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Remote Sensing of Water Clouds Temperature with an IR Camera on Board the ISS in the frame of JEM-EUSO Mission

Remote Sensing of Water Clouds Temperature with an IR Camera...

Conference on Remote Sensing of Clouds and the Atmosphere XVIII;and Optics in Atmospheric Propagation and Adaptive Systems XVI

作者： Briz, Susana de Castro, Antonio J. Fernandez-Gomez, Isabel Rodriguez, Irene Lopez, Fernando Univ Carlos III Madrid Dept Phys Leganes 28911 Madrid Spain

ISBN: (纸本)9780819497598

The Extreme Universe Space Observatory (EUSO) is an astronomical telescope that will be hosted by the Japan Experiment Module (JEM) on the International Space Station (ISS). The telescope will determine Ultra High Energy Cosmic Rays properties by measuring the UV fluorescence light emitted by the particles generated in the interaction between the cosmic rays and the atmosphere. Therefore, cloud information is crucial for a proper interpretation of the data. To obtain the cloud top height an IR camera is being designed. The design is constrained by JEM-EUSO requirements which are mainly the instrument weight, power and data rate. These requirements have led to a bi-spectral camera option with 1 mu m-wide bands centered at 10.8 and 12 mu m. The bi-spectral design has allowed us to develop a split window algorithm to correct the atmospheric effects and retrieve the cloud temperature from the brightness temperatures in the bands aforementioned. The algorithm has been checked in synthetic scenarios at pixel level. The simulations consider clouds at different levels with diverse atmospheric conditions. The results show that the algorithm is able to retrieve the temperature with accuracy much better than the required one by the JEM-EUSO mission of 3K. It has also been tested in 2D real scenarios (MODIS images). The algorithm has been applied to MODIS brightness temperatures in bands 31 and 32 which are similar to those of the IR camera. The temperatures retrieved by the algorithm are in a very good agreement with the cloud top temperatures given by MODIS.

关键词： JEM-EUSO Mission IR Camera Cloud Top Temperature split window algorithm MODIS images

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An improved algorithm to Retrieve Land Surface Temperature by GMS 5 Data

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Chinese Journal of Geophysics 2013年第5期48卷 1108-1120页

作者： Min-Yan WANG Da-Ren LU

An algorithm to retrieve Land Surface Temperature (LST) by GMS 5/VISSR thermal infrared data is developed in this paper, which is called the single time/dual channels split window algorithm. It adopts the spatial-temporal threshold criterion for cloud detection. Real-time radiosonde data are used to derive the atmospheric upward and downward radiation. The surface emissivity is derived from AVHRR-NDVI. Thus, LST in two thermal infrared channels is retrieved from the RTE (Radiation Transfer Equation). Compared with in-situ observations of surface temperature (0 centimeter) at station No. 54511 and other 43 stations in China in 2000, LSTs retrieved by the present algorithm are more accurate than other similar algorithms. Sensitivity analysis is made mainly considering of the effect from atmospheric attenuation. Based on this algorithm, diurnal cycle of LST in 4 continuous days in the middle eastern of Inner Mongolia and the spatial distribution of yearly average LST for pure clear days in East Asia are also shown.

关键词： GMS 5 Land Surface Temperature split window algorithm Cloud detection Emissivity

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Research on the Retrieval of Land Surface Temperature based on MODIS Data

Research on the Retrieval of Land Surface Temperature based ...

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2013 IEEE 5th International Symposium on Microwave,Antenna,Propagation and EMC Technologies for Wireless Communications

作者： Kang Wang Liangchao Li Yanni Guo Lanhai Jin School of Science Xidian University

ISBN: (纸本)9781467360760

Land Surface Temperature(LST) is a key parameter of the research on regional and global ground surface physical process,which synthetically reflects the result of energy and material exchange [1].In the interaction and energy exchange between land surface and atmosphere,LST is also the crucial factor of the Land Surface Energy Balance,which is affected by the Surface Radiation Balance and has important application value and plays an important significance on the ecological environment,agricultural meteorology,crop yield estimation,drought monitoring,disaster monitoring,urban environment and hydrological characteristics *** paper utilizes the split-window algorithm and related theories to retrieve the land surface temperature of southeast coast of China based on the MODIS *** 1B data of Terra satellite at 13:19 28/09/2011 is *** study area is between 21.0°N～23.0°N and 117.0°E～123.0°E [2].To begin with,the author has retrieved the atmospheric transmittance and modified its impact factors from the MODIS *** the author calculated the vegetation coverage through the calculating vegetation index and respectively estimated the Surface Emissivity according to the type of vegetation ***,the LST was retrieved by adopting the splitwindow algorithm and then the author analyzed the influence of clouds on the retrieval of LST.

关键词： MODIS data split window algorithm the retrieval of LST land surface temperature surface radiation retrieval Land surface Writers impact factor energy exchange Energy transfer Vegetation Index

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Analysis and Study of Urban Heat Environment and Respirable Particulate Matter in Beijing City

Analysis and Study of Urban Heat Environment and Respirable ...

