Application of MODIS land surface temperature in snow climatology studies, Case Study: Central Alborz Basins

Document Type : Research Paper

Authors

1 Department of Hydrology and Water Resources, Faculty of Water and Environmental Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran

2 Department of Civil Engineering, K.N. Toosi University of Technology, Tehran, Iran.

Abstract

The snow budget in mountainous river basins reacts sensitively to temperature fluctuations. Therefore, the desired temperature increase due to climate change could significantly affect the snow budget in the future. These effects could lead to significant changes in the hydrological regime of river basins such as the Central Alborz basins. The aim of this study is to investigate the application of MODIS land surface temperature (LST) data in studying snow climatology. The analysis included several important snow parameters derived from the snow depletion curves (SDCs). These curves were extracted from cloud-reduced MODIS products of daily snowpack for each river basin studied. Correlations between these snow parameters and the MODIS LST data were then investigated. The results show that several snow parameters variations are significantly correlated with MODIS LST data over past 20 years (2002-2022). Specifically, Maximum Snow Cover (MSC), Maximum Snow Cover Day (MSCD), Snow Melt Ending Day, and Accumulation-Ablation Period (AAP) exhibited substantial correlations with LST, as indicated by the Tau correlation coefficients of -0.74, -0.31, -0.51, and -0.35, respectively, at a confidence level of 90%.The SMED was found to be the most sensitive snow parameter to MODIS LST variations. Strong correlations were observed between SMED and LST across all studied subbasins, with an overall Pearson correlation coefficient of -0.79 and a Tau correlation coefficient of -0.51 for the whole study area. The results of this study show that MODIS LST data successfully explain the dependencies between snow budget and temperature in the Central Alborz basins.

