Estimation Model of Equivalent Water Thickness in the Road Area

Yunkai GUO; Xiaojiong ZHANG; Min XU; Yuling LIU; Jia QIAN; Qiong ZHANG

doi:10.12082/dqxxkx.2020.190254

[1] 张峰, 周广胜. 植被含水量高光谱遥感监测研究进展[J]. 植物生态学报, 2018,42(5):517-525. [ ZhangF, Zhou GS. Research progress on monitoring vegetation water content by using hyperspectral remote sensing[J]. Chinese Journal of Plant Ecology, 2018,42(5):517-525. ] [ Zhang F, Zhou G S. Research progress on monitoring vegetation water content by using hyperspectral remote sensing[J]. Chinese Journal of Plant Ecology, 2018,42(5):517-525. ]

[2] et alDetermining the water status of Satsuma mandarin trees "citrus unshiumarcovitch" using spectral indices and by combining hyperspectral and physiological data[J]. Agricultural and Forest Meteorology, 150, 369-379(2009).

[3] et alThe spectral response of Buxussempervirens to different types of environmental stress: A laboratory experiment[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 74, 56-65(2012).

[4] 刘良云, 王纪华, 张永江, 等. 叶片辐射等效水厚度计算与叶片水分定量反演研究[J]. 遥感学报, 2007,11(3):289-295. [ Liu LY, Wang JH, Zhang YJ, et al. Detection of leaf EWT by calculating REWT from reflectance spectra[J]. Journal of Remote Sensing, 2007,11(3):289-295. ] [ Liu L Y, Wang J H, Zhang Y J, et al. Detection of leaf EWT by calculating REWT from reflectance spectra[J].Journal of Remote Sensing, 2007,11(3):289-295. ]

[5] 赵英时等. 遥感应用分析原理与方法[M]. 北京: 科学出版社, 2003. [ Zhao YS, et al.Principles and methods of remote sensing application analysis[M]. Beijing: Science Press, 2003. ] [ Zhao Y S, et al. Principles and methods of remote sensing application analysis[M]. Beijing: Science Press, 2003. ]

[6] et alRelations of remote sensing leaf water indices to leaf water thickness in cowpea, bean, and sugarbeet plants[J]. Remote Sensing of Environment, 112, 445-455(2007).

[7] Using spectral information from the NIR water absorption features for the retrieval of canopy water content[J]. International Journal of Applied Earth Observations and Geo-information, 10, 388-397(2008).

[8] 李丹娜, 郭云开, 朱善宽, 等. 利用PRO4SAIL与支持向量机回归的组合模型反演植被等效水厚度[J]. 测绘通报, 2017(8):50-55. [ Li DN, Guo YK, Zhu SK, et al. Quantitative inversion vegetation equivalent water thickness by combined model of PRO4SAIL and the support vector regression[J]. Bulletin of Surveying and Mapping, 2017(8):50-55. ] [ Li D N, Guo Y K, Zhu S K, et al. Quantitative inversion vegetation equivalent water thickness by combined model of PRO4SAIL and the support vector regression[J]. Bulletin of Surveying and Mapping, 2017(8):50-55. ]

[9] 侯学会, 王猛, 刘思含, 等. 基于Prosail模型和Landsat 8数据的小麦冠层含水量反演比较[J]. 麦类作物学报, 2018,38(4):493-497. [ Hou XH, WangM, Liu SH, et al. Comparison between prosailmodel and Landsat 8 images in inversion of water content of wheat canopy[J]. Journal of Triticeae Crops, 2018,38(4):493-497.] [ Hou X H, Wang M, Liu S H, et al. Comparison between prosailmodel and Landsat 8 images in inversion of water content of wheat canopy[J]. Journal of Triticeae Crops, 2018,38(4)):493-497. ]

[10] Evaluation of hyperspectral indices for retrieval of canopy equivalent water thickness and gravimetric water content[J]. International Journal of Remote Sensing, 37, 3384-3399(2016).

[11] NDWI: A normalized difference water index for remote sensing of vegetation liquid water from space[J]. Remote Sensing of Environment, 58, 257-266(1996).

