Simulation of Spectral Albedo and Bidirectional Reflectance over Snow-Covered Urban Canyon: Model Development and Factor Analysis
<p>(<b>a</b>) The location of Harbin, situated in northeastern China, and (<b>b</b>) image taken from the EOSDIS Worldview website (<a href="https://worldview.earthdata.nasa.gov/" target="_blank">https://worldview.earthdata.nasa.gov/</a>, accessed 17 June 2024), which showcases a snapshot of the urban structure. Distribution of spectral albedo (<b>c</b>–<b>e</b>) within the study area (126.5°E to 126.8°E, 45.6°N to 45.9°N), with data derived from the MODIS Terra MOD09A1 product on 13 October 2019 (autumn season).</p> "> Figure 2
<p>The topographical representation of the modeled urban canyons configured with standard parameters.</p> "> Figure 3
<p>Viewing geometry and plane definitions.</p> "> Figure 4
<p>The spectral reflectance of snow as simulated by the ART model for MODIS Band 1 (469 nm), with a snow grain size of 3600 µm and a soot pollutant concentration of 500 ppb.</p> "> Figure 5
<p>Spectral albedo of selected man-made and natural materials.</p> "> Figure 6
<p>Flowchart of the proposed snow-covered urban BRDF model.</p> "> Figure 7
<p>Variations of domain-averaged relative error with incident photon numbers.</p> "> Figure 8
<p>Comparisons of simulated BRDF with truth values over flat Lambertian, RTLSR, and ART surfaces. Solar zenith angle is 45°; Lambertian albedo is 0.2; RTLSR model coefficients are <math display="inline"><semantics> <mrow> <msub> <mi>f</mi> <mrow> <mi>i</mi> <mi>s</mi> <mi>o</mi> </mrow> </msub> <mo>=</mo> <mn>0.091</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>f</mi> <mrow> <mi>v</mi> <mi>o</mi> <mi>l</mi> </mrow> </msub> <mo>=</mo> <mn>0.032</mn> </mrow> </semantics></math>, and <math display="inline"><semantics> <mrow> <msub> <mi>f</mi> <mrow> <mi>g</mi> <mi>e</mi> <mi>o</mi> </mrow> </msub> <mo>=</mo> <mn>0.012</mn> </mrow> </semantics></math>; and the ART model has a snow grain size of 500 µm and pollutant concentration of 3600 ppb.</p> "> Figure 9
<p>Time series of spectral albedos from the MOD09/MYD09 dataset over Harbin between 2018 and 2022.</p> "> Figure 10
<p>Spectral variations and deviations of MOD, MYD, and modeled albedos under snow-free, fresh snow, and snow melt scenarios.</p> "> Figure 10 Cont.
<p>Spectral variations and deviations of MOD, MYD, and modeled albedos under snow-free, fresh snow, and snow melt scenarios.</p> "> Figure 11
<p>Spectral albedo and BRDF variations over urban canyons with building coverage varying from 10% to 50%. The negative VZA in the PP represents the direction of the relative azimuth angle of 180° (forward scattering direction), and the negative VZA in the CP represents the direction of the relative azimuth angle of 270°. (<b>a</b>) Spectral albedo. (<b>b</b>) BRDF distribution at Band 1 with BC = 50%. (<b>c</b>) BRDF distribution at Band 6 with BC = 50%. (<b>d</b>) ALM reflectance at Band 1. (<b>e</b>) PP reflectance at Band 1. (<b>f</b>) CP reflectance at Band 1. (<b>g</b>) ALM reflectance at Band 6. (<b>h</b>) PP reflectance at Band 6. (<b>i</b>) CP reflectance at Band 6.</p> "> Figure 11 Cont.
