Review of forest Leaf Area Index retrieval over rugged terrain based on remotely sensed data
- Vol. 26, Issue 12, Pages: 2451-2472(2022)
Published： 07 December 2022
DOI： 10.11834/jrs.20210244
扫 描 看 全 文
浏览全部资源
扫码关注微信
- About Journal
- Journal Online
- Special Source
摘要
叶面积指数LAI（Leaf Area Index）是表征植被几何结构及生长状态的重要生物物理参数，也是陆表过程模型的重要输入参数，如何获取高精度LAI一直备受关注。近年来，随着遥感数据的不断丰富，LAI遥感估算算法得到了快速发展，全球尺度的LAI产品已被广泛应用于气候与生态环境变化研究。然而，当前主流的LAI遥感产品生成算法基本上基于平坦地表假设而忽略了地形的影响，因此在地形复杂的地区精度较差。这是因为在山地中崎岖的地表不仅会导致严重的辐射失真现象，还会因邻近的地形对地物目标造成遮挡，因此森林多样的冠层结构和山地复杂地形的相互影响给LAI遥感反演带来了较大的不确定性。山地作为一种特殊的地貌，约占全球陆地表面的1/4，在中国占了近2/3，在这些复杂区域中估算LAI考虑地形因素十分必要。在本文中，我们首先系统地总结了现有LAI反演算法和全球遥感产品的分辨率、精度等信息，并讨论了将这些算法和产品应用于崎岖地形LAI反演的主要挑战。然后，针对山地植被场景中存在的地形效应、尺度效应，总结出山地植被冠层LAI反演的策略主要包括地形校正方法和山地辐射传输模型，并讨论了不同策略的优缺点。接着，文章讨论了野外观测的LAI数据在崎岖地形上存在的地形效应和尺度效应，以及这些效应对反演结果验证的影响程度。最后，综合总结和展望表明，遥感观测、山地辐射传输建模、机器学习技术等方面的协调使用将来可以为崎岖地表的LAI精准估算和可靠验证提供了一条有希望的途径。
Abstract
Leaf Area Index (LAI)
an essential climate variable that characterizes vegetation canopy structure
is essential in ecological and hydrological processes. Global scale LAI remote sensing products had been generated and widely used in the research of ecological environment. Most existing LAI retrieval algorithms assume that the land surface is flat and homogeneous
thereby demonstrating good performance in a homogeneous land surface. However
many studies have demonstrated that neglecting the influence of topography may cause large biases and uncertainties of the estimated LAI in a mountain area. A rugged terrain can not only distort radiation in different slopes and aspects but also cause shadows due to neighboring topographic effects. Forest occupies a large proportion of the land surface and has the most complex structure over rugged terrain
attracting greater attention to estimate accurate LAI due to its great contributions to the ecological environment. In this work
we systematically summarized the LAI retrieval algorithms and global remote sensing products and investigated the major challenges when applying those algorithms to LAI inversion over rugged terrain. Thereafter
we reviewed the main LAI retrieval methods
including topographic correction methods and the mountain radiative transfer models. Finally
the topographic and scale effects of the field in situ LAI data over a rugged terrain were discussed. The comprehensive summary and prospects show that great advances in remote sensing observations
radiation transfer modeling
machine learning techniques
etc. provide a promising way toward accurate LAI estimations and reliable validation over a rugged terrain.
关键词
叶面积指数地形校正遥感反演统计模型冠层反射率模型验证
Keywords
Leaf Area Indextopographyremote sensingretrievelstatistical modelcanopy reflectance modelvalidation
references
Antonelli A, Kissling W D, Flantua S G A, Bermúdez M A, Mulch A, Muellner-Riehl A N, Kreft H, Linder H P, Badgley C, Fjeldså J, Fritz S A, Rahbek C, Herman F, Hooghiemstra H and Hoorn C. 2018. Geological and climatic influences on mountain biodiversity. Nature Geoscience, 11(10): 718-725 [DOI: 10.1038/s41561-018-0236-zhttp://dx.doi.org/10.1038/s41561-018-0236-z]
Bai L D, Gou Y P, Shao W W, Guo Y K, Wu W. 2016. On the linear relationship between vegetation indices and leaf area index based on multi-angle remote sensing. Engineering of Surveying and Mapping, 25(01):1-4+9
白兰东, 苟叶培, 邵文文, 郭云开, 伍文. 2016. 基于多角度遥感的植被指数与叶面积指数的线性关系研.测绘工程, 25(01):1-4+9 [DOI: 10.3969/j.issn.1006-7949.2016.01.001http://dx.doi.org/10.3969/j.issn.1006-7949.2016.01.001]
Baret F, Hagolle O, Geiger B, Bicheron P, Miras B, Huc M, Berthelot B, Niño F, Weiss M, Samain O, Roujean J L and Leroy M. 2007. LAI, fAPAR and fCOVER cyclopes global products derived from vegetation. Remote Sensing of Environment, 110(3): 275-286 [DOI: 10.1016/j.rse.2007.02.018http://dx.doi.org/10.1016/j.rse.2007.02.018]
Baret F, Weiss M, Allard D, Garrigue S, Leroy M, Jeanjean H, Fernandes R, Myneni R, Privette J, Morisette J, Bohbot H, Bosseno R, Dedieu G, Di Bella C, Duchemin B, Espana M, Gond V, Gu X F, Guyon D, Lelong C, Maisongrande P, Mougin E, Nilson T, Veroustraete F and Vintilla R. 2011. VALERI: a network of sites and methodology for the validation of medium spatial resolution land products. http://w3.avignon.inra.fr/valeri/documents/VALERI-RSESubmitted.pdf（Visit date：2020/12/8http://w3.avignon.inra.fr/valeri/documents/VALERI-RSESubmitted.pdf(Visitdate:2020/12/8）
Baret F, Weiss M, Lacaze R, Camacho F, Makhmara H, Pacholcyzk P and Smets B. 2013. GEOV1: LAI and FAPAR essential climate variables and FCOVER global time series capitalizing over existing products. Part1: principles of development and production. Remote Sensing of Environment, 137: 299-309 [DOI: 10.1016/j.rse.2012.12.027http://dx.doi.org/10.1016/j.rse.2012.12.027]
Baret F, Weiss M, Verger A and Smets B. 2016. ATBD for LAI, FAPAR and FCOVER from PROBA-V products at 300 m resolution (GEOV3) (IMAGINES_RP2. 1_ATBD-LAI300M). Retrieved from http://fp7-imagines.eu/media/Documents/ImagineS_RP2.1_ATBD-LAI300m_I1.73.pdf（Visit date：2020/12/8http://fp7-imagines.eu/media/Documents/ImagineS_RP2.1_ATBD-LAI300m_I1.73.pdf(Visitdate:2020/12/8）
Bériaux E, Waldner F, Collienne F, Bogaert P and Defourny P. 2015. Maize leaf area index retrieval from synthetic quad pol SAR time series using the water cloud model. Remote Sensing, 7(12): 16204-16225 [DOI: 10.3390/rs71215818http://dx.doi.org/10.3390/rs71215818]
Black T A, Chen J M, Lee X and Sagar R M. 1991. Characteristics of shortwave and longwave irradiances under a Douglas-fir forest stand. Canadian Journal of Forest Research, 21(7): 1020-1028 [DOI: 10.1139/x91-140http://dx.doi.org/10.1139/x91-140]
Caicedo J P R, Verrelst J, Munoz-Mari J, Moreno J and Camps-Valls G. 2014. Toward a semiautomatic machine learning retrieval of biophysical parameters. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(4): 1249-1259 [DOI: 10.1109/jstars.2014.2298752http://dx.doi.org/10.1109/jstars.2014.2298752]
Cao B, Du Y M, Li J, Li H, Li L, Zhang Y, Zou J and Liu Q H. 2015. Comparison of five slope correction methods for leaf area index estimation from hemispherical photography. IEEE Geoscience and Remote Sensing Letters, 12(9): 1958-1962 [DOI: 10.1109/lgrs.2015.2440438http://dx.doi.org/10.1109/lgrs.2015.2440438]