[12] 程晓娟, 杨贵军, 徐新刚, 等. 新植被水分指数的冬小麦冠层水分遥感估算[J]. 光谱学与光谱分析, 2014,34(12):3391-3396. [ Cheng XJ, Yang GJ, Xu XG, et al. Estimating canopy water content in wheat based on new vegetation water index[J]. Spectroscopy and Spectral Analysis, 2014,34(12):3391-3396. ] [ Cheng X J, Yang G J, Xu X G, et al. Estimating canopy water content in wheat based on new vegetation water index[J]. Spectroscopy and Spectral Analysis, 2014,34(12):3391-3396. ]

[13] 郑兴明, 丁艳玲, 赵凯, 等. 基于Landsat 8 OLI数据的玉米冠层含水量反演研究[J]. 光谱学与光谱分析, 2014,34(12):3385-3390. [ Zhen XM, Ding YL, ZhaoK, et al. Estimation of vegetation water content from Landsat 8 OLI data[J]. Spectroscopy and Spectral Analysis, 2014,34(12):3385-3390. ] [ Zhen X M, Ding Y L, Zhao K, et al. Estimation of vegetation water content from Landsat 8 OLI data[J]. Spectroscopy and Spectral Analysis, 2014,34(12):3385-3390. ]

[14] et alEstimation of crop LAI using hyperspectral vegetation indices and a hybrid inversion method[J]. Remote Sensing of Environment, 165, 123-134(2015).

[15] 张春兰, 杨贵军, 李贺丽, 等. 基于随机森林算法的冬小麦叶面积指数遥感反演研究[J]. 中国农业科学, 2018,51(5):855-867. [ Zhang CL, Yang GJ, Li HL, et al. Remote sensing inversion of leaf area index of winter wheat based on random forest algorithm[J]. Scientia Agricultural Sinica, 2018,51(5):855-867. ] [ Zhang C L, Yang G J, Li H L, et al. Remote sensing inversion of leaf area index of winter wheat based on random forest algorithm[J]. Scientia Agricultural Sinica,2018,51(5):855-867. ]

[16] PROSPECT+SAIL models: A review of use for vegetation characterization[J]. Remote Sensing of Environment, 113, 56-66(2009).

[17] Prospect: A model of leaf optical properties spectra[J]. Remote Sensing of Environment, 34, 75-91(1990).

[18] Regional equivalent water thickness modeling from remote sensing across a tree Cover/LAI gradient in mediterranean forests of northern Tunisia[J]. Molecular Diversity Preservation International (MDPI), 7, 1937-1961(2015).

[19] et alDetecting vegetation leaf water content using reflectance in the optical domain[J]. Remote Sensing of Environment, 77, 22-33(2001).

[20] Water content estimation in vegetation with MODIS reflectance data and model inversion methods[J]. Remote Sensing of Environment, 85, 109-124(2003).

[21] 牛亚莉. 遗传优化支持向量机的交通流量预测模型[J]. 微型电脑应用, 2017,33(8):72-74. [ Niu YL. Traffic flow forecasting model by Support Vector Machine optimized by genetic algorithm[J]. Microcomputer Applications, 2017,33(8):72-74. ] [ Niu Y L. Traffic flow forecasting model by Support Vector Machine optimized by genetic algorithm[J]. Microcomputer Applications, 2017,33(8):72-74. ]

[22] 付俊娥, 苏庆珣, 潘世兵, 等. 基于支持向量机理论的地下水动态遥感监测模型与应用[J]. 地球信息科学学报, 2010,12(4):466-472. [ Fu JE, Su QX, Pan SB, et al. Support vector machine based ground water level monitoring model by using remote scence images[J]. Journal of Geo-information Science, 2010,12(4):466-472. ] [ Fu J E, Su Q X, Pan S B, et al. Support vector machine based ground water level monitoring model by using remote scence images[J]. Journal of Geo-information Science, 2010,12(4):466-472. ]

[23] 沈润平, 郭佳, 张婧娴, 等. 基于随机森林的遥感干旱监测模型的构建[J]. 地球信息科学学报, 2017,19(1):125-133. [ Shen RJ, GuoJ, Zhang JX, et al. Construction of a drought monitoring model using the randform forest based remote sensing[J]. Journal of Geo-information Science, 2017,19(1):125-133. ] [ Shen R J, Guo J, Zhang J X, et al. Construction of a drought monitoring model using the randform forest based remote sensing[J]. Journal of Geo-information Science, 2017,19(1):125-133. ]

[24] 李鹏霞. 基于偏最小二乘和支持向量机的生物分子的定量结构—活性/性质关系研究[D]. 兰州:西北师范大学, 2011. [ Li XP. Quantitative structure-activity/property relationship studies in biomolecules based on partial least squares and support vector machine[D]. Lanzhou: Northwest Normal University, 2011. ] [ Li X P. Quantitative structure-activity/property relationship studies in biomolecules based on partial least squares and support vector machine[D]. Lanzhou: Northwest Normal University, 2011. ]