Früher|Nächster

Review on the theory， method， and research progress of leaf area index estimation in mountainous areas

  • role： First author第一作者
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China

    Beijing Engineering Research Center for Global Land Remote Sensing Products, Beijing Normal University, Beijing 100875, China

  • Email：201931051040@mail.bnu.edu.cn
  • Introduction：江海英，1992年生,女,博士研究生, 研究方向为植被定量遥感。E-mail: 201931051040@mail.bnu.edu.cn
JIANG Haiying12,  
  • role： Corresponding author通信作者
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China

    Beijing Engineering Research Center for Global Land Remote Sensing Products, Beijing Normal University, Beijing 100875, China

  • Email：jiakun@bnu.edu.cn
  • Introduction：贾坤，1983年生，男，副教授，研究方向为植被定量遥感、土地覆盖遥感分类和生态遥感。E-mail: jiakun@bnu.edu.cn
JIA Kun12*,  
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China

    Beijing Engineering Research Center for Global Land Remote Sensing Products, Beijing Normal University, Beijing 100875, China

ZHAO Xiang12,  
  • Affiliation：

    Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China

WEI Xiangqin3,  
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China

    Beijing Engineering Research Center for Global Land Remote Sensing Products, Beijing Normal University, Beijing 100875, China

WANG Bing12,  
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China

    Beijing Engineering Research Center for Global Land Remote Sensing Products, Beijing Normal University, Beijing 100875, China

YAO Yunjun12,  
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China

    Beijing Engineering Research Center for Global Land Remote Sensing Products, Beijing Normal University, Beijing 100875, China

ZHANG Xiaotong12,  
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China

    Beijing Engineering Research Center for Global Land Remote Sensing Products, Beijing Normal University, Beijing 100875, China

JIANG Bo12

Resümee

Leaf Area Index (LAI) is an important vegetation parameter that represents leaf density and canopy structure characteristics. This parameter plays an important role in climate change, crop growth model, and carbon and water cycle studies. Remote sensing is an important means to estimate LAI on regional and global scales. LAI products are currently mainly obtained by remote sensing retrieval. However, most LAI product algorithms ignore the effect of topographic features, which results in the great uncertainty in the accuracy of retrieved LAI in mountainous areas. The influence of topographic factors on the canopy reflectance needs to be considered to improve the accuracy of mountain LAI retrieval. Generally, there are mainly two methods to eliminate the influence of topography on mountain LAI retrieval. One method is to use the mountain canopy reflectance model to simulate reflectance, and the other method is to perform topographic correction on remote sensing data.In this paper, the research progress of mountain canopy reflectance model and topographic correction method were comprehensively analyzed on the basis of the theories and methods of LAI retrieval in mountainous areas. For mountain LAI retrieval method based on mountain canopy reflectance simulation, some mountain canopy reflectance models simplify the influence of topographic factors on atmospheric scattering and adjacent terrain scattering, resulting in poor model simulation and low LAI retrieval accuracy. Some complex mountain canopy reflectance models, such as geometric-optical hybrid model or computer simulation model, can accurately simulate topographic effect on reflectance, but it is difficult to invert due to complex input parameters. For mountain LAI retrieval method based on image topographic correction, it is difficult to choose suitable topographic correction method, because the generality of the existing models is poor that a single topographic correction model may only be applicable to a certain terrain condition, a certain area, a certain sensor or a certain waveband. In addition to the above two methods, some studies directly add topographic factors into the statistical regression equation of LAI as a control variable, so as to retrieve mountain LAI. However, this method may cause over fitting phenomenon and does not have robustness and portability.Based on the existing problems of mountain canopy reflectance model, topographic correction method and mountain LAI retrieval method, this paper summarizes and discusses the development trend of future research of mountain LAI retrieval. For mountain canopy reflectance model, it is necessary to develop a model that takes into account the non-Lambertian characteristics of the surface, the geotropic growth of trees, and diffuse radiation and other factors to improve the accuracy of model simulation. In addition, the parameter optimization and retrievability of the model should also be considered. For topographic correction method, it can be combined with BRDF correction or atmospheric correction in the future, especially for complex terrain. To accurately and efficiently retrieve mountain LAI, it is necessary to comprehensively consider factors such as the size of the study area, the heterogeneity of the ground surface, and the degree of terrain undulations, and choose an appropriate topographic correction method or mountain canopy reflectance model. Moreover, it is necessary to carry out more in-depth research on the validation of LAI retrieval accuracy in mountainous areas.

