Remote sensing retrieval of high-spatial-resolution land surface albedo
- Vol. 27, Issue 3, Pages: 724-737(2023)
Published: 07 March 2023
DOI: 10.11834/jrs.20231732
扫 描 看 全 文
浏览全部资源
扫码关注微信
Published: 07 March 2023 ,
扫 描 看 全 文
赵聪聪,闻建光,游冬琴,唐勇,陈曦,刘强.2023.高分辨率地表反照率遥感估算研究进展.遥感学报,27(3): 724-737
Zhao C C,Wen J G,You D Q,Tang Y,Chen X and Liu Q. 2023. Remote sensing retrieval of high-spatial-resolution land surface albedo. National Remote Sensing Bulletin, 27(3):724-737
地表反照率是研究地表能量收支平衡、中长期天气预报和全球气候变化的一个重要参数。利用卫星数据估算地表反照率需要地表二向反射特征知识的支持,目前针对中低分辨率卫星数据的反照率反演算法相对成熟,且发布了不同分辨率和精度的全球产品。随着全球高分辨率对地观测技术的不断发展,特别是中国高分系列卫星的发展,为高分辨率地表反照率精确估算提供了丰富的数据源。然而高分辨率地表反照率遥感估算面临着观测角度数据不足、波段信息少等有效数据缺失的问题,国内外学者开展了一系列针对高分辨率地表反照率估算方法的研究。本文首先简述了地表反照率遥感估算的基本原理与目前存在的主要问题,总结并分析了近几年高分辨率反照率估算的相关算法,并对未来高分辨率反照率估算的进一步发展提出了展望和设想。论文研究可为高分辨率反照率算法研究和产品发展提供理论支持。
Land surface albedo is an important variable for controlling Earth radiation budget. It is also recognized as an Essential Climate Variable by the Global Climate Observing System. The progress of the high-spatial-resolution satellite development allows high-spatial-resolution albedo to provide important data for the research of local radiation and energy balance
regional climate
and ecological environment. However
albedo is a variable related to solar angle
wavelength
and atmospheric status. Thus
the estimation of high-spatial-resolution land surface albedo becomes challenging. Different methods of land surface albedo remote sensing estimation have been developed in the last two decades. These methods greatly improve the high-resolution albedo mapping ability. Summarizing and analyzing the proposed methods of high-spatial-resolution albedo are important to improve the accuracy of its product estimation further.
Two fundamental problems of insufficient multiangle observation and multisensor band information for high-resolution albedo estimation are proposed by analyzing the basic principle of the existing albedo estimation. Four main methods of high-spatial-resolution albedo are summarized in the characteristics of the algorithm and application cases according to how the problem of insufficient understanding of land surface reflectance anisotropy is overcome. Lastly
the conclusion and prospect of the high-spatial-resolution albedo method development are also summarized.
According to whether the land surface anisotropic reflection characteristics are considered
the current high-resolution albedo retrieval methods are divided into two basic types: the method of narrowband to broadband conversions and the method of Bidirectional Reflectance Distribution Functionconsideration. The latter type considers the surface anisotropic reflection characteristics in different ways. It also includes estimation based on high-resolution multiangle reflectance data
estimation based on combining high-resolution reflectance data with low-resolution reflectance data
and estimation based on empirical knowledge of BRDF/albedo.
These proposed methods of obtaining high-spatial-resolution land surface albedo with the surface bidirectional reflection characteristic information directly or indirectly are still the mainstream idea. They alleviate the problem of missing high-resolution valid data to a certain extent. However
they are still limited because of the lack of effective data
such as the angle and band of the high-resolution remote sensing data and the lack of high-resolution BRDF a priori knowledge information. The development of a high-resolution albedo algorithm is a prospect for future research. It can potentially provide theoretical support for high-resolution land surface albedo product development.
