浏览全部资源
扫码关注微信
Published： 07 October 2022 ，
扫 描 看 全 文
郭静，焦子锑，丁安心，董亚冬，张小宁，崔磊，尹思阳，常雅轩，谢蕊.2022.基于星载POLDER冰雪数据评价3个BRDF模型.遥感学报，26（10）： 2060-2072
Guo J， Jiao Z T， Ding A X， Dong Y D， Zhang X N， Cui L， Yin S Y， Chang Y X and Xie R. 2022. Evaluation of three BRDF models’ performance using spaceborne POLDER snow data. National Remote Sensing Bulletin， 26（10）：2060-2072
冰雪在短波区域具有很强的各向异性反射特征，对全球能量平衡及水循环等有重要作用。目前，国内外学者发展了一系列应用于冰雪的二向性反射分布函数BRDF（Bidirectional Reflectance Distribution Function）模型，全面比较和评估这些模型对星载多角度遥感产品的业务化模型选择有重要参考价值和指导意义。本文基于全球POLDER冰雪多角度反射率数据，选取3个模型，包括核驱动、半经验的MODIS业务化RTLSR模型、渐进辐射传输物理模型ART以及新发展的RTLSRS 模型进行了全面比较分析，研究结果表明：（1）在拟合所有POLDER数据时，RTLSRS模型都具有最高精度，对于单组纯雪数据，RTLSRS模型的最小二乘拟合的均方根误差（RMSE）比ART模型降低了45.45%，仅为RTLSR模型的18.46%。对于非纯雪数据，RTLSRS模型与RTLSR模型的拟合能力总体差别不大，但其RMSE比RTLSR模型降低了67.5%，ART模型的精度最差。（2）虽然RTLSRS可以高精度拟合所有数据，但该模型拟合纯雪（
R
2
=0.969，RMSE=0.012）精度较优于非纯雪（
R
2
=0.926，RMSE=0.013）。（3）对RTLSRS模型进行简化，仅保留其各向同性核和雪核ISM（Isotropic-Snow Model），验证结果表明：简化后的模型能够很好地表征雪的二向散射能力，使用POLDER全部纯雪数据进行拟合时，
R
2
达到了0.949，RMSE为0.034。本文有助于用户在应用冰雪多角度数据时选择更合适的BRDF模型，同时对理解这些模型的误差提供了有价值的参考
Snow and ice scatter solar radiation in a strong anisotropic fashion
especially in shortwave region
which
in turn
causes a significant difference in the study of the global energy balance and water cycles. The remote sensing community has developed a series of reflectance models for various applications in snow surface. Comprehensive comparison and evaluation of these models help in choosing an algorithm to produce satellite multi-angle remote sensing product. In this paper
we use the Polarization and Directionality of Earth Reflectances (POLDER) multi-angle snow data to compare and evaluate the performance of three models to characterize snow scattering. Three models including the kernel-driven linear Ross Thick-Li Sparse Reciprocal (RTLSR) model as the Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo operational algorithm
the Asymptotic Radiative Theory model (ART)
and the lately developed RTLSR-Snow (RTLSRS) model have been well used in studies. First
the POLDER data are divided into pure snow data and impure snow data by the using the homogeneity index provided by the POLDER database. We then use three BRDF models to fit (1) a single pure snow BRDF dataset; (2) the entire archive of the pure snow BRDF data; (3) a single impure snow BRDF dataset; and (4) the entire achieve of the impure snow BRDF data. We analyze the result on the basis of the
R
2
RMSE and bias. As the volumetric scattering kernel and geometric optical kernel contribute little to pure snow reflectances
we further simplify the RTLSRS model by keeping only isotropic scattering and snow scattering kernel in the kernel-driven model framework (i.e.
