Accurate modelling of canopy traits from seasonal Sentinel-2 imagery based on the vertical distribution of leaf traits

T.W. Gara, R. Darvishzadeh, A.K. Skidmore, Tiejun Wang, Marco Heurich

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

Leaf traits at canopy level (hereinafter canopy traits) are conventionally expressed as a product of total canopy leaf area index (LAI) and leaf trait content based on samples collected from the exposed upper canopy. This traditional expression is centered on the theory that absorption of incident photosynthetically active radiation (PAR) follow a bell-shaped function skewed to the upper canopy. However, the validity of this theory has remained untested for a suite of canopy traits in a temperate forest ecosystem across multiple seasons using multispectral imagery. In this study, we examined the effect of canopy traits expression in modelling canopy traits using Sentinel-2 multispectral data across the growing season in Bavaria Forest National Park (BFNP), Germany. To achieve this, we measured leaf mass per area (LMA), chlorophyll (Cab), nitrogen (N) and carbon content and LAI from the exposed upper and shaded lower canopy respectively over three seasons (spring, summer and autumn). Subsequently, we estimated canopy traits using two expressions, i.e. the traditional expression-based on the product of LAI and leaf traits content of samples collected from the sunlit upper canopy (hereinafter top-of-canopy expression) and the weighted expression - established on the proportion between the shaded lower and sunlit upper canopy LAI and their respective leaf traits content. Using a Random Forest machine-learning algorithm, we separately modelled canopy traits estimated from the two expressions using Sentinel-2 spectral bands and vegetation indices. Our results showed that dry matter related canopy traits (LMA, N and carbon) estimated based on the weighted canopy expression yield stronger correlations and higher prediction accuracy (NRMSECV < 0.19) compared to the top-of-canopy traits expression across all seasons. In contrast, canopy chlorophyll estimated from the top-of-canopy expression demonstrated strong fidelity with Sentinel-2 bands and vegetation indices (RMSE < 0.48 µg/cm2) compared to weighted canopy chlorophyll (RMSE > 0.48 µg/cm2) across all seasons. We also developed a generalized model that explained 52.57–67.82% variation in canopy traits across the three seasons. Using the most accurate Random Forest model for each season, we demonstrated the capability of Sentinel-2 data to map seasonal dynamics of canopy traits across the park. Results presented in this study revealed that canopy trait expression can have a profound effect on modelling the accuracy of canopy traits using satellite imagery throughout the growing seasons. These findings have implications on model accuracy when monitoring the dynamics of ecosystem functions, processes and services.
Original languageEnglish
Pages (from-to)108-123
Number of pages16
JournalISPRS journal of photogrammetry and remote sensing
Volume157
DOIs
Publication statusPublished - 12 Sep 2019

Fingerprint

vertical distribution
canopies
imagery
leaves
canopy
Ecosystems
modeling
Carbon
Satellite imagery
Chlorophyll
Learning algorithms
Learning systems
leaf area index
Nitrogen
Radiation
Monitoring
ecosystems
growing season
photosynthetically active radiation
national parks

Keywords

  • ITC-ISI-JOURNAL-ARTICLE

Cite this

@article{114f9be4ce1c4ecbb0256af3a8fe76cc,
title = "Accurate modelling of canopy traits from seasonal Sentinel-2 imagery based on the vertical distribution of leaf traits",
abstract = "Leaf traits at canopy level (hereinafter canopy traits) are conventionally expressed as a product of total canopy leaf area index (LAI) and leaf trait content based on samples collected from the exposed upper canopy. This traditional expression is centered on the theory that absorption of incident photosynthetically active radiation (PAR) follow a bell-shaped function skewed to the upper canopy. However, the validity of this theory has remained untested for a suite of canopy traits in a temperate forest ecosystem across multiple seasons using multispectral imagery. In this study, we examined the effect of canopy traits expression in modelling canopy traits using Sentinel-2 multispectral data across the growing season in Bavaria Forest National Park (BFNP), Germany. To achieve this, we measured leaf mass per area (LMA), chlorophyll (Cab), nitrogen (N) and carbon content and LAI from the exposed upper and shaded lower canopy respectively over three seasons (spring, summer and autumn). Subsequently, we estimated canopy traits using two expressions, i.e. the traditional expression-based on the product of LAI and leaf traits content of samples collected from the sunlit upper canopy (hereinafter top-of-canopy expression) and the weighted expression - established on the proportion between the shaded lower and sunlit upper canopy LAI and their respective leaf traits content. Using a Random Forest machine-learning algorithm, we separately modelled canopy traits estimated from the two expressions using Sentinel-2 spectral bands and vegetation indices. Our results showed that dry matter related canopy traits (LMA, N and carbon) estimated based on the weighted canopy expression yield stronger correlations and higher prediction accuracy (NRMSECV < 0.19) compared to the top-of-canopy traits expression across all seasons. In contrast, canopy chlorophyll estimated from the top-of-canopy expression demonstrated strong fidelity with Sentinel-2 bands and vegetation indices (RMSE < 0.48 µg/cm2) compared to weighted canopy chlorophyll (RMSE > 0.48 µg/cm2) across all seasons. We also developed a generalized model that explained 52.57–67.82{\%} variation in canopy traits across the three seasons. Using the most accurate Random Forest model for each season, we demonstrated the capability of Sentinel-2 data to map seasonal dynamics of canopy traits across the park. Results presented in this study revealed that canopy trait expression can have a profound effect on modelling the accuracy of canopy traits using satellite imagery throughout the growing seasons. These findings have implications on model accuracy when monitoring the dynamics of ecosystem functions, processes and services.",
keywords = "ITC-ISI-JOURNAL-ARTICLE",
author = "T.W. Gara and R. Darvishzadeh and A.K. Skidmore and Tiejun Wang and Marco Heurich",
year = "2019",
month = "9",
day = "12",
doi = "10.1016/j.isprsjprs.2019.09.005",
language = "English",
volume = "157",
pages = "108--123",
journal = "ISPRS journal of photogrammetry and remote sensing",
issn = "0924-2716",
publisher = "Elsevier",