Chastain R, Housman I, Goldstein J, Finco M and Tenneson K. 2019. Empirical cross sensor comparison of Sentinel-2A and 2B MSI, Landsat-8 OLI, and Landsat-7 ETM+ top of atmosphere spectral characteristics over the conterminous United States. Remote Sensing of Environment, 221: 274-285 [DOI: https://doi.org/10.1016/j.rse.2018.11.012https://doi.org/10.1016/j.rse.2018.11.012]
Chen J M and Black T A. 1992. Defining leaf area index for non-flat leaves. Plant, Cell and Environment, 15(4): 421-429 [DOI: 10.1111/j.1365-3040.1992.tb00992.xhttp://dx.doi.org/10.1111/j.1365-3040.1992.tb00992.x]
Chen J M and Cihlar J. 1996. Retrieving leaf area index of boreal conifer forests using landsat TM images. Remote Sensing of Environment, 55(2): 153-162 [DOI: 10.1016/0034-4257(95)00195-6http://dx.doi.org/10.1016/0034-4257(95)00195-6]
Chen J M, Pavlic G, Brown L, Cihlar J, Leblanc S G, White H P, Hall R J, Peddle D R, King D J, Trofymow J A, Swift E, Van der Sanden J and Pellikka P K E. 2002. Derivation and validation of canada-wide coarse-resolution leaf area index maps using high-resolution satellite imagery and ground measurements. Remote Sensing of Environment, 80(1): 165-184 [DOI: 10.1016/s0034-4257(01)00300-5http://dx.doi.org/10.1016/s0034-4257(01)00300-5]
Chen W and Cao C X. 2012. Topographic correction-based retrieval of leaf area index in mountain areas. Journal of Mountain Science, 9(2): 166-174 [DOI: 10.1007/s11629-012-2248-2http://dx.doi.org/10.1007/s11629-012-2248-2]
Claverie M, Matthews J L, Vermote E F and Justice C O. 2016. A 30+ year AVHRR LAI and FAPAR climate data record: algorithm description and validation. Remote Sensing, 8(3): 263 [DOI: 10.3390/rs8030263http://dx.doi.org/10.3390/rs8030263]
Combal B, Isaka H and Trotter C. 2000. Extending a turbid medium BRDF model to allow sloping terrain with a vertical plant stand. IEEE Transactions on Geoscience and Remote Sensing, 38(2): 798-810 [DOI: 10.1109/36.842009http://dx.doi.org/10.1109/36.842009]
Deng F, Chen J M, Plummer S, Chen M Z and Pisek J. 2006. Algorithm for global leaf area index retrieval using satellite imagery. IEEE Transactions on Geoscience and Remote Sensing, 44(8): 2219-2229 [DOI: 10.1109/tgrs.2006.872100http://dx.doi.org/10.1109/tgrs.2006.872100]
Dong L X. 2019. Multi-model estimation of forest leaf area index in the Three Gorges Reservoir area. Remote Sensing for Land and Resources, 31(2): 73-81
董立新. 2019. 三峡库区森林叶面积指数多模型遥感估算. 国土资源遥感, 31(2): 73-81 [DOI: 10.6046/gtzyyg.2019.02.11http://dx.doi.org/10.6046/gtzyyg.2019.02.11]
Donohue R J, Roderick M L and McVicar T R. 2007. On the importance of including vegetation dynamics in Budyko’s hydrological model. Hydrology and Earth System Sciences, 11(2): 983-995 [DOI: 10.5194/hess-11-983-2007http://dx.doi.org/10.5194/hess-11-983-2007]
Dorigo W A, Zurita-Milla R, de Wit A J W, Brazile J, Singh R and Schaepman M E. 2007. A review on reflective remote sensing and data assimilation techniques for enhanced agroecosystem modeling. International Journal of Applied Earth Observation and Geoinformation, 9(2): 165-193 [DOI: 10.1016/j.jag.2006.05.003http://dx.doi.org/10.1016/j.jag.2006.05.003]
Duan S B and Yan G J. 2007. A review of models for topographic correction of remotely sensed images in mountainous area. Journal of Beijing Normal University (Natural Science), 43(3): 362-366
段四波, 阎广建. 2007. 山区遥感图像地形校正模型研究综述. 北京师范大学学报(自然科学版), 43(3): 362-366 [DOI: 10.3321/j.issn:0476-0301.2007.03.025http://dx.doi.org/10.3321/j.issn:0476-0301.2007.03.025]
Duchemin B, Hadria R, Erraki S, Boulet G, Maisongrande P, Chehbouni A, Escadafal R, Ezzahar J, Hoedjes J C B, Kharrou M H, Khabba S, Mougenot B, Olioso A, Rodriguez J C and Simonneaux V. 2006. Monitoring wheat phenology and irrigation in central morocco: on the use of relationships between evapotranspiration, crops coefficients, leaf area index and remotely-sensed vegetation indices. Agricultural Water Management, 79(1): 1-27 [DOI: 10.1016/j.agwat.2005.02.013http://dx.doi.org/10.1016/j.agwat.2005.02.013]
Fan W L, Chen J M, Ju W M and Zhu G L. 2014. GOST: a geometric-optical model for sloping terrains. IEEE Transactions on Geoscience and Remote Sensing, 52(9): 5469-5482 [DOI: 10.1109/tgrs.2013.2289852http://dx.doi.org/10.1109/tgrs.2013.2289852]
Fan W L, Li J and Liu Q H. 2015. GOST2: the improvement of the canopy reflectance model GOST in separating the sunlit and shaded leaves. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8(4): 1423-1431 [DOI: 10.1109/jstars.2015.2413994http://dx.doi.org/10.1109/jstars.2015.2413994]
Fang H L, Baret F, Plummer S and Schaepman-Strub G. 2019. An overview of global leaf area index (LAI): methods, products, validation, and applications. Reviews of Geophysics, 57(3): 739-799 [DOI: 10.1029/2018rg000608http://dx.doi.org/10.1029/2018rg000608]
Fang H L, Liang S L and Kuusk A. 2003. Retrieving leaf area index using a genetic algorithm with a canopy radiative transfer model. Remote Sensing of Environment, 85(3): 257-270 [DOI: 10.1016/s0034-4257(03)00005-1http://dx.doi.org/10.1016/s0034-4257(03)00005-1]
Fang X Q and Zhang W C. 2003. The application of remotely sensed data to the estimation of the leaf area index. Remote Sensing for Land and Resources, (3): 58-62
方秀琴, 张万昌. 2003. 叶面积指数(LAI)的遥感定量方法综述. 国土资源遥感, (3): 58-62 [DOI: 10.3969/j.issn.1001-070X.2003.03.014http://dx.doi.org/10.3969/j.issn.1001-070X.2003.03.014]
Ganguly S, Nemani R R, Zhang G, Hashimoto H, Milesi C, Michaelis A, Wang W L, Votava P, Samanta A, Melton F, Dungan J L, Vermote E, Gao F, Knyazikhin Y and Myneni R B. 2012. Generating global leaf area index from landsat: algorithm formulation and demonstration. Remote Sensing of Environment, 122: 185-202 [DOI: 10.1016/j.rse.2011.10.032http://dx.doi.org/10.1016/j.rse.2011.10.032]
Ganguly S, Schull M, Samanta A, Shabanov N, Milesi C, Nemani R, Knyazikhin Y and Myneni R. 2008. Generating vegetation leaf area index earth system data record from multiple sensors. Part 1: theory. Remote Sensing of Environment, 112(12): 4333-4343 [DOI: 10.1016/j.rse.2008.07.014http://dx.doi.org/10.1016/j.rse.2008.07.014]
Gao Y N and Zhang W C. 2009. A simple empirical topographic correction method for ETM+ imagery. International Journal of Remote Sensing, 30(9): 2259-2275 [DOI: 10.1080/01431160802549336http://dx.doi.org/10.1080/01431160802549336]
