光学学报, 2019, 39 (2): 0228003, 网络出版: 2019-05-10   

基于谐波分析算法的干旱区绿洲土壤光谱特性研究 下载: 984次

Spectral Characteristics of Oasis Soil in Arid Area Based on Harmonic Analysis Algorithm
张子鹏 1,2,3,*丁建丽 1,2,3,*王敬哲 1,2,3
作者单位
1 新疆大学资源与环境科学学院, 新疆 乌鲁木齐 830046
2 新疆大学绿洲生态教育部重点实验室, 新疆 乌鲁木齐 830046
3 新疆大学智慧城市与环境建模自治区普通高校重点实验室, 新疆 乌鲁木齐 830046
引用该论文

张子鹏, 丁建丽, 王敬哲. 基于谐波分析算法的干旱区绿洲土壤光谱特性研究[J]. 光学学报, 2019, 39(2): 0228003.

Zipeng Zhang, Jianli Ding, Jingzhe Wang. Spectral Characteristics of Oasis Soil in Arid Area Based on Harmonic Analysis Algorithm[J]. Acta Optica Sinica, 2019, 39(2): 0228003.

参考文献

[1] Rossel R A V, McGlynn R N, McBratney A B. Determining the composition of mineral-organic mixes using UV-vis-NIR diffuse reflectance spectroscopy[J]. Geoderma, 2006, 137(1/2): 70-82.

[2] Ghamisi P, Yokoya N, Li J, et al. Advances in hyperspectral image and signal processing: a comprehensive overview of the state of the art[J]. Proceedings of the IEEE, 2017, 5(4): 37-78.

[3] MinasnyB, McBratney AB, Bellon-MaurelV, et al. Removing the effect of soil moisture from NIR diffuse reflectance spectra for the prediction of soil organic carbon[J]. Geoderma, 2011, 167-168: 118- 124.

[4] Peón J, Fernández S, Recondo C, et al. Evaluation of the spectral characteristics of five hyperspectral and multispectral sensors for soil organic carbon estimation in burned areas[J]. International Journal of Wildland Fire, 2017, 26(3): 230-239.

[5] Tahmasbian I, Xu Z H, Boyd S, et al. Laboratory-based hyperspectral image analysis for predicting soil carbon, nitrogen and their isotopic compositions[J]. Geoderma, 2018, 330: 254-263.

[6] Lin L X, Xue F C, Wang Y J, et al. Photography measured-value magnification improves local correlation maximization-complementary superiority method of hyperspectral analysis of soil total nitrogen[J]. Catena, 2018, 165: 106-114.

[7] Nocita M, Stevens A, Noon C, et al. Prediction of soil organic carbon for different levels of soil moisture using Vis-NIR spectroscopy[J]. Geoderma, 2013, 199: 37-42.

[8] Stenberg B. Effects of soil sample pretreatments and standardized rewetting as interacted with sand classes on Vis-NIR predictions of clay and soil organic carbon[J]. Geoderma, 2010, 158(1/2): 15-22.

[9] Gomez C, Lagacherie P. 189-[J]. Coulouma G. Regional predictions of eight common soil properties, their spatial structures from hyperspectral Vis-NIR data. Geoderma, 2012, 190: 176-185.

[10] 于雷, 洪永胜, 周勇, 等. 连续小波变换高光谱数据的土壤有机质含量反演模型构建[J]. 光谱学与光谱分析, 2016, 36(5): 1428-1433.

    Yu L, Hong Y S, Zhou Y, et al. Inversion of soil organic matter content using hyperspectral data based on continuous wavelet transformation[J]. Spectroscopy and Spectral Analysis, 2016, 36(5): 1428-1433.

[11] Hong Y S, Yu L, Chen Y Y, et al. Prediction of soil organic matter by VIS-NIR spectroscopy using normalized soil moisture index as a proxy of soil moisture[J]. Remote Sensing, 2017, 10(2): 28.

[12] Luo H L, Yang Y, Tong B, et al. Traffic sign recognition using a multi-task convolutional neural network[J]. IEEE Transactions on Intelligent Transportation Systems, 2018, 19(4): 1100-1111.

[13] Peng X T, Shi T Z, Song A H, et al. Estimating soil organic carbon using VIS/NIR spectroscopy with SVMR and SPA methods[J]. Remote Sensing, 2014, 6(4): 2699-2717.

[14] Bioucas-Dias J M, Plaza A, Camps-Valls G, et al. . Hyperspectral remote sensing data analysis and future challenges[J]. Proceedings of the IEEE, 2013, 1(2): 6-36.

[15] Xue Z H, Du P J, Su H J. Harmonic analysis for hyperspectral image classification integrated with PSO optimized SVM[J]. Proceedings of the IEEE, 2014, 7(6): 2131-2146.

[16] Donoho D L, Maleki A, Rahman I U, et al. Reproducible research in computational harmonic analysis[J]. Computing in Science & Engineering, 2009, 11(1): 8-18.