地表反照率地表二向反射遥感反演窄宽波段转换高分辨率卫星
land surface albedoBidirectional Reflectance Distribution Function (BRDF)remote sensing retrievalnarrowband to broadbandhigh spatial resolution satellite
Bacour C and Bréon F M. 2005. Variability of biome reflectance directional signatures as seen by POLDER. Remote Sensing of Environment, 98(1): 80-95 [DOI: 10.1016/j.rse.2005.06.008http://dx.doi.org/10.1016/j.rse.2005.06.008]
Bonafoni S and Sekertekin A. 2020. Albedo retrieval from sentinel-2 by new narrow-to-broadband conversion coefficients. IEEE Geoscience and Remote Sensing Letters, 17(9): 1618-1622 [DOI: 10.1109/lgrs.2020.2967085http://dx.doi.org/10.1109/lgrs.2020.2967085]
Brest C L and Goward S N. 1987. Deriving surface albedo measurements from narrow band satellite data. International Journal of Remote Sensing, 8(3): 351-367 [DOI: 10.1080/01431168708948646http://dx.doi.org/10.1080/01431168708948646]
Chen F, Li Y J, Ma Q M, Li X, Chen J, Li M, Gao C Z and Yang X Y. 2020. High-resolution BRDF and albedo parameters inversion from sentinel-2 multispectral instrument data//IGARSS 2020-2020 IEEE International Geoscience and Remote Sensing Symposium. Waikoloa: IEEE [DOI: 10.1109/igarss39084.2020.9323591http://dx.doi.org/10.1109/igarss39084.2020.9323591]
Dickinson R E. 1983. Land surface processes and climate-surface albedos and energy balance. Advances in Geophysics, 25: 305-353 [DOI: 10.1016/S0065-2687(08)60176-4http://dx.doi.org/10.1016/S0065-2687(08)60176-4]
Duguay C R and Ledrew E F. 1993. Estimating surface reflectance and albedo from landsat-5 thematic mapper over rugged terrain (VOL, 58, PG, 552, 1992). Photogrammetric Engineering and Remote Sensing, 59(4): 498-498
Franch B, Vermote E F and Claverie M. 2014b. Intercomparison of Landsat albedo retrieval techniques and evaluation against in situ measurements across the US SURFRAD network. Remote Sensing of Environment, 152: 627-637 [DOI: 10.1016/j.rse.2014.07.019http://dx.doi.org/10.1016/j.rse.2014.07.019]
Franch B, Vermote E F, Sobrino J A and Julien Y. 2014a. Retrieval of surface albedo on a daily basis: application to MODIS data. IEEE Transactions on Geoscience and Remote Sensing, 52(12): 7549-7558 [DOI: 10.1109/tgrs.2014.2313842http://dx.doi.org/10.1109/tgrs.2014.2313842]
Gao B, Gong H L and Wang T X. 2015. A method for retrieving daily land surface albedo from space at 30-m resolution. Remote Sensing, 7(8): 10951-10972 [DOI: 10.3390/rs70810951http://dx.doi.org/10.3390/rs70810951]
Gao B, Jia L and Wang T X. 2014. Derivation of land surface albedo at high resolution by combining HJ-1A/B reflectance observations with MODIS BRDF products. Remote Sensing, 6(9): 8966-8985 [DOI: 10.3390/rs6098966http://dx.doi.org/10.3390/rs6098966]
Greuell W and Oerlemans J. 2004. Narrowband-to-broadband albedo conversion for glacier ice and snow: equations based on modeling and ranges of validity of the equations. Remote Sensing of Environment, 89(1): 95-105 [DOI: 10.1016/j.rse.2003.10.010http://dx.doi.org/10.1016/j.rse.2003.10.010]
Hautecœur O and Leroy M M. 1998. Surface bidirectional reflectance distribution function observed at global scale by POLDER/ADEOS. Geophysical Research Letters, 25(22): 4197-4200 [DOI: 10.1029/1998gl900111http://dx.doi.org/10.1029/1998gl900111]
He T, Liang S L, Wang D D, Cao Y F, Gao F, Yu Y Y and Feng M. 2018. Evaluating land surface albedo estimation from Landsat MSS, TM, ETM+, and OLI data based on the unified direct estimation approach. Remote Sensing of Environment, 204: 181-196 [DOI: 10.1016/j.rse.2017.10.031http://dx.doi.org/10.1016/j.rse.2017.10.031]