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Joint Workshop on Urban Remote Sensing

作者： Li Shanshan Gong Huili Zhao Wenji Sun Yonghua Capital Normal Univ Coll Resources Environm & Tourism Beijing Peoples R China GIS Capital Normal Univ Resources Environm Beijing Municipal Lab Beijing Peoples R China

ISBN: (纸本)9781424434602

The phenomenon of temperature in urban cities is higher than that in suburbs is called Urban Beat Island (UHI), which is put forward by Lake Howard([1]) in 1833. Three main algorithms of land surface temperature retrieval of existing thermal infrared remote sensed data has been developed: single channel algorithm, split window algorithm and multi-band algorithm. The thesis selected split window algorithm and improves the split window algorithm of AVHRR to suit MODIS image data. This split window algorithm is based on the thermal radiation data which is observed by NOAA-AVHRR. This paper retrieved land surface temperature and analysis the spatial characteristics of Beijing urban heat island as well as its influencing factors. The result indicates that analysis on urban heat island effect based on split window algorithm of Beijing is available. There is a clear urban heat island in summer, and it is quite close with vegetation index. Besides, the negative correlation relationship between land surface temperature and Respirable Particulate Matter is also emphasizely analyzed.

关键词： land surface temperature urban heat island split window algorithm PM10 land surface emissivity atmospheric transmittance

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Research on Beijing Urban Heat Island and PM₁₀ in the Air

Research on Beijing Urban Heat Island and PM<sub>10</sub> in...

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International Workshop on Education Technology and Training/International Workshop on Geoscience and Remote Sensing (ETT and GRS 2008)

作者： Li, Shanshan Gong, Huili Zhao, Wenji Liu, Jianming Capital Normal Univ MOE Key Lab 3D Informat & Acquisit & Applicat Beijing Peoples R China Capital Normal Univ Coll Resources Environm & Tourism Beijing Peoples R China Capital Normal Univ Beijing Municipal Lab Resources Environm GIS Beijing Peoples R China

ISBN: (纸本)9780769535630

With the acceleration of urbanization, the phenomenon of temperature in urban cities is higher than that in suburbs is called Urban Heat Island (UHI), which is put forward by Lake Howard([1]) in 1833. This paper chose split window algorithm to retrieve land surface temperature after comparing with single window algorithm and multi-band algorithm, and improves the split window algorithm of AVHRR so as to be suitable for MODIS image data. In this paper, land surface temperature is retrieved by split window algorithm based on MODIS' images, and the temporal and spatial characteristics of Beijing urban beat island as well as its influencing factors are also analyzed. The result indicates that: Without particular atmosphere parameter, analysis on urban heat island effect based on split window algorithm of Beijing is available. There is a clear urban heat island in summer, and it is quite dose with vegetation index. Besides, the negative correlation relationship between land surface temperature and PM10 is also emphasizely analyzed.

关键词： land surface temperature urban heat island split window algorithm PM10 land surface emissivity atmospheric transmittance

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Application of MODIS data to assess the urban heat island in Shanghai City, China

Application of MODIS data to assess the urban heat island in...

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15th International Conference on Geoinformatics

作者： Yan, Feng Qin, Zhihao Wang, Yanjiao Nanjing Univ Int Inst Earth Syst Sci Nanjing 210008 Peoples R China Chinese Acad Sci Inst Agr Resources & Reg Planning Beijing Peoples R China Natl Climate Ctr China Meterolog Adm Lab Climate Studies China Beijing 100081 Peoples R China

ISBN: (纸本)9780819469120

Based on MODIS data of Shanghai City from 2003 to 2005, land surface temperature (LST) was retrieved and used indirectly to get the quarterly intensity and higher temperature area of urban heat island. Results show that (1) the mean highest LST is 13.9 degrees C in winter and 32.5 degrees C, 45.7 degrees C and 29.0 degrees C in spring, summer, and autumn, respectively;(2) the relatively higher LST area distribution has obvious seasonal variation, which moves southeast in winter, northwest in spring and summer, and draws back southeast again in autumn;(3) weak urban cold island exists in urban areas during winter mornings;urban heat island exists in spring, summer and autumn;the summer's heat island is the most notable;and (4) LST of urban cold island centre in winter morning is 2.6 degrees C lower than that of countryside and the mean urban heat island intensity is 5.7 degrees C, 10.4 degrees C and 4.2 degrees C in spring, summer and autumn, respectively.