<p>Spectral albedo and BRDF variations over urban canyons with building coverage varying from 10% to 50%. The negative VZA in the PP represents the direction of the relative azimuth angle of 180° (forward scattering direction), and the negative VZA in the CP represents the direction of the relative azimuth angle of 270°. (<b>a</b>) Spectral albedo. (<b>b</b>) BRDF distribution at Band 1 with BC = 50%. (<b>c</b>) BRDF distribution at Band 6 with BC = 50%. (<b>d</b>) ALM reflectance at Band 1. (<b>e</b>) PP reflectance at Band 1. (<b>f</b>) CP reflectance at Band 1. (<b>g</b>) ALM reflectance at Band 6. (<b>h</b>) PP reflectance at Band 6. (<b>i</b>) CP reflectance at Band 6.</p> "> Figure 12
<p>Spectral albedo and BRDF variations over urban canyons with building height varying from 10 m to 90 m. The negative VZA in the PP represents the direction of the relative azimuth angle of 180° (forward scattering direction), and the negative VZA in the CP represents the direction of the relative azimuth angle of 270°. (<b>a</b>) Spectral albedo. (<b>b</b>) BRDF distribution at Band 1 with BH = 90 m. (<b>c</b>) BRDF distribution at Band 6 with BH = 90 m. (<b>d</b>) ALM reflectance at Band 1. (<b>e</b>) PP reflectance at Band 1. (<b>f</b>) CP reflectance at Band 1. (<b>g</b>) ALM reflectance at Band 6. (<b>h</b>) PP reflectance at Band 6. (<b>i</b>) CP reflectance at Band 6.</p> "> Figure 13
<p>Spectral albedo and BRDF variations over urban canyons with snow coverage varying from 20% to 100%. The negative VZA in the PP represents the direction of the relative azimuth angle of 180° (forward scattering direction), and the negative VZA in the CP represents the direction of the relative azimuth angle of 270°. (<b>a</b>) Spectral albedo. (<b>b</b>) BRDF distribution at Band 1 with SC = 20%. (<b>c</b>) BRDF distribution at Band 6 with SC = 20%. (<b>d</b>) ALM reflectance at Band 1. (<b>e</b>) PP reflectance at Band 1. (<b>f</b>) CP reflectance at Band 1. (<b>g</b>) ALM reflectance at Band 6. (<b>h</b>) PP reflectance at Band 6. (<b>i</b>) CP reflectance at Band 6.</p> "> Figure 14
<p>Spectral albedo and BRDF variations over urban canyons with snow grain size varying from 100 to 5000 µm. The negative VZA in the PP represents the direction of the relative azimuth angle of 180° (forward scattering direction), and the negative VZA in the CP represents the direction of the relative azimuth angle of 270°. (<b>a</b>) Spectral albedo. (<b>b</b>) BRDF distribution at Band 1 with SGS = 5000 µm. (<b>c</b>) BRDF distribution at Band 6 with SGS = 5000 µm. (<b>d</b>) ALM reflectance at Band 1. (<b>e</b>) PP reflectance at Band 1. (<b>f</b>) CP reflectance at Band 1. (<b>g</b>) ALM reflectance at Band 6. (<b>h</b>) PP reflectance at Band 6. (<b>i</b>) CP reflectance at Band 6.</p> "> Figure 14 Cont.
<p>Spectral albedo and BRDF variations over urban canyons with snow grain size varying from 100 to 5000 µm. The negative VZA in the PP represents the direction of the relative azimuth angle of 180° (forward scattering direction), and the negative VZA in the CP represents the direction of the relative azimuth angle of 270°. (<b>a</b>) Spectral albedo. (<b>b</b>) BRDF distribution at Band 1 with SGS = 5000 µm. (<b>c</b>) BRDF distribution at Band 6 with SGS = 5000 µm. (<b>d</b>) ALM reflectance at Band 1. (<b>e</b>) PP reflectance at Band 1. (<b>f</b>) CP reflectance at Band 1. (<b>g</b>) ALM reflectance at Band 6. (<b>h</b>) PP reflectance at Band 6. (<b>i</b>) CP reflectance at Band 6.</p> "> Figure 15
<p>Spectral albedo and BRDF variations over urban canyons with pollutant concentration varying from <math display="inline"><semantics> <msup> <mn>10</mn> <mn>2</mn> </msup> </semantics></math> ppb to <math display="inline"><semantics> <msup> <mn>10</mn> <mn>6</mn> </msup> </semantics></math> ppb. The negative VZA in the PP represents the direction of the relative azimuth angle of 180° (forward scattering direction), and the negative VZA in the CP represents the direction of the relative azimuth angle of 270°. (<b>a</b>) Spectral albedo. (<b>b</b>) BRDF distribution at Band 1 with PC =<math display="inline"><semantics> <msup> <mn>10</mn> <mn>2</mn> </msup> </semantics></math> ppb. (<b>c</b>) BRDF distribution at Band 6 with PC =<math display="inline"><semantics> <msup> <mn>10</mn> <mn>6</mn> </msup> </semantics></math> ppb. (<b>d</b>) ALM reflectance at Band 1. (<b>e</b>) PP reflectance at Band 1. (<b>f</b>) CP reflectance at Band 1. (<b>g</b>) ALM reflectance at Band 6. (<b>h</b>) PP reflectance at Band 6. (<b>i</b>) CP reflectance at Band 6.</p> "> Figure 15 Cont.