He T, Liang S L, Wang D D, Chen X N, Song D X and Jiang B. 2015. Land surface albedo estimation from Chinese HJ satellite data based on the direct estimation approach. Remote Sensing, 7(5): 5495-5510 [DOI: 10.3390/rs70505495http://dx.doi.org/10.3390/rs70505495]
He T, Liang S L, Wang D D, Shi Q Q and Tao X. 2014a. Estimation of high-resolution land surface shortwave albedo from AVIRIS data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(12): 4919-4928 [DOI: 10.1109/jstars.2014.2302234http://dx.doi.org/10.1109/jstars.2014.2302234]
He T, Liang S L, Wang D D, Shuai Y M and Yu Y Y. 2014b. Fusion of satellite land surface albedo products across scales using a multiresolution tree method in the North Central United States. IEEE Transactions on Geoscience and Remote Sensing, 52(6): 3428-3439 [DOI: 10.1109/tgrs.2013.2272935http://dx.doi.org/10.1109/tgrs.2013.2272935]
Hu Y H, Jia G S, Pohl C, Zhang X X and Van Genderen J. 2016. Assessing surface albedo change and its induced radiation budget under rapid urbanization with Landsat and GLASS data. Theoretical and Applied Climatology, 123(3/4): 711-722 [DOI: 10.1007/s00704-015-1385-2http://dx.doi.org/10.1007/s00704-015-1385-2]
Jiao Z T, Hill M J, Schaaf C B, Zhang H, Wang Z S and Li X W. 2014. An anisotropic flat index (AFX) to derive BRDF archetypes from MODIS. Remote Sensing of Environment, 141: 168-187 [DOI: 10.1016/j.rse.2013.10.017http://dx.doi.org/10.1016/j.rse.2013.10.017]
Jiao Z T, Zhang H, Dong Y D, Liu Q, 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]
Kimes D S and Sellers P J. 1985. Inferring hemispherical reflectance of the earth’s surface for global energy budgets from remotely sensed nadir or directional radiance values. Remote Sensing of Environment, 18(3): 205-223 [DOI: 10.1016/0034-4257(85)90058-6http://dx.doi.org/10.1016/0034-4257(85)90058-6]
Knap W H, Brock B W, Oerlemans J and Willis I C. 1999a. Comparison of landsat TM-derived and ground-based albedos of Haut Glacier d’Arolla, Switzerland. International Journal of Remote Sensing, 20(17): 3293-3310 [DOI: 10.1080/014311699211345http://dx.doi.org/10.1080/014311699211345]
Knap W H, Reijmer C H and Oerlemans J. 1999b. Narrowband to broadband conversion of Landsat TM glacier albedos. International Journal of Remote Sensing, 20(10): 2091-2110 [DOI: 10.1080/014311699212362http://dx.doi.org/10.1080/014311699212362]
Leroy M, Deuzé J L, Bréon F M, Hautecoeur O, Herman M, Buriez J C, Tanré D, Bouffiès S, Chazette P and Roujean J L. 1997. Retrieval of atmospheric properties and surface bidirectional reflectances over land from POLDER/ADEOS. Journal of Geophysical Research: Atmospheres, 102(D14): 17023-17037 [DOI: 10.1029/96jd02662http://dx.doi.org/10.1029/96jd02662]
Lewis P, Guanter L, Saldana G L, Muller J P, Watson G, Shane N, Kennedy T, Fisher J, Domenech C, Preusker R, North P, Heckel A, Danne O, Krämer U, Zühlke M, Fomferra N, Brockmann C and Schaaf C. 2012. The ESA Globalbedo project: algorithm//2012 IEEE International Geoscience and Remote Sensing Symposium. Munich, Germany: IEEE [DOI: 10.1109/IGARSS.2012.6352306http://dx.doi.org/10.1109/IGARSS.2012.6352306]
Li Z, Erb A, Sun Q S, Liu Y, Shuai Y M, Wang Z S, Boucher P and Schaaf C. 2018. Preliminary assessment of 20-m surface albedo retrievals from sentinel-2A surface reflectance and MODIS/VIIRS surface anisotropy measures. Remote Sensing of Environment, 217: 352-365 [DOI: 10.1016/j.rse.2018.08.025http://dx.doi.org/10.1016/j.rse.2018.08.025]
