Evaluation of estimator variables in air temperature estimation in January and June based on land cover

Document Type : Research


1 Ph.D. Student, Department of Physical Geography, Faculty of Humanities, Tarbiat Modares University, Tehran, Iran

2 Professor, Department of Physical Geography, Faculty of Humanities, Tarbiat Modares University, Tehran, Iran

3 Associate Professor, Department of Physical Geography, Faculty of Humanities, Tarbiat Modares University, Tehran, Iran

4 Professor, Department of Space Physics, Institute of Geophysics, University of Tehran, Tehran, Iran


The near-surface temperature, Ts measured by ground stations provides limited information on the spatial distribution of Ta pattern. A correct estimation of Ta distribution pattern is necessary for a wide range of applications such as hydrology, ecology, meteorology (Wenbin et al., 2013) and biology of vector-borne diseases. In this study, near-surface air temperatures (Ta) using environmental parameters including land surface temperature (LST), altitude, slope, vegetation, latitude, albedo, and mean sea level pressure (MSLP), were estimated for January and July in the period 2001-2015 for Iran. In this study, due to the use of different data sources with different spatial resolutions, all maps were converted to the same spatial resolution of Era-Interim (0.125˚). Then spatial distributions of Ta and LST were determined. The spatial distribution patterns of these two components were also determined by applying the Moran spatial autocorrelation index. Finally, according to the land cover, multivariate regression models are presented for estimating Ta based on seven parameters, including LST, altitude, slope, vegetation and latitude, albedo and MSLP. In the following, the characteristics of each of these data are also described. Standardized regression coefficients were used to determine the most important estimator in each land cover. The correlation between the parameters involved in the study with the absolute difference between the air and surface temperature are negative in January, which means that by increasing, slope, altitude, NDVI, latitude, albedo and MSLP, the difference is reduced and vice versa. Nonetheless, this kind of relationship is not valid in the whole study area, and there are some exceptions. In July the relationship between this difference and slope and NDVI is positive, which means that with increasing altitude, latitude, albedo and MSLP, the differences also increase. In January, waters (99%), urban areas (95%), and barren or sparsely vegetated (92%) have the highest R2. While, mixed forests had the lowest R2 equal to 27% (Figure 4). The least errors are related to urban areas (0.69 ° C), water (0.75 ° C), and then forest areas (0.9 ° C). The highest errors were observed in cropland and open shrubland equal to 1.35˚C and 1.34˚C. The highest R2 was calculated for water (95%), urban areas (94%), mixed forest and open shrubland (93%). The least error occurred in mixed forest (0.3˚C). The main objective of the present study was to develop a model of air temperature estimation from surface temperature and other auxiliary variables (elevation, slope, vegetation, latitude, land cover, albedo and mean sea level pressure). Regression models were presented for estimating Ta in monthly scale. The results can be summarized as: Between the air and surface temperature, the most variability is related to the Ta which in the region of Iran has an annual variation coefficient of 92% in January and 41% in July. In January, slope and altitude are the most important variables in the estimation model so that up to 16% and 12% can explain LST-Ta differences, respectively, while latitude and MSLP are the most important variables in July so that each one of them explains up to 9.6% of these differences in July. The role of land cover in estimating Ta is very important. In addition, the number of pixels located on each land cover category can also play a decisive role in estimation model. Category of water, urban and barren area in January, exhibited the highest R2 of 99%, 95% and 92%, respectively. The lowest R2 (approximately 27%) is related to grassland and mixed forest. In July, the highest R2 is related to water and urban areas about 95 and 94%. R2 of grassland increases by approaching summer. The lowest error is recorded for urban area, water and mixed forest in January while the lowest error is related to mixed forest, open shrubland and barren areas in July. The accuracy of estimation models varies according to the months and the land cover. Based on standardized regression coefficients, in January altitude (in barren, urban and cropland area) mean sea level pressure (in grassland and shrubland), slope of mixed forest area and latitude in water area were of great importance in air temperature estimation. While, in June, due to presence of low pressure unter in Iran, the role of local climatic factors has been minimized and mean sea level pressures was the most important estimator almost in all landcovers.


