Review of the land surface BRDF inversion methods based on remotely sensed satellite data

HAN Yuan; WEN Jianguang; XIAO Qing; BAO Yunfei; CHEN Xi; LIU Qiang; HE Min

doi:10.11834/jrs.20231188

Research Progress | Views : 0 下载量: 365 CSCD: 0 更多指标

R-PDF
PDF
Export
Share
Collection
Album

Review of the land surface BRDF inversion methods based on remotely sensed satellite data
Vol. 27, Issue 9, Pages: 2024-2040(2023)
Published： 07 September 2023 ，
DOI： 10.11834/jrs.20231188

扫描看全文

韩源，闻建光，肖青，鲍云飞，陈曦，刘强，贺敏.2023.陆表二向反射（BRDF）反演方法研究进展.遥感学报，27（9）： 2024-2040

Han Y，Wen J G，Xiao Q，Bao Y F，Chen X，Liu Q and He M. 2023. Review of the land surface BRDF inversion methods based on remotely sensed satellite data. National Remote Sensing Bulletin， 27（9）：2024-2040
韩源，闻建光，肖青，鲍云飞，陈曦，刘强，贺敏.2023.陆表二向反射（BRDF）反演方法研究进展.遥感学报，27（9）： 2024-2040 DOI： 10.11834/jrs.20231188.

Han Y，Wen J G，Xiao Q，Bao Y F，Chen X，Liu Q and He M. 2023. Review of the land surface BRDF inversion methods based on remotely sensed satellite data. National Remote Sensing Bulletin， 27（9）：2024-2040 DOI： 10.11834/jrs.20231188.

摘要

陆表二向反射BRDF（Bidirectional Reflectance Distribution Function）定量刻画了地表目标在不同太阳—目标—传感器方向上的反射能力，是光学定量遥感研究的基础参量。BRDF在地表三维结构表征上起着重要作用，对地表能量平衡研究有重要意义。自20世纪80年代来的发展，BRDF在定义、反演、观测等方面的研究都取得了显著的进展。随着多角度卫星或拟多角度卫星的发射升空，其相应的BRDF产品得到了业务化的生产和发布，被广泛应用到了遥感多个领域。本文从BRDF反演的基本原理出发，分析了BRDF反演的主要问题，在此基础上重点介绍了BRDF反演方法的原理和特点，这些方法可有效缓解BRDF反演过程中的病态（ill-posed）问题，最后指出了未来提高BRDF反演精度的研究方向。

Abstract

Bidirectional Reflectance Distribution Function (BRDF) is a basic variable in optical quantitative remote sensing

which describes the reflection anisotropy of surface targets with different sun-target-sensor geometry. BRDF not only plays an important role in the characterization of land surface structure but also has great relevance for the research of earth energy balance. The definition

inversion

and observation technology of BRDF have made remarkable progress over the past 40 years. Moreover

with the launch of multiangular remote sensors

its BRDF products have been generated and released

which are widely used in remote sensing community.

Based on the principle of BRDF inversion

the most common problems associated with BRDF inversion are first analyzed

including the ill-posed problem caused by insufficient observations

the noise of observation data

and the noise accompanied by the introduced prior knowledge that causes the uncertainties of the inversion.

Then

the current BRDF inversion methods used to solve the problems above are analyzed

summarized

and classified into three: fundamental inversion methods

regularization-constrained inversion methods

and information classification and amplification inversion methods. Fundamental inversion methods are suitable when the number of observations is greater than the number of variables to be retrieved

and prior knowledge is not required. They include the least square method

the least variance method

and the robust estimation method. The least square method and the robust estimation method are only used when observations are sufficient

but the least variance method can be used even when observations are insufficient. However

prior knowledge is required for regularization-constrained inversion

information classification

and information amplification inversion methods

all of which are used to address the ill-posed problem. The regularization-constrained inversion method constrains the inversion results by regularization rules. The information classification and information amplification inversion methods include multistage target decision making

Bayesian estimation

Kalman filtering

and multisensor joint inversion. Among them

the multistage target decision-making method can allocate as much information as possible to the target parameters

and the Bayesian estimation method

the Kalman filter method

and the multisensor joint inversion method address the issue of insufficient observations by expanding data sources.

