Abstract
An accurate and spatially continuous estimation of terrestrial latent heat flux (LE) is crucial to the management and planning of water resources for arid and semi-arid areas, for which LE estimations from different satellite sensors unfortunately often contain data gaps and are inconsistent. Many integration approaches have been implemented to overcome these limitations; however, most suffer from either the persistent bias of relying on datasets at only one resolution or the spatiotemporal inconsistency of LE products. In this study, we exhibit an integration case in the midstream of the Heihe River Basin of northwest China by using a multi-resolution Kalman filter (MKF) method to develop continuous and consistent LE maps from satellite LE datasets across different resolutions. The Moderate Resolution Imaging Spectroradiometer (MODIS) LE product (MOD16), the Landsat-based LE product derived from the Landsat 7 Enhanced Thematic Mapper Plus (ETM+) sensor, and ground observations of eddy covariance flux tower from June to September 2012 are used. The integrated results illustrate that data gaps of MOD16 dropped to less than 0.4% from the original 27–52%, and the root-mean-square error (RMSE) between the LE products decreased by 50.7% on average. Our findings indicate that the MKF method has excellent capacity to fill data gaps, reduce uncertainty, and improve the consistency of multiple LE datasets at different resolutions.
Highlights
Terrestrial latent heat flux (LE) describes the heat flux of transpiration and evaporation from the land surface to the atmosphere and plays an indispensable role in understanding the global energy balance and water cycle [1,2]
There are a large amount of remotely sensed LE products available with different resolutions, uncertainties still remain among the individual LE products due to the error of algorithm inputs, the differences of model mechanisms, physical parameterization, and scaling effects [12,13]
We propose that the multi-resolution Kalman filter (MKF) method is scalable to other areas and applicable to serve other integrations of satellite LE products suffering from the issues mentioned in this study for two reasons, namely, high efficiency and comprehensive consideration of multiple datasets’ advantages across different resolutions
Summary
Terrestrial latent heat flux (LE) describes the heat flux of transpiration and evaporation from the land surface to the atmosphere and plays an indispensable role in understanding the global energy balance and water cycle [1,2]. The data gaps stated above limit the comprehensive understanding of regional water budgets over these areas To overcome these limitations, many data fusion methods have been proposed to improve accuracy and spatiotemporal consistencies of LE data by integrating multiple products. The MKF method has been substantially carried out over high-level satellite products, the advantages of this method have not been applied to resolving LE data gaps, which requires further exploration, especially in arid and semi-arid areas. Given the immense data gaps of the MOD16 in arid and semi-arid areas, we integrated two satellite-based LE products (the MOD16 and Landsat-based LE products) using the MKF method, and Remote Sens. Technical details of the data processing procedure can be found in Liu et al (2013, 2018) [33,34]
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