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

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.

Graphical abstract

Keywords

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

References

[1] Anderson G P, Berk A, Acharya P K, Matthew M W, Bernstein L S, Chetwynd J H, Dothe H, Adler-Golden S M, Ratkowski A J, Felde G W, Gardner J A, Hoke M L, Richtsmeier S C, Pukall B, Mello J B, Jeong L S (2000). MODTRAN4: radiative transfer modeling for remote sensing. In: Algorithms for Multispectral, Hyperspectral, and Ultraspectral Imagery VI.Washington: SPIE, 176–183

[2] Bouali M, Ladjal S (2011). Toward optimal destriping of MODIS data using a unidirectional variational model.IEEE Trans Geosci Remote Sens, 49(8): 2924–2935

[3] Carter A, Ramsey M (2010). Long-term volcanic activity at shiveluch volcano: nine years of ASTER spaceborne thermal infrared observations.Remote Sens (Basel), 2(11): 2571–2583

[4] Choi Y Y, Suh M S (2018). Development of Himawari-8/Advanced Himawari Imager (AHI) land surface temperature retrieval algorithm.Remote Sens (Basel), 10(12): 2013

[5] Coll C, Caselles V, Sobrino J A, Valor E (1994). On the atmospheric dependence of the split-window equation for land-surface temperature.Int J Remote Sens, 15(1): 105–122

[6] Coolbaugh M, Kratt C, Fallacaro A, Calvin W, Taranik J (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

[7] Dash P, Göttsche F M, Olesen F S, Fischer H (2002). Land surface temperature and emissivity estimation from passive sensor data: theory and practice-current trends.Int J Remote Sens, 23(13): 2563–2594

[8] Duan S B, Li Z L, Li H, Göttsche F M, Wu H, Zhao W, Leng P, Zhang X, Coll C (2019). Validation of Collection 6 MODIS land surface temperature product using in situ measurements.Remote Sens Environ, 225: 16–29

[9] Eleftheriou D, Kiachidis K, Kalmintzis G, Kalea A, Bantasis C, Koumadoraki P, Spathara M E, Tsolaki A, Tzampazidou M I, Gemitzi A (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

[10] Feng Z, Song L, Duan J, He L, Zhang Y, Wei Y, Feng W (2022). Monitoring Wheat Powdery Mildew Based on Hyperspectral, Thermal Infrared, and RGB Image Data Fusion.Sensors (Basel), 22(1): 31

[11] Fischler M A, Bolles R C (1981). Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography.Commun ACM, 24(6): 381–395

[12] Freitas S C, Trigo I F, Bioucas-Dias J M, Gottsche F M (2010). Quantifying the uncertainty of land surface temperature retrievals From SEVIRI/Meteosat.IEEE Trans Geosci Remote Sens, 48(1): 523–534

[13] Gillespie A, Rokugawa S, Matsunaga T, Cothern J S, Hook S, Kahle A B (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

[14] Guo J, Ren H, Zheng Y, Lu S, Dong J (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

[15] Herb W R, Janke B, Mohseni O, Stefan H G (2008). Ground surface temperature simulation for different land covers. J Hydrol (Amst), 356(3–4): 327–343

[16] Hoffmann L, Günther G, Li D, Stein O, Wu X, Griessbach S, Heng Y, Konopka P, Müller R, Vogel B, Wright J S (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

[17] Hu Y, Zhang Y (2007). Analysis of relative radiometric calibration accuracy of space camera. Spacecraft Recovery & Remote Sensing, 28(4): 54–57 (in Chinese)

[18] Jia H, Yang D, Deng W, Wei Q, Jiang W (2021). Predicting land surface temperature with geographically weighed regression and deep learning. Wiley Interdiscip Rev Data Min Knowl Discov, 11(1)

[19] Jiménez-Muñoz J C, Sobrino J A (2003). A generalized single-channel method for retrieving land surface temperature from remote sensing data.J Geophys Res, 108(D22): 2003JD003480

[20] Kabir S, Leigh L, Helder D (2020). Vicarious methodologies to assess and improve the quality of the optical remote sensing images: a critical review.Remote Sens (Basel), 12(24): 4029

[21] Karnieli A, Agam N, Pinker R T, Anderson M, Imhoff M L, Gutman G G, Panov N, Goldberg A (2010). Use of NDVI and land surface temperature for drought assessment: merits and limitations.J Clim, 23(3): 618–633

