References

1. 1.
   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
2. 2.
   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
3. 3.
   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
4. 4.
   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. ）
5. 5.
   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
6. 6.
   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 ）
7. 7.
   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
8. 8.
   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
9. 9.
   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
10. 10.
    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
11. 11.
    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
12. 12.
    Chen J M and Black T A. 1992. Defining leaf area index for non-flat leaves. Plant, Cell and Environment, 15(4): 421-429
13. 13.
    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
14. 14.
    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
15. 15.
    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
16. 16.
    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
17. 17.
    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
18. 18.
    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
19. 19.
    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
20. 20.
    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
21. 21.
    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
22. 22.
    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
23. 23.
    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
24. 24.
    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
25. 25.
    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
26. 26.
    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
27. 27.
    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
28. 28.
    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
29. 29.
    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
30. 30.
    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
31. 31.
    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
32. 32.
    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
33. 33.
    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
34. 34.
    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
35. 35.
    GCOS. 2016. The Global Observing System for Climate: Implementation Needs (GCOS-2016). Retrieved from ）
36. 36.
    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
37. 37.
    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
38. 38.
    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
39. 39.
    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
40. 40.
    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
41. 41.
    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
42. 42.
    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
43. 43.
    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
44. 44.
    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
45. 45.
    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
46. 46.
    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: (199808/09)12:10/11<1611::Aid-hyp684>3.0.Co;2-4]
47. 47.
    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
48. 48.
    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
49. 49.
    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
50. 50.
    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
51. 51.
    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
52. 52.
    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
53. 53.
    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
54. 54.
    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
55. 55.
    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
56. 56.
    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
57. 57.
    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
58. 58.
    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
59. 59.
    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
60. 60.
    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
61. 61.
    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
62. 62.
    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
63. 63.
    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
64. 64.
    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
65. 65.
    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
66. 66.
    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
67. 67.
    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
68. 68.
    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
69. 69.
    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
70. 70.
    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
71. 71.
    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
72. 72.
    Liu L Y. 2014. Simulation and correction of spatialscaling effects for leaf area index. Journal of Remote Sensing, 18(6): 1158-1168
73. 73.
    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
74. 74.
    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
75. 75.
    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
76. 76.
    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
77. 77.
    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
78. 78.
    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
79. 79.
    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
80. 80.
    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
81. 81.
    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
82. 82.
    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
83. 83.
    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
84. 84.
    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
85. 85.
    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
86. 86.
    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
87. 87.
    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
88. 88.
    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
89. 89.
    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
90. 90.
    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
91. 91.
    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
92. 92.
    Reeder D H. 2002. Topographic Correction of Satellite Images: Theory and Application. Hanover, NH: Dartmouth College
93. 93.
    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
94. 94.
    Richter R, Kellenberger T and Kaufmann H. 2009. Comparison of topographic correction methods. Remote Sensing, 1(3): 184-196
95. 95.
    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
96. 96.
    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
97. 97.
    Ross J. 1981. The Radiation Regime and Architecture of Plant Stands. Dordrecht: Springer
98. 98.
    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
99. 99.
    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
100. 100.
     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
101. 101.
     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
102. 102.
     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
103. 103.
     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
104. 104.
     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
105. 105.
     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
106. 106.
     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
107. 107.
     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
108. 108.
     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: [2765:Mteopv]2.0.Co;2]
109. 109.
     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
110. 110.
     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
111. 111.
     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
112. 112.
     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
113. 113.
     Verhoef W. 1984. Light scattering by leaf layers with application to canopy reflectance modeling: The SAIL model. Remote Sensing of Environment, 16, 125-141
114. 114.
     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
115. 115.
     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
116. 116.
     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
117. 117.
     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
118. 118.
     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
119. 119.
     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
120. 120.
     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
121. 121.
     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
122. 122.
     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
123. 123.
     Wang Y J, Lauret N and Gastellu-Etchegorry J P. 2020. DART radiative transfer modelling for sloping landscapes. Remote Sensing of Environment, 247: 111902
124. 124.
     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
125. 125.
     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
126. 126.
     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
127. 127.
     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
128. 128.
     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
129. 129.
     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
130. 130.
     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
131. 131.
     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
132. 132.
     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
133. 133.
     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
134. 134.
     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
135. 135.
     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
136. 136.
     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
137. 137.
     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
138. 138.
     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
139. 139.
     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
140. 140.
     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
141. 141.
     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
142. 142.
     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
143. 143.
     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
144. 144.
     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
145. 145.
     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
146. 146.
     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
147. 147.
     Zhang B. 2018. Remotely sensed big data era and intelligent information extraction. Geomatics and Information Science of Wuhan University, 43(12): 1861-1871
148. 148.
     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
149. 149.
     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
150. 150.
     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
151. 151.
     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
152. 152.
     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
153. 153.
     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
154. 154.
     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
155. 155.
     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
156. 156.
     Zhu X Z. 2014. A Comparitive Study of Three Different Methods for Estimating Forest Leaf Area Index. Hangzhou: Zhejiang A and F University
157. 157.
     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
158. 158.
     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
159. 159.
     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