Keywords

Main Subjects


  1. Otgonbayar M, Atzberger C, Mattiuzzi M, Erdenedalai A (2019) Estimation of climatologies of average monthly air temperature over mongolia using MODIS land surface temperature (LST) time series and machine learning techniques. Remote Sens (Basel) 11:1–24. https://doi.org/10.3390/rs11212588
  2. Rittger K, Painter TH, Dozier J (2013) Advances in Water Resources Assessment of methods for mapping snow cover from MODIS. Adv Water Resour 51:367–380. https://doi.org/10.1016/j.advwatres.2012.03.002
  3. Hachem S, Duguay CR, Allard M (2012) Comparison of MODIS-derived land surface temperatures with ground surface and air temperature measurements in continuous permafrost terrain. Cryosphere 6:51–69. https://doi.org/10.5194/tc-6-51-2012
  4. Zhang H, Zhang F, Zhang G, et al (2018) How Accurately Can the Air Temperature Lapse Rate Over the Tibetan Plateau Be Estimated From MODIS LSTs? Journal of Geophysical Research: Atmospheres 123:3943–3960. https://doi.org/10.1002/2017JD028243
  5. Rodrigues de Almeida C, Garcia N, Campos JC, et al (2023) Time-series analyses of land surface temperature changes with Google Earth Engine in a mountainous region. Heliyon 9:. https://doi.org/10.1016/j.heliyon.2023.e18846
  6. Shamir E, Georgakakos KP (2014) MODIS Land Surface Temperature as an index of surface air temperature for operational snowpack estimation. Remote Sens Environ 152:83–98. https://doi.org/10.1016/j.rse.2014.06.001
  7. Pérez-Díaz CL, Lakhankar T, Romanov P, et al (2017) Evaluation of MODIS land surface temperature with in-situ snow surface temperature from crest-safe. Int J Remote Sens 38:4722–4740. https://doi.org/10.1080/01431161.2017.1331055
  8. Pérez Díaz CL, Lakhankar T, Romanov P, et al (2015) Near–surface air temperature and snow skin temperature comparison from CREST-SAFE station data with MODIS land surface temperature data. Hydrology and Earth System Sciences Discussions 12:7665–7687. https://doi.org/10.5194/hessd-12-7665-2015
  9. Rawat M, Sateesh K, Raushan R, et al (2021) Snow Cover and Land Surface Temperature Assessment of Mana Basin Uttarakhand India Using MODIS Satellite Data. In: Water, Cryosphere and Climate Change in the Himalayas. A Geospatial Approach. p 355
  10. Kemper T (2018) MODIS-based climate monitoring and snow cover modeling in the area of Deadhorse / Prudhoe Bay , Alaska. Humboldt-University of Berlin
  11. Zhou J, Chen Y, Li J, Tang Y (2008) Retrieving snow surface temperature based on MODIS data. Geo-Spatial Information Science 11:247–251. https://doi.org/10.1007/s11806-008-0102-z
  12. André C, Ottlé C, Royer A, Maignan F (2015) Remote Sensing of Environment Land surface temperature retrieval over circumpolar Arctic using SSM / I – SSMIS and MODIS data. Remote Sens Environ 162:1–10. https://doi.org/10.1016/j.rse.2015.01.028
  13. Wan Z (1992) Land Surface Temperature Measurements from EOS MODIS Data
  14. Jia A, Wang D, Liang S, et al (2022) Global Daily Actual and Snow-Free Blue-Sky Land Surface Albedo Climatology From 20-Year MODIS Products. Journal of Geophysical Research: Atmospheres 127:e2021JD035987. https://doi.org/https://doi.org/10.1029/2021JD035987
  15. Dong J, Peters-Lidard C (2010) On the Relationship Between Temperature and MODIS Snow Cover Retrieval Errors in the Western U.S. IEEE J Sel Top Appl Earth Obs Remote Sens 3:132–140. https://doi.org/10.1109/JSTARS.2009.2039698
  16. Choudhury A, Yadav AC, Bonafoni S (2021) A response of snow cover to the climate in the northwest himalaya (Nwh) using satellite products. Remote Sens (Basel) 13:1–22. https://doi.org/10.3390/rs13040655
  17. Zhong L, Ma Y, Su Z, Salama MS (2010) Estimation of land surface temperature over the Tibetan Plateau using AVHRR and MODIS data. Adv Atmos Sci 27:1110–1118. https://doi.org/10.1007/s00376-009-9133-0
  18. Rani S, Mal S (2022) Trends in land surface temperature and its drivers over the High Mountain Asia. Egyptian Journal of Remote Sensing and Space Science 25:717–729. https://doi.org/10.1016/j.ejrs.2022.04.005
  19. Xie A, Zhu J, Qin X, Wang S (2023) The Antarctic Amplification Based on MODIS Land Surface Temperature and ERA5. Remote Sens (Basel) 15
  20. IRIMO (2014) Annual Report of of National Crisis Management and Climatic Hazards
  21. Riggs G, Hall D, Salomonson V (2015) MODIS Snow Products User Guide to Collection 6
  22. Riggs G, Hall D (2011) MODIS Snow Cover Algorithms and Products – Improvements for Collection 6. In: 68th EASTERN SNOW CONFERENCE. pp 163–171
  23. Vermote EF, Roger JC, Ray JP (2015) MODIS Surface Reflectance User’s Guide: Collection 6. 1–40
  24. Dariane AB, Khoramian A, Santi E (2017) Investigating Spatiotemporal Snow Cover Variability via Cloud-free MODIS Snow Cover Product in Central Alborz region. Remote Sens Environ
  25. Pearson K, Galton F (1997) VII. Note on regression and inheritance in the case of two parents. Proceedings of the Royal Society of London 58:240–242. https://doi.org/10.1098/rspl.1895.0041
  26. Kendall MG (1948) Rank correlation methods. Charles Griffin & Company Limite
  27. Montgomery DC, Peck EA, Geoffery VG (2012) Introduction to linear regression analysis, 5th ed. Wiely & Sons