isotropic and snow-kernel model
ISM). The performance of the ISM model has further been evaluated using the POLDER pure snow data. The results are as follows: (1) The RTLSRS is the most accurate model among all models being considered. For a single pure snow BRDF dataset
the RTLSRS model has an RMSE value that is 45.45% lower than that of ART models and is only 18.46% of that for RTLSR model.For a single impure snow BRDF dataset
the BRDF curve of RTLSRS model is generally similar with RTLSR model’s
but the RMSE is 67.5% lower than RTLSR. The RMSE of the ART model is the largest in this case
arriving at 0.136. (2) The accuracy of the RTLSRS model in simulating the pure snow data (
R
2
=0.969
RMSE=0.012) is higher than that of the impure snow data (
R
2
=0.926
RMSE=0.013). (3) The simplified ISM model can characterize the pure snow BRDF data well. The
R
2
and RMSE can reach 0.949 and 0.034 for the entire POLDER pure snow data
which is even better than the ART model. RTLSRS has the highest accuracy in fitting various POLDER BRDF snow data. Although the ISM has low accuracy relative to its original RTLSRS model
it shows higher accuracy than the ART model in fitting the POLDER pure snow data. Results present that the index of the “homogeneity” provided by the entire archive of the POLDER snow database cannot necessarily meet the requirement to identify the pure snow pixels of POLDER snow data. Therefore
a new method must be developed to refine the POLDER snow data and provide more details that can improve the understanding for potential users in relation to snow optical scattering.
冰雪ARTRTLSRRTLSRSPOLDER核驱动BRDF模型
snowARTRTLSRRTLSRSPOLDERkernel-driven BRDF model
Bréon F M and Maignan F. 2017. A BRDF–BPDF database for the analysis of earth target reflectances. Earth System Science Data, 9(1): 31-45 [DOI: 10.5194/essd-9-31-2017http://dx.doi.org/10.5194/essd-9-31-2017]
Chang Y X, Jiao Z T, Dong Y D, Zhang X N, He D D, Yin S Y, Cui L and Ding A X. 2019. Parameterization and correction of hotspot parameters of Ross-Li kernel driven models on POLDER dataset. Journal of Remote Sensing, 23(4): 661-672
常雅轩, 焦子锑, 董亚冬, 张小宁, 何丹丹, 尹思阳, 崔磊, 丁安心. 2019. Ross-li核驱动模型热点参数化及其校正—以POLDER数据为例. 遥感学报, 23(4): 661-672 [DOI: 10.11834/jrs.20198332http://dx.doi.org/10.11834/jrs.20198332]
Deschamps P, Bréon F, Leroy M, Podaire A, Bricaud A, Buriez J and Seve G. 1994. The POLDER mission: instrument charateristics and scientific objectives. IEEE Transactions on Geoscience and Remote Sensing, 32(3): 598-615 [DOI: 10.1109/36.297978http://dx.doi.org/10.1109/36.297978]
Ding A X, Jiao Z T, Dong Y D, Qu Y, Zhang X N, Xiong C, He D D, Yin S Y, Cui L and Chang Y X. 2019. An assessment of the performance of two snow kernels in characterizing snow scattering properties. International Journal of Remote Sensing, 40(16): 6315-6335 [DOI: 10.1080101431161.2019.1590878http://dx.doi.org/10.1080101431161.2019.1590878]
Ding A X, Jiao Z T, Dong Y D, Zhang X N, He D D, Cui L, Yin S Y and Chang Y X. 2019. Performance assessment of the operational MODIS BRDF model for snow/ice cover type. Journal of Remote Sensing, 23(6): 1147-1158
丁安心, 焦子锑, 董亚冬, 张小宁, 何丹丹, 崔磊, 尹思阳, 常雅轩. 2019. 业务化MODIS BRDF模型对冰雪BRDF/反照率的反演能力评估. 遥感学报, 23(6): 1147-1158 [DOI:10.11834/jys.20198037http://dx.doi.org/10.11834/jys.20198037]
