Evaluation of Land Surface Temperature and Snow Cover Ratio by Using Remote Sensing Data (Case Study: Kasilian Watershed)

Document Type : Research Article

Authors

1 Ph.D. Candidate of Watershed Management, Faculty of Natural Resources and Earth Science, Shahrekord University, Shahrekord

2 Assistance Professor, Faculty of Natural Resources and Earth Science, Shahrekord University, Shahrekord

3 Associated Professor, Faculty of Natural Resources and Earth Science, Shahrekord University, Shahrekord

Abstract

Land Surface Temperature (LST) affects snowpack spatiotemporal changes as one of the crucial components of water balance. Despite the ease of access to Remote Sensing (RS) data, such as Moderate Resolution Imaging Spectroradiometer (MODIS), products that are used for monitoring and evaluating LST effect on snow cover area SCA are sometimes not available for some reason. In order to overcome such a limitation, the monthly average values of SCA and LST in each 8 days for 13 years (2003-2016) were evaluated by regression relations. The results showed, minimum and maximum values of annual mean of snow covered area percent that occurred in 20009 and 2011 as being equal to 5.86%, 20.32, respectively. In addition, the annual mean of minimum and maximum values of LST related to 2004 and 2010 were 17.65 and 21.1 0C, respectively. The pattern of two variables changes illustrated that the SCA changes to LST are reverse and gradually increasing during the study period. Also, the results revealed that in power regression, the Nash-Sutcliff Efficiency (NSE) coefficient for SCA percent simulation, ,  RMSE and Bias, are 0.6, 0.64, 9.88 and -2.14, respectively. These coefficients are 0.16%, 47%, 14.37% and 86.32% in linear regression method, respectively. Thus, this study may be helpful to estimate SCA and reconstruct missing data in satellite images.

Keywords

References

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Iranian journal of Ecohydrology
Volume 8, Issue 1
April 2021
Pages 1-16

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History

  • Receive Date: 04 August 2020
  • Revise Date: 02 December 2020
  • Accept Date: 02 December 2020
  • First Publish Date: 08 March 2021
  • Publish Date: 21 March 2021

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