García-Haro F J, Campos-Taberner M, Muñoz-Marí J, Laparra V, Camacho F, Sánchez-Zapero J and Camps-Valls G. 2018. Derivation of global vegetation biophysical parameters from EUMETSAT polar system. ISPRS Journal of Photogrammetry and Remote Sensing, 139: 57-74 [DOI: 10.1016/j.isprsjprs.2018.03.005http://dx.doi.org/10.1016/j.isprsjprs.2018.03.005]
Gascon F, Gastellu-Etchegorry J P, Lefevre-Fonollosa M J and Dufrene E. 2004. Retrieval of forest biophysical variables by inverting a 3-D radiative transfer model and using high and very high resolution imagery. International Journal of Remote Sensing, 25(24): 5601-5616 [DOI: 10.1080/01431160412331291305http://dx.doi.org/10.1080/01431160412331291305]
Gastellu-Etchegorry J P, Martin E and Gascon F. 2004. DART: a 3D model for simulating satellite images and studying surface radiation budget. International Journal of Remote Sensing, 25(1): 73-96 [DOI: 10.1080/0143116031000115166http://dx.doi.org/10.1080/0143116031000115166]
GCOS. 2016. The Global Observing System for Climate: Implementation Needs (GCOS-2016). Retrieved from https://library.wmo.int/opac/doc_num.php?explnum_id=3417（Visit date：2020/12/8https://library.wmo.int/opac/doc_num.php?explnum_id=3417(Visitdate:2020/12/8）
Gonsamo A and Pellikka P. 2008. Methodology comparison for slope correction in canopy leaf area index estimation using hemispherical photography. Forest Ecology and Management, 256(4): 749-759 [DOI: 10.1016/j.foreco.2008.05.032http://dx.doi.org/10.1016/j.foreco.2008.05.032]
Gorelick N, Hancher M, Dixon M, Ilyushchenko S, Thau D and Moore R. 2017. Google earth engine: planetary-scale geospatial analysis for everyone. Remote Sensing of Environment, 202: 18-27 [DOI: 10.1016/j.rse.2017.06.031http://dx.doi.org/10.1016/j.rse.2017.06.031]
Grotti M, Calders K, Origo N, Puletti N, Alivernini A, Ferrara C and Chianucci F. 2020. An intensity, image-based method to estimate gap fraction, canopy openness and effective leaf area index from phase-shift terrestrial laser scanning. Agricultural and Forest Meteorology, 280: 107766 [DOI: 10.1016/j.agrformet.2019.107766http://dx.doi.org/10.1016/j.agrformet.2019.107766]
Gu C Y. 2013. Satellite-Based Retrieval of Canopy Parameters of Moso Bamboo Forest with PROSAIL Radiative Transfer Model. Hangzhou: Zhejiang A and F University
谷成燕. 2013. 基于PROSAIL辐射传输模型的毛竹林冠层参数遥感定量反演. 杭州: 浙江农林大学
Gu D G and Gillespie A. 1998. Topographic normalization of Landsat TM images of forest based on subpixel sun-canopy-sensor geometry. Remote Sensing of Environment, 64(2): 166-175 [DOI: 10.1016/s0034-4257(97)00177-6http://dx.doi.org/10.1016/s0034-4257(97)00177-6]
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]
Hao D L, Xiao Q, Wen J G, You D Q, Wu X D, Lin X W and Wu S B. 2018. Advances in upscaling methods of quantitative remote sensing. Journal of Remote Sensing, 22(3): 408-423
郝大磊, 肖青, 闻建光, 游冬琴, 吴小丹, 林兴稳, 吴胜标. 2018. 定量遥感升尺度转换方法研究进展. 遥感学报, 22(3): 408-423 [DOI: 10.11834/jrs.20187070http://dx.doi.org/10.11834/jrs.20187070]
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]
Hashimoto H, Wang W L, Milesi C, White M A, Ganguly S, Gamo M, Hirata R, Myneni R B and Nemani R R. 2012. Exploring simple algorithms for estimating gross primary production in forested areas from satellite data. Remote Sensing, 4(1): 303-326 [DOI: 10.3390/rs4010303http://dx.doi.org/10.3390/rs4010303]
Hedstrom N R and Pomeroy J W. 1998. Measurements and modelling of snow interception in the boreal forest. Hydrological Processes, 12(10/11): 1611-1625 [DOI: 10.1002/(sici)1099-1085http://dx.doi.org/10.1002/(sici)1099-1085(199808/09)12:10/11<1611::Aid-hyp684>3.0.Co;2-4]
Huang H G. 2019. Principles and applications of the three-dimensional remote sensing mechanism model RAPID. Remote Sensing Technology and Application, 34(5): 901-913
黄华国. 2019. 三维遥感机理模型RAPID原理及其应用. 遥感技术与应用, 34(5): 901-913 [DOI: 10.11873/j.issn.1004-0323.2019.5.0901http://dx.doi.org/10.11873/j.issn.1004-0323.2019.5.0901]
Hymus G J, Pontailler J Y, Li J H, Stiling P, Hinkle C R and Drake B G. 2002. Seasonal variability in the effect of elevated CO2 on ecosystem leaf area index in a scrub-oak ecosystem. Global Change Biology, 8(10): 931-940 [DOI: 10.1046/j.1365-2486.2002.00526.xhttp://dx.doi.org/10.1046/j.1365-2486.2002.00526.x]
Jacquemoud S, Baret F, Andrieu B, Danson F M and Jaggard K. 1995. Extraction of vegetation biophysical parameters by inversion of the PROSPECT+SAIL models on sugar beet canopy reflectance data. Application to TM and AVIRIS sensors. Remote Sensing of Environment, 52(3): 163-172 [DOI: 10.1016/0034-4257(95)00018-Vhttp://dx.doi.org/10.1016/0034-4257(95)00018-V]
Jay S, Maupas F, Bendoula R and Gorretta N. 2017. Retrieving LAI, chlorophyll and nitrogen contents in sugar beet crops from multi-angular optical remote sensing: comparison of vegetation indices and PROSAIL inversion for field phenotyping. Field Crops Research, 210: 33-46 [DOI: 10.1016/j.fcr.2017.05.005http://dx.doi.org/10.1016/j.fcr.2017.05.005]
Jiang H, Mao Z Y and Wang X Q. 2011. A topography-adjusted vegetation index(TAVI) and its application in dynamic forest monitoring. Journal of Beijing Forestry University, 33(5): 8-12
江洪, 毛政元, 汪小钦. 2011. 地形调节植被指数及其在森林动态监测中的应用. 北京林业大学学报, 33(5): 8-12 [DOI: 10.13332/j.1000-1522.2011.05.006http://dx.doi.org/10.13332/j.1000-1522.2011.05.006]
Jiang S H, Jiang H and Chen H. 2020. Vegetation FPAR retrieval based on SEVI in rugged terrain and terrain effects assessment. Journal of Geo-Information Science, 22(8): 1725-1734
蒋世豪, 江洪, 陈慧. 2020. 基于SEVI的复杂地形山区植被FPAR遥感反演与地形效应评估. 地球信息科学学报, 22(8): 1725-1734 [DOI: 10.12082/dqxxkx.2020.190316http://dx.doi.org/10.12082/dqxxkx.2020.190316]
Jin H A, Li A N, Bian J H, Nan X, Zhao W, Zhang Z J and Yin G F. 2017. Intercomparison and validation of MODIS and GLASS leaf area index (LAI) products over mountain areas: a case study in southwestern China. International Journal of Applied Earth Observation and Geoinformation, 55: 52-67 [DOI: 10.1016/j.jag.2016.10.008http://dx.doi.org/10.1016/j.jag.2016.10.008]
Jin H A, Li A N, Bian J H, Zhao W, Zhang Z J and Nan X. 2016. Leaf area index (LAI) estimationfrom remotely sensed observations in different topographic gradients over southwestern China. Remote Sensing Technology and Application, 31(1): 42-50