Dong Y D, Jiao Z T, Ding A X, Zhang H, Zhang X N, Li Y, He D D, Yin S Y and Cui L. 2018a. A modified version of the kernel-driven model for correcting the diffuse light of ground multi-angular measurements. Remote Sensing of Environment, 210: 325-344 [DOI: 10.1016/j.rse.2018.03.030http://dx.doi.org/10.1016/j.rse.2018.03.030]
Dong Y D, Jiao Z T, Yin S Y, Zhang H, Zhang X N, Cui L, He D D, Ding A X, Chang Y X and Yang S T. 2018b. Influence of snow on the magnitude and seasonal variation of the clumping index retrieved from MODIS BRDF products. Remote Sensing, 10(8): 1194 [DOI: 10.3390/rs10081194http://dx.doi.org/10.3390/rs10081194]
Dong Y D, Jiao Z T and Zhang H. 2016. To reconstruct hotspot effect for MODIS BRDF archetypes using a hotspot-corrected kernel-driven BRDF model. 2016 IEEE International Geoscience and Remote Sensing Symposium: 2654-2656 [DOI: 10.1016/j.cageo.2016.06.010http://dx.doi.org/10.1016/j.cageo.2016.06.010]
Dong Y D, Jiao Z T, Zhang H, Li J Y, Jiao G P and Shi H Y. 2014. Efficient algorithm for improving the hotspot effect of the operational MODIS BRDF product. Journal of Remote Sensing, 18(4): 804-825
董亚冬, 焦子锑, 张虎, 李佳悦, 焦广平, 石涵予. 2014. 改善MODIS BRDF产品热点效应的方法研究. 遥感学报, 18(4): 804-825 [DOI: 10.11834/jrs.20143229http://dx.doi.org/10.11834/jrs.20143229]
Dong Y D. 2017. The improvement of kernel-driven model and its application in estimating clumping index. Beijing: Beijing Normal University: 1-2
董亚冬. 2017. 核驱动模型的改进及其在估算聚集指数上的应用. 北京: 北京师范大学: 1-2
Jiang X. 2006. Progress in the research of snow and ice albedo. Journal of Glaciology and Geocryology, 28(5): 728-738
蒋熹. 2006. 冰雪反照率研究进展. 冰川冻土, 28(5): 728-738 [DOI: 10.3969/j.issn.1000-0240.2006.05.016http://dx.doi.org/10.3969/j.issn.1000-0240.2006.05.016]
Jiao Z T, Ding A X, Kokhanovsky A, Schaaf C, Bréon F M, Dong Y D, Wang Z S, Liu Y, Zhang X N, Yin S Y, Cui L, Mei L L and Chang Y X. 2019. Development of a snow kernel to better model the anisotropic reflectance of pure snow in a kernel-driven BRDF model framework. Remote Sensing of Environment, 221: 198-209 [DOI:10.16/j.rse.2018.11.001http://dx.doi.org/10.16/j.rse.2018.11.001]
Jiao Z T, Schaaf C B, Dong Y D, Román M, Michael J H, Chen J M, Wang Z S, Zhang H, Saenz E, Poudyal R, Gatebe C, Bréon F M, Li X W and Strahler A H. 2016. A method for improving hotspot directional signatures in BRDF models used for MODIS. Remote Sensing of Environment, 186: 135-151 [DOI: 10.1016/j.rse.2016.08.007http://dx.doi.org/10.1016/j.rse.2016.08.007]
Jiao Z T, Zhang H, Dong Y D, Liu Q H, Xiao Q and Li X W. 2015. An algorithm for retrieval of surface albedo from small view-angle airborne observations through the use of BRDF archetypes as prior knowledge. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8(7): 3279-3293 [DOI: 10.1109/JSTARS.2015.2414925http://dx.doi.org/10.1109/JSTARS.2015.2414925]
Jiao Z T, Zhang X N, Bréon F M, Dong Y D, Román M, Wang Z S, Cui L, Yin S Y, Ding A X and Wang J D. 2018. The influence of spatial resolution on the angular variation patterns of optical reflectance as retrieved from MODIS and polder measurements. Remote Sensing of Environment, 215: 371-385 [DOI: 10.1016/j.rse.2018.06.025http://dx.doi.org/10.1016/j.rse.2018.06.025]
Kokhanovsky A A and Bréon F. 2012. Validation of an analytical snow BRDF model using parasol multi-angular and multispectral observations. IEEE Geoscience and Remote Sensing Letters, 9(5): 928-932 [DOI: 10.1109/LGRS.2012.2185775http://dx.doi.org/10.1109/LGRS.2012.2185775]
