Performance of the Large Field of View Airborne Infrared Scanner and its application potential in land surface temperature retrieval

doi:10.1007/s11707-022-1023-0

Front. Earth Sci.

2023, Vol. 17

Issue (2) : 378-390 https://doi.org/10.1007/s11707-022-1023-0

RESEARCH ARTICLE

Performance of the Large Field of View Airborne Infrared Scanner and its application potential in land surface temperature retrieval

Chao WANG^1,², Zhiyuan LI^1,², Xiong XU^1,^2,³(

), Xiangsui ZENG^1,², Jia LI^1,², Huan XIE^1,², Yanmin JIN^1,², Xiaohua TONG^1,²

¹. College of surveying and Geo-informatics, Tongji University, Shanghai 200092, China
². Shanghai Key Laboratory for Planetary Mapping and Remote Sensing for Deep Space Exploration, Tongji University, Shanghai 200092, China
³. Frontiers Science Center for Intelligent Autonomous Systems, Tongji University, Shanghai 200092, China

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Abstract

The Large Field of View Airborne Infrared Scanner is a newly developed multi-spectral instrument that collects images from the near-infrared to long-wave infrared channels. Its data can be used for land surface temperature (LST) retrieval and environmental monitoring. Before data application, quality assessment is an essential procedure for a new instrument. In this paper, based on the data collected by the scanner near the Yellow River in Henan Province, the geometric and radiometric qualities of the images are first evaluated. The absolute geolocation accuracy of the ten bands of the scanner is approximately 5.1 m. The ground sampling distance is found to be varied with the whisk angles of the scanner and the spatial resolution of the images. The band-to-band registration accuracy between band one and the other nine bands is approximately 0.25 m. The length and angle deformations of the ten bands are approximately 0.67% and 0.3°, respectively. The signal-to-noise ratio (SNR) and relative radiometric calibration accuracy of bands 4, 9, and 10 are relatively better than those of the other bands. Secondly, the radiative transfer equation (RTE) method is used to retrieve the LST from the data of the scanner. Measurements of in situ samples are collected to evaluate the retrieved LST. Neglecting the samples with unreasonable retrieved LST, the bias and RMSE between in situ LST measured by CE312 radiometer and retrieved LST are −0.22 K and 0.94 K, and the bias and RMSE are 0.27 K and 1.59 K for the InfReC R500-D thermal imager, respectively. Overall, the images of the Large Field of View Airborne Infrared Scanner yield a relatively satisfactory accuracy for both LST retrieval and geometric and radiometric qualities.

Keywords Large Field of View Airborne Infrared Scanner quality assessment thermal infrared remote sensing land surface temperature retrieval

Corresponding Author(s): Xiong XU

Online First Date: 13 January 2023 Issue Date: 04 August 2023

Cite this article:

Chao WANG,Zhiyuan LI,Xiong XU, et al. Performance of the Large Field of View Airborne Infrared Scanner and its application potential in land surface temperature retrieval[J]. Front. Earth Sci., 2023, 17(2): 378-390.

URL:

https://academic.hep.com.cn/fesci/EN/10.1007/s11707-022-1023-0
https://academic.hep.com.cn/fesci/EN/Y2023/V17/I2/378

Tab.1 The differences between the Large Field of View Airborne Infrared Scanner and other airborne sensors

Tab.2 Specifications of the Large Field of View Airborne Infrared Scanner

Fig.1 Mosaic of the band nine images covering the GCPs (red points) and in situ measurements region (green rectangle).

Fig.2 Mosaic of the band nine images covering the in situ measurement plots (green points).

Tab.3 The average displacement in the X and Y directions, and the absolute geolocation accuracy in the ten bands

Fig.3 Comparison of the whisk angle of the image pixels containing the 63 GCPs and the averaged absolute geolocation accuracy in ten bands for each GCP.

Tab.4 The average ground sampling distance (m/pixel) under different whisk angles and the average ground sampling distance (m/pixel) of all GCP pairs

Fig.4 The relationship between the whisk angle and ground sampling distance of (a) B1, (b) B2, B3, and B4, and (c) B5, B6, B7, B8, B9, and B10.

Tab.5 The overall band-to-band registration accuracy and the band-to-band registration accuracy in the X and Y directions between band one and different bands

Fig.5 The band-to-band registration accuracy between band one and band two of different lines in the (a) X and (b) Y directions. (c) and (d) are the corresponding histogram of band-to-band registration accuracy in the X and Y directions.

Tab.6 The results of length deformation and angle deformation of the ten bands

Fig.6 Number of different (a) lengths and (b) angles used in the evaluation of geometric deformation.

Fig.7 Images of the DN value for the sample of built-up area of the ten bands.

