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.
Highlights
Land surface temperature (LST) plays an increasingly key role in thermal environment research at both local and global scales [1,2]
We use the Discrete Cosine Transform (DCT)-Penalized Least Square approach (PLS) method expressed as Equation (1) to fill data gaps of Moderate Resolution Imaging Spectroradiometer (MODIS) LST
The filling process is performed on whole MODIS LST datasets with a full time series (Figure S2), here we take the results of one month as an example
Summary
Land surface temperature (LST) plays an increasingly key role in thermal environment research at both local and global scales [1,2] It serves as an important boundary condition for energy flux between the atmosphere and ground [3,4], and is widely used in many geophysical fields such as hydrology, applied meteorology, vegetation monitoring, urban climatic effects, environmental monitoring and so on. The data gaps in satellite datasets are resultant from many factors, such as non-overlapping satellite orbits, cloud contamination, instrumental malfunction. It is well-known that there are substantial data gaps in the widely-used Moderate Resolution Imaging Spectroradiometer (MODIS) datasets, especially during daytime when convective clouds are common [5]. A fast and robust method for filling data gaps is highly required to meet the demands of complete and high-quality MODIS LST data
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