A1 Journal article (refereed)
Chlorophyll Concentration Retrieval by Training Convolutional Neural Network for Stochastic Model of Leaf Optical Properties (SLOP) Inversion (2020)

Annala, L., Honkavaara, E., Tuominen, S., & Pölönen, I. (2020). Chlorophyll Concentration Retrieval by Training Convolutional Neural Network for Stochastic Model of Leaf Optical Properties (SLOP) Inversion. Remote Sensing, 12(2), Article 283. https://doi.org/10.3390/rs12020283

JYU authors or editors

Publication details

All authors or editors: Annala, Leevi; Honkavaara, Eija; Tuominen, Sakari; Pölönen, Ilkka

Journal or series: Remote Sensing

eISSN: 2072-4292

Publication year: 2020

Volume: 12

Issue number: 2

Article number: 283

Publisher: MDPI AG

Publication country: Switzerland

Publication language: English

DOI: https://doi.org/10.3390/rs12020283

Publication open access: Openly available

Publication channel open access: Open Access channel

Publication is parallel published (JYX): https://jyx.jyu.fi/handle/123456789/67577

Abstract

Miniaturized hyperspectral imaging techniques have developed rapidly in recent years and have become widely available for different applications. Combining calibrated hyperspectral imagery with inverse physically based reflectance models is an interesting approach for estimating chlorophyll concentrations that are good indicators of vegetation health. The objective of this study was to develop a novel approach for retrieving chlorophyll a and b values from remotely sensed data by inverting the stochastic model of leaf optical properties using a one-dimensional convolutional neural network. The inversion results and retrieved values are validated in two ways: A classical machine learning validation dataset and calculating chlorophyll maps from empirical remotely sensed hyperspectral data and comparing them to TCARIOSAVI , an index that has strong negative correlation with chlorophyll concentration. With the validation dataset, coefficients of determination ( R2 ) of 0.97 were obtained for chlorophyll a and 0.95 for chlorophyll b. The chlorophyll maps correlate with the TCARIOSAVI map. The correlation coefficient (R) is −0.87 for chlorophyll a and −0.68 for chlorophyll b in selected plots. These results indicate that the approach is highly promising approach for estimating vegetation chlorophyll content.

Keywords: remote sensing; silviculture; forest mensuration; spectral imaging; chlorophyll; optical properties; machine learning; neural networks (information technology); stochastic processes

Free keywords: optical properties; convolutional neural network; deep learning; chlorophyll; stochastic modeling; physical parameter retrieval; forestry

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Ministry reporting: Yes

VIRTA submission year: 2020

JUFO rating: 1