Liang S L. 2001. Narrowband to broadband conversions of land surface albedo I: algorithms. Remote Sensing of Environment, 76(2): 213-238 [DOI: 10.1016/s0034-4257(00)00205-4http://dx.doi.org/10.1016/s0034-4257(00)00205-4]
Liang S L, Shuey C J, Russ A L, Fang H L, Chen M Z, Walthall C L, Daughtry C S T and Hunt R. 2003. Narrowband to broadband conversions of land surface albedo: II. Validation. Remote Sensing of Environment, 84(1): 25-41 [DOI: 10.1016/S0034-4257(02)00068-8http://dx.doi.org/10.1016/S0034-4257(02)00068-8]
Liang S L, Strahler A H and Walthall C. 1999. Retrieval of land surface albedo from satellite observations: a simulation study. Journal of Applied Meteorology and Climatology, 38(6): 712-725 [DOI: 10.1175/1520-0450(1999)038<0712:ROLSAF>2.0.CO;2http://dx.doi.org/10.1175/1520-0450(1999)038<0712:ROLSAF>2.0.CO;2]
Liang S L, Stroeve J and Box J E. 2005. Mapping daily snow/ice shortwave broadband albedo from Moderate Resolution Imaging Spectroradiometer (MODIS): the improved direct retrieval algorithm and validation with Greenland in situ measurement. Journal of Geophysical Research: Atmospheres, 110(D10): D10109 [DOI: 10.1029/2004jd005493http://dx.doi.org/10.1029/2004jd005493]
Liu Q, Wang L Z, Qu Y, Liu N F, Liu S H, Tang H R and Liang S L. 2013. Preliminary evaluation of the long-term GLASS albedo product. International Journal of Digital Earth, 6(S1): 69-95 [DOI: 10.1080/17538947.2013.804601http://dx.doi.org/10.1080/17538947.2013.804601]
Liu Q H, Wen J G, Zhou X, Zhao J, Li Z Y, Li X, Ma M G, Wang W Z, Liao X H, Liu S M, Fan W J, Xiao Q, Zhong B, Li J, Xin X Z, Li L, Jia L, Gao Z H, Jin J D, Liang S, Xin J, Liao C J and Wu Y R. 2023. Technique system of remote sensing product generation and validation of GF common products. National Remote Sensing Bulletin, 27(3): 544-562
柳钦火, 闻建光, 周翔, 赵坚, 李增元, 李新, 马明国, 王维真, 廖小罕, 刘绍民, 范闻捷, 肖青, 仲波, 李静, 辛晓洲, 李丽, 贾立, 高志海, 金家栋, 梁师, 邢进, 廖楚江, 吴一戎. 2023. 高分遥感共性产品生成和真实性检验技术体系. 遥感学报, 27(3): 544-562 [DOI: 10.11834/jrs.20235022http://dx.doi.org/10.11834/jrs.20235022]
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]
Martonchik J V, Diner D J, Pinty B, Verstraete M M, Myneni R B, Knyazikhin Y and Gordon H R. 1998. Determination of land and ocean reflective, radiative, and biophysical properties using multiangle imaging. IEEE Transactions on Geoscience and Remote Sensing, 36(4): 1266-1281 [DOI: 10.1109/36.701077http://dx.doi.org/10.1109/36.701077]
Nicodemus F E, Richmond J C, Hsia J J, Ginsberg I W and Limperis T. 1977. Geometrical Considerations and Nomenclature for Reflectance. Washington, DC: National Burean of Standards
Peng J J, Fan W J, Xu X R, Wang L Z, Liu Q H, Li J C and Zhao P. 2015. Estimating crop albedo in the application of a physical model based on the law of energy conservation and spectral invariants. Remote Sensing, 7(11): 15536-15560 [DOI: 10.3390/rs71115536http://dx.doi.org/10.3390/rs71115536]
Peng S, Wen J G, Xiao Q, You D Q, Dou B C, Liu Q and Tang Y. 2017. Multi-staged NDVI dependent snow-free land-surface shortwave albedo narrowband-to-broadband (NTB) coefficients and their sensitivity analysis. Remote Sensing, 9(1): 93 [DOI: 10.3390/rs9010093http://dx.doi.org/10.3390/rs9010093]
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]
