A physically based algorithm for land surface emissivity retrieval from combined mid-infrared and thermal infrared data

Li, Zhao-Liang; Petitcolin, F.; Zhang, Renhua

doi:10.1007/bf02916575

Cited by 57 publications

(37 citation statements)

References 19 publications

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“…Because C i in Equation (21) theoretically depends on the unknown surface temperature, to calculate TISI from the definition in Equation (23), the surface temperature must be estimated by an assumed initial emissivity (Nerry, Petitcolin, and Stoll 1998) using an iterative approach (Dash et al 2005) or substituted by the highest ground brightness temperature among all channels used for a given pixel (Li et al 1999b;Li, Petitcolin, and Zhang 2000). It has been shown that the difference between TISI estimated using the approximate surface temperature and TISIE is small (<1%) (Becker, and Li 1990a;Nerry, Petitcolin, and Stoll 1998).…”

Section: Temperature-independent Spectral Indices (Tisi) Methodsmentioning

confidence: 99%

“…The direct estimation of LSE from passive satellite measurements is impossible due to the combined effects of the land surface temperature (LST) and LSE or the atmospheric contamination (Li, Petitcolin, and Zhang 2000;Jiang, Li, and Nerry 2006). In other words, there are at least two problems *Corresponding author.…”

Section: Introductionmentioning

confidence: 99%

“…The solution of a set of underdetermined equations described by measured radiances is the main difficulty in the retrieval of LST and LSE. The coupling of LST and LSE makes the determination of LSE require the simultaneous determination of LST and vice versa (Li and Becker 1993;Becker and Li 1995;Li, Petitcolin, and Zhang 2000).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Land surface emissivity retrieval from satellite data

Wang

et al. 2012

International Journal of Remote Sensing

379

200

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As an intrinsic property of natural materials, land surface emissivity (LSE) is an important surface parameter and can be derived from the emitted radiance measured from space. Besides radiometric calibration and cloud detection, two main problems need to be resolved to obtain LSE values from space measurements. These problems are often referred to as land surface temperature (LST) and emissivity separation from radiance at ground level and as atmospheric corrections in the literature. To date, many LSE retrieval methods have been proposed with the same goal but different application conditions, advantages, and limitations. The aim of this article is to review these LSE retrieval methods and to provide technical assistance for estimating LSE from space. This article first gives a description of the theoretical basis of LSE measurements and then reviews the published methods. For clarity, we categorize these methods into (1) (semi-)empirical or theoretical methods, (2) multi-channel temperature emissivity separation (TES) methods, and (3) physically based methods (PBMs). This article also discusses the validation methods, which are of importance in verifying the uncertainty and accuracy of retrieved emissivity. Finally, the prospects for further developments are given.

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Section: Temperature-independent Spectral Indices (Tisi) Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Land surface emissivity retrieval from satellite data

Wang

et al. 2012

International Journal of Remote Sensing

379

200

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“…Based on radiative transfer theory, for a cloud-free atmosphere under thermodynamic equilibrium, the channel radiance Bi(Ti) measured on top of the atmosphere (TOA) in a thermal infrared channel of the sensor aboard the satellite is provided with a significant approximation as follows [16,18]:…”

Section: Split-window Algorithm Principlementioning

confidence: 99%

A Practical Split-Window Algorithm for Estimating Land Surface Temperature from Landsat 8 Data

et al. 2015

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This paper developed a practical split-window (SW) algorithm to estimate land surface temperature (LST) from Thermal Infrared Sensor (TIRS) aboard Landsat 8. The coefficients of the SW algorithm were determined based on atmospheric water vapor sub-ranges, which were obtained through a modified split-window covariance-variance ratio method. The channel emissivities were acquired from newly released global land cover products at 30 m and from a fraction of the vegetation cover calculated from visible and near-infrared images aboard Landsat 8. Simulation results showed that the new algorithm can obtain LST with an accuracy of better than 1.0 K. The model consistency to the noise of the brightness temperature, emissivity and water vapor was conducted, which indicated the robustness of the new algorithm in LST retrieval. Furthermore, based on comparisons, the new algorithm performed better than the existing algorithms in retrieving LST from TIRS data. Finally, the SW algorithm was proven to be reliable through application in different regions. To further confirm the credibility of the SW algorithm, the LST will be validated in the future.

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“…Based on radiative transfer theory, for a cloud-free atmosphere in thermodynamic equilibrium, the channel radiance measured at the TOA in the TIR channel is approximately [36]:…”

Section: Radiative Transfer For the Split-window Algorithmmentioning

confidence: 99%

Estimation and Validation of Land Surface Temperatures from Chinese Second-Generation Polar-Orbit FY-3A VIRR Data

Tang

Shao

et al. 2015

Remote Sensing

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This work estimated and validated the land surface temperature (LST) from thermal-infrared Channels 4 (10.8 µm) and 5 (12.0 µm) of the Visible and Infrared Radiometer (VIRR) onboard the second-generation Chinese polar-orbiting FengYun-3A (FY-3A) meteorological satellite. The LST, mean emissivity and atmospheric water vapor content (WVC) were divided into several tractable sub-ranges with little overlap to improve the fitting accuracy. The experimental results showed that the root mean square errors (RMSEs) were proportional to the viewing zenith angles (VZAs) and WVC. The RMSEs were below 1.0 K for VZA sub-ranges less than 30° or for VZA sub-ranges less than 60° and WVC less than 3.5 g/cm 2 , provided that the land surface emissivities were known. A preliminary validation using independently simulated data showed that the estimated LSTs were quite consistent with the actual inputs, with a maximum RMSE OPEN ACCESSRemote Sens. 2015, 7 3251 below 1 K for all VZAs. An inter-comparison using the Moderate Resolution Imaging Spectroradiometer (MODIS)-derived LST product MOD11_L2 showed that the minimum RMSE was 1.68 K for grass, and the maximum RMSE was 3.59 K for barren or sparsely vegetated surfaces. In situ measurements at the Hailar field site in northeastern China from October, 2013, to September, 2014, were used to validate the proposed method. The result showed that the RMSE between the LSTs calculated from the ground measurements and derived from the VIRR data was 1.82 K.

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A physically based algorithm for land surface emissivity retrieval from combined mid-infrared and thermal infrared data

Cited by 57 publications

References 19 publications

Land surface emissivity retrieval from satellite data

Land surface emissivity retrieval from satellite data

A Practical Split-Window Algorithm for Estimating Land Surface Temperature from Landsat 8 Data

Estimation and Validation of Land Surface Temperatures from Chinese Second-Generation Polar-Orbit FY-3A VIRR Data

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