Advanced Search
  • Included in CSCD
  • Chinese Core Journals
  • Included in WJCI Report
  • Included in Scopus, CA, DOAJ, EBSCO, JST
  • The Key Magazine of China Technology
Volume 43 Issue 5
Dec.  2024
Turn off MathJax
Article Contents
Article Navigation >  CARSOLOGICA SINICA  > 2024  >  43(5): 1047-1056, 1064
HE Wen, LI Yanqiong, YU Ling, WANG Jinye, NI Longkang, LI Ning. Hyperspectral inversion of total nitrogen content in calcareous soil in karst areas[J]. CARSOLOGICA SINICA, 2024, 43(5): 1047-1056, 1064. doi: 10.11932/karst20240505
Citation: HE Wen, LI Yanqiong, YU Ling, WANG Jinye, NI Longkang, LI Ning. Hyperspectral inversion of total nitrogen content in calcareous soil in karst areas[J]. CARSOLOGICA SINICA, 2024, 43(5): 1047-1056, 1064. doi: 10.11932/karst20240505
shu

Hyperspectral inversion of total nitrogen content in calcareous soil in karst areas

doi: 10.11932/karst20240505
  • 1.

    College of Environmental Science and Engineering, Guilin University of Technology, Guilin, Guangxi 541006, China

  • 2.

    Guangxi Key Laboratory of Plant Conservation and Restoration Ecology in Karst Terrain, Guangxi Institute of Botany, Guangxi Zhuang Autonomous Region and Chinese Academy of Sciences, Guilin, Guangxi 541006, China

  • 3.

    Key Laboratory of Vegetation Restoration and Management of Degraded Ecosystems, South China Botanical Garden, Chinese Academy of Sciences, Guangzhou, Guangdong 510650, China

  • 4.

    Guilin University of Aerospace Technology, Guilin, Guangxi 541004, China

  • 5.

    Nanning College of Technology, Nanning, Guangxi 530100, China

  • Received Date: 2023-07-23
    Available Online: 2024-12-30
    Abstract
  • Nitrogen is a component of many important compounds in plants, such as proteins, nucleic acids and enzymes, and hence is indispensable for the growth of plants. The nitrogen content in soil is one of the key indicators of soil fertility. Calcareous soil is one of the main soil types in karst areas. A rapid and accurate estimation of total nitrogen (TN) content in calcareous soil is an important guarantee for the scientific evaluation of soil environmental quality in karst areas. In recent years, the rapid development of hyperspectral remote sensing technology has brought new opportunities for a quick assessment of soil physical and chemical properties. However, it is still extremely challenging to rapidly assess soil nitrogen content in karst areas by hyperspectral remote sensing due to the weak optical signal of soil nitrogen and the interference of factors such as the complex ecological environment and the strong spatial heterogeneity of soil TN content in karst areas.Karst areas are extensively distributed in China, where calcareous soil is one of the main soil types, exerting a great influence on ecological protection and agricultural development. Therefore, it is of great theoretical and practical significance for us to develop hyperspectral inversion models suitable for the TN content of calcareous soil. Karst landforms are distributed across 70 counties/cities in Guangxi, with an area of 97,700 km2, accounting for 41% of the total area of Guangxi and 10.8% of the total area of karst landforms in China. Taking calcareous soil in the karst areas of Guangxi as the research object, this study performed five mathematical transformations on soil spectra to improve the detection ability of spectral signals while eliminating spectral noise. Meanwhile, given the coexistence of linear and nonlinear relationships between soil TN content and spectra, the hyperspectral inversion capability of three models, namely partial least squares regression (PLSR), generalized neural network (GRNN) and PLSR_GRNN (a combined model of PLSR and GRNN), for soil TN content was compared and analyzed to establish a high-precision and rapid inversion model suitable for the TN content of calcareous soils in karst areas.The results showed as follows. (1) The TN content in calcareous soil was significantly correlated with various spectral bands from 400 to 2,500 nm. Among them, the TN content was more sensitive to the reflectance of the spectral bands near 600 nm, 1,300 nm, 1,600 nm, 1,900 nm and 2,300 nm. (2) The first-order differential transform (FDR), second-order differential transform (SDR), reciprocal logarithmic transform (lg(1/R)), reciprocal logarithmic first-order differential transform ((lg(1/R))') and envelope removal transform (CR) of the original soil spectra can improve the capability of inversion of TN content in calcareous soil to some extent. The transformation effects were roughly ordered by (lg(1/R))' > SDR > CR > FDR > lg(1/R). Overall, the spectral differential transform is superior to the envelope transform as well as the reciprocal logarithmic transform, and can better exploit the detection capability of the spectral signal for soil TN. (3) The PLSR algorithm had excellent predictive ability for the variation of TN content in calcareous soil. In the SDR transform case, the model had the highest accuracy and better model robustness without overfitting, with a coefficient of determination (R2) of 0.84 and root mean square error (RMSE) of 0.55 in the modeling set and R2 of 0.82 and RMSE of only 0.64 in the validation set. Compared with the PLSR algorithm, the GRNN model had greater prediction ability. However, the robustness of GRNN model was worse and the overfitting phenomenon was obvious. In the same SDR transformation case, the modeling set R2 of the GRNN model could reach 0.92, but the validation set R2 was only 0.59, so the overall performance was inferior to that of the PLSR model. (4) The PLSR_GRNN model can integrate the advantages of PLSR and GRNN model, maintaining the high predictability of GRNN model and avoiding the overfitting phenomenon. Among them, the best inversion model was established by SDR, with R2 of 0.92 and 0.90 for the modeling set and validation set, and RMSE of 0.43 and 0.51, respectively, which were suitable for hyperspectral inversion of TN content in calcareous soil in karst areas. In addition, the FDR, (lg(1/R))' and CR transformations also had excellent performance, with R2 above 0.80 for the modeling set and R2 above 0.75 for the validation set.Although the prediction accuracy of the GRNN model cannot be improved by combining the PLSR model with the GRNN model, the overfitting phenomenon can be effectively controlled. This modeling approach, which combines linear and nonlinear models, is more widely applicable than the PLSR model or GRNN model alone, and is more adaptable to more heterogeneous soil types, and will be more widely used. Rapid and high-precision prediction of TN content in calcareous soil in karst areas can be performed based on hyperspectral models. The results can provide a basis for regional soil remediation and utilization.

