Impact of Observational Environment Change on Air Temperature Based on High-Spatial-Resolution Satellite Remote Sensing Data

Shihan Chen; Ling Li; Hongfan Jiang; Weijie Ju; Manyu Zhang; Duanyang Liu; Yuanjian Yang

doi:10.3788/AOS202040.1028001

Journals >Acta Optica Sinica >Volume 40 >Issue 10 >Page 1028001 > Article

Acta Optica Sinica
Vol. 40, Issue 10, 1028001 (2020)

Impact of Observational Environment Change on Air Temperature Based on High-Spatial-Resolution Satellite Remote Sensing Data

Shihan Chen¹, Ling Li¹, Hongfan Jiang³, Weijie Ju⁴, Manyu Zhang¹, Duanyang Liu^5、6, and Yuanjian Yang^2、7、*

Author Affiliations

¹School of Remote Sensing & Geomatics Engineering, Nanjing University of Information Science & Technology, Nanjing, Jiangsu 210044, China;

²School of Atmospheric Physics, Nanjing University of Information Science & Technology, Nanjing, Jiangsu 210044, China;

³School of Computer and Software, Nanjing University of Information Science & Technology, Nanjing, Jiangsu 210044, China;

⁴School of Mathematics and Statistics, Nanjing University of Information Science and Technology, Nanjing, Jiangsu 210044, China

⁵Key Laboratory of Transportation Meteorology, China Meteorological Administration, Nanjing, Jiangsu 210008, China

⁶Jiangsu Meteorological Observatory, Nanjing, Jiangsu 210008, China

⁷Institute of Environment, Energy and Sustainability, The Chinese University of Hong Kong, Shatin, Hong Kong 999077, China

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DOI: 10.3788/AOS202040.1028001 Cite this Article Set citation alerts

Shihan Chen, Ling Li, Hongfan Jiang, Weijie Ju, Manyu Zhang, Duanyang Liu, Yuanjian Yang. Impact of Observational Environment Change on Air Temperature Based on High-Spatial-Resolution Satellite Remote Sensing Data[J]. Acta Optica Sinica, 2020, 40(10): 1028001 Copy Citation Text

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Fig. 1. Nanjing map. (a) Geographical location, altitude, and station locations; (b) Landsat8-based synthetic false color image

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Retrieval results. (a) Population density distribution; (b) anthropogenic heat flux distribution; (c) main land usage type distribution; (d) NDVI distribution; (e) NDWI distribution; (f) impervious surface distribution

Fig. 2. Retrieval results. (a) Population density distribution; (b) anthropogenic heat flux distribution; (c) main land usage type distribution; (d) NDVI distribution; (e) NDWI distribution; (f) impervious surface distribution

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Fig. 4. Air temperature distribution in Nanjing at 30 m resolution based on regression model

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Band	Wavelength /μm	Spatial resolution /m	Usage
Band 1 (coastal)	0.433-0.453	30	Coastal monitor
Band 2 (blue)	0.450-0.515	30	Penetration of water body and discrimination of soil and vegetation
Band 3 (green)	0.525-0.600	30	Vegetation identification
Band 4 (red)	0.630-0.680	30	Observing roads, bare soil, vegetation types
Band 5 (NIR)	0.845-0.885	30	Biomass estimation and wet soil identification
Band 6 (SWIR 1)	1.560-1.660	30	Distinguish roads, bare soil, water and fog-cloud
Band 7 (SWIR 2)	2.100-2.300	30	Rock and mineral identification
Band 8 (Pan)	0.500-0.680	15	Resolution enhancement
Band 9 (cirrus)	1.360-1.390	30	Cloud detection

Table 1. Main bands of OLI and their usages

Site type	Classification map	Real scene
Urban type
Suburban type
Rural type

Table 2. Underlying surface classification and corresponding scene examples(built-up is red, farmland is yellow, vegetation is green, and water is blue)

Number	Site type	Actual air temperature /℃	Predicted air temperature /℃	Difference /℃	Relative error
1	Urban	38.000	37.401	0.599	0.016
2	Urban	37.000	37.209	-0.209	-0.006
3	Urban	37.000	37.352	-0.352	-0.010
4	Urban	37.500	37.936	-0.436	-0.012
5	Suburban	36.000	36.183	-0.183	-0.005
6	Suburban	37.500	36.085	1.415	0.038
7	Suburban	37.500	36.320	1.180	0.031
8	Suburban	37.000	36.544	0.456	0.012
9	Rural	35.500	34.447	1.053	0.030
10	Rural	37.000	34.520	2.480	0.067
11	Rural	36.500	34.503	1.997	0.055
12	Rural	36.500	35.733	0.767	0.021