Roman M O, Gatebe C K, Shuai Y M, Wang Z S, Gao F, Masek J G, He T, Liang S L and Schaaf C B. 2013. Use of in situ and airborne multiangle data to assess MODIS-and Landsat-based estimates of directional reflectance and albedo. IEEE Transactions on Geoscience and Remote Sensing, 51(3): 1393-1404 [DOI: 10.1109/tgrs.2013.2243457http://dx.doi.org/10.1109/tgrs.2013.2243457]
Roujean J L, Tanré D, Bréon F M and Deuzé J L. 1997. Retrieval of land surface parameters from airborne POLDER bidirectional reflectance distribution function during HAPEX-Sahel. Journal of Geophysical Research: Atmospheres, 102(D10): 11201-11218 [DOI: 10.1029/97jd00341http://dx.doi.org/10.1029/97jd00341]
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): 135-148 [DOI: 10.1016/s0034-4257(02)00091-3http://dx.doi.org/10.1016/s0034-4257(02)00091-3]
Shuai Y M, Masek J G, Gao F and Schaaf C B. 2011. An algorithm for the retrieval of 30-m snow-free albedo from Landsat surface reflectance and MODIS BRDF. Remote Sensing of Environment, 115(9): 2204-2216 [DOI: 10.1016/j.rse.2011.04.019http://dx.doi.org/10.1016/j.rse.2011.04.019]
Shuai Y M, Masek J G, Gao F, Schaaf C B and He T. 2014. An approach for the long-term 30-m land surface snow-free albedo retrieval from historic Landsat surface reflectance and MODIS-based a priori anisotropy knowledge. Remote Sensing of Environment, 152: 467-479 [DOI: 10.1016/j.rse.2014.07.009http://dx.doi.org/10.1016/j.rse.2014.07.009]
Tasumi M, Allen R G and Trezza R. 2008. At-surface reflectance and albedo from satellite for operational calculation of land surface energy balance. Journal of Hydrologic Engineering, 13(2): 51-63 [DOI: 10.1061/(asce)1084-0699(2008)13:2(51http://dx.doi.org/10.1061/(asce)1084-0699(2008)13:2(51)]
Valiente J A, Nunez M, Lopez-Baeza E and Moreno J F. 1995. Narrow-band to broad-band conversion for meteosat-visiible channel and broad-band albedo using both AVHRR-1 and AVHRR-2 channels. International Journal of Remote Sensing, 16(6): 1147-1166 [DOI: 10.1080/01431169508954468http://dx.doi.org/10.1080/01431169508954468]
Vermote E, Justice C O and Breon F M. 2009. Towards a generalized approach for correction of the BRDF effect in MODIS directional reflectances. IEEE Transactions on Geoscience and Remote Sensing, 47(3): 898-908 [DOI: 10.1109/tgrs.2008.2005977http://dx.doi.org/10.1109/tgrs.2008.2005977]
Wang D D, Liang S L, He T and Yu Y Y. 2013. Direct estimation of land surface albedo from VIIRS data: Algorithm improvement and preliminary validation. Journal of Geophysical Research: Atmospheres, 118(22): 12577-12586 [DOI: 10.1002/2013jd020417http://dx.doi.org/10.1002/2013jd020417]
Wang J, Cui Y H, He X B, Zhang J and Yan S J. 2015. Surface albedo variation and its influencing factors over dongkemadi glacier, Central Tibetan Plateau. Advances in Meteorology, 2015: 852098 [DOI: 10.1155/2015/852098http://dx.doi.org/10.1155/2015/852098]
Wang Z S, Erb A M, Schaaf C B, Sun Q S, Liu Y, Yang Y, Shuai Y M, Casey K A and Román M O. 2016. Early spring post-fire snow albedo dynamics in high latitude boreal forests using Landsat-8 OLI data. Remote Sensing of Environment, 185: 71-83 [DOI: 10.1016/j.rse.2016.02.059http://dx.doi.org/10.1016/j.rse.2016.02.059]
Wen J G, Dou B C, You D Q, Tang Y, Xiao Q, Liu Q and Qinhuo L. 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, Liu Q H, Xiao Q and Li X W. 2015. Remote Sensing Modeling of Land Surface Bidirectional Reflection and the Retrieval of Albedo. Beijing: Science Publishing Press, 2015: 5
闻建光, 刘强, 柳钦火, 肖青, 李小文. 2015. 陆表二向反射特性遥感建模及反照率反演. 北京: 科学出版社, 2015: 5
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]
