Residual errors in ASTER temperature and emissivity standard products AST08 and AST05

Gillespie, Alan R.; Abbott, Elsa A.; Gilson, Laura; Hulley, Glynn; Jiménez-Muñoz, Juan C.; Sobrino, José A.

doi:10.1016/j.rse.2011.09.007

Cited by 76 publications

(43 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As an example, Pérez-Planells et al in [45] showed that using interpolated NCEP profiles instead of radiosoundings yielded errors of ±0.6 K for sites close to sea level, which would be equivalent to ±0.01 in terms of emissivity. Moreover, previous studies pointed out that ASTER product atmospheric correction loses accuracy in warm and humid areas close to sea level [23,24]. Despite using an MMD relationship including additional samples for vegetated surfaces and a local recalibration and atmospheric correction, the results of emissivity for these surfaces are systematically lower for TES than for ANEM (see Figures 3 and 4), which confirms the existence of an impact of residual atmospheric correction errors that may be affecting TES emissivities more than ANEM ones.…”

Section: Lse and Lst Evaluationsupporting

confidence: 52%

“…AST05 and AST08 products are obtained by the TES algorithm, with a spatial resolution of 90 m. The nominal accuracy of these products are ±1.5 K for AST 08 and ±0.015 for AST05 [28]. These products use the radiative transfer model MODTRAN 3.5 with required atmospheric parameters obtained from the National Center of Environmental Prediction and the National Center of Atmosphere Research (NCEP and NCAR) and satellite data [23,29].…”

Section: Aster Sensormentioning

confidence: 99%

See 1 more Smart Citation

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

View full text Add to dashboard Cite

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.

show abstract

Section: Lse and Lst Evaluationsupporting

confidence: 52%

Section: Aster Sensormentioning

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

View full text Add to dashboard Cite

show abstract

“…The primary objective of the ASTER temperature and emissivity separation (TES) algorithm is to provide high accuracy narrowband emissivity for large spectral contrast surface types such as soils and rocks [40,41]. Some validation work indicated the ASTER narrowband emissivity can achieve high accuracy over arid and semi-arid areas [36,[42][43][44].…”

Section: Compare To Naalsed Bbementioning

confidence: 99%

“…Therefore, the accuracy of emissivity retrieval for soils and rocks is guaranteed. Regarding surface types with small spectral contrast such as water bodies and vegetated areas, the accuracy of emissivity inversion cannot meet the design goal as reported by several authors [41,45]. The TES algorithm has been modified several times to accommodate low emissivity spectral contrast and error in the measured data and the accuracy has been improved over the first version [46].…”

Section: Compare To Naalsed Bbementioning

confidence: 99%

A Comparative Study of Three Land Surface Broadband Emissivity Datasets from Satellite Data

et al. 2013

View full text Add to dashboard Cite

Abstract:This study compared three broadband emissivity (BBE) datasets from satellite observations. The first is a new global land surface BBE dataset known as the Global Land Surface Satellite (GLASS) BBE. The other two are the North American ASTER Land Surface Emissivity Database (NAALSED) BBE and University of Wisconsin Global Infrared Land Surface Emissivity Database (UWIREMIS) BBE, which were derived from two independent narrowband emissivity products. Firstly, NAALSED BBE was taken as the reference to evaluate the GLASS BBE and UWIREMIS BBE. The GLASS BBE was more close to NAALSED BBE with a bias and root mean square error (RMSE) of −0.001 and 0.007 for the summer season, −0.001 and 0.008 for the winter season, respectively. Then, the spatial distribution and seasonal pattern of global GLASS BBE and UWIREMIS BBE for six dominant land cover types were compared. The BBE difference between vegetated areas and non-vegetated areas can be easily seen from two BBEs. The seasonal variation of GLASS BBE was more reasonable than that of UWIREMIS BBE. Finally, the time series were calculated from GLASS BBE and UWIREMIS BBE using the data from 2003 through 2010. The periodic variations of GLASS BBE were stronger than those of UWIREMIS BBE. The long time series high quality GLASS BBE can be incorporated in land surface models for improving their simulation results.

show abstract

“…Considering the simultaneous retrieval of the LST and the land surface emissivity (LSE), the temperature and emissivity separation (TES) method may be a candidate. However, significant errors in the LST and LSE for the surfaces with low spectral contrast emissivity (e.g., water, snow, vegetation) can be caused by the TES [21,22]. Since the GF-5 satellite observes the land almost at nadir, the dual-angle algorithm was discarded.…”

Section: Introductionmentioning

confidence: 99%

Algorithm Development for Land Surface Temperature Retrieval: Application to Chinese Gaofen-5 Data

et al. 2017

View full text Add to dashboard Cite

Land surface temperature (LST) is a key variable in the study of the energy exchange between the land surface and the atmosphere. Among the different methods proposed to estimate LST, the quadratic split-window (SW) method has achieved considerable popularity. This method works well when the emissivities are high in both channels. Unfortunately, it performs poorly for low land surface emissivities (LSEs). To solve this problem, assuming that the LSE is known, the constant in the quadratic SW method was calculated by maintaining the other coefficients the same as those obtained for the black body condition. This procedure permits transfer of the emissivity effect to the constant. The result demonstrated that the constant was influenced by both atmospheric water vapour content (W) and atmospheric temperature (T 0 ) in the bottom layer. To parameterize the constant, an exponential approximation between W and T 0 was used. A LST retrieval algorithm was proposed. The error for the proposed algorithm was RMSE = 0.70 K. Sensitivity analysis results showed that under the consideration of NE∆T = 0.2 K, 20% uncertainty in W and 1% uncertainties in the channel mean emissivity and the channel emissivity difference, the RMSE was 1.29 K. Compared with AST 08 product, the proposed algorithm underestimated LST by about 0.8 K for both study areas when ASTER L1B data was used as a proxy of Gaofen-5 (GF-5) satellite data. The GF-5 satellite is scheduled to be launched in 2017.

show abstract

Residual errors in ASTER temperature and emissivity standard products AST08 and AST05

Cited by 76 publications

References 24 publications

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

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

A Comparative Study of Three Land Surface Broadband Emissivity Datasets from Satellite Data

Algorithm Development for Land Surface Temperature Retrieval: Application to Chinese Gaofen-5 Data

Contact Info

Product

Resources

About