     

    • FullText(HTML)
  • loading
    • References(33)
  • [1]
    Maire V, Wright I J, Prentice I C, Batjes N H, Bhaskar R, Van Bodegom P M, Cornwell W K, Ellsworth D, Niinemets Ü, Ordonez A. Global effects of soil and climate on leaf photosynthetic traits and rates[J]. Global Ecology and Biogeography, 2015, 24(6): 706-717. doi: 10.1111/geb.12296
    [2]
    Zhu Q L, Xing X Y, Zhang H, An S S. Soil ecological stoichiometry under different vegetation area on loess hilly-gully region[J]. Acta Ecologica Sinica, 2013, 33(15): 4674-4682. doi: 10.5846/stxb201212101772
    [3]
    张婷, 代群威, 邓远明, 李琼芳, 董发勤, Bowen Li, Bruce W Fouke, 李相邑. 九寨沟优势植物凋落物叶片淋溶的碳氮磷释放特征[J]. 中国岩溶, 2021, 40(1):133-139.

    ZHANG Ting, DAI Qunwei, DENG Yuanming, LI Qiongfang, DONG Faqin, Bowen Li, Bruce W Fouke, LI Xiangyi. Release characteristics of carbon, nitrogen and phosphorus from withered leaves of dominant plants in Jiuzhaigou valley[J]. Carsologica Sinica, 2021, 40(1): 133-139.
    [4]
    Obukhov A I, Orlov D S. Spectral reflectivity of the major soil groups and possibility of using diffuse reflection in soil investigations[J]. Soviet Soil Science, 1964, 2(2): 174-184.
    [5]
    Galvao L S, Vitorello I. Role of organic matter in obliterating the effects of iron on spectral reflectance and colour of Brazilian tropical soils[J]. International Journal of Remote Sensing, 1998, 19(10): 1969-1979. doi: 10.1080/014311698215090
    [6]
    Karnieli A, Verchovsky I, Hall J K, Oren E. Geographic information system for semi-detailed mapping of soils in a semi-arid region[J]. Geocarto International, 1998, 13(3): 29-42. doi: 10.1080/10106049809354650
    [7]
    Dalal R C, Henry R J. Simultaneous determination of moisture, organic carbon, and total nitrogen by near infrared reflectance spectrophotometry[J]. Soil Science Society of America Journal, 1986, 50(1): 120-123. doi: 10.2136/sssaj1986.03615995005000010023x
    [8]
    Reeves I J, Mccarty G, Meisinger J. Near infrared reflectance spectroscopy for the analysis of agricultural soils[J]. Journal of Near Infrared Spectroscopy, 1999, 7(1): 179.
    [9]
    吴明珠, 李小梅, 沙晋明. 亚热带红壤全氮的高光谱响应和反演特征研究[J]. 光谱学与光谱分析, 2013, 33(11): 3111-3115.