Schlüsselwort

remote sensing;optical remote sensing;LAI;topographic correction;mountain canopy reflectance model;DEM

References

  1. 1.
    Baret F, Bacour C, Béal D, Weiss M, Berthelot B and Regner P. 2006. Algorithm theoretical basis document for MERIS top of canopy land products (TOC_VEG), Contract 1-25
  2. 2.
    Biudes M S, Machado N G, De Morais Danelichen V H, Souza M C, Vourlitis G L and De Souza Nogueira J. 2014. Ground and remote sensing-based measurements of leaf area index in a transitional forest and seasonal flooded forest in Brazil. International Journal of Biometeorology, 58(6): 1181-1193
  3. 3.
    Bonan G. 2002. Ecological Climatology: concept and Applications. New York: Cambridge University Press.
  4. 4.
    Bréda N J J. 2008. Leaf area index//Jørgensen S E and Fath B D, eds. Encyclopedia of Ecology. Amsterdam: Elsevier: 2148-2154
  5. 5.
    Chen J M and Black T A. 1992. Defining leaf area index for non-flat leaves. Plant, Cell and Environment, 15(4): 421-429
  6. 6.
    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
  7. 7.
    Chen Y, Hall A and Liou K N. 2006. Application of three-dimensional solar radiative transfer to mountains. Journal of Geophysical Research: Atmosphere, 111(D21): D21111
  8. 8.
    Civco D L. 1989. Topographic normalization of Landsat thematic mapper digital imagery. Photogrammetric Engineering and Remote Sensing, 55(9): 1303-1309
  9. 9.
    Claverie M, Matthews J L, Vermote E F and Jastice C O. 2016. A 30+ year AVHRR LAI and FAPAR climate data record: Algorithm description and validation. Remote Sensing, 8(3), 263
  10. 10.
    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
  11. 11.
    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
  12. 12.
    Ding Y F, You H G, Zhang H, Chen S J, Xu B and Sun T. 2018. Topographic correction method for high-resolution remote sensing images. Journal of Beijing University of Aeronautics and Astronautics, 44(1): 27-35
  13. 13.
    Dozier J and Frew J. 1990. Rapid calculation of terrain parameters for radiation modeling from digital elevation data. IEEE Transactions on Geoscience and Remote Sensing, 28(5): 963-969
  14. 14.
    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
  15. 15.
    Fan W L, Chen J M, Ju W M and Nesbitt N. 2014a. Hybrid geometric optical–radiative transfer model suitable for forests on slopes. IEEE Transactions on Geoscience and Remote Sensing, 52(9): 5579-5586
  16. 16.
    Fan W L, Chen J M, Ju W M and Zhu G L. 2014b. GOST: a geometric-optical model for sloping terrains. IEEE Transactions on Geoscience and Remote Sensing, 52(9): 5469-5482
  17. 17.
    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
  18. 18.
    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
  19. 19.
    Fang J Y, Shen Z H and Cui H T. 2004. Ecological characteristics of mountains and research issues of mountain ecology. Biodiversity Science, 12(1): 10-19
  20. 20.
    Fu G and Wu J S. 2017. Validation of MODIS collection 6 FPAR/LAI in the alpine grassland of the northern Tibetan plateau. Remote Sensing Letters, 8(9): 831-838
  21. 21.
    Gao M L, Zhao W J, Gong Z N, Gong H L, Chen Z and Tang X M. 2014. Topographic correction of ZY-3 satellite images and its effects on estimation of shrub leaf biomass in mountainous areas. Remote Sensing, 6(4): 2745-2764
  22. 22.
    Gao Y N and Zhang W C. 2008. Comparison test and research progress of topographic correction on remotely sensed data. Geographical Research, 27(2): 467-477
  23. 23.
    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
  24. 24.
    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
  25. 25.
    Gastellu-Etchegorry J P, Yin T G, Lauret N, Cajgfinger T, Gregoire T, Grau E, Feret J B, Lopes M, Guilleux J, Dedieu G, Malenovský Z, Cook B D, Morton D, Rubio J, Durrieu S, Cazanave G, Martin E and Ristorcelli T. 2015. Discrete anisotropic radiative transfer (DART 5) for modeling airborne and satellite spectroradiometer and LIDAR acquisitions of natural and urban landscapes. Remote Sensing, 7(2): 1667-1701
  26. 26.
    Ge H L, Lu D S, He S Z, Xu A J, Zhou G M and Du H Q. 2008. Pixel-based minnaert correction method for reducing topographic effects on a Landsat 7 ETM+ image. Photogrammetric Engineering and Remote Sensing, 74(11): 1343-1350
  27. 27.