The challenges of how to improve the inversion accuracy of land surface BRDF in the future were also discussed

namely

high-resolution BRDF inversion

mountainous surface BRDF inversion

and the application of artificial intelligence technology in BRDF inversion. The BRDF model suitable for low- and medium-resolution pixel scales is not suitable for high-resolution pixel scales due to the strong proximity effect and mutual occlusion effect among high-resolution pixels. With the rapid growth in high-resolution satellite data and UAV data

the development of appropriate models for high-resolution pixel-scale BRDF inversion is imminent. The second model

mountainous surface BRDF inversion

also faces challenges due to the complex terrain and a lack of remote sensing data. To solve the problem

a multisource

multiscale joint inversion method as well as the prior knowledge dataset of mountainous surface BRDF need to be created. Finally

with the accumulation of remote sensing data over the last few decades

remote sensing has entered the “Big Data Era.” Investigating how to invert surface BRDF with remote sensing based on artificial intelligence technology is worthwhile.

关键词

BRDF多角度定量光学遥感病态问题反演原理反演方法能量平衡

Keywords

Bidirectional Reflectance Distribution Function (BRDF)multianglequantitativeoptical remote sensingill-posedinversion principlesinversion methodssurface energy balance

references

Bell J B. 1978. Solutions of ill-posed problems. by A. N. Tikhonov, V. Y. Arsenin. Mathematics of Computation, 32(144): 1320-1322 [DOI: 10.2307/2006360http://dx.doi.org/10.2307/2006360]

Chen J M and Leblanc S G. 1997. A four-scale bidirectional reflectance model based on canopy architecture. IEEE Transactions on Geoscience and Remote Sensing, 35(5): 1316-1337 [DOI: 10.1109/36.628798http://dx.doi.org/10.1109/36.628798]

Chen J M, Menges C H and Leblanc S G. 2005. Global mapping of foliage clumping index using multi-angular satellite data. Remote Sensing of Environment, 97(4): 447-457 [DOI: 10.1016/j.rse.2005.05.003http://dx.doi.org/10.1016/j.rse.2005.05.003]

Chen X, Cui H J and Yang H. 2007. The application of Bootstrap method in linear BRDF models inversion. Journal of Remote Sensing, 11(6): 845-851

陈霞, 崔恒建, 杨华. 2007. Bootstrap方法在遥感线性BRDF模型反演中的应用. 遥感学报, 11(6): 845-851 [DOI: 10.11834/jrs.200706114http://dx.doi.org/10.11834/jrs.200706114]

Chou K C, Willsky A S and Benveniste A. 1994. Multiscale recursive estimation, data fusion, and regularization. IEEE Transactions on Automatic Control, 39(3): 464-478 [DOI: 10.1109/9.280746http://dx.doi.org/10.1109/9.280746]

Cui S C, Yang S Z, Qiao Y L, Zhao Q, Wang J C and Wang Z. 2012. Adaptive regularized filtering for BRDF model inversion and land surface albedo retrieval based on spectrum cutoff technique. Optik, 123(3): 250-256 [DOI: 10.1016/j.ijleo.2011.04.008http://dx.doi.org/10.1016/j.ijleo.2011.04.008]

Fan W, Chen J M, Ju W, and Nesbitt N. 2014. Hybrid geometric optical-radiative transfer model suitable for forests on slopes. IEEE Transactions on Geoscience and Remote Sensing, 52(9): 5579-5586 [DOI: 10.1109/TGRS.2013.2290590http://dx.doi.org/10.1109/TGRS.2013.2290590]

Gao F, Jin Y F, Schaaf C B and Strahler A H. 2002. Bidirectional NDVI and atmospherically resistant BRDF inversion for vegetation canopy. IEEE Transactions on Geoscience and Remote Sensing, 40(6): 1269-1278 [DOI: 10.1109/TGRS.2002.800241http://dx.doi.org/10.1109/TGRS.2002.800241]

Gao F, Li X W, Xia Z G, Zhu Q J and Straher A H. 1998. Multi angle remote sensing inversion based on knowledge staged uncertainty. Science in China (Series D), 28(4): 346-350

高峰, 李小文, 夏宗国, 朱启疆, Straher A H. 1998. 基于知识的分阶段不确定性多角度遥感反演. 中国科学(D辑), 28(4): 346-350