    WU Mingzhu, LI Xiaomei, SHA Jinming. Spectral inversion models for prediction of red soil total nitrogen content in subtropical region (Fuzhou)[J]. Spectroscopy and Spectral Asnalysis, 2013, 33(11): 3111-3115.
    [10]
    谢文. 基于高光谱技术的森林土壤不同养分含量光谱特征及估测模型研究[D]. 南昌:江西农业大学, 2017.

    XIE Wen. Study on spectral characteristics and estimation models of different nutrient contents in forest soils based on hyperspectral technology[D]. Nanchang: Jiangxi Agriculture Universty, 2017.
    [11]
    吴俊, 郭大千, 李果, 郭熙, 钟亮, 朱青, 国佳欣, 叶英聪. 基于CARS-BPNN的江西省土壤有机碳含量高光谱预测[J]. 中国农业科学, 2022, 55(19):3738-3750.

    WU Jun, GUO Daqian, LI Guo, GUO Xi, ZHONG Liang, ZHU Qing, GUO Jiaxin, YE Yingcong. Prediction of soil organic carbon content in Jiangxi Province by vis-nir spectroscopy based on the CARS-BPNN model[J]. Scientia Agricultura Sinica, 2022, 55(19): 3738-3750.
    [12]
    文冬妮, 杨程, 杨霖, 秦兴华, 孟磊, 何秋香, 朱同彬, Christoph Müller. 岩溶区农业种植对土壤有机氮矿化的影响[J]. 中国岩溶, 2020, 39(2):189-195.

    WEN Dongni, YANG Cheng, YANG Lin, QIN Xinghua, MENG Lei, HE Qiuxiang, ZHU Tongbin, Christoph Müller. Effects of agricultural cultivation on soil organic nitrogen mineralization in karst regions[J]. Carsologica Sinica, 2020, 39(2): 189-195.
    [13]
    Linker R, Shmulevich I, Kenny A, Shaviv A. Soil identification and chemometrics for direct determination of nitrate in soils using FTIR-ATR mid-infrared spectroscopy[J]. Chemosphere, 2025, 61(5): 652-658.
    [14]
    刘秀英. 玉米生理参数及农田土壤信息高光谱监测模型研究[D]. 陕西:西北农林科技大学, 2016.

    LIU Xiuying. Monitoring models of physiological parameters of corn and farmland soil informatlon based on hyper-spectral reflectance[D]. Shaanxi:Northwest A & F Universty, 2016.
    [15]
    赖倩倩, 杨霖, 秦兴华, 田伟, 伍延正, 汤水荣, 解钰, Christoph Müller, 孟磊. 蔗渣生物质炭对喀斯特农田石灰性土壤氮转化过程的短期影响[J]. 中国岩溶, 2019, 38(3):405-457. doi: 10.11932/karst2019y03

    LAI Qianqian, YANG Lin, QIN Xinghua, TIAN Wei, WU Yanzheng, TANG Shuirong, XIE Yu, Christoph Müller, MENG Lei. Study on short-term effects of sugarcane biochar on nitrogen transformation in calcareous soils in karst farmland[J]. Carsologica Sinica, 2019, 38(3): 405-457. doi: 10.11932/karst2019y03
    [16]
    胡芳, 杜虎, 曾馥平, 宋同清, 彭晚霞, 兰斯安, 张芳. 广西不同林龄喀斯特森林生态系统碳储量及其分配格局[J]. 应用生态学报, 2017, 28(3):721-729.