<p>Spectral albedo and BRDF variations over urban canyons with pollutant concentration varying from <math display="inline"><semantics> <msup> <mn>10</mn> <mn>2</mn> </msup> </semantics></math> ppb to <math display="inline"><semantics> <msup> <mn>10</mn> <mn>6</mn> </msup> </semantics></math> ppb. The negative VZA in the PP represents the direction of the relative azimuth angle of 180° (forward scattering direction), and the negative VZA in the CP represents the direction of the relative azimuth angle of 270°. (<b>a</b>) Spectral albedo. (<b>b</b>) BRDF distribution at Band 1 with PC =<math display="inline"><semantics> <msup> <mn>10</mn> <mn>2</mn> </msup> </semantics></math> ppb. (<b>c</b>) BRDF distribution at Band 6 with PC =<math display="inline"><semantics> <msup> <mn>10</mn> <mn>6</mn> </msup> </semantics></math> ppb. (<b>d</b>) ALM reflectance at Band 1. (<b>e</b>) PP reflectance at Band 1. (<b>f</b>) CP reflectance at Band 1. (<b>g</b>) ALM reflectance at Band 6. (<b>h</b>) PP reflectance at Band 6. (<b>i</b>) CP reflectance at Band 6.</p> "> Figure 16
<p>Spectral albedo and BRDF variations over urban canyons with solar zenith angle varying from 40°to 80°. The negative VZA in the PP represents the direction of the relative azimuth angle of 180° (forward scattering direction), and the negative VZA in the CP represents the direction of the relative azimuth angle of 270°. (<b>a</b>) Spectral albedo. (<b>b</b>) BRDF distribution at Band 1 with SZA = 80°. (<b>c</b>) BRDF distribution at Band 6 with SZA = 80°. (<b>d</b>) ALM reflectance at Band 1. (<b>e</b>) PP reflectance at Band 1. (<b>f</b>) CP reflectance at Band 1. (<b>g</b>) ALM reflectance at Band 6. (<b>h</b>) PP reflectance at Band 6. (<b>i</b>) CP reflectance at Band 6.</p> "> Figure 17
<p>Spectral albedo with different settings of building heights and snow coverage (<b>a</b>–<b>c</b>) and of snow grain size and concentration of soot pollution (<b>d</b>–<b>f</b>).</p> ">
Abstract
:1. Introduction
2. Materials and Methods
2.1. Site Description
2.2. Geometric Modeling of Urban Canyons
2.3. Satellite Albedo Product
2.4. Snow ART Model
2.5. Spectral Albedo Data for Urban Surfaces
2.6. Monte Carlo Ray-Tracing Method
3. Model Validation
3.1. Validation over Flat Surfaces
3.2. Validation with MODIS Albedo Observations
4. Results
4.1. Building Coverage
4.2. Building Height
4.3. Snow Coverage
4.4. Snow Grain Size
4.5. Soot Pollutant Concentration
4.6. Solar Zenith Angle
4.7. Influence of Intertwined Factors on Snow-Covered Urban Albedo
5. Discussions
6. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Ren, Z.; Fu, Y.; Dong, Y.; Zhang, P.; He, X. Rapid urbanization and climate change significantly contribute to worsening urban human thermal comfort: A national 183-city, 26-year study in China. Urban Clim. 2022, 43, 101154. [Google Scholar] [CrossRef]
- Wang, X.; Meng, X.; Long, Y. Projecting 1 km-grid population distributions from 2020 to 2100 globally under shared socioeconomic pathways. Sci. Data 2022, 9, 563. [Google Scholar] [CrossRef] [PubMed]