Gao F, Schaaf C B, Strahler A H and Lucht W. 2001. Using a multikernel least-variance approach to retrieve and evaluate albedo from limited bidirectional measurements. Remote Sensing of Environment, 76(1): 57-66 [DOI: 10.1016/S0034-4257(00)00192-9http://dx.doi.org/10.1016/S0034-4257(00)00192-9]

Garguet-Duport B, Girel J, Chassery J M and Pautou G. 1996. The use of multiresolution analysis and wavelets transform for merging SPOT panchromatic and multispectral image data. Photogrammetric Engineering and Remote Sensing, 62(9): 1057-1066

Gastellu-Etchegorry J P, Demarez V, Pinel V and Zagolski F. 1996. Modeling radiative transfer in heterogeneous 3-D vegetation canopies. Remote Sensing of Environment, 58(2): 131-156 [DOI: 10.1016/0034-4257(95)00253-7http://dx.doi.org/10.1016/0034-4257(95)00253-7]

Goel N S, Rozehnal I and Thompson R L. 1991. A computer graphics based model for scattering from objects of arbitrary shapes in the optical region. Remote Sensing of Environment, 36(2): 73-104 [DOI: 10.1016/0034-4257(91)90032-2http://dx.doi.org/10.1016/0034-4257(91)90032-2]

Govaerts Y M and Verstraete M M. 1998. Raytran: a Monte Carlo ray-tracing model to compute light scattering in three-dimensional heterogeneous media. IEEE Transactions on Geoscience and Remote Sensing, 36(2): 493-505 [DOI: 10.1109/36.662732http://dx.doi.org/10.1109/36.662732]

Guan Y W, Zhou Y R, He B B, Liu X Z, Zhang H G and Feng S L. 2020. Improving land cover change detection and classification with BRDF correction and spatial feature extraction using Landsat Time Series: a case of urbanization in Tianjin, China. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13: 4166-4177 [DOI: 10.1109/JSTARS.2020.3007562http://dx.doi.org/10.1109/JSTARS.2020.3007562]

Hansen P C. 1998. Rank-Deficient and Discrete Ill-Posed Problems: Numerical Aspects of Linear Inversion. Philadelphia: SIAM.

Hao D L, Wen J G, Xiao Q, Wu S B, Lin X W, You D Q and Tang Y. 2018. Modeling anisotropic reflectance over composite sloping terrain. IEEE Transactions on Geoscience and Remote Sensing, 56(7): 3903-3923 [DOI: 10.1109/TGRS.2018.2816015http://dx.doi.org/10.1109/TGRS.2018.2816015]

Hao D L, Wen J G, Xiao Q, You D Q and Tang Y. 2020. An improved topography-coupled kernel-driven model for land surface anisotropic reflectance. IEEE Transactions on Geoscience and Remote Sensing, 58(4): 2833-2847 [DOI: 10.1109/TGRS.2019.2956705http://dx.doi.org/10.1109/TGRS.2019.2956705]

Hasegawa K, Matsuyama H, Tsuzuki H and Sweda T. 2010. Improving the estimation of leaf area index by using remotely sensed NDVI with BRDF signatures. Remote Sensing of Environment, 114(3): 514-519 [DOI: 10.1016/j.rse.2009.10.005http://dx.doi.org/10.1016/j.rse.2009.10.005]

Huang H G, Qin W H and Liu Q H. 2013. RAPID: a radiosity applicable to porous IndiviDual objects for directional reflectance over complex vegetated scenes. Remote Sensing of Environment, 132: 221-237 [DOI: 10.1016/j.rse.2013.01.013http://dx.doi.org/10.1016/j.rse.2013.01.013]

Huemmrich K F. 2001. The GeoSail model: a simple addition to the SAIL model to describe discontinuous canopy reflectance. Remote Sensing of Environment, 75(3): 423-431 [DOI: 10.1016/s0034-4257(00)00184-xhttp://dx.doi.org/10.1016/s0034-4257(00)00184-x]

Jackson D D. 1979. The use of a priori data to resolve non-uniqueness in linear inversion. Geophysical Journal International, 57(1): 137-157 [DOI: 10.1111/j.1365-246X.1979.tb03777.xhttp://dx.doi.org/10.1111/j.1365-246X.1979.tb03777.x]