    Geng J, Chen J M, Fan W L, Tu L L, Tian Q J, Yang R R, Yang Y J, Wang L, Lv C G and Wu S B. 2017. GOFP: a geometric-optical model for forest plantations. IEEE Transactions on Geoscience and Remote Sensing, 55(9): 5230-5241
  28. 28.
    Ghasemi N, Mohammadzadeh A and Sahebi M R. 2013. Assessment of different topographic correction methods in ALOS AVNIR-2 data over a forest area. International Journal of Digital Earth, 6(5): 504-520
  29. 29.
    Gonsamo A and Chen J M. 2014. Improved LAI algorithm implementation to MODIS data by incorporating background, topography, and foliage clumping information. IEEE Transactions on Geoscience and Remote Sensing, 52(2): 1076-1088
  30. 30.
    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
  31. 31.
    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
  32. 32.
    Gupta S K and Shukla D P. 2020. Evaluation of topographic correction methods for LULC preparation based on multi-source DEMs and landsat-8 imagery. Spatial Information Research, 28(1): 113-127
  33. 33.
    Hantson S and Chuvieco E. 2011. Evaluation of different topographic correction methods for landsat imagery. International Journal of Applied Earth Observation and Geoinformation, 13(5): 691-700
  34. 34.
    Hao D L, Wen J G, Xiao Q, You D Q and Tang. An improved topography-coupled Kernel-driven model for land surface anisotropic reflectance. 2020. IEEE Transactions on Geoscience and Remote Sensing,58(4): 2833-2847 [ DOI: ]
  35. 35.
    Heiskanen J. 2006. Estimating aboveground tree biomass and leaf area index in a mountain birch forest using ASTER satellite data. International Journal of Remote Sensing, 27(6): 1135-1158
  36. 36.
    Helbig N, Löwe H and Lehning M. 2009. Radiosity approach for the shortwave surface radiation balance in complex terrain. Journal of the Atmospheric Sciences, 66(9): 2900-2912
  37. 37.
    Houborg R, Soegaard H and Boegh E. 2007. Combining vegetation index and model inversion methods for the extraction of key vegetation biophysical parameters using Terra and Aqua MODIS reflectance data. Remote Sensing of Environment, 106(1): 39-58
  38. 38.
    Huang D, Knyazikhin Y, Wang W, Deering D W, Stenberg P, Shabanov N, Tan B and Myneni R B. 2008. Stochastic transport theory for investigating the three-dimensional canopy structure from space measurements. Remote Sensing of Environment, 112(1), 35-50
  39. 39.
    Huang H G and Lian J. 2015. A 3D approach to reconstruct continuous optical images using lidar and MODIS. Forest Ecosystems, 2: 20
  40. 40.
    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.
  41. 41.
    Huang W, Zhang L P and Li P X. 2005. An improved topographic correction approach for satellite image. Journal of Image and Graphics, 10(9): 1124-1128
  42. 42.
    Jiang H, Zhang Z M, Wang X Q and He G J. 2015. Bamboo forest LAI retrieval and analysis in mountainous area based on TAVI. Journal of Geo-information Science, 17(4): 500-504
  43. 43.
    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
  44. 44.
    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
  45. 45.
    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
  46. 46.
    Jing J C, Jin H A, Tang B and Li A N. 2019. Intercomparison and evaluation of influencing factors among different LAI products over mountainous areas. Journal of Natural Resources, 34(2): 400-411
  47. 47.
    Johnson R L, Peddle D R and Hall R J. 2000. A Modeled-Based Sub-pixel scale mountain terrain normalization algorithm for improved LAI estimation from airborne CASI imagery//Proceedings of the 22nd Canadian Remote Sensing Symposium. Ottawa: Canadian Remote Sensing Society: 415-424.
  48. 48.
    Kamal M, Phinn S and Johansen K. 2016. Assessment of multi-resolution image data for mangrove leaf area index mapping. Remote Sensing of Environment, 176: 242-254
  49. 49.
    Kondratyev K Y. 1969. Radiation in the Atmosphere. New York: Academic Press
  50. 50.
    Li A N, Bian J H, Zhang Z J, Zhao W and Yin G F. 2016a. Progresses, opportunities, and challenges of mountain remote sensing research. Jourcal of Remote Sensing, 20(5): 1199-1215
  51. 51.
    Li A N, Yin G F, Jin H A, Bian J H and Zhao W. 2016. Principles and methods for the retrieval of biophysical variables in mountainous areas. Remote Sensing Technology and Application, 31(1): 1-11
  52. 52.
    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.2012.06.018]
  53. 53.
