Research Article of American Journal of Geographical Research and Reviews
A Verification Method of Thermo- infrared Remote Sensing Temperature Retrieval Algorithm with a CFD Model
Yong Zhang1,*, Zhiqiang Yao1, Yingbao Yang2, Leqin Zhang1, Xin Pan2
1College of Natural Resources and Environment, Chizhou University, No.199 Muzhi Road,Chizhou 247000, China
2School of Earth Science and Engineering, Hohai University, 8 Buddha CityWest Road, Nanjing 210098,China
Due to the thermal infrared remote sensing inversion of surface corresponds to the surface temperature of the image pixels is planar, the inversion algorithm of authentication, rarely considering the reference temperature data and inversion of surface temperature on like yuan scale problems.ENVI – met based on the CFD model to simulate the nanjing jiangning district land surface temperature, and by the methods of measurement of analog temperature verification, proved ENVI – met the error of the model to simulate the surface temperature of 0.6 0C, meet the verification accuracy of temperature inversion algorithm.Using ENVI – met model of Landsat8 data.
Keywords: land surface temperature; CFD; ENVI-met; cross validation
How to cite this article:
Yong Zhang,Zhiqiang Yao,Yingbao Yang,Leqin Zhang,Xin Pan. A Verification Method of Thermo – infrared Remote Sensing Temperature Retrieval Algorithm with a CFD Model. American Journal of Geographical Research and Reviews, 2018; 1:9. DOI:10.28933/ajgrr-2018-03-2801
1. Pan X, Liu Y, Fan X. Comparative assessment of satellite-retrieved surface net radiation: An examination on CERES and SRB datasets in China[J]. Remote Sensing, 2015, 7(4): 4899-4918.
2. Pan X, Liu Y, Fan X. Satellite retrieval of surface evapotranspiration with nonparametric approach: Accuracy assessment over a semiarid region[J]. Advances in Meteorology, 2016, 2016.
3. Pan X, Liu Y, Gan G, et al. Estimation of Evapotranspiration Using a Nonparametric Approach Under All Sky: Accuracy Evaluation and Error Analysis[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2017, 10(6): 2528-2539.
4. Li Z L, Tang B H, Wu H, et al. Satellite-derived land surface temperature: Current status and perspectives[J]. Remote Sensing of Environment, 2013, 131: 14-37.
5. Yang Y, Yao L. The influence of urban design factors on urban heat environment in urban residential area with remote sensing[C]//MIPPR 2009: Remote Sensing and GIS Data Processing and Other Applications. International Society for Optics and Photonics, 2009, 7498: 74984K.
6. Tang C S, Shi B, Gao L, et al. Urbanization effect on soil temperature in Nanjing, China[J]. Energy and Buildings, 2011, 43(11): 3090-3098.
7. Su W, Zhang Y, Yang Y, et al. Examining the impact of greenspace patterns on land surface temperature by coupling LiDAR data with a CFD model[J]. Sustainability, 2014, 6(10): 6799-6814.
8. Fahmy M, Hathway A, Pattacini L, et al. Environmental thermal impact assessment of regenerated urban form: A case study in Sheffield[C]//World Renewable Energy Congress-Sweden; 8-13 May; 2011; Linköping; Sweden. Linköping University Electronic Press, 2011 (057): 3201-3208.
9. Yang Y, Zhang X, Lu X, et al. Effects of Building Design Elements on Residential Thermal Environment[J]. Sustainability, 2017, 10(1): 57.
10. Cheng L, Gong J, Li M, et al. 3D building model reconstruction from multi-view aerial imagery and lidar data[J]. Photogrammetric Engineering & Remote Sensing, 2011, 77(2): 125-139.