The Experts below are selected from a list of 53925 Experts worldwide ranked by ideXlab platform
Zhang Zhao-ming - One of the best experts on this subject based on the ideXlab platform.
-
Land Surface Temperature Retrieval Based on MODIS and TM Data
Journal of Image and Graphics, 2007Co-Authors: Zhang Zhao-mingAbstract:Land Surface Temperature(LST) retrieval has been a key issue in the thermal infrared remote sensing research area.Landsat5 TM data with a higher spatial resolution thermal infrared band of 120m was often used to retrieve Land Surface Temperature.However,the fact that Landsat 5 possesses only one thermal infrared band is also a critical limitation for LST retrieval.In most cases,only at-satellite brightness Temperature was thus obtained from TM6 data,which is far different from the Land Surface Temperature.Hence the precision of Land Surface Temperature retrieval was actually not so satisfied.While the proposal of the generalized single-channel algorithm in 2003 makes it possible to figure out Land Surface Temperature from TM6 data with high precision.Based on this algorithm,a test for Land Surface Temperature retrieval of Beijing region was carried out with Landsat 5 TM data acquired on 6 May 2005.MODIS data received on the same day was used to compute the total atmospheric water vapor content which is necessary for the algorithm.Furthermore,the retrieving result has been validated using simultaneously measured in situ data,and compared with that of using standard atmosphere data.A significantly high precision with a root mean square deviation(rmsd) of 1.67℃ has been achieved by the approach introduced in this paper,which shows the advantages of synthetically utilizing multi-satellite data.
-
IGARSS - Land Surface Temperature Retrieval of Beijing City using MODIS and TM Data
2006 IEEE International Symposium on Geoscience and Remote Sensing, 2006Co-Authors: Zhang Zhao-ming, He Guojin, Xiao Rong-bo, Wang Wei, Ouyang Zhi-yunAbstract:Land Surface Temperature (LST) retrieval has always been a key issue in the thermal infrared remote sensing research area. Landsat5 TM data with a higher spatial resolution thermal infrared band of 120 m was often used to retrieve Land Surface Temperature. However, the fact that Landsat 5 possesses only one thermal infrared band is also a critical limitation for LST retrieval, which does not allow applying a split-window method. LST retrieval from the radiative transfer equation using in situ radiosounding data is often not practical because of the scarcity of in situ radiosounding data. Therefore in most cases, only at-satellite brightness Temperature was obtained from TM6 data, which is far different from the Land Surface Temperature. Hence the precision of Land Surface Temperature retrieval was actually not so satisfied. While the proposal of the generalized single-channel algorithm in 2003 makes it possible to figure out Land Surface Temperature from TM6 data with high precision. Based on this algorithm, a test for Land Surface Temperature retrieval of Beijing region was carried out with Landsat5 TM data acquired on 6 May 2005. MODIS data received on the same date was used to compute the total atmospheric water vapor content which is necessary for the algorithm. Furthermore, the retrieving result has been validated using simultaneously measured in situ data. A significantly high precision with a root mean square deviation (rmsd) of 1.67 K has been achieved by the approach introduced in this paper, which shows the advantages of synthetically utilizing multi-satellite data.
Ouyang Zhi-yun - One of the best experts on this subject based on the ideXlab platform.
-
IGARSS - Land Surface Temperature Retrieval of Beijing City using MODIS and TM Data
2006 IEEE International Symposium on Geoscience and Remote Sensing, 2006Co-Authors: Zhang Zhao-ming, He Guojin, Xiao Rong-bo, Wang Wei, Ouyang Zhi-yunAbstract:Land Surface Temperature (LST) retrieval has always been a key issue in the thermal infrared remote sensing research area. Landsat5 TM data with a higher spatial resolution thermal infrared band of 120 m was often used to retrieve Land Surface Temperature. However, the fact that Landsat 5 possesses only one thermal infrared band is also a critical limitation for LST retrieval, which does not allow applying a split-window method. LST retrieval from the radiative transfer equation using in situ radiosounding data is often not practical because of the scarcity of in situ radiosounding data. Therefore in most cases, only at-satellite brightness Temperature was obtained from TM6 data, which is far different from the Land Surface Temperature. Hence the precision of Land Surface Temperature retrieval was actually not so satisfied. While the proposal of the generalized single-channel algorithm in 2003 makes it possible to figure out Land Surface Temperature from TM6 data with high precision. Based on this algorithm, a test for Land Surface Temperature retrieval of Beijing region was carried out with Landsat5 TM data acquired on 6 May 2005. MODIS data received on the same date was used to compute the total atmospheric water vapor content which is necessary for the algorithm. Furthermore, the retrieving result has been validated using simultaneously measured in situ data. A significantly high precision with a root mean square deviation (rmsd) of 1.67 K has been achieved by the approach introduced in this paper, which shows the advantages of synthetically utilizing multi-satellite data.
