temperature Time Series Observed by TERRA/MODIS. In Proceedings of the 1st EARSeL Workshop on Temporal Analysis of Satellite Images (21-24). Mykonos, Greece. https://www.researchgate.net/profile/Francesca-De-Lorenzi/publication/260979297_Mapping_Air_Temperature_by_Fourier_Analysis_of_Land_Surface_Temperature_time_series_observed_by_TERRAMODIS/links/5cdc2718a6fdccc9ddaebe19/Mapping-Air-Temperature-by-Fourier-Analysis-of-Land-Surface-Temperature-time-series-observed-by-TERRA-MODIS.pdf
Aliaga, V., Ferrelli, F., and Piccolo, M. C. (2017). Regionalization of climate over the Argentine Pampas. International Journal of Climatology, 37 (Suppl.1), 1237-1247. https://doi.org/10.1002/joc.5079
Bartkowiak, P., Castelli, M., and Notarnicola, C. (2019). Downscaling Land Surface Temperature from MODIS Dataset with Random Forest Approach over Alpine Vegetated Areas. Remote Sensing, 11(11), 1319. https://doi.org/10.3390/rs11111319
Benali, A., Carvalho, A. C., Nunes, J. P., Carvalhais, N., and Santos, A. (2012). Estimating air surface temperature in Portugal using MODIS LST data. Remote Sensing of Environment, 124, 108-121. https://doi.org/10.1016/j.rse.2012.04.024
Breiman, L. (2001). Random forests. Machine learning, 45(1), 5-32. https://doi.org/10.1023/A:1010933404324
Chang, Y., Ding, Y., Zhao, Q., and Zhang, S. (2020). A Comprehensive Evaluation of 4-Parameter Diurnal Temperature Cycle Models with In Situ and MODIS LST over Alpine Meadows in the Tibetan Plateau. Remote Sensing, 12(1), 103. https://doi.org/10.3390/rs12010103
Chronopoulos, K. I., Tsiros, I. X., Dimopoulos, I. F., and Alvertos, N. (2008). An application of artificial neural network models to estimate air temperature data in areas with sparse network of meteorological stations. Journal of Environmental Science and Health Part A, 43(14), 1752-1757. https://doi.org/10.1080/10934520802507621
Deery, D., Jimenez-Berni, J., Jones, H., Sirault, X., and Furbank, R. (2014). Proximal Remote Sensing Buggies and Potential Applications for Field-Based Phenotyping. Agronomy, 4(3), 349-379. https://doi.org/10.3390/agronomy4030349
Emamifar, S., Rahimikhoob, A., and Noroozi, A. A. (2013). Daily mean air temperature estimation from MODIS land surface temperature products based on M5 model tree. International Journal of Climatology, 33(15), 3174-3181. https://doi.org/10.1002/joc.3655
Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D. and Moore, R. (2017) Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment. Google. https://earthengine.google.com/).
Heck, E., de Beurs, K. M., Owsley, B. C., and Henebry, G. M. (2019). Evaluation of the MODIS collections 5 and 6 for change analysis of vegetation and land surface temperature dynamics in North and South America. ISPRS Journal of Photogrammetry and Remote Sensing, 156, 121-134. https://doi.org/10.1016/j.isprsjprs.2019.07.011
Huang, W. R., Chang, Y. H., and Liu, P. Y. (2018). Assessment of IMERG precipitation over Taiwan at multiple timescales. AtmosphericResearch, 214, 239-249. https://doi.org/10.1016/j.atmosres.2018.08.004
Infraestructura de Datos Espaciales de la Provincia de Córdoba (2019). Mapas Córdoba - Geoportal IDE de la Provincia de Córdoba. Ministerio de Finanzas, Provincia de Córdoba. https://www.mapascordoba.gob.ar/Documentos/METADATOS_LIMITANTES_DE_SUELO.pdf
Janatian, N., Sadeghi, M., Sanaeinejad, S. H., Bakhshian, E., Farid, A., Hasheminia, S. M., and Ghazanfari, S. (2017). A statistical framework for estimating air temperature using MODIS land surface temperature data. International Journal of Climatology, 37(3), 1181-1194. https://doi.org/10.1002/joc.4766
Kamoutsis, A. P., Matsoukis, A. S., and Chronopoulos, K. I. (2013). Air temperature estimation by using artificial neural network models in the greater Athens area, Greece. International Scholarly Research Notices, 2013, 489350. https://doi.org/10.1155/2013/489350