    Li J. 2010.The study of modeling and parameterization methods for LAI retrieval in mountains area. Beijing: Beijing Normal University
  54. 54.
    Li X 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
  55. 55.
    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
  56. 56.
    Lin Q N, Huang H G, Chen L and Chen E X. 2017. Topographic correction method for steep terrain mountain images. Journal of Remote Sensing, 21(5): 776-784
  57. 57.
    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
  58. 58.
    Liu Y, Liu R G and Chen J M. 2012. Retrospective retrieval of long-term consistent global leaf area index (1981—2011) from combined AVHRR and MODIS data. Journal of Geophysical Research, 2012, 117(G4)： G04003
  59. 59.
    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
  60. 60.
    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
  61. 61.
    Luisa E M, Frédéric B and Marie W. 2008. Slope correction for LAI estimation from gap fraction measurements. Agricultural and Forest Meteorology, 148(10): 1553-1562
  62. 62.
    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
  63. 63.
    Nichol J, Hang L K and Sing W M. 2006. Empirical correction of low sun angle images in steeply sloping terrain: a slope-matching technique. International Journal of Remote Sensing, 27(3): 629-635
  64. 64.
    Olyphant G A. 1986a. Longwave radiation in mountainous areas and its influence on the energy balance of alpine snowfields. Water Resources Research, 22(1): 62-66
  65. 65.
    Olyphant G A. 1986b. The components of incoming radiation within a mid-latitude alpine watershed during the snowmelt season. Arctic and Alpine Research, 18(2): 163-169
  66. 66.
    Park S H and Jung H S. 2015. Comparative performance analysis between topographic correction models for landsat-8 OLI images. 36th Asian Conference on Remote Sensing (ACRS2015), 1:315-320.
  67. 67.
    Park S H, Jung H S, Choi J and Jeon S. 2017. A quantitative method to evaluate the performance of topographic correction models used to improve land cover identification. Advances in Space Research, 60(7): 1488-1503
  68. 68.
    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
  69. 69.
    Proy C, Tanré D and Deschamps P Y. 1989. Evaluation of topographic effects in remotely sensed data. Remote Sensing of Environment, 30(1): 21-32
  70. 70.
    Qi J B, Xie D H, Guo D S and Yan G J. 2017. A large-scale emulation system for realistic three-dimensional (3-D) forest simulation. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10(11): 4834-4843
  71. 71.
    Reeder D H. 2002. Topographic Correction of Satellite Images: theory and Application. Hanover: Dartmouth College
  72. 72.
    Richter R and Schläpfer D. 2002. Geo-atmospheric processing of airborne imaging spectrometry data. Part 2: atmospheric/topographic correction. International Journal of Remote Sensing, 2002, 23(13): 2631-2649
  73. 73.
    Richter R, Kellenberger T and Kaufmann H. 2009. Comparison of topographic correction methods. Remote Sensing, 1(3): 184-196
  74. 74.
    Richter R. 1998. Correction of satellite imagery over mountainous terrain. Applied Optics, 37(18): 4004-4015
  75. 75.
    Sandmeier S and Itten K I. 1997. A physically-based model to correct atmospheric and illumination effects in optical satellite data of rugged terrain. IEEE Transactions on Geoscience and Remote Sensing, 35(3): 708-717
  76. 76.
    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
  77. 77.
    Shepherd J D and Dymond J R. 2003. Correcting satellite imagery for the variance of reflectance and illumination with topography. International Journal of Remote Sensing, 24(17): 3503-3514
  78. 78.
    Singh S, Sharma J K and Mishra V D. 2011. Comparison of different topographic correction methods using AWiFS Satellite data. International Journal of Advanced Engineering Sciences and Technologies, 7(1): 85-91
  79. 79.
    Singh S and Talwar R. 2013. A systematic survey on different topographic correction techniques for rugged terrain satellite imagery. International Journal of Electronics and Communication Technology, 4(5): 14-18
  80. 80.
    Smith J A, Lin T L and Ranson K J. 1980. The Lambertian assumption and Landsat data. Photogrammetric Engineering and Remote Sensing, 46(9): 1183-1189
  81. 81.
    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.
  82. 82.