Kokhanovsky A A and Schreier M. 2009. The determination of snow specific surface area, albedo and effective grain size using AASTR space-borne measurements. International Journal of Remote Sensing, 30(4): 919-933 [DOI: 10.1080/01431160802395250http://dx.doi.org/10.1080/01431160802395250]
Kokhanovsky A A and Zege E P. 2004. Scattering optics of snow. Applied Optics, 43(7): 1589-1602 [DOI: 10.1364/AO.43.001589http://dx.doi.org/10.1364/AO.43.001589]
Lacaze P. 2009. POLDER-3/PARASOL BRDF Databases User Manual. POSTEL: 10-16[2019-09-02]. https://cnes.fr/sites/default/files/migration/smsc/parasolhttps://cnes.fr/sites/default/files/migration/smsc/parasol
Li X W and Strahler A H. 1985. Geometric-optical modeling of a conifer forest canopy. IEEE Transactions on Geoscience and Remote Sensing, GE-23(5): 705-721 [DOI: 10.1109/36.134078http://dx.doi.org/10.1109/36.134078]
Li X W and Strahler A H. 1992. Geometric-optical bidirectional reflectance modeling of the discrete crown vegetation canopy: effect of crown shape and mutual shadowing. IEEE Transactions on Geoscience and Remote Sensing, 30(2): 276-292 [DOI: 10.1109/36.134078http://dx.doi.org/10.1109/36.134078]
Lucht W and Roujean J L. 2000. Considerations in the parametric modeling of BRDF and albedo from multiangular satellite sensor observations. Remote Sensing Reviews, 18(2/4): 343-379 [DOI: 10.1080/02757250009532395http://dx.doi.org/10.1080/02757250009532395]
Pan H Z, Wang J and Li H Y. 2015. Accuracy validation of the MODIS snow albedo products and estimate of the snow albedo under cloud over the Qilian Mountains. Journal of Glaciology and Geocryology, 37(1): 49-57
潘海珠, 王建, 李弘毅. 2015. 祁连山区MODIS积雪反照率产品的精度验证及云下积雪反照率估算研究. 冰川冻土, 37(1): 49-57 [DOI: 10.7522/j.issn.1000-0240.2015.0005http://dx.doi.org/10.7522/j.issn.1000-0240.2015.0005]
Peltoniemi J I, Kaasalainen S, Naranen J, Matikainen L and Piironen J. 2005. Measurement of directional and spectral signatures of light reflectance by snow. IEEE Transactions on Geoscience and Remote Sensing, 43(10): 2294-2304 [DOI: 10.1109/TGRS.2005.855131http://dx.doi.org/10.1109/TGRS.2005.855131]
Peltoniemi J I, Suomalainen J, Hakala T, Näränen J, Puttonen E, Kaasalainen S, Hirschmugl M and Torppa J. 2010. Reflectance of various snow types: measurements, modeling, and potential for snow melt monitoring. Light Scattering Reviews, 5: 393-449 [DOI: 10.1007/978-3-642-10336-0_9http://dx.doi.org/10.1007/978-3-642-10336-0_9]
Qu Y, Liu Q, Liang S L, Wang L Z, Liu N F and Liu S H. 2014. Direct-estimation algorithm for mapping daily land-surface broadband albedo from MODIS data. IEEE Transactions on Geoscience and Remote Sensing, 52(2): 907-919 [DOI: 10.1109/TGRS.2013.2245670http://dx.doi.org/10.1109/TGRS.2013.2245670]
Qu Y, Liu Q and Liu S H. 2016. A forward kernel function for fitting in situ measured snow bidirectional reflectance factor. Spectroscopy and Spectral Analysis, 36(9): 2749-2754
瞿瑛, 刘强, 刘素红. 2016. 基于前向散射核函数拟合冰雪反射光谱各向异性. 光谱学与光谱分析, 36(9): 2749-2754 [DOI: 10.3964/j.issn.1000-0593(2016)09-2749-06http://dx.doi.org/10.3964/j.issn.1000-0593(2016)09-2749-06]
Rahmah H, Pinty B and Verstraete M M. 1993. Coupled surface-atmosphere reflectance (CSAR) model: 2. Semiempirical surface model usable with NOAA Advanced Very High Resolution Radiometer data. Journal of Geophysical Research, 98(D11): 20791-20801 [DOI: 10.1029/93jd02072http://dx.doi.org/10.1029/93jd02072]