Tab.7 The average signal-to-noise ratio and relative radiometric calibration accuracy of the ten bands

Tab.8 The average signal-to-noise ratio and relative radiometric calibration accuracy of the four regions

Fig.8 (a) Signal-to-noise ratio and (b) relative radiometric calibration accuracy of the ten band in the four regions.

Fig.9 The in situ LST of the ten plots measured by the InfReC R500-D.

Tab.9 Comparison between the LST measured in situ by CE312 and retrieved with the RTE method

Tab.10 Comparison between the LST measured in situ by InfReC R500-D and retrieved with the RTE method

Fig.10 Correlation between the in situ LST measured by (a) CE312 and (b) InfReC R500-D and the LST obtained from the RTE method.

Tab.11 The statistics of the comparison between the LST measured in situ and the RTE-retrieved LST

1	G P, Anderson A, Berk P K, Acharya M W, Matthew L S, Bernstein J H, Chetwynd H, Dothe S M, Adler-Golden A J, Ratkowski G W, Felde J A, Gardner M L, Hoke S C, Richtsmeier B, Pukall J B, Mello L S Jeong (2000). MODTRAN4: radiative transfer modeling for remote sensing. In: Algorithms for Multispectral, Hyperspectral, and Ultraspectral Imagery VI.Washington: SPIE, 176–183
2	M, Bouali S Ladjal (2011). Toward optimal destriping of MODIS data using a unidirectional variational model.IEEE Trans Geosci Remote Sens, 49(8): 2924–2935 https://doi.org/10.1109/TGRS.2011.2119399
3	A, Carter M Ramsey (2010). Long-term volcanic activity at shiveluch volcano: nine years of ASTER spaceborne thermal infrared observations.Remote Sens (Basel), 2(11): 2571–2583 https://doi.org/10.3390/rs2112571
4	Y Y, Choi M S Suh (2018). Development of Himawari-8/Advanced Himawari Imager (AHI) land surface temperature retrieval algorithm.Remote Sens (Basel), 10(12): 2013 https://doi.org/10.3390/rs10122013
5	C, Coll V, Caselles J A, Sobrino E Valor (1994). On the atmospheric dependence of the split-window equation for land-surface temperature.Int J Remote Sens, 15(1): 105–122 https://doi.org/10.1080/01431169408954054
6	M, Coolbaugh C, Kratt A, Fallacaro W, Calvin J Taranik (2007). Detection of geothermal anomalies using Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) thermal infrared images at Bradys Hot Springs, Nevada, USA.Remote Sens Environ, 106(3): 350–359 https://doi.org/10.1016/j.rse.2006.09.001
7	P, Dash F M, Göttsche F S, Olesen H Fischer (2002). Land surface temperature and emissivity estimation from passive sensor data: theory and practice-current trends.Int J Remote Sens, 23(13): 2563–2594 https://doi.org/10.1080/01431160110115041
8	S B, Duan Z L, Li H, Li F M, Göttsche H, Wu W, Zhao P, Leng X, Zhang C Coll (2019). Validation of Collection 6 MODIS land surface temperature product using in situ measurements.Remote Sens Environ, 225: 16–29 https://doi.org/10.1016/j.rse.2019.02.020
9	D, Eleftheriou K, Kiachidis G, Kalmintzis A, Kalea C, Bantasis P, Koumadoraki M E, Spathara A, Tsolaki M I, Tzampazidou A Gemitzi (2018). Determination of annual and seasonal daytime and nighttime trends of MODIS LST over Greece - climate change implications.Sci Total Environ, 616-617: 937–947 https://doi.org/10.1016/j.scitotenv.2017.10.226 pmid: 29107377
10	Z, Feng L, Song J, Duan L, He Y, Zhang Y, Wei W Feng (2022). Monitoring Wheat Powdery Mildew Based on Hyperspectral, Thermal Infrared, and RGB Image Data Fusion.Sensors (Basel), 22(1): 31 https://doi.org/10.3390/s22010031 pmid: 35009575
11	M A, Fischler R C Bolles (1981). Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography.Commun ACM, 24(6): 381–395 https://doi.org/10.1145/358669.358692
12	S C, Freitas I F, Trigo J M, Bioucas-Dias F M Gottsche (2010). Quantifying the uncertainty of land surface temperature retrievals From SEVIRI/Meteosat.IEEE Trans Geosci Remote Sens, 48(1): 523–534 https://doi.org/10.1109/TGRS.2009.2027697
13	A, Gillespie S, Rokugawa T, Matsunaga J S, Cothern S, Hook A B Kahle (1998). A temperature and emissivity separation algorithm for Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) images.IEEE Trans Geosci Remote Sens, 36(4): 1113–1126 https://doi.org/10.1109/36.700995
