Laser & Optoelectronics Progress, Volume. 60, Issue 7, 0730005(2023)

Fractional Differential-Based Hyperspectral Inversion of Soil Organic Matter Content

Wuyao Li1, Mamat Sawut1,2,3、*, and Maihemuti Balati1,2,3
Author Affiliations
  • 1College of Geography and Remote Sensing Science, Xinjiang University, Urumqi 830046, Xinjiang, China
  • 2Xinjiang Key Laboratory of Oasis Ecology, Urumqi 830046, Xinjiang, China
  • 3Key Laboratory of Smart City and Environment Modelling of Higher Education Institute, Urumqi 830046, Xinjiang, China
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    Figures & Tables(7)
    Geographical position of study area and distribution of sampling
    Reflectance curves of soil with different organic matter contents
    Correlation coefficient between SOM content and spectral reflectance transform form. (a) 1-order differential; (b) 1.2-order differential; (c) 1.4-order differential; (d) 1.6-order differential; (e) 1.8-order differential; (f) 2-order differential
    Optimal mathematical transformation form of SOM results in the three models. (a) 1-order differential of SVR model; (b) 1-order differential of PLSR model; (c) 1.2-order differential of RF model
    • Table 1. SOM content statistical characteristics of study area samples

      View table

      Table 1. SOM content statistical characteristics of study area samples

      Sample typeQuantityMaxMinMeanStandard deviationCV /%
      Calibration4925.4162.75811.6025.43646.86
      Validation2424.1332.74212.5315.81046.37
      Totality7325.4162.74211.9595.60146.84
    • Table 2. Characteristic bands of SOM under fractional differentiation

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      Table 2. Characteristic bands of SOM under fractional differentiation

      Spectral transformationCharacteristic band /nmNumber of wavelengthsr
      0/0/
      0.2/0/
      0.4/0/
      0.6/0/
      0.8399、43520.293、0.316
      1588、597、630、648、675、792、855、101380.308、0.323、0.290、-0.286、-0.308、-0.350、-0.291、0.290
      1.2407、464、588、597、648、671、675、723、792、1013100.285、-0.312、0.290、0.319、-0.310、0.280、-0.287、-0.293、-0.368、0.353
      1.4407、425、464、597、648、671、723、775、792、965、1013110.316、-0.294、-0.335、0.315、-0.308、0.287、-0.307、0.282、-0.372、-0.278、0.283
      1.6387、407、425、464、597、648、671、676、723、775、792、965120.296、0.343、-0.283、-0.343、0.305、-0.300、0.285、0.278、-0.311、0.298、-0.366、-0.278
      1.8387、407、464、597、648、671、676、723、775、792、797110.309、0.364、-0.347、0.288、-0.291、0.278、0.289、-0.307、0.309、-0.353、0.277
      2387、407、464、648、676、723、775、792、79790.303、0.377、-0.349、-0.282、0.293、-0.297、0.314、-0.336、0.278
    • Table 3. SOM content modeling and validation results

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      Table 3. SOM content modeling and validation results

      ModelSample typeModel formulaR2RMSERPD
      SVR1Calibration sety=0.76x+2.85830.832.292.43
      SVR1.2y=0.7074x+3.59830.732.841.96
      SVR1.4y=0.6341x+4.79630.673.191.74
      SVR1.6y=0.7051x+3.95630.713.011.85
      SVR1.8y=0.7537x+2.7770.812.452.27
      SVR2y=0.7488x+2.57890.812.484.97
      SVR1Validation sety=0.777x+3.00560.821.753.38
      SVR1.2y=0.8659x+1.96670.931.135.21
      SVR1.4y=0.897x+1.45250.970.797.52
      SVR1.6y=0.9153x+1.2740.970.767.75
      SVR1.8y=0.9031x+1.54410.960.866.83
      SVR2y=0.8639x+2.25840.931.193.73
      PLSR1Calibration sety=0.5492x+5.30370.554.241.31
      PLSR1.2y=0.4062x+6.98540.414.241.30
      PLSR1.4y=0.3676x+7.43940.374.371.27
      PLSR1.6y=0.3553x+7.58380.364.421.26
      PLSR1.8y=0.3697x+7.4150.374.371.27
      PLSR2y=0.3697x+7.41530.374.371.05
      PLSR1Validation sety=0.6591x+4.21270.712.182.71
      PLSR1.2y=0.7094x+3.59080.713.121.86
      PLSR1.4y=0.6275x+4.60230.632.472.39
      PLSR1.6y=0.65x+4.32440.652.392.47
      PLSR1.8y=0.6028x+4.90780.602.552.32
      PLSR2y=0.6137x+4.77390.612.522.09
      RF1

      Calibration set

      y=0.6285x+4.31530.822.612.13
      RF1.2y=0.5551x+5.25840.922.602.14
      RF1.4y=0.5674x+5.11310.912.582.16
      RF1.6y=0.5544x+5.26860.882.702.06
      RF1.8y=0.5864x+4.94690.912.502.22
      RF2y=0.6319x+4.71150.802.712.05
      RF1Validation sety=0.5873x+5.03450.792.072.85
      RF1.2y=0.6418x+4.40540.931.623.65
      RF1.4y=0.604x+4.9530.921.773.34
      RF1.6y=0.5824x+5.25980.901.873.16
      RF1.8y=0.5654x+5.46470.901.933.07
      RF2y=0.6048x+4.6390.861.883.14
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    Wuyao Li, Mamat Sawut, Maihemuti Balati. Fractional Differential-Based Hyperspectral Inversion of Soil Organic Matter Content[J]. Laser & Optoelectronics Progress, 2023, 60(7): 0730005

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    Paper Information

    Category: Spectroscopy

    Received: Feb. 14, 2022

    Accepted: Mar. 18, 2022

    Published Online: May. 24, 2023

    The Author Email: Sawut Mamat (korxat@xju.edu.cn)

    DOI:10.3788/LOP220715

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