[22] Lemus-Canovas M, Martin-Vide J, Moreno-Garcia M C, Lopez-Bustins J A (2020). Estimating Barcelona’s metropolitan daytime hot and cold poles using Landsat-8 land surface temperature.Sci Total Environ, 699: 134307

[23] Li C, Tian S, Li S, Yin M (2016). Temperature and emissivity separation via sparse representation with thermal airborne hyperspectral imager data.J Appl Remote Sens, 10(4): 042003

[24] Li Z, Tang B, Wu H, Ren H, Yan G, Wan Z, Trigo I F, Sobrino J A (2013). Satellite-derived land surface temperature: current status and perspectives.Remote Sens Environ, 131: 14–37

[25] Liu D, Pu R (2008). Downscaling thermal infrared radiance for subpixel land surface temperature retrieval.Sensors (Basel), 8(4): 2695–2706

[26] Lowe D G (1999). Object recognition from local scale-invariant features. In: Proceedings of the Seventh IEEE International Conference on Computer Vision.Kerkyra: IEEE, 1150–1157

[27] Mao K, Li S, Wang D, Zhang L, Wang X, Tang H, Li Z (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

[28] Nie J, Ren H, Zheng Y, Ghent D, Tansey K (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

[29] Parida B R, Bar S, Roberts G, Mandal S P, Pandey A C, Kumar M, Dash J (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

[30] Price J C (1983). Estimating surface temperatures from satellite thermal infrared data—a simple formulation for the atmospheric effect.Remote Sens Environ, 13(4): 353–361

[31] Qian Y, Zhao E, Gao C, Wang N, Ma L (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

[32] Qin Z, Karnieli A, Berliner P (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

[33] Quattrochi D A, Luvall J C (1999). Thermal infrared remote sensing for analysis of landscape ecological processes: methods and applications.Landsc Ecol, 14(6): 577–598

[34] Ren H, Ye X, Nie J, Meng J, Fan W, Qin Q, Liang Y, Liu H (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

[35] Sobrino J A, Jiménez-Muñoz J C, El-Kharraz J, Gómez M, Romaguera M, Sòria G (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

[36] Sobrino J A, Jiménez-Muñoz J C, Sòria G, Romaguera M, Guanter L, Moreno J, Plaza A, Martínez P (2008). Land surface emissivity retrieval from different VNIR and TIR sensors.IEEE Trans Geosci Remote Sens, 46(2): 316–327

[37] Urban M, Eberle J, Hüttich C, Schmullius C, Herold M (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

[38] Wan Z (2008). New refinements and validation of the MODIS Land-Surface temperature/emissivity products.Remote Sens Environ, 112(1): 59–74

[39] Wan Z, Li Z (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

[40] Wang Z, Wu X, Qian H, Yu F, Iacovazzi R, Shao X, Kondratovich V, Yoo H (2018). Radiometric Quality Assessment of GOES-16 ABI L1b Images. In: Earth Observing Systems XXIII. California: SPIE

[41] Weng Q, Fu P, Gao F (2014). Generating daily land surface temperature at Landsat resolution by fusing Landsat and MODIS data.Remote Sens Environ, 145: 55–67

[42] Yamamoto Y, Ishikawa H (2018). Thermal land surface emissivity for retrieving land surface temperature from Himawari-8.J Meteorol Soc Jpn, 96B(0): 43–58

[43] Yang S, Zhang D, Sun L, Wang Y, Gao Y (2020). Assessing drought conditions in cloudy regions using reconstructed land surface temperature.J Meteorol Res, 34(2): 264–279

[44] Ye S, Jiang W, Li J, Liu X (2017). Imaging simulation and error analysis of large field of view airborne infrared scanner. Infrared Laser Eng, 46(4): 134–139 (in Chinese)

[45] Zarei A, Shah-Hosseini R, Ranjbar S, Hasanlou M (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

[46] Zhang R, Tian J, Su H, Sun X, Chen S, Xia J (2008). Two improvements of an operational two-layer model for terrestrial surface heat flux retrieval.Sensors (Basel), 8(10): 6165–6187

[47] Zhu L, Zhou J, Liu S, Li M, Li G (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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