Main Subjects

بابایی فینی، ا.، 1394، بررسی رابطه دمای سطح زمین و شاخص بهنجار‌شده پوشش گیاهی در محیط شهری (مطالعه موردی: کلان‌شهراصفهان)، م. علمی-پژوهشی (دانشگاه آزاد) 90، 75-29.
واعظ موسوی، س.ع. و مختارزاده، م.، 1394، تخمین دمای هوای سطح زمین با استفاده از داده LST سنجنده MODIS، بیست و دومین همایش ملی ژئوماتیک.
پرویز، ل.، خلقی، م. و ولیزاده، خ.، 1389، تخمین دمای هوا با استفاده از روش شاخص پوشش گیاهی-دما (TVX)، مجله علوم و فنون کشاورزی و منابع طبیعی، علوم آب و خاک، سال پانزدهم، شماره پنجاه و ششم، 33-21.
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 Sens. Environ., 124,108–121.
Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P., Kobayashi, S., Andae, U., Balmaseda, M. A., Balsamo, G., Bauer, P., Bechtold, P., Beljaars, A. C. M., Van de Berg, L., Bidlot, J., Bormann, N., Delsol, C., Dragani, R., Fuentes, M., Geer, A. J., Haimberger, L., Healy, S. B., Hersbach, H., Holm, E.V., Isaksen, L., Kallberg, P., Kohler, M., Matricardi, M., McNally, A. P., Monge-Sanz, B. M., Morcrette, J.-J., Park, B.-K., Peubey, C., Rosnay, P.de, Tavolato, C., Thepaut, J.-N. and Vitrat, F., 2011, The ERA-Interim reanalysis: configuration and performance of the data assimilation system. Q. J. R. Meteorol. Soc, 137, 553–597, DOI:10.1002/qj.828.
Friedl, M. A., Sulla-Menashe, D., Tan, B., Schneider, A., Ramankutty, N., Sibley, A. and Huang, X., 2010, MODIS Collection 5 global land cover: Algorithm refinements and characterization of new datasets. Remote Sens. Environ., 114, 168–182.
Ghasemi, A., 2015, Changes and trends in maximum, minimum and mean temperature series in Iran. Atmos. Sci. Lett., 16, 366-372.
Huete, A., Justice, C. and Leeuwen, W. V., 1999, Modis vegetation index (mod 13), algorithm theoretical basis document, version3. 2University of Virginia Department of Environmental Sciences Clark Hall, Charlottesville, VA 22903.
Janatian, N., Sadeghi, M., Sanaeinejad, S. H., Bakhshian, E., Farid, A., Hasheminia, S. M. and Ghazanfari, S., 2016, A statistical framework for estimating air temperature using MODIS land surface temperature data. Int. j. climatol., 37, 1181-1194.
Kloog, I., Nordio, F., Coull, B. A. and Schwartz, J., 2014, Predicting spatiotemporal mean air temperature using MODIS satellite surface temperature measurements across the Northeastern USA. Remote Sens. Environ., 150, 132-139.
Mooney, P. M., Mulligan, F. J. and Fealy, R., 2011, Comparison of ERA-40, ERA-Interim and NCEP/NCAR reanalysis data with observed surface air temperatures over Ireland. Int. j. climatol., 31, 545–557.
Moradi, M., Salahi, B. and Masoodian, S. A., 2016, Land surface temperature zoning of Iran with MODIS data. Journal of natural environment hazards, 5, 101-116.
Mutiibwa, D., Strachan, S. and Albright, T., 2015, Land Surface Temperature and Surface Air Temperature in Complex Terrain. Journal of selected topics in applied earth observation and remote sensing, 8, 4762 – 4774, doi: 10.1109/JSTARS.2015.2468594.
Phan, T. N., Kappas, M. and Degener, J., 2017, Different combination of MODIS land surface temperature data for daily air surface temperature estimation in North West Vietnam. Geophysical Research Abstracts, 19, 5213-1.
Revadekar, J. V., Hameed, S., Collins, D., Manton, M., Shikh, M., Bogaonkar, H. P., Kothawale, D. R., Adnan, M., Ahmed, A. U., Ashraf, J., Baidya, S., Islam, N., Jayasinghearachchi, D., Manzoor, N., Premalal, K.H.M.S. and Shreshta, M. L., 2013, Impact of altitude and latitude on changes in temperature extremes over South Asia during 1971–2000. Int. j. climatol., 33, 199–209.
Shah, D. B., Pandya, M. R., Trivedi, H. J. and Jani, A. R., 2013, Estimating minimum and maximum air temperature using MODIS data over Indo-Gangetic Plain. J. Earth Syst. Sci., 122, 1593–1605.
Shen, S. and Leptoukh, G. G., 2011, Estimation of surface air temperature over central and eastern Eurasia from MODIS land surface temperature. Environ. Res. Lett., 6, 045206.
Shi, Y., Jiang, Z., Dong, L. and Shen, S., 2017, Statistical Estimation of High-Resolution Surface Air Temperature from MODIS over the Yangtze River Delta, China. Journal of meteorological research, 31, 448.454.
Sun, H., Chen, Y., Gong, A., Zhao, X., Zhan, W. and Wang, M., 2014, Estimating mean air temperature using MODIS day and night land surface temperatures. Theor. Appl. Climatol., 118, 81–92.
Vancutsem, C., Ceccato, P., Dinku, T. and Connor, S. J., 2010, Evaluation of MODIS land surface temperature data to estimate air temperature in different ecosystems over Africa. Remote Sens. Environ., 114, 449-465.
Wan, Z., 1999, MODIS Land-Surface Temperature Algorithm Theoretical Basis Document (LST ATBD),v.3.3
Wan, Z. and Dozier, J., 1989, Land-surface temperature measurement from space: physical principles and inverse modeling. IEEE Trans. Geosci. Remote Sens., 27, 3, 268-278.
Wenbin, Z., Aifeng, L. and Shaofeng, J., 2013, Estimation of daily maximum and minimum air temperature using MODIS land surface temperature products. Remote Sens. Environ., 130, 62-73.
Xu, Y., Knudby, A. and Chak, H. H., 2014, Estimating daily maximum air temperature from MODIS in British Columbia, Canada. Int. J. Remote Sens., 35, 8108-8121.