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Theoretical and Applied Climatology

, Volume 130, Issue 3–4, pp 1149–1161 | Cite as

Estimation of daily minimum land surface air temperature using MODIS data in southern Iran

  • Shohreh Didari
  • Hamidreza Norouzi
  • Shahrokh Zand-ParsaEmail author
  • Reza Khanbilvardi
Original Paper

Abstract

Land surface air temperature (LSAT) is a key variable in agricultural, climatological, hydrological, and environmental studies. Many of their processes are affected by LSAT at about 5 cm from the ground surface (LSAT5cm). Most of the previous studies tried to find statistical models to estimate LSAT at 2 m height (LSAT2m) which is considered as a standardized height, and there is not enough study for LSAT5cm estimation models. Accurate measurements of LSAT5cm are generally acquired from meteorological stations, which are sparse in remote areas. Nonetheless, remote sensing data by providing rather extensive spatial coverage can complement the spatiotemporal shortcomings of meteorological stations. The main objective of this study was to find a statistical model from the previous day to accurately estimate spatial daily minimum LSAT5cm, which is very important in agricultural frost, in Fars province in southern Iran. Land surface temperature (LST) data were obtained using the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard Aqua and Terra satellites at daytime and nighttime periods with normalized difference vegetation index (NDVI) data. These data along with geometric temperature and elevation information were used in a stepwise linear model to estimate minimum LSAT5cm during 2003–2011. The results revealed that utilization of MODIS Aqua nighttime data of previous day provides the most applicable and accurate model. According to the validation results, the accuracy of the proposed model was suitable during 2012 (root mean square difference (RMSD) = 3.07 °C, \( {R}_{adj}^2 \) = 87 %). The model underestimated (overestimated) high (low) minimum LSAT5cm. The accuracy of estimation in the winter time was found to be lower than the other seasons (RMSD = 3.55 °C), and in summer and winter, the errors were larger than in the remaining seasons.

Keywords

Normalize Difference Vegetation Index Root Mean Square Difference Land Surface Temperature Mean Absolute Difference Normalize Difference Vegetation Index Data 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

Authors would like to acknowledge “The online Data Pool at the NASA Land Processes Distributed Active Archive Center (LP DAAC)” and also the Meteorological Office (IRIMO) Data Center of Islamic Republic of Iran for preparing the data and NOAA-CREST for their support.

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Copyright information

© Springer-Verlag Wien 2016

Authors and Affiliations

  • Shohreh Didari
    • 1
    • 3
  • Hamidreza Norouzi
    • 2
  • Shahrokh Zand-Parsa
    • 1
    Email author
  • Reza Khanbilvardi
    • 3
  1. 1.Water Engineering Department, Agricultural CollegeShiraz UniversityShirazIran
  2. 2.New York City College of TechnologyNOAA – Cooperative Remote Sensing and Technology (CREST) CenterNew YorkUSA
  3. 3.The City College of the City University of New York, NOAA-CRESTNew YorkUSA

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