    HU Fang, DU Hu, ZENG Fuping, SONG Tongqing, PENG Wanxia, LAN Sian, ZHANG Fang. Carbon storage and its allocation in karst forest at different stand ages in Guangxi, China[J]. Chinese Journal of Applied Ecology, 2017, 28(3): 721-729.
    [17]
    宋玉, 塔西甫拉提·特依拜, 李崇博, 侯艳军, 陶兰花, 张飞. 基于偏最小二乘法的土壤汞含量高光谱反演[J]. 地理与地理信息科学, 2015, 31(3):44-47, 53. doi: 10.3969/j.issn.1672-0504.2015.03.009

    SONG Yu, TASHPOLAT·Teyip, LI Chongbo, HOU Yanjun, TAO Lanhua, ZHANG Fei. PLSR based hyperspectral remote sensing retrieval of soil Hg content[J]. Geography and Geo-information Science, 2015, 31(3): 44-47, 53. doi: 10.3969/j.issn.1672-0504.2015.03.009
    [18]
    郭超凡, 郭逍宇. 基于可见光波段包络线去除的湿地植物叶片叶绿素估算[J]. 生态学报, 2016, 36(20):6538-6546.

    GUO Chaofan, GUO Xiaoyu. Estimation of wetland plant leaf chlorophyll content based on continuum removal in the visible domain[J]. Acta Ecologica Sinica, 2016, 36(20): 6538-6546.
    [19]
    胡芳, 蔺启忠, 王钦军, 王亚军. 土壤钾含量高光谱定量反演研究[J]. 国土资源遥感, 2012, 24(4):157-162.

    HU Fang, LIN Qizhong, WANG Qinjun, WANG Yajun. Quantitative inversion of soil potassium content by using hyperspectral reflectance[J]. Remote Sensing for Land & Resources, 2012, 24(4): 157-162.
    [20]
    Specht D F. A general regression neural network[J]. IEEE Transactions on Neural Networks, 1991, 2(6): 568-576. doi: 10.1109/72.97934
    [21]
    蒋烨林, 王让会, 李焱, 李成, 彭擎, 吴晓全. 艾比湖流域不同土地覆盖类型土壤养分高光谱反演模型研究[J]. 中国生态农业学报, 2016, 24(11):1555-1564.

    JIANG Yelin, WANG Ranghui, LI Yan, LI Cheng, PENG Qing, WU Xiaoquan. Hyper-spectral retrieval of soil nutrient content of various land-cover types in Ebinur lake basin[J]. Chinese Journal of Eco-Agriculture, 2016, 24(11): 1555-1564.
    [22]
    Shi T Z, Chen Y Y, Liu Y L, Wu G F. Visible and near-infrared reflectance spectroscopy: An alternative for monitoring soil contamination by heavy metals[J]. Journal of Hazardous Materials, 2014, 265: 166-176. doi: 10.1016/j.jhazmat.2013.11.059
    [23]
    卢志宏, 刘辛瑶, 常书娟, 杨胜利, 赵薇薇, 杨勇, 刘爱军. 基于BP神经网络的草原矿区表层土壤N/P高光谱反演模型[J]. 草业科学, 2018, 35(9):2127-2136.

    LU Zhihong, LIU Xinyao, CHANG Shujuan, YANG Shengli, ZHAO Weiwei, YANG Yong, LIU Aijun. Hyperspectral inversion of the surface soil N/P ratio in a grassland mining area based on the BP neural network[J]. Pratacultural Science, 2018, 35(9): 2127-2136.
    [24]
    郭鹏, 李婷, 张世熔, 李智平, 梁俊捷. 西河流域不同海拔区土壤有效钾的高光谱反演[J]. 土壤通报, 2019, 50(2):274-281.

    GUO Peng, LI Ting, ZHANG Shirong, LI Zhiping, LIANG Junjie. Hyperspectral estimation of soil available potassium at different altitudes of the Xihe watershed[J]. Chinese Journal of Soil Science, 2019, 50(2): 274-281.
    [25]
    李焱, 王让会, 管延龙, 蒋烨林, 吴晓全, 彭擎. 基于高光谱反射特性的土壤全氮含量预测分析[J]. 遥感技术与应用, 2017, 32(1):173-179.

    LI Yan, WANG Ranghui, GUAN Yanlong, JIANG Yelin, WU Xiaoquan, PENG Qing. Prediction analysis of soil total nitrogen content based on hyperspectral[J]. Remote Sensing Technology and Application, 2017, 32(1): 173-179.
    [26]
    王世东, 石朴杰, 张合兵, 王新闯. 基于高光谱的矿区复垦农田土壤全氮含量反演[J]. 生态学杂志, 2019, 38(1):294-301.