靳华安, 李爱农, 边金虎, 赵伟, 张正健, 南希. 2016. 西南地区不同山地环境梯度叶面积指数遥感反演. 遥感技术与应用, 31(1): 42-50 [DOI: 10.11873/j.issn.1004-0323.2016.1.0042http://dx.doi.org/10.11873/j.issn.1004-0323.2016.1.0042]
Jin H A, Li A N, Xu W X, Xiao Z Q, Jiang J Y and Xue H Z. 2019. Evaluation of topographic effects on multiscale leaf area index estimation using remotely sensed observations from multiple sensors. ISPRS Journal of Photogrammetry and Remote Sensing, 154: 176-188 [DOI: 10.1016/j.isprsjprs.2019.06.008http://dx.doi.org/10.1016/j.isprsjprs.2019.06.008]
Knyazikhin Y, Martonchik J V, Myneni R B, Diner D J and Running S W. 1998. Synergistic algorithm for estimating vegetation canopy leaf area index and fraction of absorbed photosynthetically active radiation from MODIS and misr data. Journal of Geophysical Research: Atmospheres, 103(D24): 32257-32275 [DOI: 10.1029/98jd02462http://dx.doi.org/10.1029/98jd02462]
Körner C, Paulsen J and Spehn E M. 2011. A definition of mountains and their bioclimatic belts for global comparisons of biodiversity data. Alpine Botany, 121(2): 73-78 [DOI: 10.1007/s00035-011-0094-4http://dx.doi.org/10.1007/s00035-011-0094-4]
Leonenko G, Los S O and North P R J. 2013. Retrieval of leaf area index from MODIS surface reflectance by model inversion using different minimization criteria. Remote Sensing of Environment, 139: 257-270 [DOI: 10.1016/j.rse.2013.07.012http://dx.doi.org/10.1016/j.rse.2013.07.012]
Leuning R, Zhang Y Q, Rajaud A, Cleugh H and Tu K. 2008. A simple surface conductance model to estimate regional evaporation using MODIS leaf area index and the penman-monteith equation. Water Resources Research, 44(10): W10419 [DOI: 10.1029/2007wr006562http://dx.doi.org/10.1029/2007wr006562]
Li A N, Bian J H, Yin G F, Jin H A, Zhao W, Zhang Z J, Nan X and Lei G B. 2018. Study on retrieving key ecological parameters in mountainous regions by remote sensing methods and evaluating their spatio-temporal representativeness. Advances in Earth Science, 33(2): 141-151
李爱农, 边金虎, 尹高飞, 靳华安, 赵伟, 张正健, 南希, 雷光斌. 2018. 山地典型生态参量遥感反演建模及其时空表征能力研究. 地球科学进展, 33(2): 141-151 [DOI: 10.11867/j.issn.1001-8166.2018.02.0141http://dx.doi.org/10.11867/j.issn.1001-8166.2018.02.0141]
Li A N, Bian J H, Zhang Z J, Zhao W and Yin G F. 2016. Progresses, opportunities, and challenges of mountain remote sensing research. Journal of Remote Sensing, 20(5): 1199-1215
李爱农, 边金虎, 张正健, 赵伟, 尹高飞. 2016. 山地遥感主要研究进展、发展机遇与挑战. 遥感学报, 20(5): 1199-1215 [DOI: 10.11834/jrs.20166227http://dx.doi.org/10.11834/jrs.20166227]
Li C R, Song J L and Wang J D. 2015. Modifying geometric-optical bidirectional reflectance model for direct inversion of forest canopy leaf area index. Remote Sensing, 7(9): 11083-11104 [DOI: 10.3390/rs70911083http://dx.doi.org/10.3390/rs70911083]
Li F Q, Jupp D L B, Thankappan M, Lymburner L, Mueller N, Lewis A and Held A. 2012. A physics-based atmospheric and BRDF correction for landsat data over mountainous terrain. Remote Sensing of Environment, 124: 756-770 [DOI: 10.1016/j.rse.2012.06.018http://dx.doi.org/10.1016/j.rse.2012.06.018]
Li X, Vereecken H and Ma C F. 2019. Observing ecohydrological processes: Challenges and perspectives//Li X and Vereecken H eds. Observation and Measurement of Ecohydrological Processes. Berlin, Heidelberg: Springer: 1-27 [DOI: 10.1007/978-3-662-48297-1_1http://dx.doi.org/10.1007/978-3-662-48297-1_1]
Li X J, Mao F J, Du H Q, Zhou G M, Xu X J, Han N, Sun S B, Gao G L and Chen L. 2017. Assimilating leaf area index of three typical types of subtropical forest in China from MODIS time series data based on the integrated ensemble kalman filter and PROSAIL model. ISPRS Journal of Photogrammetry and Remote Sensing, 126: 68-78 [DOI: 10.1016/j.isprsjprs.2017.02.002http://dx.doi.org/10.1016/j.isprsjprs.2017.02.002]
Li X W and Strahler A H. 1986. Geometric-optical bidirectional reflectance modeling of a conifer forest canopy. IEEE Transactions on Geoscience and Remote Sensing, GE-24(6): 906-919 [DOI: 10.1109/tgrs.1986.289706http://dx.doi.org/10.1109/tgrs.1986.289706]
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 Strahler A H. 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 Z H, Li P, Ding D and Wang H J. 2018. Research progress of global high resolution digital elevation models. Geomatics and Information Science of Wuhan University, 43(12):1927-1942
李振洪, 李鹏, 丁咚, 王厚杰. 2018.全球高分辨率数字高程模型研究进展与展望.武汉大学学报(信息科学版), 43(12):1927-1942 [DOI:10.13203/j.whugis20180295http://dx.doi.org/10.13203/j.whugis20180295]
Liao Y B, Chen X F, Chen X, Zhang D R, Guan B H and Zhou F. 2011. Effect of topographic correction on the estimation of leaf area index based on landsat TM. Remote Sensing Information, 5: 47-51, 64
廖钰冰, 陈新芳, 陈喜, 张丹荣, 关保华, 周峰. 2011. 地形校正对叶面积指数遥感估算的影响. 遥感信息, (5): 47-51, 64 [DOI: 10.3969/j.issn.1000-3177.2011.05.008http://dx.doi.org/10.3969/j.issn.1000-3177.2011.05.008]
Lin X W, Wen J G, Wu S B, Hao D L, Xiao Q and Liu Q H. 2020. Advances in topographic correction methods for optical remote sensing imageries. Journal of Remote Sensing (Chinese), 24(8): 958-974
林兴稳, 闻建光, 吴胜标, 郝大磊, 肖青, 柳钦火. 2020. 地表反射率地形校正物理模型与效果评价方法研究进展. 遥感学报, 24(8): 958-974 [DOI: 10.11834/jrs.20209167http://dx.doi.org/10.11834/jrs.20209167]
Liu L Y. 2014. Simulation and correction of spatialscaling effects for leaf area index. Journal of Remote Sensing, 18(6): 1158-1168
刘良云. 2014. 叶面积指数遥感尺度效应与尺度纠正. 遥感学报, 18(6): 1158-1168 [DOI: 10.11834/jrs.20144103http://dx.doi.org/10.11834/jrs.20144103]
Liu Q H, Yan G J, Jiao Z T, Xiao Q, Wen J G, Liang S L and Wang J D. 2019. Geometric-optical remote sensing modeling to quantitative remote sensing theory and methodology development: in memory of academician Li Xiaowen. Journal of Remote Sensing, 23(1): 1-10
柳钦火, 阎广建, 焦子锑, 肖青, 闻建光, 梁顺林, 王锦地. 2019. 发展几何光学遥感建模理论, 推动定量遥感科学前行——深切缅怀李小文院士. 遥感学报, 23(1): 1-10 [DOI: 10.11834/jrs.20198077http://dx.doi.org/10.11834/jrs.20198077]
Liu T, Chen C, Fan W Y, Mao X G and Yu Y. 2019. Variation of leaf area index estimation in forests based on remote sensing images of different spatial scales. Chinese Journal of Applied Ecology, 30(5): 1687-1698
刘婷, 陈晨, 范文义, 毛学刚, 于颖. 2019. 基于不同空间尺度遥感影像估算森林叶面积指数的差异. 应用生态学报, 30(5): 1687-1698 [DOI: 10.13287/j.1001-9332.201905.014http://dx.doi.org/10.13287/j.1001-9332.201905.014]