14	J, Guo H, Ren Y, Zheng S, Lu J Dong (2020). Evaluation of land surface temperature retrieval from Landsat 8/TIRS images before and after stray light correction using the SURFRAD dataset.Remote Sens (Basel), 12(6): 1023 https://doi.org/10.3390/rs12061023
15	W R Herb, B Janke, O Mohseni, H G Stefan (2008). Ground surface temperature simulation for different land covers. J Hydrol (Amst), 356(3–4): 327–343 https://doi.org/10.1016/j.jhydrol.2008.04.020
16	L, Hoffmann G, Günther D, Li O, Stein X, Wu S, Griessbach Y, Heng P, Konopka R, Müller B, Vogel J S Wright (2019). From ERA-Interim to ERA5: the considerable impact of ECMWF’s next-generation reanalysis on Lagrangian transport simulations.Atmos Chem Phys, 19(5): 3097–3124 https://doi.org/10.5194/acp-19-3097-2019
17	Y Hu, Y Zhang (2007). Analysis of relative radiometric calibration accuracy of space camera. Spacecraft Recovery & Remote Sensing, 28(4): 54–57 (in Chinese)
18	H Jia, D Yang, W Deng, Q Wei, W Jiang (2021). Predicting land surface temperature with geographically weighed regression and deep learning. Wiley Interdiscip Rev Data Min Knowl Discov, 11(1) https://doi.org/10.1002/widm.1396
19	J C, Jiménez-Muñoz J A Sobrino (2003). A generalized single-channel method for retrieving land surface temperature from remote sensing data.J Geophys Res, 108(D22): 2003JD003480 https://doi.org/10.1029/2003JD003480
20	S, Kabir L, Leigh D Helder (2020). Vicarious methodologies to assess and improve the quality of the optical remote sensing images: a critical review.Remote Sens (Basel), 12(24): 4029 https://doi.org/10.3390/rs12244029
21	A, Karnieli N, Agam R T, Pinker M, Anderson M L, Imhoff G G, Gutman N, Panov A Goldberg (2010). Use of NDVI and land surface temperature for drought assessment: merits and limitations.J Clim, 23(3): 618–633 https://doi.org/10.1175/2009JCLI2900.1
22	M, Lemus-Canovas J, Martin-Vide M C, Moreno-Garcia J A Lopez-Bustins (2020). Estimating Barcelona’s metropolitan daytime hot and cold poles using Landsat-8 land surface temperature.Sci Total Environ, 699: 134307 https://doi.org/10.1016/j.scitotenv.2019.134307 pmid: 31520942
23	C, Li S, Tian S, Li M Yin (2016). Temperature and emissivity separation via sparse representation with thermal airborne hyperspectral imager data.J Appl Remote Sens, 10(4): 042003 https://doi.org/10.1117/1.JRS.10.042003
24	Z, Li B, Tang H, Wu H, Ren G, Yan Z, Wan I F, Trigo J A Sobrino (2013). Satellite-derived land surface temperature: current status and perspectives.Remote Sens Environ, 131: 14–37 https://doi.org/10.1016/j.rse.2012.12.008
25	D, Liu R Pu (2008). Downscaling thermal infrared radiance for subpixel land surface temperature retrieval.Sensors (Basel), 8(4): 2695–2706 https://doi.org/10.3390/s8042695 pmid: 27879844
26	D G Lowe (1999). Object recognition from local scale-invariant features. In: Proceedings of the Seventh IEEE International Conference on Computer Vision.Kerkyra: IEEE, 1150–1157
27	K, Mao S, Li D, Wang L, Zhang X, Wang H, Tang Z Li (2011). Retrieval of land surface temperature and emissivity from ASTER1B data using a dynamic learning neural network.Int J Remote Sens, 32(19): 5413–5423 https://doi.org/10.1080/01431161.2010.501043
28	J, Nie H, Ren Y, Zheng D, Ghent K Tansey (2021). Land surface temperature and emissivity retrieval from nighttime middle-infrared and thermal-infrared Sentinel-3 images.IEEE Geosci Remote Sens Lett, 18(5): 915–919 https://doi.org/10.1109/LGRS.2020.2986326
29	B R, Parida S, Bar G, Roberts S P, Mandal A C, Pandey M, Kumar J Dash (2021). Improvement in air quality and its impact on land surface temperature in major urban areas across India during the first lockdown of the pandemic.Environ Res, 199: 111280 https://doi.org/10.1016/j.envres.2021.111280 pmid: 34029544
30	J C Price (1983). Estimating surface temperatures from satellite thermal infrared data—a simple formulation for the atmospheric effect.Remote Sens Environ, 13(4): 353–361 https://doi.org/10.1016/0034-4257(83)90036-6