    Soenen S A, Peddle D R, Hall R J, Coburn C A and Hall F G. 2010. Estimating aboveground forest biomass from canopy reflectance model inversion in mountainous terrain. Remote Sensing of Environment, 114(7): 1325-1337
  83. 83.
    Song J L, Wang J D, Shuai Y M and Xiao Z Q. 2009. The research on bidirectional reflectance computer simulation of forest canopy at pixel scale. Spectroscopy and Spectral Analysis, 29(8): 2141-2147
  84. 84.
    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
  85. 85.
    Temps R C and Coulson K L. 1977. Solar radiation incident upon slopes of different orientations. Solar Energy, 19(2): 179-184
  86. 86.
    Tong Q X. 2005. Earth observation from space and human demension for global change studies. Advance in Earth Sciences, 20(1): 1-5
  87. 87.
    Verger A, Baret F, Weiss M, Kandasamy S and Vermote E. 2013. The CACAO method for smoothing, gap filling, and characterizing seasonal anomalies in satellite time series. IEEE Transactions on Geoscience and Remote Sensing, 51(4), 1963-1972
  88. 88.
    Wang G X, Deng W, Yang Y and Cheng G W. 2011. The advances, priority and developing trend of alpine ecology. Journal of Mountain Science, 29(2): 129-140
  89. 89.
    Wei X Q, Gu X F, Meng Q Y, Yu T, Zhou X, Wei Z, Jia K and Wang C M. 2017. Leaf area index estimation using Chinese GF-1 wide field view data in an agriculture region. Sensors, 2017, 17(7): 1593
  90. 90.
    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
  91. 91.
    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
  92. 92.
    Wu S B, Wen J G, Lin X W, Hao D L, You D Q, Xiao Q, Liu Q H and Yin T G. 2019. 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
  93. 93.
    Xia X Q, Tian Q J and Du F L. 2004. Analysis of topographical effect on retrieval of LAI from remotely sensed data. Remote Sensing Information, (2): 16-19, 37
  94. 94.
    Xiang Y, Xiao Z Q, Liang S L, Wang J D and Song J L. 2014. Validation of global land surface satellite (GLASS) leaf area index product. Journal of Remote Sensing, 18(3): 573-596
  95. 95.
    Xiao Z Q, Wang T T, Liang S L and Sun R. 2016. Estimating the fractional vegetation cover from GLASS leaf area index product. Remote Sensing, 8(4), 337
  96. 96.
    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.
  97. 97.
    Yan G J, Zhu C G, Guo J, Wang J D and Li X W. 2000. A model based radiative transfer algorithm to correct remotely sensed image in mountainous area. Journal of Image and Graphics, 5(1): 11-15
  98. 98.
    Yang Y S, Li A N, Jin H A, Yin G F, Zhao W, Lei G B and Bian J H. 2016. Intercomparison among GEOV1, GLASS and MODIS LAI products over mountainous area in southwestern China. Remote Sensing Technology and Application, 31(3): 438-450
  99. 99.
    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
  100. 100.
    Yin G F, Li A N, Wu S B, Fan W L, Zeng Y L, Yan K, Xu B D, Li J and Liu Q H. 2018. PLC: a simple and semi-physical topographic correction method for vegetation canopies based on path length correction. Remote Sensing of Environment, 215: 184-198
  101. 101.
    Yin G F, Li A N, Zhao W, Jin H A, Bian J H, 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
  102. 102.
    Yin G F, Ma L, Zhao W, Zeng Y L, Xu B D and Wu S B. 2020b. Topographic Correction for Landsat 8 OLI vegetation reflectances through path length correction: a comparison between explicit and implicit method. IEEE Transactions on Geoscience and Remote Sensing, 1-13(accepted)
  103. 103.
    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.
  104. 104.
    Zhang H L, Ni S X and Zhang J. 2001. Progress on the study of topographic normalization methods on remotely sensed imagery on abroad. Remote Sensing Information, (3): 24-26
  105. 105.
    Zhang Z M, He G J, Zhang X M, Long T F, Wang G Z and Wang M M. 2018. A coupled atmospheric and topographic correction algorithm for remotely sensed satellite imagery over mountainous terrain. Giscience and Remote Sensing, 55(3): 400-416
  106. 106.
    Zhong X H and Liu S Z 2014. China Mountain Classification Research. Journal of Mountain Science, 32(002): 129-140

Lesen Sie die ganze Passage

The above content is generated by Large Model Translation. The translated content is for reference only. We do not assume any commercial or legal responsibilty for any consequences arising from the use of our website