Yan G J, Jiang H L, Yan K, Cheng S Y, Song W J, Tong Y Y, Liu Y N, Qi J B, Mu X H, Zhang W M, Xie D H and Zhou H M. 2021. Review of optical multi-angle quantitative remote sensing. Journal of Remote Sensing, 25(1): 83-108
阎广建, 姜海兰, 闫凯, 程诗宇, 宋婉娟, 童依依, 刘雅楠, 漆建波, 穆西晗, 张吴明, 谢东辉, 周红敏. 2021. 多角度光学定量遥感. 遥感学报, 25(1): 83-108 [DOI: 10.11834/jrs.20218355http://dx.doi.org/10.11834/jrs.20218355]
You D Q, Wen J G, Xiao Q, Liu Q, Liu Q H, Tang Y, Dou B C and Peng J J. 2015. Development of a high resolution BRDF/albedo product by fusing airborne CASI reflectance with MODIS daily reflectance in the oasis area of the Heihe River Basin, China. Remote Sensing, 7(6): 6784-6807 [DOI: 10.3390/rs70606784http://dx.doi.org/10.3390/rs70606784]
Zhang G, Huang F and Zheng M L. 2017a. Generating time series of medium-resolution albedo images by Kalman filtering algorithm//2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics. Shanghai: IEEE [DOI: 10.1109/CISP-BMEI.2017.8302030http://dx.doi.org/10.1109/CISP-BMEI.2017.8302030]
Zhang G D, Zhou H M, Wang C J, Xue H Z, Wang J D and Wan H W. 2019. Time series high-resolution land surface albedo estimation based on the ensemble Kalman filter algorithm. Remote Sensing, 11(7): 753 [DOI: 10.3390/rs11070753http://dx.doi.org/10.3390/rs11070753]
Zhang H, Jiao Z T, Chen L, Dong Y D, Zhang X N, Lian Y, Qian D and Cui T J. 2018. Quantifying the reflectance anisotropy effect on albedo retrieval from remotely sensed observations using archetypal BRDFs. Remote Sensing, 10(10): 1628 [DOI: 10.3390/rs10101628http://dx.doi.org/10.3390/rs10101628]
Zhang H, Jiao Z T, Dong Y D and Li X W. 2015. Evaluation of BRDF archetypes for representing surface reflectance anisotropy using MODIS BRDF data. Remote Sensing, 7(6): 7826-7845 [DOI: 10.3390/rs70607826http://dx.doi.org/10.3390/rs70607826]
Zhang H, Liu P F, He L, Lian Y and Cui T J. 2017b. Effects of reflectance anisotropy on albedo retrieval from satellite observations//2017 IEEE International Geoscience and Remote Sensing Symposium. Fort Worth: IEEE [DOI: 10.1109/IGARSS.2017.8127665http://dx.doi.org/10.1109/IGARSS.2017.8127665]
Zhang K, Zhou H M, Wang J D and Xue H Z. 2014. Estimation and validation of high spatio-temporal resolution albedo by fusing Landsat ETM+and MODIS data. Journal of Remote Sensing, 18(3): 497-517
张开, 周红敏, 王锦地, 薛华柱. 2014. 融合Landsat ETM+和MODIS数据估算高时空分辨率地表短波反照率. 遥感学报, 18(3): 497-517 [DOI: 10.11834/jrs.20143147http://dx.doi.org/10.11834/jrs.20143147]
Zhang X N, Jiao Z T, Dong Y D, He T, Ding A X, Yin S Y, Zhang H, Cui L, Chang Y X, Guo J and Xie R. 2020. Development of the direct-estimation albedo algorithm for snow-free Landsat TM albedo retrievals using field flux measurements. IEEE Transactions on Geoscience and Remote Sensing, 58(3): 1550-1567 [DOI: 10.1109/tgrs.2019.2946598http://dx.doi.org/10.1109/tgrs.2019.2946598]
Zhou H M, Hu N, He T, Liang S L and Wang J D. 2018. High resolution albedo estimation with Chinese GF-1 WFV data//IGARSS 2018-2018 38th IEEE International Geoscience and Remote Sensing Symposium (IGARSS). Valencia, Spain: IEEE [DOI: 10.1109/IGARSS.2018.8518467http://dx.doi.org/10.1109/IGARSS.2018.8518467]
Zhou Y, Wang D D, Yu Y Y, Liang S L and IEEE. 2017. VIIRS land surface albedo product: algorithm development and validation//2017 IEEE International Geoscience and Remote Sensing Symposium. Fort Worth: IEEE [DOI: 10.1109/IGARSS.2017.8126891http://dx.doi.org/10.1109/IGARSS.2017.8126891]
相关文章
相关作者
相关机构