Estimation of sugarcane field temperature using Split Window Algorithm and OLI LandSat 8 satellite images

Subject Areas : Geospatial systems development

Shadman Veysi ¹ ( PhD. Student of Irrigation & Drainage, Shahid Chamran University of Ahvaz )
Abd Ali Naseri ² ( Prof. College of Water Sciences Engineering, Shahid Chamran University of Ahvaz )
Saeid Hamzeh ³ ( Assis. Prof. College of Geography, University of Tehran )
Poria Moradi ⁴ ( MSc. of Remote Sensing & Geographic Information System, Shahid Chamran University of Ahvaz )

Received: 2015-04-25 Accepted : 2015-10-01 Published : 2016-03-20

Keywords: LANDSAT 8, Sugarcane fields temperature, Infrared thermometer, Split window algorithm, Salman Farsi agro industry unit,

Abstract :

Land Surface Temperature (LST) is one of important parameters that is measured using Remote-sensing tools and thermal bands of satellites. The importance of this issue is revealed when direct effects of temperature are shown on the increase and decrease of evaporation, evapotranspiration and as a result, the moisture content changes in the plant. In this study, the temperature of sugarcane canopy cover was measured by LandSat 8 satellite data in 8 sugarcane fields out of Salman Farsi Sugacane Industry involving 5 points from each field (totally 40 points); these points were irrigated in different days and measured by the infrared thermometer. The points were selected at the edges of fields with the intervals of 30 m in order to avoid the combination of them with the pixels with no vegetation. To calibrate the Split Window (SW) algorithm, the input data of water evaporation, emissivity and transmittance as well as LandSat 8 satellite images were applied. Results have shown that the estimation of vegetation temperature of sugarcane fields in different days of irrigation was of an acceptable accuracy. Also, in the points with the same vegetation, irrigation is the main factor for the changes of temperature. In this research, Residual Mean Error Square (RMSe), and Mean Average Error for the measured field temperature and extracted one by the satellite images were given as 0.925 and 0.766 °C, respectively.

References:

1. اکبری، ا. و ح. کوهبنانی. 1389. استفاده از  الگوریتم DTC و SEBAL به منظور برآورد دمای سطح از باند حرارتی ETM⁺. همایش ملی ژئوماتیک، تهران، سازمان نقشه­برداری، 19 و 20 اردیبهشت.

2. بهرامی، ش.، ا. اکبری و ع. دوران. 1392. بررسی تاثیر عوامل جغرافیایی بر حرارت سطحی زمین با استفاده از تصاویر ماهواره­ای در مخروط آتشفشان تفتان. سنجش از دور و سامانه اطلاعات جغرافیایی در منابع طبیعی، 4(4): 11-24.

3. مرکز تحقیقات نیشکر. 1389. گزارش نتایج مطالعات فاز یک کشت و صنعت سلمان فارسی، آمار سالانه. 135 صفحه.

 

4. Asner GP, Heidebrecht KB. 2002. Spectral unmixing of vegetation, soil and dry carbon cover in arid regions: comparing multispectral and hyperspectral observations. International Journal of Remote Sensing, 23(19): 3939-3958.

5. Bastiaanssen W, Menenti M, Feddes R, Holtslag A. 1998. A remote sensing surface energy balance algorithm for land (SEBAL). 1. Formulation. Journal of Hydrology, 212: 198-212.

6. Bolgrien DW, Granin NG, Levin L. 1995. Surface temperature dynamics of Lake Baikal observed from AVHRR images. Photogrammetric Engineering and Remote Sensing, 61(2): 211-216.

7. Carlson TN, Ripley DA. 1997. On the relation between NDVI, fractional vegetation cover, and leaf area index. Remote Sensing of Environment, 62(3): 241-252.

8. Friedel MJ. 2012. Data-driven modeling of surface temperature anomaly and solar activity trends. Environmental Modelling & Software, 37: 217-232.

9. Gao L, Qin Z. 2007. Research on the fitting relation of the planck equation expansion parameter model in split window algorithm. Geography and Geo-Information Science, 23(4): 9-12.

10. Herb WR, Janke B, Mohseni O, Stefan HG. 2008. Ground surface temperature simulation for different land covers. Journal of Hydrology, 356(3): 327-343.

