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Sensitivity analysis of artificial neural network for chlorophyll prediction using hyperspectral data

Author

Listed:
  • Prashant K. Srivastava

    (Banaras Hindu University
    Banaras Hindu University)

  • Manika Gupta

    (University of Delhi)

  • Ujjwal Singh

    (Banaras Hindu University)

  • Rajendra Prasad

    (IIT (BHU))

  • Prem Chandra Pandey

    (Shiv Nadar University)

  • A. S. Raghubanshi

    (Banaras Hindu University)

  • George P. Petropoulos

    (Harokopio University of Athens)

Abstract

Hyperspectral acquisition provides the spectral response in narrow and continuous spectral channel. The high number of contiguous bands in hyperspectral remote sensing provides significant improvements in assessing subtle changes as compared to the multispectral satellite datasets in context of spectral resolution. Therefore, the main goal of the present research is to evaluate the sensitivity of the artificial neural networks (ANNs) for chlorophyll prediction in the winter wheat crop using different hyperspectral spectral indices. For evaluating relative variable significance in the study, the Olden’s function has been applied. The Lek’s profile method is used for sensitivity analysis of ANNs for chlorophyll prediction using the vegetation indices such as Red Edge Inflection Point (REIP), Normalized Difference Vegetation Index (NDVI), Soil-Adjusted Vegetation Index (SAVI), and Structure-Insensitive Pigment Index (SIPI) derived from hyperspectral radiometer. The analysis indicates a high sensitivity of SAVI followed by NDVI, REIP and SIPI for chlorophyll retrieval using ANNs. The statistical performance indices obtained from calibration (RMSE = 0.27; index of agreement = 0.96) and validation (RMSE = 0.66; index of agreement = 0.83) suggested that the ANN model is appropriate for chlorophyll prediction with good efficiency. The outcome of this work can be used by upcoming hyperspectral missions such as Airborne Visible Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) and Hyperspectral Infrared Imager (HyspIRI) for large-scale estimation of chlorophyll and could help in the real-time monitoring of crop health status.

Suggested Citation

  • Prashant K. Srivastava & Manika Gupta & Ujjwal Singh & Rajendra Prasad & Prem Chandra Pandey & A. S. Raghubanshi & George P. Petropoulos, 2021. "Sensitivity analysis of artificial neural network for chlorophyll prediction using hyperspectral data," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 23(4), pages 5504-5519, April.
    • Handle: RePEc:spr:endesu:v:23:y:2021:i:4:d:10.1007_s10668-020-00827-6
    DOI: 10.1007/s10668-020-00827-6
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    References listed on IDEAS

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    1. Zhang, Guoqiang & Eddy Patuwo, B. & Y. Hu, Michael, 1998. "Forecasting with artificial neural networks:: The state of the art," International Journal of Forecasting, Elsevier, vol. 14(1), pages 35-62, March.
    2. Tian, Wenchong & Liao, Zhenliang & Zhang, Jin, 2017. "An optimization of artificial neural network model for predicting chlorophyll dynamics," Ecological Modelling, Elsevier, vol. 364(C), pages 42-52.
    3. Ozgur Kisi & Jalal Shiri, 2011. "Precipitation Forecasting Using Wavelet-Genetic Programming and Wavelet-Neuro-Fuzzy Conjunction Models," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 25(13), pages 3135-3152, October.
    4. Prashant Srivastava & Dawei Han & Miguel Rico-Ramirez & Deleen Al-Shrafany & Tanvir Islam, 2013. "Data Fusion Techniques for Improving Soil Moisture Deficit Using SMOS Satellite and WRF-NOAH Land Surface Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(15), pages 5069-5087, December.
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    1. Yuanyuan Shi & Junyu Zhao & Xianchong Song & Zuoyu Qin & Lichao Wu & Huili Wang & Jian Tang, 2021. "Hyperspectral band selection and modeling of soil organic matter content in a forest using the Ranger algorithm," PLOS ONE, Public Library of Science, vol. 16(6), pages 1-15, June.

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