关键词： urban heat island split window algorithm land surface temperature seasonal change

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Development of a daily long term record of NOAA-14 AVHRR land surface temperature over Africa

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REMOTE SENSING OF ENVIRONMENT 2006年第2期103卷 153-164页

作者： Pinheiro, A. C. T. Mahoney, R. Privette, J. L. Tucker, C. J. NASA Goddard Space Flight Ctr Greenbelt MD USA Global Sci & Technol Inc Greenbelt MD USA

We developed a new 6-year daily, daytime and nighttime, NOAA-14 AVHRR based land surface temperature (LST) dataset over continental Africa for the period 1995 through 2000. The processing chain was developed within the Global Inventory Modeling and Mapping System (GIMMS) at NASA's Goddard Space Flight Center. This paper describes the processing methodology used to convert the Global Area Coverage Level-1b data into LST and collateral data layers, such as sun and view geometries, cloud mask, local time of observation, and latitude and longitude. We used the Ulivieri et al. [Ulivieri, C., M.M. Castronuovo, R. Francioni, and A. Cardillo (1994), A split window algorithm for estimating land surface temperature from satellites, Adv. Space Research, 14(3):59-65.] split window algorithm to determine LST values. This algorithm requires as input values of surface emissivity in AVHRR channels 4 and 5. Thus, we developed continental maps of emissivity using an ensemble approach that combines laboratory emissivity spectra, MODIS-derived maps of herbaceous and woody fractional cover, and the UNESCO FAO soil map. A preliminary evaluation of the resulting LST product over a savanna woodland in South Africa showed a bias of < 0.3 K and an uncertainty of < 1.3 K for daytime retrievals (< 2.5 K for night). More extensive validation is required before statistically significant uncertainties can be determined. The LST production chain described here could be adapted for any wide field of view sensor (e.g., MODIS, VIIRS), and the LST product may be suitable for monitoring spatial and temporal temperature trends, or as input to many process models (e.g., hydrological, ecosystem). (c) 2006 Elsevier Inc. All rights reserved.

关键词： land surface temperature Advanced Very High Resolution Radiometer Global Area Coverage split window algorithm Africa

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An algorithm to retrieve land surface temperature from ASTER thermal band data for agricultural drought monitoring

An algorithm to retrieve land surface temperature from ASTER...

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Conference on Remote Sensing for Agriculture, Ecosystems, and Hydrology VIII

作者： Qin, Zhihao Li, Wenjuan Gao, Maofang Zhang, Hong'ou Chinese Acad Agr Sci Inst Agroresource & Reg Planning Beijing 100081 Peoples R China Guangdong Publ Lab Environm Sci & Technol Guangzhou 510650 Guangdong Peoples R China Nanjing Univ Int Inst Earth Syst Sci Nanjing 210093 Jiangsu Peoples R China

ISBN: (纸本)0819464546

High spatial resolution ASTER data have 5 thermal bands, of which band 13 and 14 are especially suitable for land surface temperature (LST) estimation. Generally, LST retrieval from two thermal bands is done through so-called split window technique. In the past two decades above 17 split window algorithms have been proposed. However, such algorithm for ASTER data has not been reported, probably due to the new availability of the data for environmental application. In the study, a new split window algorithm has been developed for LST retrieval from ASTER data. Our algorithm only involves two essential parameters for LST retrieval while keeping the same accuracy as those having more parameters. Detailed derivation of the split window algorithm has been given in the paper, which including formulation of thermal radiation transfer equation, determination of algorithm constants, and estimation of the essential parameters. Comparison of our algorithm with the existing ones for validation of its accuracy and applicability in the real world indicates that our algorithm has an average root mean square (RMS) error of 0.67 degrees C when transmittance has an error of 0.05 and emissivity has an error of 0.01. Thus we can conclude that our algorithm is a very good alternative for accurate LST retrieval from ASTER data. Application of the algorithm to Wuxi-Suchou region in Yangtze River Delta produces a very reseasonable LST image of the region, hence confirins the applicability of the algorithm.

关键词： land surface temperature ASTER split window algorithm agricultural drought monitoring emissivity

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Estimation of land surface emissivity for Landsat TM6 and its application to Lingxian Region in north China

Estimation of land surface emissivity for Landsat TM6 and it...

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Conference on Remote Sensing for Environmental Monitoring, GIS Applications, and Geology VI

ISBN: (纸本)0819464619

Landsat TM has a thermal band (TM6) operating in 10.45-12.6 mu m, which can be used for land surface temperature (LST) retrieval. Land surface emissivity (LSE) is an essential parameter for LST retrieval. However, LSE information is generally no available for many applications. In this paper we intend to develop an applicable approach for LSE estimation so that LST can be retrieved from Landsat TM6 data. Spatial resolution of TM6 is 120m under nadir. Pixels under this scale can be viewed as composed of three land cover patterns for most natural surfaces: vegetation, bare soil/rock and water. Emissivities of these land cover patterns are relatively stable and well known, which enables us to propose a method for LSE estimation using the visible and near infrared (NIR) bands. The composition ratio of vegetation and bare soil or building under pixel scale can be estimated from bands 3 and 4 (TM3 and TM4). LSE for TM6 can then be estimated through thermal radiance equation with the composition ratio and the emissivities of the patterns known. The proposed methodology for LSE estimation is simple and easy to use, hence provides opportunity to promote the application of TM6 data to agriculture and environments. Finally we apply this methodology to Lingxian region of Shangdong Province in North China Plain, the most important agricultural region in China, for LSE estimation and LST retrieval, which has produced a reasonable estimation of thermal variation of the region.

关键词： land surface temperature ASTER split window algorithm surface emissivity atmospheric transmittance

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