Liu Y, Liu R G, Chen J M, Cheng X and Zheng G. 2013. Current status and perspectives of leaf area index retrieval from optical remote sensing data. Journal of Geo-Information Science, 15(5): 734-743
刘洋, 刘荣高, 陈镜明, 程晓, 郑光. 2013. 叶面积指数遥感反演研究进展与展望. 地球信息科学学报, 15(5): 734-743 [DOI: 10.3724/SP.J.1047.2013.00734http://dx.doi.org/10.3724/SP.J.1047.2013.00734]
Ma B, Li J, Fan W J, Ren H Z, Xu X R, Cui Y K and Peng J J. 2018. Application of an LAI Inversion Algorithm Based on the Unified Model of Canopy Bidirectional Reflectance Distribution Function to the Heihe River Basin. Journal of Geophysical Research: Atmospheres, 123(10): 671-610,687 [DOI: 10.1029/2018jd028415http://dx.doi.org/10.1029/2018jd028415]
Ma P P, Li J, Liu Q H, He B B and Zhao J. 2019. Multisensor synergistic quantitative leaf area index product of China. Journal of Remote Sensing, 23(6): 1232-1252
马培培, 李静, 柳钦火, 何彬彬, 赵静. 2019. 中国区域MuSyQ叶面积指数产品验证与分析. 遥感学报, 23(6): 1232-1252 [DOI:10.11834/jrs.20198071http://dx.doi.org/10.11834/jrs.20198071]
Ma Y, Wu H P, Wang L Z, Huang B, Ranjan R, Zomaya A and Jie W. 2015. Remote sensing big data computing: challenges and opportunities. Future Generation Computer Systems, 51: 47-60 [DOI: 10.1016/j.future.2014.10.029http://dx.doi.org/10.1016/j.future.2014.10.029]
María Luisa E, Frédéric B and Marie W. 2008. Slope correction for lai estimation from gap fraction measurements. Agricultural and Forest Meteorology, 148(10): 1553-1562 [DOI: 10.1016/j.agrformet.2008.05.005http://dx.doi.org/10.1016/j.agrformet.2008.05.005]
Masson V, Champeaux J L, Chauvin F, Meriguet C and Lacaze R. 2003. A global database of land surface parameters at 1-km resolution in meteorological and climate models. Journal of Climate, 16(9): 1261-1282 [DOI: 10.1175/1520-0442-16.9.1261http://dx.doi.org/10.1175/1520-0442-16.9.1261]
Meyer L H, Heurich M, Beudert B, Premier J and Pflugmacher D. 2019. Comparison of Landsat-8 and Sentinel-2 data for estimation of leaf area index in temperate forests. Remote Sensing, 11(10): 1160 [DOI: 10.3390/rs11101160http://dx.doi.org/10.3390/rs11101160]
Montes F, Pita P, Rubio A and Cañellas I. 2007. Leaf area index estimation in mountain even-aged Pinus silvestris L. stands from hemispherical photographs. Agricultural and Forest Meteorology, 145(3/4): 215-228 [DOI: 10.1016/j.agrformet.2007.04.017http://dx.doi.org/10.1016/j.agrformet.2007.04.017]
Mousivand A, Verhoef W, Menenti M and Gorte B. 2015. Modeling top of atmosphere radiance over heterogeneous non-lambertian rugged terrain. Remote Sensing, 7(6): 8019-8044 [DOI: 10.3390/rs70608019http://dx.doi.org/10.3390/rs70608019]
Myneni R B, Asrar G and Hall F G. 1992. A three-dimensional radiative transfer method for optical remote sensing of vegetated land surfaces. Remote Sensing of Environment, 41(2/3): 105-121 [DOI: 10.1016/0034-4257(92)90071-Qhttp://dx.doi.org/10.1016/0034-4257(92)90071-Q]
Myneni R B, Hoffman S, Knyazikhin Y, Privette J L, Glassy J, Tian Y, Wang Y, Song X, Zhang Y, Smith G R, Lotsch A, Friedl M, Morisette J T, Votava P, Nemani R R and Running S W. 2002. Global products of vegetation leaf area and fraction absorbed PAR from year one of MODIS data. Remote Sensing of Environment, 83(1/2): 214-231 [DOI: 10.1016/s0034-4257(02)00074-3http://dx.doi.org/10.1016/s0034-4257(02)00074-3]
Pasolli L, Asam S, Castelli M, Bruzzone L, Wohlfahrt G, Zebisch M and Notarnicola C. 2015. Retrieval of leaf area index in mountain grasslands in the Alps from MODIS satellite imagery. Remote Sensing of Environment, 165: 159-174 [DOI: 10.1016/j.rse.2015.04.027http://dx.doi.org/10.1016/j.rse.2015.04.027]
Piao S, Yin G D, Tan J G, Cheng L, Huang M T, Li Y, Liu R G, Mao J F, Myneni R B, Peng S S, Poulter B, Shi X Y, Xiao Z Q, Zeng N, Zeng Z Z and Wang Y P. 2015. Detection and attribution of vegetation greening trend in China over the last 30 years. Global Change Biology, 21(4): 1601-1609 [DOI: 10.1111/gcb.12795http://dx.doi.org/10.1111/gcb.12795]
Proy C, Tanre D and Deschamps P Y. 1989. Evaluation of topographic effects in remotely sensed data. Remote Sensing of Environment, 30(1): 21-32 [DOI: 10.1016/0034-4257(89)90044-8http://dx.doi.org/10.1016/0034-4257(89)90044-8]
Qi J B, Xie D H, Xu Y and Yan G J. 2019. Principles and applications of the 3D radiative transfer model LESS. Remote Sensing Technology and Application, 34(5): 914-924
漆建波, 谢东辉, 许月, 阎广建. 2019. 三维辐射传输模型LESS原理及其应用. 遥感技术与应用, 34(5): 914-924 [DOI: 10.11873/j.issn.1004-0323.2019.5.0914http://dx.doi.org/10.11873/j.issn.1004-0323.2019.5.0914]
Qu Y H, Han W C, Fu L Z, Li C R, Song J L, Zhou H M, Bo Y C and Wang J D. 2014. LAINet—A wireless sensor network for coniferous forest leaf area index measurement: design, algorithm and validation. Computers and Electronics in Agriculture, 108: 200-208 [DOI: 10.1016/j.compag.2014.08.003http://dx.doi.org/10.1016/j.compag.2014.08.003]
Qu Y H, Shaker A, Korhonen L, Silva C A, Jia K, Tian L and Song J L. 2020. Direct estimation of forest leaf area index based on spectrally corrected airborne LiDAR pulse penetration ratio. Remote Sensing, 12(2): 217 [DOI: 10.3390/rs12020217http://dx.doi.org/10.3390/rs12020217]
Reeder D H. 2002. Topographic Correction of Satellite Images: Theory and Application. Hanover, NH: Dartmouth College [DOI: 10.1349/ddlp.2867http://dx.doi.org/10.1349/ddlp.2867]
Reichstein M, Camps-Valls G, Stevens B, Jung M, Denzler J, Carvalhais N and Prabhat. 2019. Deep learning and process understanding for data-driven earth system science. Nature, 566(7743): 195-204 [DOI: 10.1038/s41586-019-0912-1http://dx.doi.org/10.1038/s41586-019-0912-1]
Richter R, Kellenberger T and Kaufmann H. 2009. Comparison of topographic correction methods. Remote Sensing, 1(3): 184-196 [DOI: 10.3390/rs1030184http://dx.doi.org/10.3390/rs1030184]
Rivera J P, Verrelst J, Delegido J, Veroustraete F and Moreno J. 2014. On the semi-automatic retrieval of biophysical parameters based on spectral index optimization. Remote Sensing, 6(6): 4927-4951 [DOI: 10.3390/rs6064927http://dx.doi.org/10.3390/rs6064927]
Rivera J P, Verrelst J, Leonenko G and Moreno J. 2013. Multiple cost functions and regularization options for improved retrieval of leaf chlorophyll content and lai through inversion of the PROSAIL model. Remote Sensing, 5(7): 3280-3304 [DOI: 10.3390/rs5073280http://dx.doi.org/10.3390/rs5073280]
