Remote Sensing for Land & Resources 

About: Remote Sensing for Land & Resources is an academic journal. The journal publishes majorly in the area(s): Remote sensing (archaeology) & Hyperspectral imaging. It has an ISSN identifier of 1001-070X. Over the lifetime, 1225 publications have been published receiving 2774 citations.

The estimation of land surface emissivity for landsat tm6

Abstract: Land surface emissivity (LSE) is an essential parameter for land surface temperature (LST) retrieval from thermal remote sensing data. Up till now, three methods have been proposed for LST retrieval from TM6 data, namely, atmospheric correction, mono-window algorithm and single channel algorithm, which all require LSE as a parameter. In this paper the authors have first reviewed the three methods and then dealt emphatically with the estimation of this parameter. The method was applied to Lingxian area of Shangdong Province in North China Plain, the most important agricultural area in China, for LSE estimation and LST retrieval. The result shows that the method can yield a reasonable estimation of thermal variation of that area.

Estimating of the essential atmospheric parameters of mono-window algorithm for land surface temperature retrieval from landsat tm 6

Abstract: The thermal band data of Landsat TM (TM6) is highly suitable for analyzing the spatial patterns of the Earth's heat flux variation and surface temperature. Based on the thermal radiance transfer equation and several approximations to its terms, the authors developed a mono-window algorithm for retrieving land surface temperature (LST) from TM6 data. Unlike the conventional atmospheric correction which requires the in situ atmospheric profile data to estimate the atmospheric thermal radiance and absorption, the proposed mono-window algorithm directly involves the impacts of both atmosphere and the emitted ground into its computation, hence avoids the consequence of inaccurate atmospheric radiance estimate. The proposed algorithm requires two essential atmospheric parameters for LST retrieval: transmittance and atmospheric average temperature. This paper discusses the estimation of the essential atmospheric parameters. Atmospheric effective mean temperature can be estimated using the ground meteorological observation data. Equations also have been constructed for estimation of atmospheric transmittance using the atmospheric water vapor content in the profile. Moderate errors in estimating the two essential atmospheric parameters may cause a bias of about 1.2℃ to the LST retrieval, which is within the acceptable accuracy of 1.5℃ for most applications.

A study on urban thermal field in summer based on satellite remote sensing

Abstract: The luminance temperature distribution of Shanghai urban area was surveyed by means of locating, rectifying , image processing, noise erasing, temperature calculating and characteristic extracting from TM infrared data of 8/4,1998. The intensity, spatial structure and cause of UHIE (Urban Heat Island Effect) were investigated and discussed. It is shown that Thematic Mapper data are quite useful for thermal environmental quality appraisal and ecological urban planning.

The validation of chinese land surface temperature products retrieved from moderate resolution imaging spectroratiometer data

Abstract: As an important parameter governing energy balance over land,Land Surface Temperature(LST) plays an important role in meteorology,hydrology,ecology and some other disciplines.In this paper,the authors have analyzed the probable sources of errors in retrieving land surface temperature using MODIS data and dealt in detail with the validation method for simultaneous retrieval using relatively high resolution remote sensing data.A comparison between ASTER land surface temperature and MODIS LST products was conducted in Taihu area on August 1,2003.Linear regression of three typical areas in water surface,city ground,and outskirts of Wuxi respectively shows that the result is satisfactory for validation,with R~2 being 0.9666.