- Liou, K.; Takano, Y.; Yang, P. Light absorption and scattering by aggregates: Application to black carbon and snow grains. J. Quant. Spectrosc. Radiat. Transf. 2011, 112, 1581–1594. [Google Scholar] [CrossRef]
- Wu, H.; Liang, S.; Tong, L.; He, T.; Yu, Y. Bidirectional Reflectance for Multiple Snow-Covered Land Types from MISR Products. IEEE Geosci. Remote Sens. Lett. 2012, 9, 994–998. [Google Scholar] [CrossRef]
- Varentsov, M.; Konstantinov, P.; Baklanov, A.; Esau, I.; Miles, V.; Davy, R. Anthropogenic and natural drivers of a strong winter urban heat island in a typical Arctic city. Atmos. Chem. Phys. 2018, 18, 17573–17587. [Google Scholar] [CrossRef]
- Mei, L.; Xue, Y.; de Leeuw, G.; von Hoyningen-Huene, W.; Kokhanovsky, A.A.; Istomina, L.; Guang, J.; Burrows, J.P. Aerosol optical depth retrieval in the Arctic region using MODIS data over snow. Remote Sens. Environ. 2013, 128, 234–245. [Google Scholar] [CrossRef]
- He, C.; Liou, K.N.; Takano, Y. Resolving Size Distribution of Black Carbon Internally Mixed with Snow: Impact on Snow Optical Properties and Albedo. Geophys. Res. Lett. 2018, 45, 2697–2705. [Google Scholar] [CrossRef]
- Mei, L.; Vandenbussche, S.; Rozanov, V.; Proestakis, E.; Amiridis, V.; Callewaert, S.; Vountas, M.; Burrows, J.P. On the retrieval of aerosol optical depth over cryosphere using passive remote sensing. Remote Sens. Environ. 2020, 241, 111731. [Google Scholar] [CrossRef]
- Chu, Q.; Yan, G.; Qi, J.; Mu, X.; Li, L.; Tong, Y.; Zhou, Y.; Liu, Y.; Xie, D.; Wild, M. Quantitative Analysis of Terrain Reflected Solar Radiation in Snow-Covered Mountains: A Case Study in Southeastern Tibetan Plateau. J. Geophys. Res. Atmos. 2021, 126, e2020JD034294. [Google Scholar] [CrossRef]
- Lee, W.L.; Liou, K.N.; Wang, C.C.; Gu, Y.; Hsu, H.H.; Li, J.L.F. Impact of 3-D Radiation-Topography Interactions on Surface Temperature and Energy Budget over the Tibetan Plateau in Winter. J. Geophys. Res. Atmos. 2019, 124, 1537–1549. [Google Scholar] [CrossRef]
- Chen, S.; Xiao, P.; Zhang, X.; Qi, J.; Yin, G.; Ma, W.; Liu, H. Simulating snow-covered forest bidirectional reflectance by extending hybrid geometric optical–radiative transfer model. Remote Sens. Environ. 2023, 296, 113713. [Google Scholar] [CrossRef]
- Ji, W.; Hao, X.; Shao, D.; Yang, Q.; Wang, J.; Li, H.; Huang, G. A New Index for Snow/Ice/Ice-Snow Discrimination Based on BRDF Characteristic Observation Data. J. Geophys. Res. Atmos. 2022, 127, e2021JD035742. [Google Scholar] [CrossRef]
- Gatebe, C.K.; King, M.D. Airborne spectral BRDF of various surface types (ocean, vegetation, snow, desert, wetlands, cloud decks, smoke layers) for remote sensing applications. Remote Sens. Environ. 2016, 179, 131–148. [Google Scholar] [CrossRef]
- Tian, X.; Liu, Q.; Song, Z.; Dou, B.; Li, X. Aerosol Optical Depth Retrieval from Landsat 8 OLI Images over Urban Areas Supported by MODIS BRDF/Albedo Data. IEEE Geosci. Remote Sens. Lett. 2018, 15, 976–980. [Google Scholar] [CrossRef]