    WANG Shidong, SHI Pujie, ZHANG Hebing, WANG Xinchuang. Retrieval of soil total nitrogen content in reclaimed farmland of mining area based on hyperspectral imaging[J]. Chinese Journal of Ecology, 2019, 38(1): 294-301.
    [27]
    岳祥飞, 李衍青, 刘鹏. 广西岩溶区灌木林地凋落物—土壤碳、氮、磷化学计量特征[J]. 中国岩溶, 2023, 42(5):1106-1116.

    YUE Xiangfei, LI Yanqing, LIU Peng. Stoichiometric characteristics of C, N and P in soil and litter of shrublands in karst areas of Guangxi[J]. Carsologica Sinica, 2023, 42(5): 1106-1116.
    [28]
    鲁如坤. 我国土壤氮、磷、钾的基本状况[J]. 土壤学报, 1989(3):280-286.

    LU Rukun. General status od nutrients (N, P, K) in soils of China[J]. Acta Pedologica Sinica, 1989(3): 280-286.
    [29]
    王莉雯, 卫亚星. 湿地土壤全氮和全磷含量高光谱模型研究[J]. 生态学报, 2016, 36(16):5116-5125.

    WANG Liwen, WEI Yaxing. Estimating the total nitrogen and total phosphorus content of wetland soils using hyperspectral models[J]. Acta Ecologica Sinica, 2016, 36(16): 5116-5125.
    [30]
    Vohland Michael, Ludwig Marie, Harbich Monika, Emmerling Christoph, Thiele Bruhn Soeren. Using variable selection and wavelets to exploit the full potential of visible−near infrared spectra for predicting soil properties[J]. Journal of Near Infrared Spectroscopy, 2016, 24(3): 255-269. doi: 10.1255/jnirs.1233
    [31]
    国佳欣, 赵小敏, 郭熙, 徐喆, 朱青, 江叶枫. 基于PLSR-BP复合模型的红壤有机质含量反演研究[J]. 土壤学报, 2020, 57(3):636-645.

    GUO Jiaxin, ZHAO Xiaomin, GUO Xi, XU Zhe, ZHU Qing, JIANG Yefeng. Inversion of organic matter content in red soil based on PLSR-BP composite model[J]. Acta Pedologica Sinica, 2020, 57(3): 636-645.
    [32]
    谷佳慧, 杨奇勇, 蒋忠诚, 罗为群, 曾红春, 覃星铭, 蓝芙宁. 广南县幅岩溶区与非岩溶区土壤碳氮磷生态化学计量比空间变异分析[J]. 中国岩溶, 2018, 37(5):761-769.

    GU Jiahui, YANG Qiyong, JIANG Zhongcheng, LUO Weiqun, ZENG Hongchun, QIN Xingming, LAN Funing. Spatial variation analysis of soil carbon, nitrogen and phosphorus eco-stoichiometric ratios in karst and non-karst areas of Guangnan county, Yunnan, China[J]. Carsologica Sinica, 2018, 37(5): 761-769.
    [33]
    陈秋帆, 卢琦, 王妍, 刘云根. 西南石漠化区林下土壤养分特征及差异性[J]. 中国岩溶, 2023, 42(2):290-300.

    CHEN Qiufan, LU Qi, WANG Yan, LIU Yungen. Nutrient characteristics and differences of forest soil in rocky desertification areas of Southwest China[J]. Carsologica Sinica, 2023, 42(2): 290-300.
    • Relative Articles
    • Supplements(0)
    • Cited By
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索
    Get Citation
    PDF(1536.0KB)
    XML

    Article Metrics

    Article views (32) PDF downloads(7) Cited by()
    Proportional views
    Related

    Website Copyright © CARSOLOGICA SINICA 桂ICP备05009877号-1

    Sponsor: Chinese Academy of Geological Sciences Sponsored by: Institute of Karst Geology, Chinese Academy of Geological Sciences Address: No.50, Qixing Road, Guilin, Guangxi

    Post Code: 541004 Tel: 0773-5812949 7796657 Email: zgyr@mail.cgs.gov.cn; zgyrbjb@163.com

    Supported by: Beijing Renhe Information Technology Co., Ltd.    

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return