Ross J. 1981. The Radiation Regime and Architecture of Plant Stands. Dordrecht: Springer [DOI: 10.1007/978-94-009-8647-3http://dx.doi.org/10.1007/978-94-009-8647-3]
Rossi C, Gonzalez F R, Fritz T, Yague-Martinez N and Eineder M. 2012. TanDEM-X calibrated Raw DEM generation. ISPRS Journal of Photogrammetry and Remote Sensing, 73:12-20 [DOI: 10.1016/j.isprsjprs.2012.05.014http://dx.doi.org/10.1016/j.isprsjprs.2012.05.014]
Roupioz L, Jia L, Nerry F and Menenti M. 2014. Correction of sub-pixel topographical effects on land surface albedo retrieved from geostationary satellite (FengYun-2D) observations. IOP Conference Series: Earth and Environmental Science, 17: 012270 [DOI: 10.1088/1755-1315/17/1/012270http://dx.doi.org/10.1088/1755-1315/17/1/012270]
Saltelli A, Annoni P, Azzini I, Campolongo F, Ratto M and Tarantola S. 2010. Variance based sensitivity analysis of model output. Design and estimator for the total sensitivity index. Computer Physics Communications, 181(2): 259-270 [DOI: 10.1016/j.cpc.2009.09.018http://dx.doi.org/10.1016/j.cpc.2009.09.018]
Sandmeier S, Müller C, Hosgood B and Andreoli G. 1998. Physical mechanisms in hyperspectral BRDF data of grass and watercress. Remote Sensing of Environment, 66(2): 222-233 [DOI: 10.1016/s0034-4257(98)00060-1http://dx.doi.org/10.1016/s0034-4257(98)00060-1]
Schaaf C B, Li X W and Strahler A H. 1994. Topographic effects on bidirectional and hemispherical reflectances calculated with a geometric-optical canopy model. IEEE Transactions on Geoscience and Remote Sensing, 32(6): 1186-1193 [DOI: 10.1109/36.338367http://dx.doi.org/10.1109/36.338367]
Schleppi P, Conedera M, Sedivy I and Thimonier A. 2007. Correcting non-linearity and slope effects in the estimation of the leaf area index of forests from hemispherical photographs. Agricultural and Forest Meteorology, 144(3/4): 236-242 [DOI: 10.1016/j.agrformet.2007.02.004http://dx.doi.org/10.1016/j.agrformet.2007.02.004]
Shao Y, Li G L, Guenther E and Campbell J B. 2015. Evaluation of topographic correction on subpixel impervious cover mapping with CBERS-2B data. IEEE Geoscience and Remote Sensing Letters, 12(8): 1675-1679 [DOI: 10.1109/lgrs.2015.2419135http://dx.doi.org/10.1109/lgrs.2015.2419135]
Singha M, Dong J W, Zhang G L and Xiao X M. 2019. High resolution paddy rice maps in cloud-prone Bangladesh and Northeast India using Sentinel-1 data. Scientific Data, 6(1): 26 [DOI: 10.1038/s41597-019-0036-3http://dx.doi.org/10.1038/s41597-019-0036-3]
Sinha S K, Padalia H, Dasgupta A, Verrelst J and Rivera J P. 2020. Estimation of leaf area index using PROSAIL based LUT inversion, MLRA-GPR and empirical models: case study of tropical deciduous forest plantation, North India. International Journal of Applied Earth Observation and Geoinformation, 86: 102027 [DOI: 10.1016/j.jag.2019.102027http://dx.doi.org/10.1016/j.jag.2019.102027]
Soenen S A, Peddle D R and Coburn C A. 2005. SCS+C: a modified Sun-canopy-sensor topographic correction in forested terrain. IEEE Transactions on Geoscience and Remote Sensing, 43(9): 2148-2159 [DOI: 10.1109/TGRS.2005.852480http://dx.doi.org/10.1109/TGRS.2005.852480]
Steltzer H and Welker J M. 2006. Modeling the effect of photosynthetic vegetation properties on the NDVI-LAI relationship. Ecology, 87(11): 2765-2772 [DOI: 10.1890/0012-9658(2006)87http://dx.doi.org/10.1890/0012-9658(2006)87[2765:Mteopv]2.0.Co;2]
Tang H, Dubayah R, Brolly M, Ganguly S and Zhang G. 2014. Large-scale retrieval of leaf area index and vertical foliage profile from the spaceborne waveform LiDAR (GLAS/ICESat). Remote Sensing of Environment, 154: 8-18 [DOI: 10.1016/j.rse.2014.08.007http://dx.doi.org/10.1016/j.rse.2014.08.007]
Teillet P M, Guindon B and Goodenough D G. 1982. On the slope-aspect correction of multispectral scanner data. Canadian Journal of Remote Sensing, 8(2): 84-106 [DOI: 10.1080/0703899 2.1982.10855028http://dx.doi.org/10.1080/07038992.1982.10855028]
Tian J Y, Wang L, Li X J, Gong H L, Shi C, Zhong R F and Liu X M. 2017. Comparison of UAV and WorldView-2 imagery for mapping leaf area index of mangrove forest. International Journal of Applied Earth Observation and Geoinformation, 61: 22-31 [DOI: 10.1016/j.jag.2017.05.002http://dx.doi.org/10.1016/j.jag.2017.05.002]
Turner D P, Cohen W B, Kennedy R E, Fassnacht K S and Briggs J M. 1999. Relationships between leaf area index and Landsat TM spectral vegetation indices across three temperate zone sites. Remote Sensing of Environment, 70(1): 52-68 [DOI: 10.1016/s0034-4257(99)00057-7http://dx.doi.org/10.1016/s0034-4257(99)00057-7]
Verhoef W. 1984. Light scattering by leaf layers with application to canopy reflectance modeling: The SAIL model. Remote Sensing of Environment, 16, 125-141 [DOI: 10.1016/0034-4257(84)90057-9http://dx.doi.org/10.1016/0034-4257(84)90057-9]
Verhoef W and Bach H. 2007. Coupled soil-leaf-canopy and atmosphere radiative transfer modeling to simulate hyperspectral multi-angular surface reflectance and TOA radiance data. Remote Sensing of Environment, 109(2): 166-182 [DOI: 10.1016/j.rse.2006.12.013http://dx.doi.org/10.1016/j.rse.2006.12.013]
Verrelst J, Rivera J P, Leonenko G, Alonso L and Moreno J. 2014. Optimizing LUT-based RTM inversion for semiautomatic mapping of crop biophysical parameters from Sentinel-2 and -3 data: role of cost functions. IEEE Transactions on Geoscience and Remote Sensing, 52(1): 257-269 [DOI: 10.1109/tgrs.2013.2238242http://dx.doi.org/10.1109/tgrs.2013.2238242]
Verrelst J, Rivera J P, van der Tol C, Magnani F, Mohammed G and Moreno J. 2015a. Global sensitivity analysis of the SCOPE model: what drives simulated canopy-leaving sun-induced fluorescence?. Remote Sensing of Environment, 166: 8-21 [DOI: 10.1016/j.rse.2015.06.002http://dx.doi.org/10.1016/j.rse.2015.06.002]
Verrelst J, Rivera J P, Veroustraete F, Muñoz-Marí J, Clevers J G P W, Camps-Valls G and Moreno J. 2015b. Experimental Sentinel-2 LAI estimation using parametric, non-parametric and physical retrieval methods - A comparison. ISPRS Journal of Photogrammetry and Remote Sensing, 108: 260-272 [DOI: 10.1016/j.isprsjprs.2015.04.013http://dx.doi.org/10.1016/j.isprsjprs.2015.04.013]
