Surface emissivity and temperature retrieval for a hyperspectral sensor

Borel, Christoph C.

doi:10.1109/igarss.1998.702966

Cited by 114 publications

(78 citation statements)

References 3 publications

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“…Unfortunately, because the images in the visible band were not successfully calibrated, only two algorithms (FR97 and GCI) were used. The leaf and soil material emissivity values were measured using an ABB BOMEM MR304 Fourier transform infrared spectrometer and retrieved using the iterative spectrally smooth temperature and emissivity separation (ISSTES) algorithm [41]. The leaf and soil brightness temperatures in the validation were acquired from TIR images that were simultaneously captured using an FLIR S60 thermal camera during the flights.…”

Section: Measured Datasetmentioning

confidence: 99%

A Robust Inversion Algorithm for Surface Leaf and Soil Temperatures Using the Vegetation Clumping Index

Bian

Cao

et al. 2017

Remote Sensing

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Abstract:The inversion of land surface component temperatures is an essential source of information for mapping heat fluxes and the angular normalization of thermal infrared (TIR) observations. Leaf and soil temperatures can be retrieved using multiple-view-angle TIR observations. In a satellite-scale pixel, the clumping effect of vegetation is usually present, but it is not completely considered during the inversion process. Therefore, we introduced a simple inversion procedure that uses gap frequency with a clumping index (GCI) for leaf and soil temperatures over both crop and forest canopies. Simulated datasets corresponding to turbid vegetation, regularly planted crops and randomly distributed forest were generated using a radiosity model and were used to test the proposed inversion algorithm. The results indicated that the GCI algorithm performed well for both crop and forest canopies, with root mean squared errors of less than 1.0 • C against simulated values. The proposed inversion algorithm was also validated using measured datasets over orchard, maize and wheat canopies. Similar results were achieved, demonstrating that using the clumping index can improve inversion results. In all evaluations, we recommend using the GCI algorithm as a foundation for future satellite-based applications due to its straightforward form and robust performance for both crop and forest canopies using the vegetation clumping index.

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Section: Measured Datasetmentioning

confidence: 99%

A Robust Inversion Algorithm for Surface Leaf and Soil Temperatures Using the Vegetation Clumping Index

Bian

Cao

et al. 2017

Remote Sensing

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“…The emissivity spectra obtained from Equation (8) are then used in Equation (4), from which five new temperatures are retrieved. These new temperatures should be equal, but often, they present small differences.…”

Section: Tes Algorithmmentioning

confidence: 99%

Comparison and Evaluation of the TES and ANEM Algorithms for Land Surface Temperature and Emissivity Separation over the Area of Valencia, Spain

et al. 2017

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Land Surface temperature (LST) is a key magnitude for numerous studies, especially for climatology and assessment of energy fluxes between surface and atmosphere. Retrieval of accurate LST requires a good characterization of surface emissivity. Both quantities are coupled in a single radiance measurement; for this reason, for N spectral bands available in a remote sensor, there will always be N + 1 unknowns. To solve the indeterminacy, temperature-emissivity separation methods have been proposed, among which the Temperature Emissivity Separation (TES) algorithm is one of the most widely used. The Adjusted Normalized Emissivity Method (ANEM) was proposed as a modification of the Normalized Emissivity Method (NEM) algorithm by adjusting the initial emissivity guess using an estimation provided by the Vegetation Cover Method (VCM). In this work, both methods were applied to a set of five ASTER scenes over the area of Valencia, Spain, which were recalibrated and atmospherically corrected using local radiosoundings and ground measurements. These scenes were compared to the ASTER temperature and emissivity standard products (AST08 and AST05, respectively). The comparison to reference measurements showed a better agreement of ANEM LST in low spectral contrast surfaces, with biases of +0.4 K, +0.8 K for TES and +1.4 K for the AST08 product in a rice crop site. For sea surface temperature, bias was −0.1 K for ANEM, +0.3 K for TES and +1.3 K for the AST08 product. The larger differences of the AST08 product could be ascribed mainly to the atmospheric correction based on NCEP profiles in contrast to the local correction used in TES and ANEM and to a lesser extent the Maximum-Minimum Difference (MMD) empirical relationship used by TES. In terms of emissivity, ANEM obtained biases up to ±0.007 (positive over vegetation and negative over water), while TES biases were up to −0.015. The AST05 product showed differences up to −0.050, although for high contrast areas, such as sand surfaces, it showed better accuracy than both TES and ANEM. A comparison between TES and ANEM on four different classes within the scene showed a systematic difference between both algorithms, which was more pronounced for low spectral contrast surfaces. Therefore, ANEM improves the accuracy at low spectral contrast surfaces, while obtaining similar results to TES at higher spectral contrast surfaces, such as urban areas. The combination of both methods could provide a procedure benefiting from the strengths shown by each of them.

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“…22 To compare, the fitted index of refraction for each pixel was forward modeled to an emissivity using the Fresnel equations and Kirchhoff's law. The true emissivity, as a function of viewing angle, was obtained by forward modeling the ellipsometry measurements in the same way.…”

Section: Comparison With Temperature-emissivity Separationmentioning

confidence: 99%

Estimating index of refraction for specular reflectors using passive polarimetric hyperspectral radiance measurements

Martin

Gross

2017

Opt. Eng

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Abstract. Results of a method of estimating index of refraction from passive, polarimetric hyperspectral imaging radiance measurements are presented. As off-nadir viewing hyperspectral imaging platforms gain prominence, estimating index of refraction, which is invariant to viewing angle, may prove advantageous to estimating the emissivity, which is not. Results show that index of refraction can be retrieved to within 8% rms error for fused silica and sapphire glass targets, while simultaneously estimating object temperature. The accuracy and self-consistency of this technique for estimating index of refraction are shown to compare favorably to the maximum smoothness temperature-emissivity separation algorithm. Additionally, the results show that atmospheric downwelling radiance can also be accurately estimated, to within the noise of the instrument, concurrently with index of refraction.

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Surface emissivity and temperature retrieval for a hyperspectral sensor

Cited by 114 publications

References 3 publications

A Robust Inversion Algorithm for Surface Leaf and Soil Temperatures Using the Vegetation Clumping Index

A Robust Inversion Algorithm for Surface Leaf and Soil Temperatures Using the Vegetation Clumping Index

Comparison and Evaluation of the TES and ANEM Algorithms for Land Surface Temperature and Emissivity Separation over the Area of Valencia, Spain

Estimating index of refraction for specular reflectors using passive polarimetric hyperspectral radiance measurements

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