31	Y, Qian E, Zhao C, Gao N, Wang L Ma (2015). Land surface temperature retrieval using nighttime mid-infrared channels data from airborne hyperspectral scanner.IEEE J Sel Top Appl Earth Obs Remote Sens, 8(3): 1208–1216 https://doi.org/10.1109/JSTARS.2014.2364853
32	Z, Qin A, Karnieli P Berliner (2001). A mono-window algorithm for retrieving land surface temperature from Landsat TM data and its application to the Israel-Egypt border region.Int J Remote Sens, 22(18): 3719–3746 https://doi.org/10.1080/01431160010006971
33	D A, Quattrochi J C Luvall (1999). Thermal infrared remote sensing for analysis of landscape ecological processes: methods and applications.Landsc Ecol, 14(6): 577–598 https://doi.org/10.1023/A:1008168910634
34	H, Ren X, Ye J, Nie J, Meng W, Fan Q, Qin Y, Liang H Liu (2022). Retrieval of land surface temperature, emissivity, and atmospheric parameters from hyperspectral thermal infrared image using a feature-band linear-format hybrid algorithm.IEEE Trans Geosci Remote Sens, 60: 1–15 https://doi.org/10.1109/TGRS.2020.3047381
35	J A, Sobrino J C, Jiménez-Muñoz J, El-Kharraz M, Gómez M, Romaguera G Sòria (2004). Single-channel and two-channel methods for land surface temperature retrieval from dais data and its application to the barrax site.Int J Remote Sens, 25(1): 215–230 https://doi.org/10.1080/0143116031000115210
36	J A, Sobrino J C, Jiménez-Muñoz G, Sòria M, Romaguera L, Guanter J, Moreno A, Plaza P Martínez (2008). Land surface emissivity retrieval from different VNIR and TIR sensors.IEEE Trans Geosci Remote Sens, 46(2): 316–327 https://doi.org/10.1109/TGRS.2007.904834
37	M, Urban J, Eberle C, Hüttich C, Schmullius M Herold (2013). Comparison of satellite-derived land surface temperature and air temperature from meteorological stations on the pan-arctic scale.Remote Sens (Basel), 5(5): 2348–2367 https://doi.org/10.3390/rs5052348
38	Z Wan (2008). New refinements and validation of the MODIS Land-Surface temperature/emissivity products.Remote Sens Environ, 112(1): 59–74 https://doi.org/10.1016/j.rse.2006.06.026
39	Z, Wan Z Li (1997). A physics-based algorithm for retrieving land-surface emissivity and temperature from EOS/MODIS data.IEEE Trans Geosci Remote Sens, 35(4): 980–996 https://doi.org/10.1109/36.602541
40	Z Wang, X Wu, H Qian, F Yu, R Iacovazzi, X Shao, V Kondratovich, H Yoo (2018). Radiometric Quality Assessment of GOES-16 ABI L1b Images. In: Earth Observing Systems XXIII. California: SPIE
41	Q, Weng P, Fu F Gao (2014). Generating daily land surface temperature at Landsat resolution by fusing Landsat and MODIS data.Remote Sens Environ, 145: 55–67 https://doi.org/10.1016/j.rse.2014.02.003
42	Y, Yamamoto H Ishikawa (2018). Thermal land surface emissivity for retrieving land surface temperature from Himawari-8.J Meteorol Soc Jpn, 96B(0): 43–58 https://doi.org/10.2151/jmsj.2018-004
43	S, Yang D, Zhang L, Sun Y, Wang Y Gao (2020). Assessing drought conditions in cloudy regions using reconstructed land surface temperature.J Meteorol Res, 34(2): 264–279 https://doi.org/10.1007/s13351-020-9136-4
44	S Ye, W Jiang, J Li, X Liu (2017). Imaging simulation and error analysis of large field of view airborne infrared scanner. Infrared Laser Eng, 46(4): 134–139 (in Chinese)
45	A, Zarei R, Shah-Hosseini S, Ranjbar M Hasanlou (2021). Validation of non-linear split window algorithm for land surface temperature estimation using Sentinel-3 satellite imagery: case study; Tehran Province, Iran.Adv Space Res, 67(12): 3979–3993 https://doi.org/10.1016/j.asr.2021.02.019
46	R, Zhang J, Tian H, Su X, Sun S, Chen J Xia (2008). Two improvements of an operational two-layer model for terrestrial surface heat flux retrieval.Sensors (Basel), 8(10): 6165–6187 https://doi.org/10.3390/s8106165 pmid: 27873864
47	L, Zhu J, Zhou S, Liu M, Li G Li (2016). Comparison of diurnal temperature cycle model and polynomial regression technique in temporal normalization of airborne land surface temperature. In: 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS).Beijing: IEEE, 4309–4312

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