Jonsson P and Eklundh L. 2002. Seasonality extraction by function fitting to time-series of satellite sensor data. IEEE Transactions on Geoscience and Remote Sensing, 40(8): 1824-1832 [DOI: 10.1109/TGRS.2002.802519http://dx.doi.org/10.1109/TGRS.2002.802519]

Lacaze R. 2006.POLDER-2 BRDF Database-User Document. https://www.‍theia-land.‍fr/wp-content/uploads/2018/12/polder-2_brdf_usermanual-i1.10_doc.pdfhttps://www.‍theia-land.‍fr/wp-content/uploads/2018/12/polder-2_brdf_usermanual-i1.10_doc.pdf

Lacaze R. 2008. POLDER-3 / PARASOL BRDF Databases User Manual. https://web.‍gps.‍caltech.‍edu/~vijay/pdf/POLDER-3_BRDF_UserManual-I1.10.pdfhttps://web.‍gps.‍caltech.‍edu/~vijay/pdf/POLDER-3_BRDF_UserManual-I1.10.pdf

León-Tavares J, Swinnen E, Smets B and Roujean J L. 2017. Angular normalisation of PROBA-V 300m NDVI//Proceedings of the 9th International Workshop on the Analysis of Multitemporal Remote Sensing Images. Brugge: IEEE: 1-5 [DOI: 10.1109/Multi-Temp.2017.8035249http://dx.doi.org/10.1109/Multi-Temp.2017.8035249]

Li X W, Gao F, Wang J D and Strahler A. 2001a. A priori knowledge accumulation and its application to linear BRDF model inversion. Journal of Geophysical Research: Atmospheres, 106(D11): 11925-11935 [DOI: 10.1029/2000JD900639http://dx.doi.org/10.1029/2000JD900639]

Li X W, Gao F, Wang J D and Zhu Q J. 1997. Uncertainty and sensitivity matrix of parameters in inversion of physical BRDF model. Journal of Remote Sensing, 1(1): 5-14

李小文, 高峰, 王锦地, 朱启疆. 1997. 遥感反演中参数的不确定性与敏感性矩阵. 遥感学报, 1(1): 5-14 [DOI: 10.11834/jrs.19970102http://dx.doi.org/10.11834/jrs.19970102]

Li X W, Gao F, Wang J F, Straher A H, Lucht W and Schaaf C. 2000. Estimation of the parameter error propagation in inversion based BRDF observations at single sun position. Science in China (Series E), 30(S1): 6-11

李小文, 高峰, 王锦地, Straher A H, Lucht W and Schaaf C. 2000. 单一太阳角BRDF数据反演过程中误差传播的估计. 中国科学(E辑), 30(S1): 6-11

Li X W, Gao F, Yang H, Wang J D, Strahler A and Schaaf C. 2001b. Bi-directional NDVI and atmosphere-coupled BRDF inversion//Proceedings of the Scanning the Present and Resolving the Future. Proceedings. IEEE 2001 International Geoscience and Remote Sensing Symposium. Sydney: IEEE: 2391-2393 [DOI: 10.1109/IGARSS.2001.978012http://dx.doi.org/10.1109/IGARSS.2001.978012]

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/tgrs.1985.289389http://dx.doi.org/10.1109/tgrs.1985.289389]

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]

Li X W, Wang J D, Hu B X and Alan H S. 1998. On utilization of a priori knowledge in inversion of remote sensing models. Science in China Series D: Earth Sciences, 41(6): 580-585 [DOI: 10.1007/bf02878739http://dx.doi.org/10.1007/bf02878739]

Li X W, Strahler A H and Woodcock C E. 1995. A hybrid geometric optical-radiative transfer approach for modeling albedo and directional reflectance of discontinuous canopies. IEEE Transactions on geoscience and remote sensing, 33(2), 466-480 [DOI: 10.1109/TGRS.1995.8746028http://dx.doi.org/10.1109/TGRS.1995.8746028]

Liu S H, Liu Q, Liu Q H, Wen J G and Li X W. 2010. The angular and spectral kernel model for BRDF and albedo retrieval. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 3(3): 241-256 [DOI: 10.1109/JSTARS.2010.2048745http://dx.doi.org/10.1109/JSTARS.2010.2048745]

