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Laser & Optoelectronics Progress, Volume. 57, Issue 9, 093002(2020)

Coupled Machine Learning and Unmanned Aerial Vehicle Based Hyperspectral Data for Soil moisture Content Estimation

Meiling Tian1,2,3、**, Xiangyu Ge1,2,3, Jianli Ding1,2,3、*, Jingzhe Wang1,2,3, and Zhenhua Zhang1,2,3
Author Affiliations
  • 1College of Resource and Environment Sciences, Xinjiang University, Urumqi, Xinjiang 830046, China
  • 2Key Laboratory of Oasis Ecology, Xinjiang University, Urumqi, Xinjiang 830046, China
  • 3Key Laboratory of Smart City and Environment Modelling of Higher Education Institute, Xinjiang University, Urumqi, Xinjiang 830046, China
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    AbstractGet PDF(in Chinese)
    Figures&Tables (7)
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    References (24)
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    Figures & Tables(7)
    Statistical characteristics of SMC

    Fig. 1. Statistical characteristics of SMC

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    Hyperspectral images based on different pretreatments. (a) Three-dimensional image; (b) R; (c) FDR; (d) SDR; (e) CR; (f) A; (g) FDA; (h) SDA

    Fig. 2. Hyperspectral images based on different pretreatments. (a) Three-dimensional image; (b) R; (c) FDR; (d) SDR; (e) CR; (f) A; (g) FDA; (h) SDA

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    Spectral curves based on different pretreatments. (a) R; (b) FDR; (c) SDR; (d) CR; (e) A; (f) FDA; (g) SDA

    Fig. 3. Spectral curves based on different pretreatments. (a) R; (b) FDR; (c) SDR; (d) CR; (e) A; (f) FDA; (g) SDA

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    Characteristic bands selected by different algorithms. (a)-(c) Characteristic bands of R after RF, GBRT, XGBoost screening; (d)-(f) characteristic bands of FDR after RF, GBRT, XGBoost screening; (g)-(i) characteristic bands of SDR after RF, GBRT, XGBoost screening; (J)-(l) characteristic bands of CR after RF, GBRT, XGBoost screening; (m)-(o) characteristic bands of RF, GBRT, XGBoost screening; (p)-(r) characteristic bands of FDA after RF, GBRT, XGBoost screening; (s)-(u) characteristic band of S

    Fig. 4. Characteristic bands selected by different algorithms. (a)-(c) Characteristic bands of R after RF, GBRT, XGBoost screening; (d)-(f) characteristic bands of FDR after RF, GBRT, XGBoost screening; (g)-(i) characteristic bands of SDR after RF, GBRT, XGBoost screening; (J)-(l) characteristic bands of CR after RF, GBRT, XGBoost screening; (m)-(o) characteristic bands of RF, GBRT, XGBoost screening; (p)-(r) characteristic bands of FDA after RF, GBRT, XGBoost screening; (s)-(u) characteristic band of S

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    SMC estimation results based on different preferred methods. (a)-(c) SMC estimation effect of R optimized by RF, GBRT and XGBoost; (d)-(f) SMC estimation effect of FDR optimized by RF, GBRT and XGBoost; (g)-(i) SMC estimation effect of SDR optimized by RF, GBRT and XGBoost; (j)-(l) SMC estimation effect of CR optimized by RF, GBRT and XGBoost; (m)-(o) SMC estimation effect of A optimized by RF, GBRT and XGBoost; (p)-(r) SMC estimation effect of FDA optimized by RF, GBRT and XGBoost; (s)-(u) SMC

    Fig. 5. SMC estimation results based on different preferred methods. (a)-(c) SMC estimation effect of R optimized by RF, GBRT and XGBoost; (d)-(f) SMC estimation effect of FDR optimized by RF, GBRT and XGBoost; (g)-(i) SMC estimation effect of SDR optimized by RF, GBRT and XGBoost; (j)-(l) SMC estimation effect of CR optimized by RF, GBRT and XGBoost; (m)-(o) SMC estimation effect of A optimized by RF, GBRT and XGBoost; (p)-(r) SMC estimation effect of FDA optimized by RF, GBRT and XGBoost; (s)-(u) SMC

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    Distribution of characteristic bands

    Fig. 6. Distribution of characteristic bands

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    • Table 1. GWR model of optimal variable SMC under different preferred methods

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      Table 1. GWR model of optimal variable SMC under different preferred methods

      Independent variableModeling setValidation set
      R2RMSE /%R2RMSE /%RPIQ
      R-RF0.6903.3070.6942.0681.682
      R-GBRT0.7003.2140.6982.0191.890
      R-XGBoost0.6533.4400.6572.2301.410
      FDR-RF0.6213.6140.6212.2371.401
      FDR-GBRT0.8002.6240.8011.6543.007
      FDR-XGBoost0.7712.8020.7721.7642.943
      SDR-RF0.7123.1320.7122.0651.895
      SDR-GBRT0.7442.9730.7451.902.724
      SDR-XGBoost0.6903.2680.6922.5631.688
      CR-RF0.7263.0620.7241.9322.212
      CR-GBRT0.6813.3120.6802.2021.436
      CR-XGBoost0.6883.2760.6892.3221.483
      A-RF0.6943.2390.6922.0761.724
      A-GBRT0.6853.2800.6882.1911.437
      A-XGBoost0.6903.2570.6912.0531.588
      FDA-RF0.8422.4340.8431.4543.115
      FDA-GBRT0.8902.0240.8901.3373.490
      FDA-XGBoost0.7642.8520.7641.8352.801
      SDA-RF0.5993.7270.5982.3171.362
      SDA-GBRT0.7382.9980.7401.8812.315
      SDA-XGBoost0.8602.2850.8611.6323.238
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    Meiling Tian, Xiangyu Ge, Jianli Ding, Jingzhe Wang, Zhenhua Zhang. Coupled Machine Learning and Unmanned Aerial Vehicle Based Hyperspectral Data for Soil moisture Content Estimation[J]. Laser & Optoelectronics Progress, 2020, 57(9): 093002

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    Paper Information

    Category: Spectroscopy

    Received: Sep. 4, 2019

    Accepted: Sep. 16, 2019

    Published Online: May. 6, 2020

    The Author Email: Meiling Tian (tianmeiling_0911@163.com), Jianli Ding (watarid@xju.edu.cn)

    DOI:10.3788/LOP57.093002

    Topics

    laser devices and laser physics
    Lasers and Laser Optics
    Laser physics
    laser manufacturing
    Instrumentation, Measurement and Metrology