Vincini M, Reeder D and Frazzi E. 2002. An empirical topographic normalization method for forest TM data//IEEE International Geoscience and Remote Sensing Symposium. Toronto, ON: IEEE: 2091-2093 [DOI: 10.1109/IGARSS.2002.1026454http://dx.doi.org/10.1109/IGARSS.2002.1026454]
Walter J M N and Torquebiau E F. 2000. The computation of forest leaf area index on slope using fish-eye sensors. Comptes Rendus de l’Académie des Sciences—Series Ⅲ： Sciences de la Vie, 323(9): 801-813 [DOI: 10.1016/s0764-4469(00)01229-4http://dx.doi.org/10.1016/s0764-4469(00)01229-4]
Wang Q, Pang Y, Li Z Y, Chen E X, Sun G Q, Tan B X and Liu D D. 2016. Inversion of leaf area index based on a simple physical model. Journal of China University of Mining and Technology, 45(3): 623-629, 645
王强, 庞勇, 李增元, 陈尔学, 孙国清, 谭炳香, 刘丹丹. 2016. 基于一种简单物理模型的叶面积指数反演. 中国矿业大学学报, 45(3): 623-629, 645 [DOI: 10.13247/j.cnki.jcumt.000519http://dx.doi.org/10.13247/j.cnki.jcumt.000519]
Wang Q, Pang Y, L, Z, Chen E, Sun G and Tan B. 2013. Improvement and application of the Conifer Forest Multiangular Hybrid GORT Model MGeoSAIL. IEEE Transactions on Geoscience and Remote Sensing, 51: 5047-5059 [DOI: 10.1109/tgrs.2012.2234466http://dx.doi.org/10.1109/tgrs.2012.2234466]
Wang T T, Xiao Z Q and Liu Z G. 2017. Performance evaluation of machine learning methods for leaf area index retrieval from time-series MODIS reflectance data. Sensors (Basel), 17(1): 81 [DOI: 10.3390/s17010081http://dx.doi.org/10.3390/s17010081]
Wang Y J, Lauret N and Gastellu-Etchegorry J P. 2020. DART radiative transfer modelling for sloping landscapes. Remote Sensing of Environment, 247: 111902 [DOI: 10.1016/j.rse.2020.111902http://dx.doi.org/10.1016/j.rse.2020.111902]
Watson D J. 1947. Comparative physiological studies on the growth of field crops: I. Variation in net assimilation rate and leaf area between species and varieties, and within and between years. Annals of Botany, 11(1): 41-76 [DOI: 10.1093/oxfordjournals.aob.a083148http://dx.doi.org/10.1093/oxfordjournals.aob.a083148]
Wen J G, Liu Q, Tang Y, Dou B C, You D Q, Xiao Q, Liu Q H and Li X W. 2015. Modeling land surface reflectance coupled BRDF for HJ-1/CCD data of rugged terrain in Heihe River Basin, China. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8(4): 1506-1518 [DOI: 10.1109/jstars.2015.2416254http://dx.doi.org/10.1109/jstars.2015.2416254]
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]
Wen J G, Liu Q H, Liu Q, Xiao Q and Li X W. 2009. Parametrized BRDF for atmospheric and topographic correction and albedo estimation in Jiangxi rugged terrain, China. International Journal of Remote Sensing, 30(11): 2875-2896 [DOI: 10.1080/01431160802558618http://dx.doi.org/10.1080/01431160802558618]
Wenge N, Xiaowen L, 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: 987-999 [DOI: 10.1109/36.752217http://dx.doi.org/10.1109/36.752217]
Wigmosta M S, Vail L W and Lettenmaier D P. 1994. A distributed hydrology-vegetation model for complex terrain. Water Resources Research, 30(6): 1665-1679 [DOI: 10.1029/94wr00436http://dx.doi.org/10.1029/94wr00436]
Wilson A M and Jetz W. 2016. Remotely sensed high-resolution global cloud dynamics for predicting ecosystem and biodiversity distributions. PLoS Biology, 14(3): e1002415 [DOI: 10.1371/journal.pbio.1002415http://dx.doi.org/10.1371/journal.pbio.1002415]
Wu S B, Wen J G, Gastellu-Etchegorry J P, Liu Q H, You D Q, Xiao Q, Hao D L, Lin X W and Yin T G. 2019a. The definition of remotely sensed reflectance quantities suitable for rugged terrain. Remote Sensing of Environment, 225: 403-415 [DOI: 10.1016/j.rse.2019.01.005http://dx.doi.org/10.1016/j.rse.2019.01.005]
Wu S B, Wen J G, Lin X W, Hao D L, You D Q, Xiao Q, Liu Q H and Yin T G. 2019b. 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, Tang Y, You D Q and Zhao J. 2017. Modeling anisotropic bidirectional reflectance of sloping forest//2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). Fort Worth, TX: IEEE: 3874-3877 [DOI: 10.1109/IGARSS.2017.8127848http://dx.doi.org/10.1109/IGARSS.2017.8127848]
Wu Z J, He G J, Huang S L, Wang M M and Lin J T. 2017. Terrain effects assessment on remotely sensed fractional vegetation cover in hilly area of southern China. Journal of Remote Sensing, 21(1): 159-167
吴志杰, 何国金, 黄绍霖, 王猛猛, 林金堂. 2017. 南方丘陵区植被覆盖度遥感估算的地形效应评估. 遥感学报, 21(1): 159-167 [DOI: 10.11834/jrs.20176016http://dx.doi.org/10.11834/jrs.20176016]
Xiao Z Q, Liang S L and Jiang B. 2017. Evaluation of four long time-series global leaf area index products. Agricultural and Forest Meteorology, 246: 218-230 [DOI: 10.1016/j.agrformet.2017.06.016http://dx.doi.org/10.1016/j.agrformet.2017.06.016]
Xiao Z Q, Liang S L, Wang J D, Chen P, Yin X J, Zhang L Q and Song J L. 2014. Use of general regression neural networks for generating the GLASS leaf area index product from time-series MODIS surface reflectance. IEEE Transactions on Geoscience and Remote Sensing, 52(1): 209-223 [DOI: 10.1109/tgrs.2013.2237780http://dx.doi.org/10.1109/tgrs.2013.2237780]
Xie Q Y, Dash J, Huang W J, Peng D L, Qin Q M, Mortimer H, Casa R, Pignatti S, Laneve G, Pascucci S, Dong Y Y and Ye H C. 2018. Vegetation indices combining the red and red-edge spectral information for leaf area index retrieval. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 11(5): 1482-1493 [DOI: 10.1109/jstars.2018.2813281http://dx.doi.org/10.1109/jstars.2018.2813281]
Xu B D. 2018. Leaf Area Index Retrieval and Product Validation over Heterogeneous Land Surfaces. Beijing: Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences
徐保东. 2018. 非均质地表叶面积指数反演及产品真实性检验. 北京: 中国科学院遥感与数字地球研究所
Xu X R, Fan W J, Li J C, Zhao P and Chen G X. 2017. A unified model of bidirectional reflectance distribution function for the vegetation canopy. Science China Earth Sciences, 60(3): 463-477 [DOI: 10.1007/s11430-016-5082-6http://dx.doi.org/10.1007/s11430-016-5082-6]
Yan G J, Hu R H, Luo J H, Weiss M, Jiang H L, Mu X H, Xie D H and Zhang W M. 2019. Review of indirect optical measurements of leaf area index: recent advances, challenges, and perspectives. Agricultural and Forest Meteorology, 265: 390-411 [DOI: 10.1016/j.agrformet.2018.11.033http://dx.doi.org/10.1016/j.agrformet.2018.11.033]
Yang X B, Wang C, Pan F F, Nie S, Xi X H and Luo S Z. 2019. Retrieving leaf area index in discontinuous forest using ICESat/GLAS full-waveform data based on gap fraction model. ISPRS Journal of Photogrammetry and Remote Sensing, 148: 54-62 [DOI: 10.1016/j.isprsjprs.2018.12.010http://dx.doi.org/10.1016/j.isprsjprs.2018.12.010]