Lucht W, Schaaf C B and Strahler A H. 2000. An algorithm for the retrieval of albedo from space using semiempirical BRDF models. IEEE Transactions on Geoscience and Remote Sensing, 38(2): 977-998 [DOI: 10.1109/36.841980http://dx.doi.org/10.1109/36.841980]

Minnaert M. 1941. The reciprocity principle in lunar photometry. Astrophysical Journal, 93(3): 403-410 [DOI: 10.1086/144279http://dx.doi.org/10.1086/144279]

Muller J P, López G, Watson G, Shane N, Kennedy T and Lewis P. 2012. The ESA GlobAlbedo Project for mapping the Earth’s land surface albedo for 15 Years from European Sensors. IEEE Geoscience and Remote Sensing Symposium. http://globalbedo.org/docs/Muller-GlobAlbedo-abstractV4.pdfhttp://globalbedo.org/docs/Muller-GlobAlbedo-abstractV4.pdf

Ni W G, Li X W, Woodcock C E, Caetano M R and Strahler A H. 1999. An analytical hybrid GORT model for bidirectional reflectance over discontinuous plant canopies. IEEE Transactions on Geoscience and Remote Sensing, 37(2): 987-999 [DOI: 10.1109/36.752217http://dx.doi.org/10.1109/36.752217]

Nicodemus F E, Richmond J C, Hsia J J, Ginsberg I W and Limperis T. 1977. Geometrical Considerations and Nomenclature for Reflectance. Washington: National Bureau of Standards

North P R J. 1996. Three-dimensional forest light interaction model using a Monte Carlo method. IEEE Transactions on Geoscience and Remote Sensing, 34(4): 946-956 [DOI: 10.1109/36.508411http://dx.doi.org/10.1109/36.508411]

Pocewicz A, Vierling L A, Lentile L B and Smith R. 2007. View angle effects on relationships between MISR vegetation indices and leaf area index in a recently burned ponderosa pine forest. Remote Sensing of Environment, 107(1/2): 322-333 [DOI: 10.1016/j.rse.2006.06.019http://dx.doi.org/10.1016/j.rse.2006.06.019]

Privette J L, Eck T F and Deering D W. 1997. Estimating spectral albedo and nadir reflectance through inversion of simple BRDF models with AVHRR/MODIS-like data. Journal of Geophysical Research: Atmospheres, 102(D24): 29529-29542 [DOI: 10.1029/97jd01215http://dx.doi.org/10.1029/97jd01215]

Qi J B, Xie D H, Yin T G, Yan G J, Gastellu-Etchegorry J P, Li L Y, Zhang W M, Mu X H and Norford L K. 2019. LESS: LargE-scale remote sensing data and image simulation framework over heterogeneous 3D scenes. Remote Sensing of Environment, 221: 695-706 [DOI: 10.1016/j.rse.2018.11.036http://dx.doi.org/10.1016/j.rse.2018.11.036]

Qin J, Yan G J, Liu S M, Liang S L, Zhang H, Wang J D and Li X W. 2006. Application of ensemble Kalman filter to geophysical parameters retrieval in remote sensing: a case study of kernel-driven BRDF model inversion. Science in China Series D, 49(6): 632-640 [DOI: 10.1007/s11430-006-0632-xhttp://dx.doi.org/10.1007/s11430-006-0632-x]

Qin W H and Gerstl S A W. 2000. 3-D scene modeling of semidesert vegetation cover and its radiation regime. Remote Sensing of Environment, 74(1): 145-162 [DOI: 10.1016/s0034-4257(00)00129-2http://dx.doi.org/10.1016/s0034-4257(00)00129-2]

Quaife T and Lewis P. 2010. Temporal constraints on linear BRDF model parameters. IEEE Transactions on Geoscience and Remote Sensing, 48(5): 2445-2450 [DOI: 10.1109/TGRS.2009.2038901http://dx.doi.org/10.1109/TGRS.2009.2038901]

Rahman H, Verstraete M M and Pinty B. 1993. Coupled surface-atmosphere reflectance (CSAR) model: 1. Model description and inversion on synthetic data. Journal of Geophysical Research: Atmospheres, 98(D11): 20779-20789 [DOI: 10.1029/93jd02071http://dx.doi.org/10.1029/93jd02071]

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: Atmospheres, 97(D18): 20455-20468 [DOI: 10.1029/92jd01411http://dx.doi.org/10.1029/92jd01411]