[17] Ding JL, Yu D L.Monitoring and evaluating spatial variability of soil salinity in dry and wet seasons in the Werigan-Kuqa Oasis, China, using remotesensing and electromagnetic induction instruments[J].Geoderma, 2014, 235-236: 316- 322.

[18] 曹雷, 丁建丽, 于海洋. 渭-库绿洲多尺度景观格局与盐度关系[J]. 农业工程学报, 2016, 32(3): 101-110.

    Cao L, Ding J L, Yu H Y. Relationship between multi-scale landscape pattern and salinity in Weigan and Kuqa rivers delta oasis[J]. Transactions of the Chinese Society of Agricultural Engineering, 2016, 32(3): 101-110.

[19] 鲍士旦. 土壤农化分析[M]. 3版. 北京: 中国农业出版社, 2000.

    Bao SD. Soil agrochemical analysis[M]. 3rd ed. Beijing: China Agriculture Press, 2000.

[20] Wang J Z, Ding J L, Abulimiti A, et al. Quantitative estimation of soil salinity by means of different modeling methods and visible-near infrared (VIS-NIR) spectroscopy, Ebinur Lake Wetland, Northwest China[J]. PeerJ, 2018, 6: e4703.

[21] Shi Z, Wang Q L, Peng J, et al. Development of a national VNIR soil-spectral library for soil classification and prediction of organic matter concentrations[J]. Science China Earth Sciences, 2014, 57(7): 1671-1680.

[22] Savitzky A. Golay M J E. Smoothing and differentiation of data by simplified least squares procedures[J]. Analytical Chemistry, 1964, 36(8): 1627-1639.

[23] 杜剑, 胡炳樑, 张周锋. 基于卷积神经网络与显微高光谱的胃癌组织分类方法研究[J]. 光学学报, 2018, 38(6): 0617001.

    Du J, Hu B L, Zhang Z F. Gastric carcinoma classification based on convolutional neural network and micro-hyperspectral imaging[J]. Acta Optica Sinica, 2018, 38(6): 0617001.

[24] 杨可明, 张涛, 王立博, 等. 高光谱影像的谐波分析融合算法研究[J]. 中国矿业大学学报, 2014, 43(3): 547-553.

    Yang K M, Zhang T, Wang L B, et al. A new algorithm on hyperspectral image fusion based on the harmonic analysis[J]. Journal of China University of Mining & Technology, 2014, 43(3): 547-553.

[25] 葛翔宇, 丁建丽, 王敬哲, 等. 基于竞争适应重加权采样算法耦合机器学习的土壤含水量估算[J]. 光学学报, 2018, 38(10): 1030001.

    Ge X Y, Ding J L, Wang J Z, et al. Estimation of soil moisture content based on competitive adaptive reweighted sampling algorithm coupled with machine learning[J]. Acta Optica Sinica, 2018, 38(10): 1030001.

[26] Wang W X, Tang R C, Li C, et al. A BP neural network model optimized by Mind Evolutionary Algorithm for predicting the ocean wave heights[J]. Ocean Engineering, 2018, 162: 98-107.

[27] Shi Z, Ji W. Viscarra Rossel R A, et al. Prediction of soil organic matter using a spatially constrained local partial least squares regression and the Chinese vis-NIR spectral library[J]. European Journal of Soil Science, 2015, 66(4): 679-687.

[28] 姜雪芹, 叶勤, 林怡, 等. 基于谐波分析和高光谱遥感的土壤含水量反演研究[J]. 光学学报, 2017, 37(10): 1028001.

    Jiang X Q, Ye Q, Lin Y, et al. Inverting study on soil water content based on harmonic analysis and hyperspectral remote sensing[J]. Acta Optica Sinica, 2017, 37(10): 1028001.

[29] Liu H J, Zhang Y Z, Zhang X L, et al. Quantitative analysis of moisture effect on black soil reflectance[J]. Pedosphere, 2009, 19(4): 532-540.

[30] 周倩倩, 丁建丽, 唐梦迎, 等. 干旱区典型绿洲土壤有机质的反演及影响因素研究[J]. 土壤学报, 2018, 55(2): 313-324.

    Zhou Q Q, Ding J L, Tang M Y, et al. Inversion of soil organic matter content in oasis typical of arid area and its influencing factors[J]. Acta Pedologica Sinica, 2018, 55(2): 313-324.

[31] 栾福明, 张小雷, 熊黑钢, 等. 基于不同模型的土壤有机质含量高光谱反演比较分析[J]. 光谱学与光谱分析, 2013, 33(1): 196-200.

    Luan F M, Zhang X L, Xiong H G, et al. Comparative analysis of soil organic matter content based on different inversion models[J]. Spectroscopy and Spectral Analysis, 2013, 33(1): 196-200.

张子鹏, 丁建丽, 王敬哲. 基于谐波分析算法的干旱区绿洲土壤光谱特性研究[J]. 光学学报, 2019, 39(2): 0228003. Zipeng Zhang, Jianli Ding, Jingzhe Wang. Spectral Characteristics of Oasis Soil in Arid Area Based on Harmonic Analysis Algorithm[J]. Acta Optica Sinica, 2019, 39(2): 0228003.

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