Yin G F, Cao B, Li J, Fan W L, Zeng Y L, Xu B D and Zhao W. 2020a. Path length correction for improving leaf area index measurements over sloping terrains: a deep analysis through computer simulation. IEEE Transactions on Geoscience and Remote Sensing, 58(7): 4573-4589 [DOI: 10.1109/TGRS.2019.2963366http://dx.doi.org/10.1109/TGRS.2019.2963366]
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]
Yin T G, Qi J B, Cook B D, Morton D C, Wei S S and Gastellu-Etchegorry J P. 2020b. Modeling small-footprint airborne LiDAR-derived estimates of gap probability and leaf area index. Remote Sensing, 12(1): 4 [DOI: 10.3390/rs12010004http://dx.doi.org/10.3390/rs12010004]
Yu W T, Li J, Liu Q H, Yin G F, Zeng Y L, Lin S R and Zhao J. 2020. A simulation-based analysis of topographic effects on LAI inversion over sloped terrain. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13: 794-806 [DOI: 10.1109/JSTARS.2020.2970999http://dx.doi.org/10.1109/JSTARS.2020.2970999]
Zan M, Li D Q, Ju W M, Wang X Q and Chen S J. 2013. Measurement and retrieval of leaf area index using remote sensing data in Kanas National Nature Reserve, Xinjiang. Acta Ecologica Sinica, 33(15): 4744-4757
昝梅, 李登秋, 居为民, 王希群, 陈蜀江. 2013. 新疆喀纳斯国家自然保护区植被叶面积指数观测与遥感估算. 生态学报, 33(15): 4744-4757 [DOI: 10.5846/stxb201205040641http://dx.doi.org/10.5846/stxb201205040641]
Zhang B. 2018. Remotely sensed big data era and intelligent information extraction. Geomatics and Information Science of Wuhan University, 43(12): 1861-1871
张兵. 2018. 遥感大数据时代与智能信息提取. 武汉大学学报(信息科学版), 43(12): 1861-1871 [DOI: 10.13203/j.whugis20180172http://dx.doi.org/10.13203/j.whugis20180172]
Zhang H K, Roy D P, Yan L, Li Z, Huang H, Vermote E, Skakun S and Roger J C. 2018. Characterization of Sentinel-2A and Landsat-8 top of atmosphere, surface, and nadir BRDF adjusted reflectance and NDVI differences. Remote Sensing of Environment, 215: 482-494 [DOI: https://doi.org/10.1016/j.rse.2018.04.031https://doi.org/10.1016/j.rse.2018.04.031]
Zhang D F, Liu J L, Ni W J, Sun G Q, Zhang Z Y, Liu Q H and Wang Q. 2019. Estimation of forest leaf area index using height and canopy cover information extracted from unmanned aerial vehicle stereo imagery. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 12(2): 471-481 [DOI: 10.1109/jstars.2019.2891519http://dx.doi.org/10.1109/jstars.2019.2891519]
Zhang X Y, Li J F. 1995. The derivation of a reflectance model for the estimation of leaf area index using perpendicular vegetation index. Remote Sensing Technology and Application, 10(3):6
张晓阳, 李劲峰. 1995. 利用垂直植被指数推算作物叶面积系数的理论模式. 遥感技术与应用, 10(3): 6 [DOI: CNKI:SUN:YGJS.0.1995-03-002http://dx.doi.org/CNKI:SUN:YGJS.0.1995-03-002]
Zhang Y L, Yan G J and Bai Y L. 2015. Sensitivity of topographic correction to the dem spatial scale. IEEE Geoscience and Remote Sensing Letters, 12(1): 53-57 [DOI: 10.1109/lgrs.2014.2326000http://dx.doi.org/10.1109/lgrs.2014.2326000]
Zhang Z M, He G J, Zhang X M, Long T F, Wang G Z and Wang M M. 2017. A coupled atmospheric and topographic correction algorithm for remotely sensed satellite imagery over mountainous terrain. GIScience and Remote Sensing, 55(3): 400-416 [DOI: 10.1080/15481603.2017.1382066http://dx.doi.org/10.1080/15481603.2017.1382066]
Zheng G and Moskal L M. 2009. Retrieving leaf area index (LAI) using remote sensing: theories, methods and sensors. Sensors (Basel), 9(4): 2719-2745 [DOI: 10.3390/s90402719http://dx.doi.org/10.3390/s90402719]
Zhu G L, Liu Y B, Ju W M and Chen J M. 2013. Evaluation of topographic effects on four commonly used vegetation indices. Journal of Remote Sensing, 17(1): 210-234
朱高龙, 柳艺博, 居为民, 陈镜明. 2013. 4种常用植被指数的地形效应评估. 遥感学报, 17(1): 210-234 [DOI: 10.11834/jrs.20131380http://dx.doi.org/10.11834/jrs.20131380]
Zhu X X, Tuia D, Mou L C, Xia G S, Zhang L P, Xu F and Fraundorfer F. 2017a. Deep learning in remote sensing: a comprehensive review and list of resources. IEEE Geoscience and Remote Sensing Magazine, 5(4): 8-36 [DOI: 10.1109/MGRS.2017.2762307http://dx.doi.org/10.1109/MGRS.2017.2762307]
Zhu X Z. 2014. A Comparitive Study of Three Different Methods for Estimating Forest Leaf Area Index. Hangzhou: Zhejiang A and F University
朱旭珍. 2014. 三种不同方法估算森林叶面积指数的比较研究. 杭州: 浙江农林大学
Zhu Y H, Liu K, Liu L, Myint S W, Wang S G, Liu H X and He Z. 2017b. Exploring the potential of WorldView-2 red-edge band-based vegetation indices for estimation of mangrove leaf area index with machine learning algorithms. Remote Sensing, 9(10): 1060 [DOI: 10.3390/rs9101060http://dx.doi.org/10.3390/rs9101060]
Zhu Z C, Bi J, Pan Y Z, Ganguly S, Anav A, Xu L, Samanta A, Piao S L, Nemani R R and Myneni R B. 2013. Global data sets of vegetation leaf area index (LAI)3g and fraction of photosynthetically active radiation (FPAR)3g derived from global inventory modeling and mapping studies (GIMMS) normalized difference vegetation index (NDVI3g) for the period 1981 to 2011. Remote Sensing, 5(2): 927-948 [DOI: 10.3390/rs5020927http://dx.doi.org/10.3390/rs5020927]
Zink M, Bachmann M, Brautigam B, Fritz T, Hajnsek I, Moreira A, Wessel B and Krieger G.2014. TanDEM-X: The New Global DEM Takes Shape. IEEE Geoscience and Remote Sensing Magazine, 2: 8-23 [DOI: 10.1109/MGRS.2014.2318895http://dx.doi.org/10.1109/MGRS.2014.2318895]
Alert me when the article has been cited
提交
相关文章
相关作者
暂无数据
相关机构
State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing Normal University
Macro Agriculture Research Institute, College of Resources and Environment, Huazhong Agricultural University
Key Laboratory of Digital Earth, Aerospace Information Research Institute, Chinese Academy of Sciences
School of Geography, South China Normal University
Beijing Normal University, State Key Laboratory of Remote Sensing Science
选择翻译语言
- Postal code：100190
- Tel：010-58887052 Email：nrsb@aircas.ac.cn
- Technical support is provided by Beijing Founder electronics co., LTD 京ICP备20021838号-9
京公网安备11010802024621 - It is recommended to read the content of this site in Chrome&IE9+. Please switch to extreme mode in browser 360.
- Cookies We use cookies to help provide and enhance our service and tailor content. By continuing, you agree to the use of cookies.
0
Bulk Citation
Export to
TOP