- Lin, H.; Li, S.; Xing, J.; He, T.; Yang, J.; Wang, Q. High resolution aerosol optical depth retrieval over urban areas from Landsat-8 OLI images. Atmos. Environ. 2021, 261, 118591. [Google Scholar] [CrossRef]
- Mei, L.; Rozanov, V.; Jiao, Z.; Burrows, J.P. A new snow bidirectional reflectance distribution function model in spectral regions from UV to SWIR: Model development and application to ground-based, aircraft and satellite observations. ISPRS J. Photogramm. Remote Sens. 2022, 188, 269–285. [Google Scholar] [CrossRef]
- Shi, Z.; Xing, T.; Guang, J.; Xue, Y.; Che, Y. Aerosol Optical Depth over the Arctic Snow-Covered Regions Derived from Dual-Viewing Satellite Observations. Remote Sens. 2019, 11, 891. [Google Scholar] [CrossRef]
- Swain, B.; Vountas, M.; Deroubaix, A.; Lelli, L.; Ziegler, Y.; Jafariserajehlou, S.; Gunthe, S.S.; Herber, A.; Ritter, C.; Bösch, H.; et al. Retrieval of aerosol optical depth over the Arctic cryosphere during spring and summer using satellite observations. Atmos. Meas. Tech. 2024, 17, 359–375. [Google Scholar] [CrossRef]
- Kokhanovsky, A.A.; Breon, F.M. Validation of an Analytical Snow BRDF Model Using PARASOL Multi-Angular and Multispectral Observations. IEEE Geosci. Remote Sens. Lett. 2012, 9, 928–932. [Google Scholar] [CrossRef]
- Hsu, N.C.; Lee, J.; Sayer, A.M.; Kim, W.; Bettenhausen, C.; Tsay, S.C. VIIRS Deep Blue Aerosol Products over Land: Extending the EOS Long-Term Aerosol Data Records. J. Geophys. Res. Atmos. 2019, 124, 4026–4053. [Google Scholar] [CrossRef]
- Levy, R.C.; Mattoo, S.; Munchak, L.A.; Remer, L.A.; Sayer, A.M.; Patadia, F.; Hsu, N.C. The Collection 6 MODIS aerosol products over land and ocean. Atmos. Meas. Tech. 2013, 6, 2989–3034. [Google Scholar] [CrossRef]
- Malmros, J.K.; Mernild, S.H.; Wilson, R.; Tagesson, T.; Fensholt, R. Snow cover and snow albedo changes in the central Andes of Chile and Argentina from daily MODIS observations (2000–2016). Remote Sens. Environ. 2018, 209, 240–252. [Google Scholar] [CrossRef]
- Zhang, Y.; Kang, S.; Cong, Z.; Schmale, J.; Sprenger, M.; Li, C.; Yang, W.; Gao, T.; Sillanpää, M.; Li, X.; et al. Light-absorbing impurities enhance glacier albedo reduction in the southeastern Tibetan plateau. J. Geophys. Res. Atmos. 2017, 122, 6915–6933. [Google Scholar] [CrossRef]
- Wang, X.; Shi, T.; Zhang, X.; Chen, Y. An Overview of Snow Albedo Sensitivity to Black Carbon Contamination and Snow Grain Properties Based on Experimental Datasets across the Northern Hemisphere. Curr. Pollut. Rep. 2020, 6, 368–379. [Google Scholar] [CrossRef]
- Cooper, K.D.; Smith, J.A. A Monte Carlo Reflectance Model for Soil Surfaces with Three-Dimensional Structure. IEEE Trans. Geosci. Remote Sens. 1985, GE-23, 668–673. [Google Scholar] [CrossRef]
- Lee, W.L.; Liou, K.N.; Hall, A. Parameterization of solar fluxes over mountain surfaces for application to climate models. J. Geophys. Res. Atmos. 2011, 116, D01101. [Google Scholar] [CrossRef]
