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

Abstract

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.

Temperature and Emissivity Smoothing Separation with Nonlinear Relation of Brightness Temperature and Emissivity for Thermal Infrared Sensors

“…These two regions were used to test the performance of different methods for different MMD levels. For the third test, the average emissivity of these two regions were calculated, and a new method named Adjusted Normalized Emissivity Method (ANEM) [18,36] was introduced for comparison.…”

“…For urban areas, the ASTER library spectra of manmade materials were used. It was shown by Pérez et al [36] that the ANEM got better emissivity retrieval accuracy compared with TES.…”

“…The Adjusted Normalized Emissivity Method (ANEM) was proposed as a modification of the Normalized Emissivity Method (NEM) algorithm by adjusting the initial emissivity guess depended on different kinds of areas, such as natural areas, urban areas, and water [18,36]. For natural areas, the Vegetation Cover Method (VCM) was applied for initial emissivity.…”

“…Both retrievals of different methods and expected ASTER library values of sea water emissivity are included. Moreover, the reference water emissivity values mentioned in [36,39] are also included. The emissivity results were averaged for all samples in the selected region for every scene.…”

Evaluation of Six Directional Canopy Emissivity Models in the Thermal Infrared Using Emissivity Measurements

“…The TES method [31] was first devised and extensively applied to ASTER sensor [39][40][41], but because of the similarities between the ASTER and the CE312-2 spectral bands, it can also be applied to the latter using the same empirical equations for both instruments [33,42]. The TES algorithm starts with the normalized emissivity method (NEM) [43] using the at-surface and the downwelling radiance measurements taken by the five ASTER TIR channels.…”