Effect of emissivity uncertainty on surface temperature retrieval over urban areas: Investigations based on spectral libraries

Chen, Feng; Yang, Song; Su, Zhongbo; Wang, Kai

doi:10.1016/j.isprsjprs.2016.01.007

Cited by 50 publications

(29 citation statements)

References 45 publications

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“…The spectra in ASTER Spectral Library Version 2.0 are available from http://speclib.jpl.nasa.gov. However, as discussed previously (Chen et al, 2016), difference in wavelength range and spectral resolution between the SRFs of the channels and the spectra may result in bias in channel reflectance calculation (see Eq.…”

Section: Spectra Datamentioning

confidence: 99%

“…Accordingly, a collection of 897 spectra obtained from ASTER Spectral Library Version 2.0 was used in further investigations. An interpolation procedure (Chen et al, 2016) was used to tackle the difference in spectral resolution. Additionally, a collection of spectra from Hyperion data archives was selected (Claverie et al, 2017).…”

Section: Spectra Datamentioning

confidence: 99%

“…Consequently, some differences are expected in the recorded radiometric values (e.g., channel reflectance) of the corresponding channels between different sensors (i.e., MSI and OLI). Spectral response function (SRF) was taken as an important aspect of sensor settings, as discussed previously (Chander et al, 2013;Chen et al, 2016;Chen et al, 2017). Previous findings suggest that the effect of differences due to SRFs of the sensors need to be quantified and compensated to avoid large uncertainties in cross-calibration results (Chander et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

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A Preliminary Investigation on Comparison and Transformation of Sentinel-2 Msi and Landsat 8 Oli

Chen

Lou

Fan

et al. 2018

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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A PRELIMINARY INVESTIGATION ON COMPARISON AND TRANSFORMATION OF SENTINEL-2 MSI AND LANDSAT 8 OLI Timely and accurate earth observation with short revisit interval is usually necessary, especially for emergency response. Currently, several new generation sensors provided with similar channel characteristics have been operated onboard different satellite platforms, including Sentinel-2 and Landsat 8. Joint use of the observations by different sensors offers an opportunity to meet the demands for emergency requirements. For example, through the combination of Landsat and Sentinel-2 data, the land can be observed every 2&ndash;3 days at medium spatial resolution. However, differences are expected in radiometric values (e.g., channel reflectance) of the corresponding channels between two sensors. Spectral response function (SRF) is taken as an important aspect of sensor settings. Accordingly, between-sensor differences due to SRFs variation need to be quantified and compensated. The comparison of SRFs shows difference (more or less) in channel settings between Sentinel-2 Multi-Spectral Instrument (MSI) and Landsat 8 Operational Land Imager (OLI). Effect of the difference in SRF on corresponding values between MSI and OLI was investigated, mainly in terms of channel reflectance and several derived spectral indices. Spectra samples from ASTER Spectral Library Version 2.0 and Hyperion data archives were used in obtaining channel reflectance simulation of MSI and OLI. Preliminary results show that MSI and OLI are well comparable in several channels with small relative discrepancy (&lt;&thinsp;5&thinsp;%), including the Costal Aerosol channel, a NIR (855&ndash;875&thinsp;nm) channel, the SWIR channels, and the Cirrus channel. Meanwhile, for channels covering Blue, Green, Red, and NIR (785&ndash;900&thinsp;nm), the between-sensor differences are significantly presented. Compared with the difference in reflectance of each individual channel, the difference in derived spectral index is more significant. In addition, effectiveness of linear transformation model is not ensured when the target belongs to another spectra collection. If an improper transformation model is selected, the between-sensor discrepancy will even largely increase. In conclusion, improvement in between-sensor consistency is possibly a challenge, through linear transformation based on model(s) generated from other spectra collections.