Kärnä, T. and Baptista, A. M. (2016). Evaluation of a long-term hindcast simulation for the Columbia River estuary. Ocean Modelling, 99, 1-14. https://doi.org/10.1016/j.ocemod.2015.12.007
Long, D., Yan, L., Bai, L., Zhang, C., Li, X., Lei, H., Yang, H., Tian, F., Zeng, C., Meng, X., and Shi, C. (2020). Generation of MODIS-like land surface temperatures under all-weather conditions based on a data fusion approach. Remote Sensing of Environment, 246, 111863. https://doi.org/10.1016/j.rse.2020.111863
Ministerio de Agricultura y Ganadería (2021). Gestión de Estaciones Meteorológicas Clima. Gobierno de la Provincia de Córdoba. https://newmagya.omixom.com/accounts/login/?next=/
Marzban, F., Conrad, T., Marzban, P., and Sodoudi, S. (2018). Estimation of the Near-Surface Air Temperature during the Day and Nighttime from MODIS in Berlin, Germany. International Journal of Advanced Remote Sensing and GIS, 7(1), 2478-2517. https://doi.org/10.23953/cloud.ijarsg.337
Noi, P. T., Degener, J., and Kappas, M. (2017). Comparison of Multiple Linear Regression, Cubist Regression, and Random Forest Algorithms to Estimate Daily Air Surface Temperature from Dynamic Combinations of MODIS LST Data. Remote Sensing, 9(5), 398. https://doi.org/10.3390/rs9050398
Oyler, J. W., Dobrowski, S. Z., Holden, Z. A., and Running, S. W. (2016). Remotely Sensed Land Skin Temperature as a Spatial Predictor of Air Temperature across the Conterminous United States. Journal of Applied Meteorology and Climatology, 55(7), 1441-1457. https://doi.org/10.1175/JAMC-D-15-0276.1
Sayago, S. and Bocco, M. (2018). Crop yield estimation using satellite images: comparison of linear and non-linear models. AgriScientia, 35(1), 1-9. https://doi.org/10.31047/1668.298x.v1.n35.20447
Sayago, S., Ovando, G., and Bocco, M. (2017). Landsat images and crop model for evaluating water stress of rainfed soybean. Remote Sensing of Environment, 198, 30-39. https://doi.org/10.1016/j.rse.2017.05.008
Sobrino, J. A. and Irakulis, I. (2020). A Methodology for Comparing the Surface Urban Heat Island in Selected Urban Agglomerations Around the World from Sentinel-3 SLSTR Data. Remote Sensing, 12(12), 2052. https://doi.org/10.3390/rs12122052
Taylor, K. E. (2001). Summarizing multiple aspects of model performance in a single diagram. Journal of Geophysical Research: Atmospheres, 106(D7), 7183-7192. https://doi.org/10.1029/2000JD900719
Trenberth, K. E. and Shea, D. J. (2005). Relationships between precipitation and surface temperature. Geophysical Research Letters, 32(14). https://doi.org/10.1029/2005GL022760
Wehbe, M. B., Seiler, R. A., Vinocur, M. G., and Tarasconi, I. E. (2018). Is It Possible to Completely Adapt Agriculture Production to the Effects of Climate Variability and Change in Central Argentina? New Approaches in Face of New Challenges. In F. Alves et al. (Eds.), Theory and Practice of Climate Adaptation (443-463). Springer, Cham.
Xu, Y., Knudby, A., and Ho, H. C. (2014). Estimating daily maximum air temperature from MODIS in British Columbia, Canada. International Journal of Remote Sensing, 35(24), 8108-8121. https://doi.org/10.1080/01431161.2014.978957
Yang, Y. Z., Cai, W. H., and Yang, J. (2017). Evaluation of MODIS Land Surface Temperature Data to Estimate Near-Surface Air Temperature in Northeast China. Remote Sensing, 9(5), 410. https://doi.org/10.3390/rs9050410
Zeng, L., Wardlow, B. D., Tadesse, T., Shan, J., Hayes, M. J., Li, D., and Xiang, D. (2015). Estimation of Daily Air Temperature Based on MODIS Land Surface Temperature Products over the Corn Belt in the US. Remote Sensing, 7(1), 951-970. https://doi.org/10.3390/rs70100951
Zhao, W., Wu, H., Yin, G., and Duan, S. B. (2019). Normalization of the temporal effect on the MODIS land surface temperature product using random forest regression. ISPRS Journal of Photogrammetry and Remote Sensing, 152, 109-118. https://doi.org/10.1016/j.isprsjprs.2019.04.008