- Jin, S.Y.; Susaki, J. A 3-D Topographic-Relief-Correlated Monte Carlo Radiative Transfer Simulator for Forest Bidirectional Reflectance Estimation. IEEE Geosci. Remote Sens. Lett. 2017, 14, 964–968. [Google Scholar] [CrossRef]
- Kuchiki, K.; Aoki, T.; Niwano, M.; Motoyoshi, H.; Iwabuchi, H. Effect of sastrugi on snow bidirectional reflectance and its application to MODIS data. J. Geophys. Res. Atmos. 2011, 116. [Google Scholar] [CrossRef]
- Xiong, C.; Shi, J. Simulating polarized light scattering in terrestrial snow based on bicontinuous random medium and Monte Carlo ray tracing. J. Quant. Spectrosc. Radiat. Transf. 2014, 133, 177–189. [Google Scholar] [CrossRef]
- Larue, F.; Picard, G.; Arnaud, L.; Ollivier, I.; Delcourt, C.; Lamare, M.; Tuzet, F.; Revuelto, J.; Dumont, M. Snow albedo sensitivity to macroscopic surface roughness using a new ray-tracing model. Cryosphere 2020, 14, 1651–1672. [Google Scholar] [CrossRef]
- Zege, E.; Katsev, I.; Malinka, A.; Prikhach, A.; Heygster, G.; Wiebe, H. Algorithm for retrieval of the effective snow grain size and pollution amount from satellite measurements. Remote Sens. Environ. 2011, 115, 2674–2685. [Google Scholar] [CrossRef]
- Qu, Y.; Liang, S.; Liu, Q.; Li, X.; Feng, Y.; Liu, S. Estimating Arctic sea-ice shortwave albedo from MODIS data. Remote Sens. Environ. 2016, 186, 32–46. [Google Scholar] [CrossRef]
- Kokhanovsky, A.; Lamare, M.; Di Mauro, B.; Picard, G.; Arnaud, L.; Dumont, M.; Tuzet, F.; Brockmann, C.; Box, J.E. On the reflectance spectroscopy of snow. Cryosphere 2018, 12, 2371–2382. [Google Scholar] [CrossRef]
- Shui, T.; Liu, J.; Xiao, Y.; Shi, L. Effects of snow cover on urban surface energy exchange: Observations in Harbin, China during the winter season. Int. J. Climatol. 2019, 39, 1230–1242. [Google Scholar] [CrossRef]
- Fu, D.L.; Zhang, W.; Xing, Y.; Li, H.; Wang, P.; Li, B.; Shi, X.; Jinxiang, Z.; Yabo, S.; Thapa, S.; et al. Impacts of maximum snow albedo and land cover changes on meteorological variables during winter in northeast China. Atmos. Res. 2021, 254, 105449. [Google Scholar] [CrossRef]
- Chen, Q.X.; Shen, W.X.; Yuan, Y.; Tan, H.P. Verification of aerosol classification methods through satellite and ground-based measurements over Harbin, Northeast China. Atmos. Res. 2019, 216, 167–175. [Google Scholar] [CrossRef]
- Chen, Q.; Yuan, Y.; Huang, X.; He, Z.; Tan, H. Assessment of column aerosol optical properties using ground-based sun-photometer at urban Harbin, Northeast China. J. Environ. Sci. 2018, 74, 50–57. [Google Scholar] [CrossRef] [PubMed]
- Cheng, Y.; Zhong, Y.-J.; Liu, J.-M.; Cao, X.-B.; Zhang, Q.; He, K.-B. Response of Harbin aerosol to latest clean air actions in China. J. Hazard. Mater. 2024, 467, 133728. [Google Scholar] [CrossRef] [PubMed]
- Skakun, S.; Justice, C.; Vermote, E.; Roger, J.C. Transitioning from MODIS to VIIRS: An analysis of inter-consistency of NDVI data sets for agricultural monitoring. Int. J. Remote Sens. 2018, 39, 971–992. [Google Scholar] [CrossRef] [PubMed]
