Abstract

The Earth surface thermal infrared (TIR) radiation shows conspicuously an anisotropic behavior just like the bi-directional reflectance of visible and near infrared spectral domains. The importance of thermal radiation directionality (TRD) is being more and more widely recognized in the applications because of the magnitude of the effects generated. The effects of TRD were originally evidenced through experiments in 1962, showing that two sensors simultaneously measuring temperature of the same scene may get significantly different values when the viewing geometry is different. Such effect limits inter-comparison of measurement datasets and land surface temperature (LST) products acquired at different view angles, while raising the question of measurement reliability when used to characterize land surface processes. These early experiments fostered the development of modeling approaches to quantify TRD with the aim of developing a correction for Earth surface TIR radiation. Initiatives for pushing the analysis of TIR data through modeling have been lasted since 1970s. They were initially aimed at mimicking the observed TIR radiance with consideration of canopy structure, component emissivities and temperatures, and Earth surface energy exchange processes. Presently, observing the Earth surface TRD effect is still a challenging task because the TIR status changes rapidly. Firstly, a brief theoretical background and the basic radiative transfer equation are presented. Then, this paper reviews the historical development and current status of observing TRD in the laboratory, in-situ, from airborne and space-borne platforms. Accordingly, the TRD model development, including radiative transfer models, geometric models, hybrid models, 3D models, and parametric models are reviewed for surfaces of water, ice and sea, snow, barren lands, vegetation and urban landscapes, respectively. Next, we introduce three potential applications, including normalizing the LST products, estimating the hemispheric upward longwave radiation using multi-angular TIR observations and separating surface component temperatures. Finally, we give hints and directions for future research work. The last section summarizes the study and stresses three main conclusions.

Highlights

  • Land surface temperature (LST) and sea surface temperature (SST) are two of the 54 Essential Climate Variables (ECVs) of the Global Climate Observation System (World Meteorological Organization, 2016)

  • The r-emissivity relies on reflection mechanism obeying Kirchhoff's law (Jacob et al, 2017). It remains that a precise description of the components' temperature distribution is difficult to be known. Considering this criterion, effective r-emissivity defined by Eq (6) is appropriate for land surface emissivity (LSE) and LST retrievals from space measurements because it can be directly determined by the radiance measured over visible and near infrared (VNIR) spectral domain (e.g. NDVI (Valor and Caselles, 1996))

  • Most of the operational satellite LST and LSE products assume that the land surface radiation is isotropic, which leads to large uncertainties in the validation and/or inter-comparison

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Summary

Introduction

Land surface temperature (LST) and sea surface temperature (SST) are two of the 54 Essential Climate Variables (ECVs) of the Global Climate Observation System (World Meteorological Organization, 2016). For inland water bodies (3.56% of land surface, according to FROMGLC dataset (Gong et al, 2013)), barren lands (16.51% of land surface) (Garcia-Santos et al, 2012; Sobrino and Cuenca, 1999), and snow and ice (12.81% of land surface) (Cheng et al, 2010b), most of the models are trying to simulate the emissivity directionality, as this is the main source of directional effects on LST over relatively homogeneous surfaces The structure of such surfaces is simpler when compared with those of vegetation and urban surfaces.

Basic theoretical background
Basic radiative transfer equation
Basic definition of LSE
Basic definition of LST
Ways to describe TRD
TRD measurement and observation
Laboratory and in-situ measurements of homogeneous samples
In-situ measurements
Airborne observations
Space borne observations
TRD modeling over Earth surface
A Monte Carlo ray-tracing model considering polarization
TRD modeling over snow surface
TRD modeling over barren lands
TRD modeling over vegetation
TRD modeling over urban scenes
Potential applications of TRD models
LST products angular normalization
SULR estimation considering the TRD effect
Generating global LSCT product
TRD modeling over complex Earth surface
Two ways for developing dynamic TRD model
Developing TIR kernel driven models aiming at LST product harmonization
Designing simultaneous multi-angle multi-band satellite sensors
Establishing multi-angle datasets from ground and UAV observations
Findings
Summary

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