Filling Gaps of Monthly Terra/MODIS Daytime Land Surface Temperature Using Discrete Cosine Transform Method

Abstract

Land surface temperature (LST) is a key parameter in geophysical fields. The Moderate Resolution Imaging Spectroradiometer (MODIS) onboard Terra provides an accurate LST dataset with global coverage and monthly series, but the monthly MODIS LST data are often obscured by clouds and other atmospheric disturbances and consequently exhibit significant data gaps at a global scale, resulting in a difficult interpretation of LST trends and climatological characteristics. In this paper, an effective and fast LST reconstruction method to fill data gaps in monthly MODIS LST is presented. The proposal combines the Discrete Cosine Transform (DCT) and the Penalized Least Square approach (PLS) together with the Generalized Cross-Validation (GCV) criterion. It depends only on the spatial high-frequency information from original LST estimates and allows a fast and automatic filling process without the help of any other ancillary data. To analyze its performance, the method is applied to fill data gaps on three continents with synthetic random missing values introduced as validation sets. The statistical evaluation shows that this method is capable of filling a large number of missing values in MODIS LST datasets with very high accuracy. In addition, the trend differences between the original LST and reconstructed LST have assessed the significance by computing 95% confidence intervals for a time series of trend differences is examined. Simulated experiments show that data gaps with large missing counts lead to significant differences in trend patterns and the patterns on validation sets are well estimated by this method, which confirms that the filling process of MODIS LST is necessary and favorable results can be produced for substantial data gaps by the DCT-PLS method.

Gap-Free LST Generation for MODIS/Terra LST Product Using a Random Forest-Based Reconstruction Method

“…can be obtained by the FY-4A SSI product, and the RF approach can be applied to the entire hemisphere's LSTs with a longitude centered at 104°42′ E, theoretically. Compared with other methods [34,36], this reconstruction method is much more practical and independent, with the auxiliary data obtained from the FY-4A SSI product to describe the change of LST impacted by the cloud-covered conditions. To further analyze the validation effects at different sites, the accuracy of the reconstructed LSTs was evaluated separately with in situ measurements at each site.…”

Variance of vegetation coverage and its sensitivity to climatic factors in the Irtysh River basin

“… Garcia (2010) have developed a fast and robust smooth regression algorithm that combines the Discrete Cosine Transform (DCT) and the Penalized Least Square approach (PLS) together with the Generalized Cross-Validation (GCV) criterion to fill data gaps. Liu et al (2020) tested and applied Garcia’s method on the reconstruction of the MODIS LST datasets covering the three continents of South America, Africa and Asia, and confirmed its capability and robustness. We applied this method in our data processing to get a high quality LST dataset and further guarantee our TVDI dataset can reflect the drought of soil more accurately.…”

Deep Interpolation of Remote Sensing Land Surface Temperature Data with Partial Convolutions

“…Validation is carried out against ground-based LST data on real-world gaps, revealing RMSE values between 2 °C and 3.9 °C. Similarly, Liu et al [ 10 ] use the same discrete cosine transform and penalization of least squares to fill gaps in the MODIS LST data. Synthetic gaps are produced by the random blinking algorithm to create evaluation data.…”