[1] SELLERS P J, TUCKER C J, COLLATZ G J, et al. A revised land surface parameterization (SiB2) for atmospheric gcms. Part II: The generation of global fields of terrestrial biophysical parameters from satellite data [J]. Journal of Climate, 1996, 9(4): 706-737.
[2] DE ROO A P J, OFFERMANS R J E, CREMERS N H D T. LISEM: A single-event, physically based hydrological and soil erosion model for drainage basins. II: Sensitivity analysis, validation and application [J]. Hydrological Processes, 1996, 10(8): 1119-1126.
[3] 李斌, 张金屯. 不同植被盖度下的黄土高原土壤侵蚀特征分析 [J]. 中国生态农业学报, 2010, 18(2): 241-244. [LI B, ZHANG J T. Soil erosion characteristics under different vegetation coverage in the Loess Plateau. Chinese Journal of Eco-Agriculture, 2010, 18(2): 241-244. ]
[4] 杨胜天, 周旭, 刘晓燕, 等. 黄河中游多沙粗沙区(渭河段)土地利用对植被盖度的影响 [J]. 地理学报, 2014, 69(1): 31-41. [YANG S T, ZHOU X, LIU X Y, et al. Impacts of land use on vegetation coverage in the rich and coarse sediment area of Yellow River Basin. Acta Geographica Sinica, 2014, 69(1): 31-41. ]
[5] 任世龙, 宜树华, 陈建军, 等. 高山草地植被盖度对气候变暖和人类活动的响应 [J]. 草业科学, 2013, 30(4): 506-514. [REN S L, YI S H, CHEN J J, et al. Responses of green fractional vegetation cover of alpine grassland to climate warming and human activities. Pratacultural Science, 2013, 30(4): 506-514. ]
[6] 包刚, 包玉海, 覃志豪, 等. 高光谱植被覆盖度遥感估算研究 [J]. 自然资源学报, 2013, 28(7): 1243-1254. [BAO G, BAO Y H, QIN Z H, et al. Hyper-spectral remote sensing estimation for the vegetation cover. Journal of Natural Resources, 2013, 28(7): 1243-1254. ]
[7] 李晓松, 李增元, 吴波, 等. 基于光谱混合分析的毛乌素沙地油蒿群落覆盖度提取 [J]. 遥感学报, 2007, 11(6): 923-930. [LI X S, LI Z Y, WU B, et al. Retrieval of the coverage of Artemisia ordosica community in Mu Us Sandland based on spectral mixture analysis (SMA). Journal of Remote Sensing, 2007, 11(6): 923-930. ]
[8] HAERTEL V, SHIMABUKURO Y. Spectral linear mixing model in low spatial resolution image data [J]. IEEE Transactions on Geoscience and Remote Sensing, 2005, 43(11): 2555-2562.
[9] CAPELLA Z D, HAERTEL V, SHIMABUKURO Y, et al. Linear spectral mixing model for identifying potential missing endmembers in spectral mixture analysis [J]. IEEE Transactions on Geoscience and Remote Sensing, 2014, 52(5): 3005-3012.
[10] SOMERS B, COOLS K, DELALIEUX S, et al. Nonlinear hyperspectral mixture analysis for tree cover estimates in orchards [J]. Remote Sensing of Environment, 2009, 113(6): 1183-1193.
[11] 唐晓燕, 高昆, 倪国强. 高光谱图像非线性解混方法的研究进展 [J]. 遥感技术与应用, 2013, 28(4): 731-738. [TANG X Y, GAO K, NI G Q. Advances in nonlinear spectral unmixing of hyperspectral images. Remote Sensing Technology and Application, 2013, 28(4): 731-738. ]
[12] BOREL C C, GERSTL S A. Nonlinear spectral mixing models for vegetative and soil surfaces [J]. Remote Sensing of Environment, 1994, 47(3): 403-416.