Sahoo S, Russo T A, Elliott J and Foster I. 2017. Machine learning algorithms for modeling groundwater level changes in agricultural regions of the U.S. Water Resources Research, 53(5): 3878-3895 [DOI: 10.1002/2016WR019933http://dx.doi.org/10.1002/2016WR019933]

Salomonson V V. 1968. Anisotropy in Reflected Solar Radiation. Fort Collins: Department of Atmospheric Science Colorado State University

Samain O, Geiger B and Roujean J L. 2006. Spectral normalization and fusion of optical sensors for the retrieval of BRDF and albedo: application to VEGETATION, MODIS, and MERIS data sets. IEEE Transactions on Geoscience and Remote Sensing, 44(11): 3166-3179 [DOI: 10.1109/TGRS.2006.879545http://dx.doi.org/10.1109/TGRS.2006.879545]

Samain O, Roujean J L and Geiger B. 2008. Use of a Kalman filter for the retrieval of surface BRDF coefficients with a time-evolving model based on the ECOCLIMAP land cover classification. Remote Sensing of Environment, 112(4): 1337-1346 [DOI: 10.1016/j.rse.2007.07.007http://dx.doi.org/10.1016/j.rse.2007.07.007]

Schaaf C, Martonchik J, Pinty B, Govaerts Y, Gao F, Lattanzio A, Liu J C, Strahler A and Taberner M. 2008. Retrieval of surface albedo from satellite sensors//Liang S L, ed. Advances in Land Remote Sensing. Dordrecht: Springer: 219-243 [DOI: 10.1007/978-1-4020-6450-0_9http://dx.doi.org/10.1007/978-1-4020-6450-0_9]

Schaaf C B, Gao F, Strahler A H, Lucht W, Li X W, Tsang T, Strugnell N C, Zhang X Y, Jin Y F, Muller J P, Lewis P, Barnsley M, Hobson P, Disney M, Roberts G, Dunderdale M, Doll C, d'Entremont R P, Hu B X, Liang S L, Privette J L and Roy D. 2002. First operational BRDF, albedo nadir reflectance products from MODIS. Remote Sensing of Environment, 83(1/2): 135-184 [DOI: 10.1016/S0034-4257(02)00091-3http://dx.doi.org/10.1016/S0034-4257(02)00091-3]

Schowengerdt R A. 1980. Reconstruction of multispatial, multispectral image data using spatial frequency content. Photogrammetric Engineering and Remote Sensing, 46(10): 1325-1334

Shi W Z, Zhang M, Zhang R, Chen S X and Zhan Z. 2020. Change detection based on artificial intelligence: state-of-the-art and challenges. Remote Sensing, 12(10): 1688 [DOI: 10.3390/rs12101688http://dx.doi.org/10.3390/rs12101688]

Staylor W F and Suttles J T. 1986. Reflection and emission models for deserts derived from nimbus-7 ERB scanner measurements. Journal of Climate and Applied Meteorology, 25(2): 196-202 [DOI: 10.1175/1520-0450(1986)025<0196:Raemfd>2.0.Co;2http://dx.doi.org/10.1175/1520-0450(1986)025<0196:Raemfd>2.0.Co;2]

Strahler A H, Lucht W, Schaaf C B, Tsang T, Gao F, Li X, Muller J P, Lewis P and Barnsley M J. 1999. MODIS BRDF/Albedo product: algorithm theoretical basis document version 5.0. http://modis.gsfc.nasa.gov/data/atbd/atbd_mod09.pdfhttp://modis.gsfc.nasa.gov/data/atbd/atbd_mod09.pdf

Strugnell N C and Lucht W. 2001. An algorithm to infer continental-scale albedo from AVHRR data, land cover class, and field observations of typical BRDFs. Journal of Climate, 14(7): 1360-1376 [DOI: 10.1175/1520-0442(2001)014<1360:Aatics>2.0.Co;2http://dx.doi.org/10.1175/1520-0442(2001)014<1360:Aatics>2.0.Co;2]

Suits G H. 1971. The calculation of the directional reflectance of a vegetative canopy. Remote Sensing of Environment, 2: 117-125 [DOI: 10.1016/0034-4257(71)90085-Xhttp://dx.doi.org/10.1016/0034-4257(71)90085-X]