- Salomon, J.; Schaaf, C.; Strahler, A.; Gao, F.; Jin, Y. Validation of the MODIS bidirectional reflectance distribution function and albedo retrievals using combined observations from the aqua and terra platforms. IEEE Trans. Geosci. Remote Sens. 2006, 44, 1555–1565. [Google Scholar] [CrossRef]
- Schaaf, C.B.; Liu, J.; Gao, F.; Strahler, A.H. Aqua and Terra MODIS Albedo and Reflectance Anisotropy Products. In Land Remote Sensing and Global Environmental Change: NASA’s Earth Observing System and the Science of ASTER and MODIS; Ramachandran, B., Justice, C.O., Abrams, M.J., Eds.; Springer: New York, NY, USA, 2011; pp. 549–561. [Google Scholar] [CrossRef]
- Li, X.; Strahler, A.H. Geometric-optical bidirectional reflectance modeling of the discrete crown vegetation canopy: Effect of crown shape and mutual shadowing. IEEE Trans. Geosci. Remote Sens. 1992, 30, 276–292. [Google Scholar] [CrossRef]
- Friedl, M.; McIver, D.; Hodges, J.; Zhang, X.; Muchoney, D.; Strahler, A.; Woodcock, C.; Gopal, S.; Schneider, A.; Cooper, A.; et al. Global land cover mapping from MODIS: Algorithms and early results. Remote Sens. Environ. 2002, 83, 287–302. [Google Scholar] [CrossRef]
- Abercrombie, S.P.; Friedl, M.A. Improving the Consistency of Multitemporal Land Cover Maps Using a Hidden Markov Model. IEEE Trans. Geosci. Remote Sens. 2016, 54, 703–713. [Google Scholar] [CrossRef]
- Hansen, M.C.; DeFries, R.S.; Townshend, J.R.; Sohlberg, R. Global land cover classification at 1 km spatial resolution using a classification tree approach. Int. J. Remote Sens. 2000, 21, 1331–1364. [Google Scholar] [CrossRef]
- Kokhanovsky, A.; Aoki, T.; Hachikubo, A.; Hori, M.; Zege, E. Reflective properties of natural snow: Approximate asymptotic theory versus in situ measurements. IEEE Trans. Geosci. Remote Sens. 2005, 43, 1529–1535. [Google Scholar] [CrossRef]
- Kokaly, R.; Clark, R.; Swayze, G.; Livo, K.; Hoefen, T.; Pearson, N.; Wise, R.; Benzel, W.; Lowers, H.; Driscoll, R. USGS Spectral Library Version 7; USGS: Reston, VA, USA, 2017. [CrossRef]
- Pincus, R.; Evans, K.F. Computational Cost and Accuracy in Calculating Three-Dimensional Radiative Transfer: Results for New Implementations of Monte Carlo and SHDOM. J. Atmos. Sci. 2009, 66, 3131–3146. [Google Scholar] [CrossRef]
- Qi, J.; Xie, D.; Yin, T.; Yan, G.; Gastellu-Etchegorry, J.P.; Li, L.; Zhang, W.; Mu, X.; Norford, L.K. LESS: LargE-Scale remote sensing data and image simulation framework over heterogeneous 3D scenes. Remote Sens. Environ. 2019, 221, 695–706. [Google Scholar] [CrossRef]
- Chen, Y.; Hall, A.; Liou, K.N. Application of three-dimensional solar radiative transfer to mountains. J. Geophys. Res. Atmos. 2006, 111. [Google Scholar] [CrossRef]
- Leroux, C.; Fily, M. Modeling the effect of sastrugi on snow reflectance. J. Geophys.-Res.-Planets 1998, 103, 25779–25788. [Google Scholar] [CrossRef]
- Liou, K.N.; Lee, W.L.; Hall, A. Radiative transfer in mountains: Application to the Tibetan Plateau. Geophys. Res. Lett. 2007, 34. [Google Scholar] [CrossRef]