Xia Zhang - One of the best experts on this subject based on the ideXlab platform.
-
A new thermal infrared channel configuration for accurate Land Surface Temperature retrieval from satellite data
Remote Sensing of Environment, 2019Co-Authors: Xiaopo Zheng, Françoise Nerry, Xia ZhangAbstract:Land Surface Temperature (LST) is an important parameter in many research fields. Many algorithms have been developed to retrieve LST from satellite thermal infrared (TIR) measurements; of these, the most widely used are the split window (SW) and Temperature
Aj Nisbet - One of the best experts on this subject based on the ideXlab platform.
-
Night and day: The influence and relative importance of urban characteristics on remotely sensed Land Surface Temperature
Remote Sensing of Environment, 2020Co-Authors: Tom M. Logan, Benjamin F. Zaitchik, Seth D. Guikema, Aj NisbetAbstract:Abstract The characteristics of urban Land Surfaces contribute to the urban heat isLand, and, in turn, can exacerbate the severity of heat wave impacts. However, the mechanisms and complex interactions in urban areas underlying Land Surface Temperature are still being understood. Understanding these mechanisms is necessary to design strategies that mitigate Land Temperatures in our cities. Using the recently available night-time moderate-resolution thermal satellite imagery and employing advanced nonlinear statistical models, we seek to answer the question “What is the influence and relative importance of urban characteristics on Land Surface Temperature, during both the day and night?” To answer this question, we analyze urban Land Surface Temperature in four cities across the United States. We devise techniques for training and validating nonlinear statistical models on geostatistical data and use these models to assess the interdependent effects of urban characteristics on urban Surface Temperature. Our results suggest that vegetation and impervious Surfaces are the most important urban characteristics associated with Land Surface Temperature. While this may be expected, this is the first study to quantify this relationship for Landsat-resolution nighttime Temperature estimates. Our results also demonstrate the potential for using nonlinear statistical analysis to investigate Land Surface Temperature and its relationships with urban characteristics. Improved understanding of these relationships influencing both night and day Land Surface Temperature will assist planners undertaking climate change adaptation and heat wave mitigation.
Winston T L Chow - One of the best experts on this subject based on the ideXlab platform.
-
Landscape configuration and urban heat isLand effects assessing the relationship between Landscape characteristics and Land Surface Temperature in phoenix arizona
Landscape Ecology, 2013Co-Authors: John P Connors, Christopher S Galletti, Winston T L ChowAbstract:The structure of urban environments is known to alter local climate, in part due to changes in Land cover. A growing subset of research focuses specifically on the UHI in terms of Land Surface Temperature by using data from remote sensing platforms. Past research has established a clear relationship between Land Surface Temperature and the proportional area of Land covers, but less research has specifically examined the effects of the spatial patterns of these covers. This research considers the rapidly growing City of Phoenix, Arizona in the United States. To better understand how Landscape structure affects local climate, we explored the relationship between Land Surface Temperature and spatial pattern for three different Land uses: mesic residential, xeric residential, and industrial/commercial. We used high-resolution (2.4 m) Land cover data and an ASTER Temperature product to examine 90 randomly selected sample sites of 240 square-meters. We (1) quantify several Landscape-level and class-level Landscape metrics for the sample sites, (2) measure the Pearson correlation coefficients between Land Surface Temperature and each Landscape metric, (3) conduct an analysis of variance among the three Land uses, and (4) model the determinants of Land Surface Temperature using ordinary least squares linear regression. The Pearson’s correlation coefficients reveal significant relationships between several measures of spatial configuration and LST, but these relationships differ among the Land uses. The ANOVA confirmed that mean Land Surface Temperature and spatial patterns differed among the three Land uses. Although a relationship was apparent between Surface Temperatures and spatial pattern, the results of the linear regression indicate that proportional Land cover of grass and impervious Surfaces alone best explains Temperature in mesic residential areas. In contrast, Temperatures in industrial/commercial areas are explained by changes in the configuration of grass and impervious Surfaces.