Susaki J, Hara K, Kajiwara K and Honda Y. 2004. Robust estimation of BRDF model parameters. Remote Sensing of Environment, 89(1): 63-71 [DOI: 10.1016/j.rse.2003.10.004http://dx.doi.org/10.1016/j.rse.2003.10.004]

Tarantola A. 1987. Inverse Problem Theory: Methods for Data Fitting and Model Parameter Estimation. Amsterdam: Elsevier

van Leeuwen W J D and Roujean J L. 2002. Land surface albedo from the synergistic use of polar (EPS) and geo-stationary (MSG) observing systems: an assessment of physical uncertainties. Remote Sensing of Environment, 81(2/3): 273-289 [DOI: 10.1016/S0034-4257(02)00005-6http://dx.doi.org/10.1016/S0034-4257(02)00005-6]

Verhoef W. 1984. Light scattering by leaf layers with application to canopy reflectance modeling: the SAIL model. Remote Sensing of Environment, 16(2): 125-141 [DOI: 10.1016/0034-4257(84)90057-9http://dx.doi.org/10.1016/0034-4257(84)90057-9]

Walthall C L, Norman J M, Welles J M, Campbell G and Blad B L. 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]

Wang L Z, Yan J N, Mu L and Huang L. 2020. Knowledge discovery from remote sensing images: a review. WIREs Data Mining and Knowledge Discovery, 10(5): e1371 [DOI: 10.1002/widm.1371http://dx.doi.org/10.1002/widm.1371]

Wang Y F, Li X W, Nashed Z, Zhao F, Yang H, Guan Y N and Zhang H. 2007. Regularized kernel-based BRDF model inversion method for ill-posed land surface parameter retrieval. Remote Sensing of Environment, 111(1): 36-50 [DOI: 10.1016/j.rse.2007.03.007http://dx.doi.org/10.1016/j.rse.2007.03.007]

Wanner W, Li X and Strahler A H. 1995. On the derivation of kernels for kernel-driven models of bidirectional reflectance. Journal of Geophysical Research: Atmospheres, 100(D10): 21077-21089 [DOI: 10.1029/95JD02371http://dx.doi.org/10.1029/95JD02371]

Wei S S and Fang H L. 2016. Estimation of canopy clumping index from MISR and MODIS sensors using the normalized difference hotspot and darkspot (NDHD) method: the influence of BRDF models and solar zenith angle. Remote Sensing of Environment, 187: 476-491 [DOI: 10.1016/j.rse.2016.10.039http://dx.doi.org/10.1016/j.rse.2016.10.039]

Wen J G, Dou B C, You D Q, Tang Y, Xiao Q, Liu Q and Liu Q H. 2017. Forward a small-timescale BRDF/albedo by multisensor combined BRDF inversion model. IEEE Transactions on Geoscience and Remote Sensing, 55(2): 683-697 [DOI: 10.1109/tgrs.2016.2613899http://dx.doi.org/10.1109/tgrs.2016.2613899]

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]

Wolanin A, Mateo-García G, Camps-Valls G, Gómez-Chova L, Meroni M, Duveiller G, You L Z and Guanter L. 2020. Estimating and understanding crop yields with explainable deep learning in the Indian Wheat Belt. Environmental Research Letters, 15(2): 024019 [DOI: 10.1088/1748-9326/ab68achttp://dx.doi.org/10.1088/1748-9326/ab68ac]

Wu S B, Wen J G, Lin X W, Hao D L, You D Q, Xiao Q, Liu Q H and Yin T G. 2019a. Modeling discrete forest anisotropic reflectance over a sloped surface with an extended GOMS and SAIL model. IEEE Transactions on Geoscience and Remote Sensing, 57(2): 944-957 [DOI: 10.1109/TGRS.2018.2863605http://dx.doi.org/10.1109/TGRS.2018.2863605]

Wu S B, Wen J G, Xiao Q, Liu Q H, Hao D L, Lin X W and You D Q. 2019b. Derivation of kernel functions for kernel-driven reflectance model over sloping terrain. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 12(2): 396-409 [DOI: 10.1109/JSTARS.2018.2854771http://dx.doi.org/10.1109/JSTARS.2018.2854771]