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Section: Spectra Datamentioning

confidence: 99%

Section: Spectra Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Preliminary Investigation on Comparison and Transformation of Sentinel-2 Msi and Landsat 8 Oli

Chen

Lou

Fan

et al. 2018

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…Emissivity is a critical variable for the LST estimation, since a small uncertainty in the emissivity (1%) can lead to large errors in the LST (up to 1 K) depending on the setting of the sensor, the climatological conditions and geographical setting of the area [41,42]. In this study, three different sources of emissivity are used: ASTER and MODIS emissivity that are available in the GEE catalogue and NDVI-based emissivity estimated from Landsat red and near-infrared data.…”

Section: Emissivitymentioning

confidence: 99%

Online Global Land Surface Temperature Estimation from Landsat

et al. 2017

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This study explores the estimation of land surface temperature (LST) for the globe from Landsat 5, 7 and 8 thermal infrared sensors, using different surface emissivity sources. A single channel algorithm is used for consistency among the estimated LST products, whereas the option of using emissivity from different sources provides flexibility for the algorithm's implementation to any area of interest. The Google Earth Engine (GEE), an advanced earth science data and analysis platform, allows the estimation of LST products for the globe, covering the time period from 1984 to present. To evaluate the method, the estimated LST products were compared against two reference datasets: (a) LST products derived from ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer), as higher-level products based on the temperature-emissivity separation approach; (b) Landsat LST data that have been independently produced, using different approaches. An overall RMSE (root mean square error) of 1.52 • C was observed and it was confirmed that the accuracy of the LST product is dependent on the emissivity; different emissivity sources provided different LST accuracies, depending on the surface cover. The LST products, for the full Landsat 5, 7 and 8 archives, are estimated "on-the-fly" and are available on-line via a web application.

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“…Even if in most investigations the importance and potential impacts of urban surface emissivity were ignored, often due to data unavailability, for accurate quantitative temperature analysis they must necessarily be considered. 13 Several methods can be followed to separate the effects of both surface emissivity and temperature on the radiance at ground level; however, even with multi-spectral sensors with N thermal channels available, the system composed of N radiative transfer equations (one for each channel) remains mathematically unsolvable, because there will always be N + 1 unknowns, corresponding to the N emissivities in each wavelength and the surface temperature. 11 On the basis of the number of thermal infrared channels available, the methods to derive the land surface temperature were categorized in four main groups: 14, 15 single channel methods, double channel (or split-window) methods, two angle methods and other methods, developed for sensors operating in more than two infrared channels or based on different techniques.…”

Section: Methodsmentioning

confidence: 99%

Aerial thermography for energy efficiency of buildings: the ChoT project

Mandanici

Conte

2016

SPIE Proceedings

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The ChoT project aims at analysing the potential of aerial thermal imagery to produce large scale datasets for energetic efficiency analyses and policies in urban environments. It is funded by the Italian Ministry of Education, University and Research (MIUR) in the framework of the SIR 2014 (Scientific Independence of young Researchers) programme. The city of Bologna (Italy) was chosen as the case study. The acquisition of thermal infrared images at different times by multiple aerial flights is one of the main tasks of the project. The present paper provides an overview of the ChoT project, but it delves into some specific aspects of the data processing chain: the computing of the radiometric quantities of the atmosphere, the estimation of surface emissivity (through an object-oriented classification applied on a very high resolution multispectral image, to distinguish among the major roofing materials) and sky-view factor (by means of a digital surface model). To collect ground truth data, the surface temperature of roofs and road pavings was measured at several locations at the same time as the aircraft acquired the thermal images. Furthermore, the emissivity of some roofing materials was estimated by means of a thermal camera and a contact probe. All the surveys were georeferenced by GPS. The results of the first surveying campaign demonstrate the high sensitivity of the model to the variability of the surface emissivity and the atmospheric parameters.

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Effect of emissivity uncertainty on surface temperature retrieval over urban areas: Investigations based on spectral libraries

Cited by 50 publications

References 45 publications

A Preliminary Investigation on Comparison and Transformation of Sentinel-2 Msi and Landsat 8 Oli

A Preliminary Investigation on Comparison and Transformation of Sentinel-2 Msi and Landsat 8 Oli

Online Global Land Surface Temperature Estimation from Landsat

Aerial thermography for energy efficiency of buildings: the ChoT project

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