11. Jiménez-Muñoz JC, Sobrino JA. 2010. A single-channel algorithm for land-surface temperature retrieval from ASTER data. Geoscience and Remote Sensing Letters, IEEE, 7(1): 176-179.

12. Li Z-L, Tang B-H, Wu H, Ren H, Yan G, Wan Z, Trigo IF, Sobrino JA. 2013. Satellite-derived land surface temperature: Current status and perspectives. Remote Sensing of Environment, 131: 14-37.

13. Liang S. 2004. Quantitative Remote Sensing of Land Surfaces, Wiley-interscience, John Wiley & Sons In. ISBN 0-471-28166-2, Ch. 2, 10. 560 pp.

14. Lillesand T, Kiefer RW, Chipman J. 2014. Remote sensing and image interpretation. 6^th Edition. John Wiley & Sons,. 804 pp.

15. Lin L, Chen J, Cai C. 2012. High rate of nitrogen fertilization increases the crop water stress index of corn under soil drought. Communications in Soil Science and Plant Analysis, 43(22): 2865-2877.

16. Maimaitiyiming M, Ghulam A, Tiyip T, Pla F, Latorre-Carmona P, Halik Ü, Sawut M, Caetano M. 2014. Effects of green space spatial pattern on land surface temperature: Implications for sustainable urban planning and climate change adaptation. ISPRS Journal of Photogrammetry and Remote Sensing, 89: 59-66.

17. Miller W, Millis E. 1989. Estimating evaporation from Utah's Great Salt Lake using thermal infrared satellite imagery. Water Resources Bulletin, 25: 541-550.

18. Peng S-S, Piao S, Zeng Z, Ciais P, Zhou L, Li LZ, Myneni RB, Yin Y, Zeng H. 2014. Afforestation in China cools local land surface temperature. Proceedings of the National Academy of Sciences, 111(8): 2915-2919.

19. Petitcolin F, Vermote E. 2002. Land surface reflectance, emissivity and temperature from MODIS middle and thermal infrared data. Remote Sensing of Environment, 83(1): 112-134.

20. Pôças I, Cunha M, Pereira LS, Allen RG. 2013. Using remote sensing energy balance and evapotranspiration to characterize montane landscape vegetation with focus on grass and pasture lands. International Journal of Applied Earth Observation and Geoinformation, 21: 159-172.

21. Qin Z, Zhang M, Arnon K. 2001. Split window algorithms for retrieving land surface temperature from NOAA-AVHRR data. Remote Sensing For Land & Resources, 56(2): 33-42.

22. Sobrino JA, Jiménez-Muñoz JC, Paolini L. 2004. Land surface temperature retrieval from LANDSAT TM 5. Remote Sensing of environment, 90(4): 434-440.

23. Son N, Chen C, Chen C, Chang L, Minh V. 2012. Monitoring agricultural drought in the Lower Mekong Basin using MODIS NDVI and land surface temperature data. International Journal of Applied Earth Observation and Geoinformation, 18: 417-427 7.

24. Vinnikov KY, Yu Y, Goldberg MD, Chen M, Tarpley D. 2011. Scales of temporal and spatial variability of midlatitude land surface temperature. Journal of Geophysical Research: Atmospheres, 116(D2): 2156–2202.

25. Wan Z. 2008. New refinements and validation of the MODIS land-surface temperature/emissivity products. Remote Sensing of Environment, 112(1): 59-74.

26. Yu X, Guo X, Wu Z. 2014. Land surface temperature retrieval from Landsat 8 TIRS—Comparison between radiative transfer equation-based method, split window algorithm and single channel method. Remote Sensing, 6(10): 9829-9852

27. Zhou C, Zhang S, Wang L, Miao F. 2005. Effect of fertilization on the canopy temperature of winter wheat and its relationship with biological characteristics. Acta Ecologica Sinica, 25(1): 18-221.

28. Zhou J, Zhan W, Hu D, Zhao X. 2010. Improvement of mono-window algorithm for retrieving land surface temperature from HJ-1B satellite data. Chinese Geographical Science, 20(2): 123-131.