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Open AccessArticle

Chlorophyll Concentration Retrieval by Training Convolutional Neural Network for Stochastic Model of Leaf Optical Properties (SLOP) Inversion

1
Faculty of Information Technology, University of Jyväskylä, FI-40014 Jyväskylän Yliopisto, Jyväskylä, Finland
2
Finnish Geospatial Research Institute, National Land Survey of Finland, FI-02430 Masala, Finland
3
Natural Resources Institute Finland, FI-00790 Helsinki, Finland
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(2), 283; https://doi.org/10.3390/rs12020283
Received: 24 October 2019 / Revised: 1 December 2019 / Accepted: 4 January 2020 / Published: 15 January 2020
(This article belongs to the Section Forest Remote Sensing)
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 TCARI OSAVI , an index that has strong negative correlation with chlorophyll concentration. With the validation dataset, coefficients of determination ( R 2 ) of 0.97 were obtained for chlorophyll a and 0.95 for chlorophyll b. The chlorophyll maps correlate with the TCARI OSAVI 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. View Full-Text
Keywords: optical properties; convolutional neural network; deep learning; chlorophyll; stochastic modeling; physical parameter retrieval; forestry optical properties; convolutional neural network; deep learning; chlorophyll; stochastic modeling; physical parameter retrieval; forestry
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MDPI and ACS Style

Annala, L.; Honkavaara, E.; Tuominen, S.; Pölönen, I. Chlorophyll Concentration Retrieval by Training Convolutional Neural Network for Stochastic Model of Leaf Optical Properties (SLOP) Inversion. Remote Sens. 2020, 12, 283.

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