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Cheng Y.,CAS Institute of Remote Sensing | Cheng Y.,The Center for National Spaceborne Demonstration | Cheng Y.,University of Chinese Academy of Sciences | Zhao L.,CAS Institute of Remote Sensing | And 8 more authors.
Journal of Geographical Sciences | Year: 2016

DMSP/OLS nighttime light (NTL) image is a widely used data source for urbanization studies. Although OLS NTL data are able to map nighttime luminosity, the identification accuracy of distribution of urban areas (UAD) is limited by the overestimation of the lit areas resulting from the coarse spatial resolution. In view of geographical condition, we integrate NTL with Biophysical Composition Index (BCI) and propose a new spectral index, the BCI Assisted NTL Index (BANI) to capture UAD. Comparisons between BANI approach and NDVI-assisted SVM classification are carried out using UAD extracted from Landsat TM/ETM+ data as reference. Results show that BANI is capable of improving the accuracy of UAD extraction using NTL data. The average overall accuracy (OA) and Kappa coefficient of sample cities increased from 88.53% to 95.10% and from 0.56 to 0.84, respectively. Moreover, with regard to cities with more mixed land covers, the accuracy of extraction results is high and the improvement is obvious. For other cities, the accuracy also increased to varying degrees. Hence, BANI approach could achieve better UAD extraction results compared with NDVI-assisted SVM method, suggesting that the proposed method is a reliable alternative method for a large-scale urbanization study in China’s mainland. © 2016, Institute of Geographic Science and Natural Resources Research (IGSNRR), Science China Press and Springer-Verlag Berlin Heidelberg. Source

Wang L.,CAS Institute of Remote Sensing | Wang L.,University of Chinese Academy of Sciences | Zhao L.,CAS Institute of Remote Sensing | Zhao L.,The Center for National Spaceborne Demonstration | And 8 more authors.
Zhongguo Kexue Jishu Kexue/Scientia Sinica Technologica | Year: 2016

Using near-infrared radiance ratio between absorption channel (940 nm) and window channel (860 nm) to estimate water vapor content is a common method to retrieve water vapor with remote sensing data. For troposphere water vapor estimation with airborne remote sensing data, ready-made near-infrared ratio method introduces the influence of moisture between flight platform and the top of atmosphere. According to the aerial infrared camera image characteristics, using Modtran and TIGR data, define the following parameters: 1) R, the ratio between the water vapor content from surface to flight platform and the total atmospheric water vapor content; 2) G, the logarithm ratio between the water vapor transmittance from flight platform to land surface and the one from sun to surface on the incident path; 3) H, the logarithm ratio between the water vapor transmittance from flight platform to land surface on the incident path and the one from surface to flight platform on the emergent path, and build functional relation between G and R, H and solar incident angle respectively, and considering surface characteristics, build troposphere water vapor content retrieving model. Modtran simulation shows that the estimation precision is 0.22 g/cm2 in a camera height of 1-7 km, and precision less than 0.5 g/cm2 account for 95.30%. The measured data from airborne remote sensing experiment in Shangjie, Zhengzhou on May 28, 2014 shows that the estimation error is 0.16 g/cm2 (12.8%) compared with simultaneous balloon sounding data, and priori knowledge about underlying surface improves model precision. This paper eliminates the influence of atmosphere above flight platform, increases model accuracy and adaptability, and provides reliable input for real-time thermal infrared remote sensing atmospheric correction. © 2016, Science Press. All right reserved. Source

Xu J.-P.,Beijing Normal University | Xu J.-P.,The Center for National Spaceborne Demonstration | Li F.,National Marine Environmental Monitoring Center | Zhang B.,CAS Changchun Northeast Institute of Geography and Agroecology | And 4 more authors.
International Journal of Remote Sensing | Year: 2010

In remote chlorophyll-a (chl-a) retrieval in Case-II waters, there always exists some limitations from empirical parameters in the estimating models and uncertainties from the chl-a specific absorption coefficient. In this study, we present a newly improved three-band model in a case study in Shitoukoumen Reservoir for direct calculation of chl-a without any empirical parameters derived from regression. Inherent optical properties of the reservoir were investigated to determine parameters in the improved model. Results show that taking into account variations from chl-a specific absorption, as well as absorption by pure water, the improved model performed well in the field study used for model calibration and also robustly in two other field studies. The findings underline the rationale behind the model and demonstrate a potentially general solution for assessing chl-a in Case-II waters. In the evaluation of P6 data, the traditional near-infrared-to-red reflectance ratio (band 4/band 3) could estimate chl-a with root mean square error below 2.17μg l-1, which confirms that P6 data could be used potentially to determine chl-a concentration. © 2010 Taylor & Francis. Source

Zhang Z.-W.,Peking University | Yu T.,CAS Institute of Remote Sensing | Yu T.,The Center for National Spaceborne Demonstration | Meng Q.-Y.,CAS Institute of Remote Sensing | And 3 more authors.
Wuhan Ligong Daxue Xuebao/Journal of Wuhan University of Technology | Year: 2013

In this paper, we used four bands single-CCD imager data in Tianjin area as fundamental datum and processed them. Firstly, integrating data characteristics, the authors determined methods of radiometric and geometric correction. Secondly, technique flowchart was designed by changing the radiation and geometric correction order. Finally, the results obtained by different orders of radiometric calibration and geometric correction were quantitative analyzed from two aspects of statistical characteristics and texture features of images. On this basis, product rating system was built up. The experimental results showed that: For different underlying surface types of UAV remote sensing data, different geometric correction models have influence on the preprocessing results, but the difference can be ignored. This fact can provide a reference for the preprocessing of UAV remote sensing data and product rating. Source

Zhao L.-M.,CAS Institute of Remote Sensing Applications | Zhao L.-M.,The Center for National Spaceborne Demonstration | Gu X.-F.,CAS Institute of Remote Sensing Applications | Gu X.-F.,The Center for National Spaceborne Demonstration | And 7 more authors.
Hongwai Yu Haomibo Xuebao/Journal of Infrared and Millimeter Waves | Year: 2012

The multiple scattering effect between heterogeneous non-isothermal surfaces is described rigorously using the configuration factor in engineering thermophysics. Based on the results a directional thermal radiance model is built, and the numerical calculation of the model is discussed. The model is integrated with the gap probability model and applied to a row structure to simulate the change of Directional Brightness Temperature (DBT). The results show that the modeled DBT agrees well with the observed DBT, especially under the condition that the gap probability is very low. It is also shows that the DBT is aggrandized because of the multiple scattering effects, whereas the change of DBT tend to average out. The temperature difference, spatial distribution, emissivity of the components can all lead to the change of DBT. That is to say, the higher of the temperature difference, the craggier of the surface and the lower of the emissivity, the more exquisite DBT changes along with the viewing zenith. The model developed in this paper can explain "hot spot effect" of thermal radiance, and it is confirmed that the existence of "hot spot" is mainly related to the spatial distribution of components' temperature. This model can be used for the study of directional effect on emissivity, the LST retrieval over urban areas and the adjacency effect of thermal remote sensing pixels. Source

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