Investigating the capability of the Harmonic Analysis of Time Series (HANTS) algorithm in reconstructing time series images of daytime and nighttime land surface temperature from the MODIS sensor

A continuous, high-resolution surface temperature time series is necessary for hydrology, meteorology, and ecology. However, challenges such as cloud cover, aerosols, and algorithmic disturbances in satellite-based temperature images, particularly from MODIS, result in irregular observations, data loss, noise, and spatial-temporal outliers. The effectiveness of the Harmonic Analysis of Time Series (HANTS) algorithm in reconstructed day and night temperature series from MODIS in desert regions are assessed in this study. Utilizing daily and nightly surface temperature data from 2014 to 2020 (4380 images), data gap analysis revealed peak loss during spring and winter, averaging 6.19% during the day and 8.20% at night over seven years. Because of temperature differences between day and night, the HANTS algorithm was unable to reconstruct the day-night sequence in an accurate way, highlighting the potential of the algorithm in addressing challenges associated with desert environments.