- Manninen, T.; Anttila, K.; Jaaskelainen, E.; Riihela, A.; Peltoniemi, J.; Raisanen, P.; Lahtinen, P.; Siljamo, N.; Tholix, L.; Meinander, O.; et al. Effect of small-scale snow surface roughness on snow albedo and reflectance. Cryosphere 2021, 15, 793–820. [Google Scholar] [CrossRef]
- Bertoncini, A.; Aubry-Wake, C.; Pomeroy, J.W. Large-area high spatial resolution albedo retrievals from remote sensing for use in assessing the impact of wildfire soot deposition on high mountain snow and ice melt. Remote Sens. Environ. 2022, 278, 113101. [Google Scholar] [CrossRef]
- Jääskeläinen, E.; Manninen, T. The effect of snow at forest floor on boreal forest albedo diurnal and seasonal variation during the melting season. Cold Reg. Sci. Technol. 2021, 185, 103249. [Google Scholar] [CrossRef]
Parameter | Unit | Range | Default |
---|---|---|---|
building coverage | - | 10–50% | 25% |
building height | meter | 10–90 | 50 |
length of x | km | - | 2.5 |
length of y | km | - | 2.5 |
snow coverage | - | 20–100% | 80% |
snow grain size | µm | 1000–5000 | 3600 |
pollutant concentration | ppb | – | 500 |
zenith angle | degree | 40–80 | 70 |
azimuth angle | degree | 0–180 | 0 |
Band | Wavelength | Spectra |
---|---|---|
Band 1 | 620–670 nm | Red |
Band 2 | 841–876 nm | NIR |
Band 3 | 459–479 nm | Blue |
Band 4 | 545–565 nm | Green |
Band 5 | 1230–1250 nm | SWIR |
Band 6 | 1628–1652 nm | SWIR |
Band 7 | 2105–2155 nm | SWIR |
No. | Surface | Method | Reference |
---|---|---|---|
1 | sastrugi over rough snow | Photometric Roughness Model and a multiple scattering model | Leroux and Fily [51] |
2 | macroscopic rough snow | Monte Carlo ray-tracing method | Larue et al. [30] |
3 | small-scale rough snow | Ray-tracing method with photon recollision probability theory | Manninen et al. [53] |
4 | mountain | Monte Carlo ray-tracing method | Lee et al. [26], Liou et al. [52] |
5 | mountain | Monte Carlo ray-tracing method | Chu et al. [9] |
6 | mountain | RTLSR BRDF model | Bertoncini et al. [54] |
7 | forest | hybrid geometric optical-radiative transfer model | Chen et al. [11] |
8 | forest | photon recollision probability theory | Jääskeläinen and Manninen [55] |
9 | urban | Monte Carlo ray-tracing method | This Study |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Chen, Q.-X.; Gao, Z.-Y.; Huang, C.-L.; Dong, S.-K.; Lin, K.-F. Simulation of Spectral Albedo and Bidirectional Reflectance over Snow-Covered Urban Canyon: Model Development and Factor Analysis. Remote Sens. 2024, 16, 2340. https://doi.org/10.3390/rs16132340
Chen Q-X, Gao Z-Y, Huang C-L, Dong S-K, Lin K-F. Simulation of Spectral Albedo and Bidirectional Reflectance over Snow-Covered Urban Canyon: Model Development and Factor Analysis. Remote Sensing. 2024; 16(13):2340. https://doi.org/10.3390/rs16132340
Chicago/Turabian StyleChen, Qi-Xiang, Zi-Yi Gao, Chun-Lin Huang, Shi-Kui Dong, and Kai-Feng Lin. 2024. "Simulation of Spectral Albedo and Bidirectional Reflectance over Snow-Covered Urban Canyon: Model Development and Factor Analysis" Remote Sensing 16, no. 13: 2340. https://doi.org/10.3390/rs16132340