Xiao Z Q, Liang S L, Wang J D, Jiang B and Li X J. 2011. Real-time retrieval of Leaf Area Index from MODIS time series data. Remote Sensing of Environment, 115(1): 97-106 [DOI: 10.1016/j.rse.2010.08.009http://dx.doi.org/10.1016/j.rse.2010.08.009]

Yan K, Li H L, Song W J, Tong Y Y, Hao D L, Zeng Y L, Mu X H, Yan G J, Fang Y, Myneni R B and Schaaf C. 2022. Extending a linear kernel-driven BRDF model to realistically simulate reflectance anisotropy over rugged terrain. IEEE Transactions on Geoscience and Remote Sensing, 60: 4401816 [DOI: 10.1109/TGRS.2021.3064018http://dx.doi.org/10.1109/TGRS.2021.3064018]

Yang H, Xu W L, Zhao H R, Chen X and Wang J D. 2005. Information flow and controlling in regularization inversion of quantitative remote sensing. Science in China Series D: Earth Sciences, 48(1): 74-83

杨华, 许王莉, 赵红蕊, 陈雪, 王锦地. 2003. 定量遥感正则化反演中的信息流及其控制. 中国科学(D辑), 33(8): 799-808 [DOI: 10.3969/j.issn.1674-7240.2003.08.012http://dx.doi.org/10.3969/j.issn.1674-7240.2003.08.012]

Yin G F, Li A N, Zhao W, Jin H A, Bian J H and Wu S B. 2017. Modeling canopy reflectance over sloping terrain based on path length correction. IEEE Transactions on Geoscience and Remote Sensing, 55(8): 4597-4609 [DOI: 10.1109/TGRS.2017.2694483http://dx.doi.org/10.1109/TGRS.2017.2694483]

Zeng Y L, Li J, Liu Q H, Huete A R, Xu B D, Yin G F, Zhao J, Yang L, Fan W L, Wu S B and Yan K. 2016. An iterative BRDF/NDVI inversion algorithm based on a posteriori variance estimation of observation errors. IEEE Transactions on Geoscience and Remote Sensing, 54(11): 6481-6496 [DOI: 10.1109/tgrs.2016.2585301http://dx.doi.org/10.1109/tgrs.2016.2585301]

Zhang X Y, Friedl M A, Schaaf C B, Strahler A H, Strahler J C F, Gao F, Reed B C and Huete A. 2003. Monitoring vegetation phenology using MODIS. Remote Sensing of Environment, 84(3): 471-475 [DOI: 10.1016/S0034-4257(02)00135-9http://dx.doi.org/10.1016/S0034-4257(02)00135-9]

Zhao H R, Tang Z S and Li X W. 2007. A regularization parameter choice method on linear quantitative remote sensing inversion. Geomatics and Information Science of Wuhan University, 32(6): 531-535

赵红蕊, 唐中实, 李小文. 2007. 线性正则化遥感反演中正则化参数的确定方法. 武汉大学学报(信息科学版), 32(6): 531-535 [DOI: 10.3969/j.issn.1671-8860.2007.06.015http://dx.doi.org/10.3969/j.issn.1671-8860.2007.06.015]

Zhao X, Liu S H, Tang Y M, Yu K and Li X W. 2006. Studying on multi-stage robust estimation of BRDF model parameters. Journal of Remote Sensing, 10(6): 901-909

赵祥, 刘素红, 唐义闵, 于凯, 李小文. 2006. BRDF模型参数分阶段鲁棒性反演方法. 遥感学报, 10(6): 901-909 [DOI: 10.11834/jrs.200606132http://dx.doi.org/10.11834/jrs.200606132]

Zobitz J M, Quaife T and Nichols N K. 2020. Efficient hyper-parameter determination for regularised linear BRDF parameter retrieval. International Journal of Remote Sensing, 41(4): 1437-1457 [DOI: 10.1080/01431161.2019.1667552http://dx.doi.org/10.1080/01431161.2019.1667552]

Alert me when the article has been cited

提交

High-precision monitoring of green tide biomass in the Yellow Sea of China through optical remote sensing

On-orbit absolute radiometric calibration for optical remote sensing satellites： Progress and trends

Spatial-temporal water area monitoring of the Miyun Reservoir using remote sensing imagery from 1984 to 2020

Optical extraction of oil spills based on sunglint reflection difference in HY-1C CZI images