Study of emissivity scaling and relativity of homogeneity of surface temperature

Zhang, RH; Zl, Li; Tang, X.-Z.; Sun, X.; Su, Hongbo; Zhu, Chenchen; Zhu, ZL

doi:10.1080/0143116031000115184

Cited by 26 publications

(18 citation statements)

References 5 publications

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“…Balick and Hutchinson, 1986;Smith et al, 1997;Zhang et al, 2004;McCabe et al, 2008). These studies have typically focused on the effects of vegetation and directionality on surface temperatures derived from broadband measurements (e.g.…”

Section: Introductionmentioning

confidence: 99%

Effects of surface roughness and graybody emissivity on martian thermal infrared spectra

Bandfield

2009

Icarus

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Section: Introductionmentioning

confidence: 99%

Effects of surface roughness and graybody emissivity on martian thermal infrared spectra

Bandfield

2009

Icarus

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“…Another difficulty in determining soil emissivity is the non-homogeneity of the soil at the pixel scale. However, for acquisitions at a resolution of 100 m, Zhang et al [40] found that the pixels can be considered homogeneous. Figure 1.…”

Section: Emissivity Estimationmentioning

confidence: 99%

A Software Tool for Atmospheric Correction and Surface Temperature Estimation of Landsat Infrared Thermal Data

et al. 2016

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Land surface temperature (LST) is an important variable involved in the Earth's surface energy and water budgets and a key component in many aspects of environmental research. The Landsat program, jointly carried out by NASA and the USGS, has been recording thermal infrared data for the past 40 years. Nevertheless, LST data products for Landsat remain unavailable. The atmospheric correction (AC) method commonly used for mono-window Landsat thermal data requires detailed information concerning the vertical structure (temperature, pressure) and the composition (water vapor, ozone) of the atmosphere. For a given coordinate, this information is generally obtained through either radio-sounding or atmospheric model simulations and is passed to the radiative transfer model (RTM) to estimate the local atmospheric correction parameters. Although this approach yields accurate LST data, results are relevant only near this given coordinate. To meet the scientific community's demand for high-resolution LST maps, we developed a new software tool dedicated to processing Landsat thermal data. The proposed tool improves on the commonly-used AC algorithm by incorporating spatial variations occurring in the Earth's atmosphere composition. The ERA-Interim dataset (ECMWFmeteorological organization) was used to retrieve vertical atmospheric conditions, which are available at a global scale with a resolution of 0.125 degrees and a temporal resolution of 6 h. A temporal and spatial linear interpolation of meteorological variables was performed to match the acquisition dates and coordinates of the Landsat images. The atmospheric correction parameters were then estimated on the basis of this reconstructed atmospheric grid using the commercial RTMsoftware MODTRAN. The needed surface emissivity was derived from the common vegetation index NDVI, obtained from the red and near-infrared (NIR) bands of the same Landsat image. This permitted an estimation of LST for the entire image without degradation in resolution. The software tool, named LANDARTs, which stands for Landsat automatic retrieval of surface temperatures, is fully automatic and coded in the programming language Python. In the present paper, LANDARTs was used for the local and spatial validation of surface temperature obtained from a Landsat dataset covering two climatically contrasting zones: southwestern France and central Tunisia. Long-term datasets of in situ surface temperature measurements for both locations were compared to corresponding Landsat LST data. This temporal comparison yielded RMSE values ranging from 1.84 • C-2.55 • C. Landsat surface temperature data obtained with LANDARTs were then spatially compared using the ASTER data products of kinetic surface temperatures (AST08) for both geographical zones. This comparison yielded a satisfactory RMSE of about 2.55 • C. Finally, a sensitivity analysis for the effect of spatial validation on the LST correction process showed a variability of up to 2 • C for an entire Landsat image, confirming that the proposed s...

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“…F 1 means firstly retrieving the LAI from the NDVI of each small pixel, and then obtaining the LAI of a large pixel by averaging the four LAIs, which is called "average after retrieval". The difference can be expressed by the general equation [10] as…”

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confidence: 99%

Principles and methods for the validation of quantitative remote sensing products

Zhang

Tian

et al. 2010

Sci. China Earth Sci.

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We first discuss the relativity of "true value and homogeneity" for quantitative remote sensing products (QRSPs), and then propose the definitions of "eigenaccuracy" and "eigenhomogeneity" under practical conditions. The eigenaccuracy and eigenhomogeneity for land surface crucial parameters such as albedo, leaf area index (LAI), and surface temperature are analyzed based on a series of experiments. Secondly, we point out the differences and similarities between the scale-free phenomena of the QRSPs and the measurements of the coastline length (1-dimensional) and the curved surface area (2-dimensional). An information fractal algorithm for the QRSPs is presented. In a case study for the LAI, when the fractal dimension is 2.16, the ratio of the LAI retrieval values obtained respectively from remote sensing data of 30 m and 6 km pixel resolution can actually reach as high as 2.86 for the same 6 km pixel using the same retrieval model. Finally, we propose an operational validation method "one test and two matches" and multipoint observation when the real situation does not allow carrying out scanning measurement without gap and overlap on the ground surface. It would be almost impossible to understand the spatial pattern and the temporal evolution principles of land surface variables without the accurate spatially-distributed information, and is thus difficult to discover the temporal-spatial dynamics of the land surface processes (such as global climate change, water cycle, and carbon cycle) at regional or global scales. Quantitative remote sensing (QRS) has the advantage of obtaining the spatially-distributed information with a certain frequency for land surface variables retrieved from electromagnetic signals, which is a revolutionary improvement compared with the traditional observations based on discrete ground points. With the development of space technology, remote sensing and ground-based observation techniques, more and more satellites have been launched. All kinds of high quality and multiple functional sensors on ground or space borne provide us various categories of abundant and near real time observations. However, because of the insufficient study of the validation of quantitative remote sensing products (QRSPs) and the lack of the validation theories and practical methods, particularly, the scaling theory for heterogeneous land surface variables, the gap could not be filled between remotely sensed information at regional scales and ground-based measurements at field scales. This limits the further applications of remote sensing data and products.Therefore, it is urgent to develop theories to validate the QRSPs. Validation is to quantify the accuracy and reliabil-

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Study of emissivity scaling and relativity of homogeneity of surface temperature

Cited by 26 publications

References 5 publications

Effects of surface roughness and graybody emissivity on martian thermal infrared spectra

Effects of surface roughness and graybody emissivity on martian thermal infrared spectra

A Software Tool for Atmospheric Correction and Surface Temperature Estimation of Landsat Infrared Thermal Data

Principles and methods for the validation of quantitative remote sensing products

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