A wavelet-artificial intelligence fusion approach (WAIFA) for blending Landsat and MODIS surface temperature

Land surface temperature (LST) is one of the key parameters in the physics of earth surface processes from local to global scales. However, thermal infrared (TIR) images at both high temporal and spatial resolutions are limited because of the technical limitations of current thermal sensors. Therefore, development of fusion models to obtain thermal data in high spatial and temporal resolutions is crucial in environmental studies. This paper presents a hybrid wavelet-artificial intelligence fusion approach (WAIFA)to produce LST data at the spatial resolution of Landsat 8 thermal bands. The theoretical basis and the application procedures of the proposed data fusion approach are explained. A case study was performed to predict LSTs of six dates in 2014 from March to August in East Azerbaijan Province, Iran. This approach uses powerful non-linear artificial intelligence modeling systems which can cope with the non-linear nature of the land surface temperature data. In addition, multi-spectral bands and different spectral indices are used as well as thermal data in the modeling process to consider the mixture properties of MODIS pixels. Using a 2D wavelet transform to capture the properties of the main signals (original bands) in horizontal, vertical, and diagonal directions to consider the effect of neighboring pixels is the other improvement of this modeling approach. It can also help the model to deal with the non-stationary properties of the satellite and land surface temperature data. The results indicated that the prediction accuracy of the model in different dates varies from 0.47K to 1.93K. •A Hybrid wavelet-AI model was developed to downscale MODIS LST data.•AI models were used to cope with the nonlinearities of LST data.•MS bands and indices were used besides thermal data in the modeling process.•Wavelets were used to capture the properties of the signals in different directions.