Split-Window LST Algorithms Estimation From AVHRR/NOAA Satellites (7, 9, 11, 12, 14, 15, 16, 17, 18, 19) Using Gaussian Filter Function

Lahraoua, Mohammed; Raissouni, Naoufal; Chahboun, Asaad; Azyat, Abdelilah; Achhab, Nizar Ben

doi:10.11591/ijins.v2i1.1502

Cited by 2 publications

(2 citation statements)

References 49 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The emissivity ε and Δε are obtained according to the method developed by Sobrino et al (2008) using thresholds of the Normalized Difference Vegetation Index (NDVI) values from the red and near-infrared bands. The coefficients c 0 to c 6 were obtained from Lahraoua, Raissouni, Chahboun, Azyat, and Achhab (2012). For inland water bodies and given they are relatively small in Spain, we did not apply a different algorithm for them.…”

Section: Methodsmentioning

confidence: 99%

Average annual and seasonal Land Surface Temperature, Spanish Peninsular

et al. 2018

View full text Add to dashboard Cite

The first long-term Land Surface Temperature (LST) maps for the Peninsular Spain at annual and seasonal time scales for 1981-2015 is presented in this work. A robust protocol for correcting and calibrating NOAA-AVHRR images and computing LST datasets at the spatial resolution of 1.1 km has been used. Simultaneously, maximum air temperature (Tmax) maps at the same spatial resolution have been produced using data from meteorological stations. The comparison between the two datasets resulted in statistically significant spatial correlations at annual and seasonal scales. Finally, the Normalized Difference Vegetation Index (NDVI) data were also compared with the obtained LST datasets and the results showed significant negative correlations between the two variables, especially in summer.

show abstract

Section: Methodsmentioning

confidence: 99%

Average annual and seasonal Land Surface Temperature, Spanish Peninsular

et al. 2018

View full text Add to dashboard Cite

show abstract

“…(3) Spectral resolution: the HTIR imager splits the thermal infrared radiation spectrum into hundreds and thousands, approximately continuous and narrow bands, and the spectral response of each channel conforms to the Gaussian distribution [55,56]. When dealing with multi-spectral thermal infrared data, the spectral response function is usually obtained by direct measurement [57,58]. However, for the HTIR imager, the spectral response performance at each channel is usually fitted by a Gaussian function after spectral calibration by devices such as a monochromator, and described by the center wavelength and full width at half height (FWHM) obtained by fitting.…”

Section: Simulation Experimentsmentioning

confidence: 99%

Noise-sensitivity Analysis and Improvement of Automatic Retrieval of Temperature and Emissivity Using Spectral Smoothness

et al. 2020

View full text Add to dashboard Cite

There are numerous algorithms that can be used to retrieve land surface temperature (LST) and land surface emissivity (LSE) from hyperspectral thermal infrared (HTIR) data. The algorithms are sensitive to a number of factors, where noise is difficult to handle due to its unpredictability. Although there is a lot of research regarding the influence of noise on retrieval errors, few studies have focused on the mechanism. In this study, we selected the automatic retrieval of temperature and emissivity using spectral smoothness (ARTEMISS) algorithm—the representative of the iterative spectral smoothness temperature-emissivity separation algorithm family—as the research object and proposed an improved algorithm. First, we analyzed the influence mechanism of noise on the retrieval errors of ARTEMISS in theory. Second, we carried out a simulation and inversion experiment and analyzed the relationship between instrument spectral resolution, noise level, the ARTEMISS parameter setting and the retrieval errors separately. Last, we proposed an improved method (resolution-degrade-based spectral smoothness algorithm, RDSS) based on the mechanism and law of the influence of noise on retrieval errors and provided corresponding suggestions on instrument design. The results show that RDSS improves the accuracy of temperature inversion and is more effective for thermal infrared data with a high noise level and high spectral resolution, which can reduce the LST inversion error by up to 0.75 K and the LSE median absolute deviation (MAD) by 31%. In the presence of noise in HTIR data, the RDSS algorithm performs better than the ARTEMISS algorithm in terms of temperature-emissivity separation.

show abstract

Split-Window LST Algorithms Estimation From AVHRR/NOAA Satellites (7, 9, 11, 12, 14, 15, 16, 17, 18, 19) Using Gaussian Filter Function

Cited by 2 publications

References 49 publications

Average annual and seasonal Land Surface Temperature, Spanish Peninsular

Average annual and seasonal Land Surface Temperature, Spanish Peninsular

Noise-sensitivity Analysis and Improvement of Automatic Retrieval of Temperature and Emissivity Using Spectral Smoothness

Contact Info

Product

Resources

About