A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
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-tekijät tai -toimittajat


Julkaisun tiedot

Julkaisun kaikki tekijät tai toimittajat: Annala, Leevi; Honkavaara, Eija; Tuominen, Sakari; Pölönen, Ilkka

Lehti tai sarja: Remote Sensing

eISSN: 2072-4292

Julkaisuvuosi: 2020

Volyymi: 12

Lehden numero: 2

Artikkelinumero: 283

Kustantaja: MDPI AG

Julkaisumaa: Sveitsi

Julkaisun kieli: englanti

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

Julkaisun avoin saatavuus: Avoimesti saatavilla

Julkaisukanavan avoin saatavuus: Kokonaan avoin julkaisukanava

Julkaisu on rinnakkaistallennettu (JYX): https://jyx.jyu.fi/handle/123456789/67577


Tiivistelmä

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.


YSO-asiasanat: kaukokartoitus; metsänhoito; metsänarviointi; spektrikuvaus; klorofylli; optiset ominaisuudet; koneoppiminen; neuroverkot; stokastiset prosessit

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


Liittyvät organisaatiot


Hankkeet, joissa julkaisu on tehty


OKM-raportointi: Kyllä

Raportointivuosi: 2020

JUFO-taso: 1


Viimeisin päivitys 2021-09-08 klo 12:09