Abstract
The influence of the detection direction on the quality of spectral data was not taken into account, which is catastrophic for some samples where the reflectivity direction was very different. To explore the directional characteristics of mixed pixels reflectivity, we used an ASD FieldSpec3 spectroradiometer to carry out multi-angle spectral measurement experiments in the laboratory, and the direction error model is established. As a result, the proportions of black area (PBA) and the observed azimuths (Φ) will affect the reflectivity of the mixed pixel in the visible light band. It is found that when the PBA is close to 0, the reflectivity distribution of mixed pixels is characterized by the reflected energy reflecting uniformly around the entire hemisphere space. When PBA is close to 1, there is a significant difference in the reflectivity of mixed pixels in 2π space. The direction error model better reflects the reflectivity changes caused by PBA and observed azimuth. The mean absolute error of the estimated reflectivity compared with the measured value is only 0.047. When the PBA is large, the estimation accuracy of the model is higher. When the PBA is small, and the observed azimuth is large, the accuracy of the model is slightly worse. The “two-block” mixed pixel is an ideal sample to satisfy the direction error model, while a dispersed “multi-block” mixed pixel is not applicable to the correction model. When conducting mixed pixel spectral measurement experiment, the influence of the detection azimuth on the spectral reflectivity should be fully considered, which is beneficial to improve the reliability of experiments on multi-angle spectral measurements of mixed pixels.
References
Abuelgasim AA, Gopal S, Irons JR, Strahler AH (1996) Classification of ASAS multiangle and multispectral measurements using artificial neural networks. Remote Sens Environ 57(2):79–87. https://doi.org/10.1016/0034-4257(95)00197-2
Battaglia C, Boccard M, Haug FJ, Ballif C (2012) Light trapping in solar cells: when does a Lambertian scatterer scatter Lambertianly. Journal of applied physics 112:094504. https://doi.org/10.1063/1.4761988
Chen F, Wang K, Tang TF (2016) Spectral Unmixing using a sparse multiple-Endmember spectral mixture model. IEEE Transactions on Geoscience & Remote Sensing 54(10):5846–5861. https://doi.org/10.1109/TGRS.2016.2574331
Chen X, Wang D, Chen J et al (2018) The mixed pixel effect in land surface phenology: A simulation study 211:338–344. https://doi.org/10.1016/j.rse.2018.04.030
Chen Y, Ge Y, Heuvelink GB, Hu J, Jiang Y (2015) Hybrid constraints of pure and mixed pixels for soft-then-hard super-resolution mapping with multiple shifted images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 5(8):2040–2052. https://doi.org/10.1109/JSTARS.2015.2417191
Diner DJ, Boland SW, Brauer M, Bruegge C, Burke KA, Chipman RA, Girolamo LD, Garay MJ, Hasheminassab S, Hyer EJ (2018) Advances in multiangle satellite remote sensing of speciated airborne particulate matter and association with adverse health effects: from MISR to MAIA. J Appl Remote Sens 12(4):042603. https://doi.org/10.1117/1.JRS.12.042603
Fitzgibbon A, Pilu M, Fisher RB (1999) Direct least square fitting of ellipses. IEEE Transactions on Pattern Analysis & Machine Intelligence 21(5):476–480. https://doi.org/10.1109/34.765658
Gander W, Golub GH, Strebel R (1994) Least-squares fitting of circles and ellipses. Bit 34(4):558–578. https://doi.org/10.1007/BF01934268
Green MA (2002) Lambertian light trapping in textured solar cells and light-emitting diodes: analytical solutions. Prog Photovolt Res Appl 10(4):235–241. https://doi.org/10.1002/pip.404
Hommersom A, Kratzer S, Laanen M, Ansko I, Ligi M, Bresciani M, Giardino C, Beltrán-Abaunza JM, Moore G, Wernand M, Peters S (2012) Intercomparison in the field between the new. WISP-3 and other radiometers (TriOS Ramses, ASD FieldSpec, and TACCS) 6(1):063–615. https://doi.org/10.1117/1.JRS.6.063615
Hsieh PF, Lee LC, Chen NY (2002) Effect of spatial resolution on classification errors of pure and mixed pixels in remote sensing. IEEE Transactions on Geoscience & Remote Sensing 39(12):2657–2663. https://doi.org/10.1109/36.975000
Hueni A. (2017) ASD spectrometer app-user manual. https://doi.org/10.13140/RG.2.2.16636.85128
Jawak SD, Luis AJ (2013) Improved land cover mapping using high resolution multiangle 8-band WorldView-2 satellite remote sensing data. J Appl Remote Sens 7(1):78–84. https://doi.org/10.1117/1.JRS.7.073573
Kalashnikova OV, Kahn R, Sokolik IN (1999) Retrieving mineral dust composition, size and shape (CSS) properties from multi-angle remote sensing observations. International Conference on Medical Image Computing & Computer-assisted Intervention. https://doi.org/10.1007/10704282_72
