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

Prashant K. Srivastava (), Manika Gupta, Ujjwal Singh, Rajendra Prasad, Prem Chandra Pandey (), A. S. Raghubanshi and George P. Petropoulos
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Prashant K. Srivastava: 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

Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, 2021, vol. 23, issue 4, No 36, 5504-5519

Abstract: 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.

Keywords: Hyperspectral Radiometry; Vegetation indices; Sensitivity analysis; Neural network; Chlorophyll (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/s10668-020-00827-6

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