Robinson D A, Dewey K F and Heimand R R. 1993. Global snow cover monitoring: an update. Bulletin of the American Meteorological Society, 74(9): 1689-1696 [DOI:10.1175/1520-0477(1993)074<1689:GSCMAU>2.0.CO;2http://dx.doi.org/10.1175/1520-0477(1993)074<1689:GSCMAU>2.0.CO;2]
Roujean J L, Leroy M and Deschamps P Y. 1992. A bidirectional reflectance model of the earth's surface for the correction of remote sensing data. Journal of Geophysical Research, 97(D18): 20455-20468 [DOI:10.1029/92JD01411http://dx.doi.org/10.1029/92JD01411]
Schaaf C B, Gao F, Strahler A H, Lucht W, Li X, Tsang T, Strugnell N C, Zhang X, Jin Y, Muller J, Lewis P, Barnsley M, Hobson P, Disney M, Roberts G, Dunderdale M, Doll C, D'Entremont R P, Hu B, Liang S, Privette J L and Roy D. 2002. First operational BRDF, albedo nadir reflectance products from MODIS. Remote Sensing of Environment, 83(1/2): 135-148 [DOI: 10.1016/s0034-4257(02)00091-3http://dx.doi.org/10.1016/s0034-4257(02)00091-3]
Schaepman-Strub G, Schaepman M E, Painter T, Dangel S and Martonchik J. 2006. Reflectance quantities in optical remote sensing—definitions and case studies. Remote Sensing of Environment, 103(1): 27-42 [DOI: 10.1016/j.rse.2006.03.002http://dx.doi.org/10.1016/j.rse.2006.03.002]
Singh S K, Kulkarni A V and Chaudhary B S. 2010. Hyperspectral analysis of snow reflectance to understand the effects of contamination and grain size. Annals of Glaciology, 51(54): 83-88 [DOI:10.3189/172756410791386535http://dx.doi.org/10.3189/172756410791386535]
Stamnes K, Tsay S C, Wiscombe W J and Jayaweera K. 1988. Numerically stable algorithm for discrete-ordinate-method radiative transfer in multiple scattering and emitting layered media. Applied Optics, 27(12): 2502-2509 [DOI: 10.1364/AO.27.002502http://dx.doi.org/10.1364/AO.27.002502]
Walthall C L, Norman J M, Welles J M, Campbell G and Blad B. 1985. Simple equation to approximate the bidirectional reflectance from vegetative canopies and bare soil surfaces. Applied Optics, 24(3): 383-387 [DOI: 10.1364/AO.24.000383http://dx.doi.org/10.1364/AO.24.000383]
Wen J G, Liu Q, Xiao Q, Liu Q H, You D Q, Hao D L, Wu S B and Lin X W. 2018. Characterizing land surface anisotropic reflectance over rugged terrain: a review of concepts and recent developments. Remote Sensing, 10(3): 370 [DOI: 10.3390/rs10030370http://dx.doi.org/10.3390/rs10030370]
Wiscombe W J and Warren S G. 1980. A model for the spectral albedo of snow. Ⅰ: pure snow. Journal of the Atmospheric Sciences, 37(12): 2712-2733 [DOI: 10.1175/1520-0469(1980)037<2712:amftsa>2.0.co;2http://dx.doi.org/10.1175/1520-0469(1980)037<2712:amftsa>2.0.co;2]
Wu H Y, Liang S L, Tong L, He T and Yu Y Y. 2012. Bidirectional reflectance for multiple snow-covered land types from MISR products. IEEE Geoscience and Remote Sensing Letters, 9(5): 994-998 [DOI: 10.1109/LGRS.2012.2187041http://dx.doi.org/10.1109/LGRS.2012.2187041]
Wu H Y, Liang S L, Tong L and He T. 2011. Snow BRDF characteristics from MODIS and misr data. 2011 IEEE International Geoscience and Remote Sensing Symposium, 2011: 3178-3190 [DOI: 10.1109/IGARSS.2011.6049896http://dx.doi.org/10.1109/IGARSS.2011.6049896]
Xiong C and Shi J C. 2014. Simulating polarized light scattering in terrestrial snow based on bicontinuous random medium and monte -carlo ray tracing. Journal of Quantitative Spectroscopy and Radiative Transfer, 133: 179-189 [DOI: 10.1016/j.jqsrt.2013.07.026http://dx.doi.org/10.1016/j.jqsrt.2013.07.026]
相关文章
相关作者
相关机构
京公网安备11010802024621