Khalilzadeh J, Tasci ADA (2017) Large sample size, significance level, and the effect size: solutions to perils of using big data for academic research. Tour Manag 62:89–96. https://doi.org/10.1016/j.tourman.2017.03.026
Kuester M, Thome K, Krause K, Canham K, Whittington E (2001) Comparison of surface reflectance measurements from three ASD FieldSpec FR spectroradiometers and one ASD FieldSpec VNIR spectroradiometer. IEEE International Geoscience & Remote Sensing Symposium IEEE Xplore. https://doi.org/10.1109/IGARSS.2001.976060
Li C, Fang F, Zhou A, Zhang G (2014) A novel blind spectral Unmixing method based on error analysis of linear mixture model. IEEE Geoence and Remote Sensing Letters 11(7):1180–1184. https://doi.org/10.1109/LGRS.2013.2285926
Li JS, Zhang B, Shen Q, Zhang H, Zhang FF, Wang Q (2013) Analysis of directional reflectance properties of lake taihu using multi-angle measurements. Spectrosc Spectr Anal 33(9):2506–2511. https://doi.org/10.3964/j.issn.1000-0593(2013)09-2506-06
Li XW, Strahler AH (1986) Geometric-optical bidirectional reflectance modeling of a conifer Forest canopy. IEEE Transactions on Geoscience & Remote Sensing 24(6):906–919. https://doi.org/10.1109/TGRS.1986.289706
Liang SL, Strahler AH, Barnsley MJ (2000) Multiangle remote sensing: past, present and future. Remote Sens Rev 18:83–102. https://doi.org/10.1080/02757250009532386
Liangrocapart S, Petrou M (1998) Mixed pixels classification. Proc SPIE Int Soc Opt Eng 3500:72–83. https://doi.org/10.1117/12.331889
Liu HJ, Wang X, Li HX, Meng XT, Jiang BW, Zhang XL, Yu ZY (2018) Effect mechanism of soil Minerlas on spectral Characterisitics of Main soil classes in Songnen plain. Spectrosc Spectr Anal 038(010):3238–3244. https://doi.org/10.3964/j.issn.1000-0593(2018)10-3238-07
Liu Y, Li Y (2014) Observations of Spectral Data and Characteristics Analysis of Snow-Bare Soil Mixed Pixel Generated by Micro-Simulation 34(7):1903. https://doi.org/10.3964/j.issn.1000-0593(2014)07-1903-06
Ma X, Liu Y (2020) A Modified Geometrical Optical Model of Row Crops Considering Multiple Scattering Frame. 12(21):3600. https://doi.org/10.3390/rs12213600
Mac AA, Maclellan CJ, Malthus T (2012) The fields of view and directional response functions of two field Spectroradiometers. Geoscience & Remote Sensing IEEE Transactions on 50(10):3892–3907. https://doi.org/10.1109/TGRS.2012.2185055
Sazonov DS (2017) Correlation analysis of experimental remote-sensing data and models of microwave Rough Sea-surface emission. Izvestiya Atmospheric & Oceanic Physics 53(9):1174–1184. https://doi.org/10.1134/S0001433817090274
Schuster CS, Bozzola A, Andreani LC, Krauss TF (2014) How to assess light trapping structures versus a Lambertian Scatterer for solar cells. Opt Express 22(S2):A542. https://doi.org/10.1364/OE.22.00A542
Sun TL, Zhao YS, Liang RF (2012) Study on the reflected and Hyperspectral mixed-pixel character of aquatic plants and water. Spectrosc Spectr Anal 32(2):449–452. https://doi.org/10.3964/j.issn.1000-0593(2012)02-0449-04
Tang SH, Zhu QJ, Li XW, Wang JD, Yan GJ (2003) The study of subpixel Unmixing using Hyperspectral and multiangular data. Journal of remote sensing 137(10):31–42. https://doi.org/10.11834/jrs.20030304
Vieilleville F, Ristorcelli T, Delvit (2016) Dem reconstruction using light field and bidirectional reflectance function from multi-view high resolution spatial images. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B3:503–509. https://doi.org/10.5194/isprs-archives-XLI-B3-503-2016
Yousuf B , Shukla A , Arora MK , Bindal A, Jasrotia AS (2020) On drivers of sub-pixel classification accuracy– an example from glacier Facies. IEEE journal of selected topics in applied earth observations and remote sensing 13:1-1. 13:1-1. https://doi.org/10.1109/JSTARS.2019.2955955, 601, 608
Zhang HN, Wen XP, Xu JL, et al (2020) Study on the effect of surface roughness on the spectral Unmixing of mixed pixels (12):1–10. https://doi.org/10.1155/2020/5796860
Zhang J, Wang X (1997) Selecting the best regression equation via the P-value of F-test. Metrika 46(1):33–40. https://doi.org/10.1007/BF02717164
Acknowledgments
The study was supported by the remote sensing geochemistry disciplinary innovation team, Kunming University of Science and Technology, Kunming, China. Furthermore, the manuscript is funded by the National Natural Science Fund of China (41101343) and 2018 Yunnan Province Ph.D. Academic Newcomer Award.
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Communicated by: H. Babaie
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Zhang, H., Wen, X., Luo, D. et al. Study on directional reflectivity characteristics analysis of mixed pixels using multi-angle spectral measurements. Earth Sci Inform 14, 1159–1172 (2021). https://doi.org/10.1007/s12145-021-00622-5
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DOI: https://doi.org/10.1007/s12145-021-00622-5