Local Air and Soil Temperature Modeling Using Himawari 8 Satellite Imagery

Adhia Azhar Fauzan, Komariah Komariah, Sumani Sumani, Dwi Priyo Ariyanto, Tuban Wiyoso

Abstract

Himawari 8 satellite image, which was launched in October 2014 and began the operational in July 2015, serves to identify and track the phenomenon of rapid changes in weather. The purpose of this research was to determine the model of local air and soil temperatures using Himawari 8 satellite image. Local air and soil temperatures information was collected from the Climatology Station of Semarang district, Central Java, Indonesia. Interpretation of the Himawari 8 satellite image was performed, as well as the statistical tests of correlation and regression, according to the sun's pseudo motion. Pair correlation and regression analysis on satellite image with air temperature; and air temperature with soil temperature (bare and grass). The results showed the satellite imagery of Himawari 8 could predict the air and soil temperatures, especially bare soil. In specific, the accuracies were higher on soil temperature at 0 (surface) and 5 cm depth. But each period produced vary accuracy, due to many weather elements had may affect the air and soil temperatures.

Keywords

Bare soil; Grass soil; Climatology Station of Semarang District

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References

Bessho, K., Date, K., Hayashi, M., Ikeda, A., Imai, T., Inoue, H., … Yoshida, R. (2016). An Introduction to Himawari-8/9 – Japan's New-Generation Geostationary Meteorological Satellites. Journal of the Meteorological Society of Japan. Ser. II, 94(2), 151–183. https://doi.org/10.2151/jmsj.2016-009

Fan, J., McConkey, B., Wang, H., & Janzen, H. (2016). Root distribution by depth for temperate agricultural crops. Field Crops Research, 189, 68–74. https://doi.org/10.1016/J.FCR.2016.02.013

Flores P, F., & Lillo S, M. (2010). Simple Air Temperature Estimation Method from MODIS Satellite Images on a Regional Scale. Chilean Journal of Agricultural Research, 70(3), 436–445. https://doi.org/10.4067/S0718-58392010000300011

Forsythe, N., Hardy, A. J., Fowler, H. J., Blenkinsop, S., Kilsby, C. G., Archer, D. R., … Hashmi, M. Z. (2015). A Detailed Cloud Fraction Climatology of the Upper Indus Basin and Its Implications for Near-Surface Air Temperature. Journal of Climate, 28(9), 3537–3556. https://doi.org/10.1175/JCLI-D-14-00505.1

Geiger, R. (1959). The Climate Near The Ground. Cambridge: Havard University Press.

Jungqvist, G., Oni, S. K., Teutschbein, C., & Futter, M. N. (2014). Effect of Climate Change on Soil Temperature in Swedish Boreal Forests. PLoS ONE, 9(4), e93957. https://doi.org/10.1371/journal.pone.0093957

Kätterer, T., & Andrén, O. (2009). Predicting daily soil temperature profiles in arable soils in cold temperate regions from air temperature and leaf area index. Acta Agriculturae Scandinavica, Section B - Plant Soil Science, 59(1), 77–86. https://doi.org/10.1080/09064710801920321

Liang, L. L., Riveros-Iregui, D. A., Emanuel, R. E., & McGlynn, B. L. (2014). A simple framework to estimate distributed soil temperature from discrete air temperature measurements in data-scarce regions. Journal of Geophysical Research: Atmospheres, 119(2), 407–417. https://doi.org/10.1002/2013JD020597

Menzel, W. P., Tobin, D. C., & Revercomb, H. E. (2016). Infrared Remote Sensing with Meteorological Satellites. Advances In Atomic, Molecular, and Optical Physics, 65, 193–264. https://doi.org/10.1016/BS.AAMOP.2016.04.001

Özkan, U., & Gökbulak, F. (2017). Effect of vegetation change from forest to herbaceous vegetation cover on soil moisture and temperature regimes and soil water chemistry. CATENA, 149, 158–166. https://doi.org/10.1016/J.CATENA.2016.09.017

Patkó, I., Szeder, A., & Patkó, C. (2013). Evaluation the Impact Tilt Angle on the Sun Collectors. Energy Procedia, 32, 222–231. https://doi.org/10.1016/J.EGYPRO.2013.05.029

Rahaman, K. R., & Hassan, Q. K. (2017). Quantification of Local Warming Trend: A Remote Sensing-Based Approach. PLOS ONE, 12(1), e0169423. https://doi.org/10.1371/journal.pone.0169423

Sadeghi, M., Babaeian, E., Tuller, M., & Jones, S. B. (2017). The optical trapezoid model: A novel approach to remote sensing of soil moisture applied to Sentinel-2 and Landsat-8 observations. Remote Sensing of Environment, 198, 52–68. https://doi.org/10.1016/J.RSE.2017.05.041

Song, Y., Zhou, D., Zhang, H., Li, G., Jin, Y., & Li, Q. (2013). Effects of vegetation height and density on soil temperature variations. Chinese Science Bulletin, 58(8), 907–912. https://doi.org/10.1007/s11434-012-5596-y

Yener, D., Ozgener, O., & Ozgener, L. (2017). Prediction of soil temperatures for shallow geothermal applications in Turkey. Renewable and Sustainable Energy Reviews, 70, 71–77. https://doi.org/10.1016/J.RSER.2016.11.065

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