[13] 罗红霞, 龚健雅. 线性和非线性光谱混合模型模拟土壤、植被混合光谱的效果分析 [J]. 测绘通报, 2005(5): 6-10. [LUO H X, GONG J Y. Effects of linear and nonlinear spectra mixing models on simulating mixing spectra of soil and canopy. Bulletin of Surveying and Mapping, 2005(5): 6-10. ]
[14] RAY T W, MURRAY B C. Nonlinear spectral mixing in desert vegetation [J]. Remote Sensing of Environment, 1996, 55(1): 59-64.
[15] ZHANG J, RIVARD B, SÁNCHEZ A A, et al. Intra-and inter-class spectral variability of tropical tree species at La Selva, Costa Rica: Implications for species identification using hydice imagery [J]. Remote Sensing of Environment, 2006, 105(2): 129-141.
[16] SOMERS B, ASNER G P, TITS L, et al. Endmember variability in spectral mixture analysis: A review [J]. Remote Sensing of Environment, 2011, 115(7): 1603-1616.
[17] ROBERTS D, GARDNER M, CHURCH R, et al. Mapping chaparral in the Santa Monica Mountains using multiple endmember spectral mixture models [J]. Remote Sensing of Environment, 1998, 65(3): 267-279.
[18] 赵子傑, 赵云升. 不同粒径沙地表面双向反射特性研究 [J]. 物理学报, 2014, 63(18): 435-441. [ZHAO Z J, ZHAO Y S. Bidirectional reflectance of sandy land surface with different particle sizes. Acta Physica Sinica, 2014, 63(18): 435-441. ]
[19] 申广荣, 王人潮. 基于神经网络的水稻双向反射模型研究 [J]. 遥感学报, 2002, 6(4): 252-258. [SHEN G R, WANG R C. Study on bi-directional reflectance model of rice using an artificial neural network. Journal of Remote Sensing, 2002, 6(4): 252-258. ]
[20] PROUD S R, ZHANG Q, SCHAAF C, et al. The normalization of surface anisotropy effects present in SEVIRI reflectances by using the MODIS BRDF method [J]. IEEE Transactions on Geoscience and Remote Sensing, 2014, 52(10): 6026-6039.
[21] 童新, 刘廷玺, 杨大文, 等. 半干旱沙地-草甸区水面蒸发模拟及其影响因子辨识 [J]. 干旱区地理, 2015, 38(1): 10-17. [TONG X, LIU T X, YANG D W, et al. Simulating evaporation from a water surface for the sand-meadow ecotone of the semiarid region in North China. Arid Land Geography, 2015, 38(1): 10-17. ]
[22] ASD. Analytical Spectral Devices, Inc. Technical Guide [M]. Boulder, Colorado: ASD, 1999.
[23] SETTLE J. On the residual term in the linear mixture model and its dependence on the point spread function [J]. IEEE Transactions on Geoscience and Remote Sensing, 2005, 43(2): 398-401.
[24] 周玉洁, 王卷乐, 郭海会. 基于谐波分析和线性光谱模型的耕地信息提取 [J]. 遥感技术与应用, 2015, 30(4): 706-713. [ZHOU Y J, WANG J L, GUO H H. Application of harmonic analysis and linear spectral mixture model to extract the cultivated resource. Remote Sensing Technology and Application, 2015, 30(4): 706-713. ]
[25] KIMES D S. Dynamics of directional reflectance factor distributions for vegetation canopie [J]. Applied Opticks, 1983, 22(9): 1364-1372.
[26] ASNER G P, LOBELL D B. A biogeophysical approach for automated SWIR unmixing of soils and vegetation [J]. Remote Sensing of Environment, 2000, 74(1): 99-112.
[27] SETTLE J. On the effect of variable endmember spectra in the linear mixture model [J]. IEEE Transactions on Geoscience and Remote Sensing, 2006, 44(2): 389-396.
[28] GAO F, SCHAAF C B, STRAHLER A H, et al. Detecting vegetation structure using a kernel-based BRDF model [J]. Remote Sensing of Environment, 2003, 86(2): 198-205.
[29] ROY D P, ZHANG H K, JU J, et al. A general method to normalize Landsat reflectance data to nadir BRDF adjusted reflectance [J]. Remote Sensing of Environment, 2016, 176: 255-271.