Statistical Relationship between Satellite observed Land Surface Temperature and in-situ measured Surface Air Temperature over the Indian Region: An Exploratory Study

Authors

  • Utkarsh Tyagi ISRO
  • Ujjwal K. Gupta Space Applications Centre

DOI:

https://doi.org/10.58825/jog.2024.18.2.142

Keywords:

Land Surface Temperature (LST), IMD, ARIMA, Weather Stations, LR, Ensembled Machine Learning

Abstract

This paper presents an investigation of spatio-temporal estimation of daily minimum (Tmin) and maximum (Tmax) surface air temperature using satellite (INSAT and MODIS) derived Land surface temperature and in-situ observational data over the Indian region. Least Absolute Shrinkage and Selection Operator (LASSO) regression technique is used to identify influencing neighboring stations. To capture spatial and temporal variability of surface air temperature of a particular IMD station, Linear Regression and Auto-Regressive Integrated Moving Average, which is known as ARIMA, are used respectively. These models are statistically ensembled using stack generalization. Obtained station-by-station relationship is validated on an independent test data using Root Mean Squared Error (RMSE) to check the validity of the model under consideration. Results show ensembled model outperform (has lowest RMSE) the traditional methods for prediction of Tmin and Tmax with RMSE within a range of [1, 2] range for most of the regions and seasons.

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Published

2024-10-30

How to Cite

Tyagi, U., & Gupta, U. K. (2024). Statistical Relationship between Satellite observed Land Surface Temperature and in-situ measured Surface Air Temperature over the Indian Region: An Exploratory Study. Journal of Geomatics, 18(2), 55–63. https://doi.org/10.58825/jog.2024.18.2.142