Abstract
Land surface temperature (LST) is an important variable in the physics of land–surface processes controlling the heat and water fluxes over the interface between the Earth’s surface and the atmosphere. Space-borne remote sensing provides the only feasible way for acquiring high-precision LST at temporal and spatial domain over the entire globe. Passive microwave (PMW) satellite observations have the capability to penetrate through clouds and can provide data under both clear and cloud conditions. Nonetheless, compared with thermal infrared data, PMW data suffer from lower spatial resolution and LST retrieval accuracy. Various methods for estimating LST from PMW satellite observations were proposed in the past few decades. This paper provides an extensive overview of these methods. We first present the theoretical basis for retrieving LST from PMW observations and then review the existing LST retrieval methods. These methods are mainly categorized into four types, i.e., empirical methods, semi-empirical methods, physically-based methods, and neural network methods. Advantages, limitations, and assumptions associated with each method are discussed. Prospects for future development to improve the performance of LST retrieval methods from PMW satellite observations are also recommended.
Highlights
Land surface temperature (LST) is an important parameter in the interaction between the Earth’s surface and the atmosphere [1,2,3]
This paper provides an overview of various categories of algorithms that were established to estimate LST from Passive microwave (PMW) satellite observations
The disadvantage of multi-channel method is that it cannot explain how surface conditions function in the LST retrieval procedure and the improvement of accuracy basically relies on repeated trial and error
Summary
Land surface temperature (LST) is an important parameter in the interaction between the Earth’s surface and the atmosphere [1,2,3]. TIR radiation is influenced by atmospheric water vapor, wind speed, cloud, and rain-fall This disadvantage shall be considered since cloud-covered surface occupies 60% of the surface of the Earth [23]. This paper provides an overview of various categories of algorithms that were established to estimate LST from PMW satellite observations.
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