}

Accurate modelling of canopy traits from seasonal Sentinel-2 imagery based on the vertical distribution of leaf traits. / Gara, T.W.; Darvishzadeh, R.; Skidmore, A.K.; Wang, Tiejun; Heurich, Marco.

In: ISPRS journal of photogrammetry and remote sensing, Vol. 157, 12.09.2019, p. 108-123.

Research output: Contribution to journalArticleAcademicpeer-review

TY - JOUR

T1 - Accurate modelling of canopy traits from seasonal Sentinel-2 imagery based on the vertical distribution of leaf traits

AU - Gara, T.W.

AU - Darvishzadeh, R.

AU - Skidmore, A.K.

AU - Wang, Tiejun

AU - Heurich, Marco

PY - 2019/9/12

Y1 - 2019/9/12

N2 - Leaf traits at canopy level (hereinafter canopy traits) are conventionally expressed as a product of total canopy leaf area index (LAI) and leaf trait content based on samples collected from the exposed upper canopy. This traditional expression is centered on the theory that absorption of incident photosynthetically active radiation (PAR) follow a bell-shaped function skewed to the upper canopy. However, the validity of this theory has remained untested for a suite of canopy traits in a temperate forest ecosystem across multiple seasons using multispectral imagery. In this study, we examined the effect of canopy traits expression in modelling canopy traits using Sentinel-2 multispectral data across the growing season in Bavaria Forest National Park (BFNP), Germany. To achieve this, we measured leaf mass per area (LMA), chlorophyll (Cab), nitrogen (N) and carbon content and LAI from the exposed upper and shaded lower canopy respectively over three seasons (spring, summer and autumn). Subsequently, we estimated canopy traits using two expressions, i.e. the traditional expression-based on the product of LAI and leaf traits content of samples collected from the sunlit upper canopy (hereinafter top-of-canopy expression) and the weighted expression - established on the proportion between the shaded lower and sunlit upper canopy LAI and their respective leaf traits content. Using a Random Forest machine-learning algorithm, we separately modelled canopy traits estimated from the two expressions using Sentinel-2 spectral bands and vegetation indices. Our results showed that dry matter related canopy traits (LMA, N and carbon) estimated based on the weighted canopy expression yield stronger correlations and higher prediction accuracy (NRMSECV < 0.19) compared to the top-of-canopy traits expression across all seasons. In contrast, canopy chlorophyll estimated from the top-of-canopy expression demonstrated strong fidelity with Sentinel-2 bands and vegetation indices (RMSE < 0.48 µg/cm2) compared to weighted canopy chlorophyll (RMSE > 0.48 µg/cm2) across all seasons. We also developed a generalized model that explained 52.57–67.82% variation in canopy traits across the three seasons. Using the most accurate Random Forest model for each season, we demonstrated the capability of Sentinel-2 data to map seasonal dynamics of canopy traits across the park. Results presented in this study revealed that canopy trait expression can have a profound effect on modelling the accuracy of canopy traits using satellite imagery throughout the growing seasons. These findings have implications on model accuracy when monitoring the dynamics of ecosystem functions, processes and services.

AB - Leaf traits at canopy level (hereinafter canopy traits) are conventionally expressed as a product of total canopy leaf area index (LAI) and leaf trait content based on samples collected from the exposed upper canopy. This traditional expression is centered on the theory that absorption of incident photosynthetically active radiation (PAR) follow a bell-shaped function skewed to the upper canopy. However, the validity of this theory has remained untested for a suite of canopy traits in a temperate forest ecosystem across multiple seasons using multispectral imagery. In this study, we examined the effect of canopy traits expression in modelling canopy traits using Sentinel-2 multispectral data across the growing season in Bavaria Forest National Park (BFNP), Germany. To achieve this, we measured leaf mass per area (LMA), chlorophyll (Cab), nitrogen (N) and carbon content and LAI from the exposed upper and shaded lower canopy respectively over three seasons (spring, summer and autumn). Subsequently, we estimated canopy traits using two expressions, i.e. the traditional expression-based on the product of LAI and leaf traits content of samples collected from the sunlit upper canopy (hereinafter top-of-canopy expression) and the weighted expression - established on the proportion between the shaded lower and sunlit upper canopy LAI and their respective leaf traits content. Using a Random Forest machine-learning algorithm, we separately modelled canopy traits estimated from the two expressions using Sentinel-2 spectral bands and vegetation indices. Our results showed that dry matter related canopy traits (LMA, N and carbon) estimated based on the weighted canopy expression yield stronger correlations and higher prediction accuracy (NRMSECV < 0.19) compared to the top-of-canopy traits expression across all seasons. In contrast, canopy chlorophyll estimated from the top-of-canopy expression demonstrated strong fidelity with Sentinel-2 bands and vegetation indices (RMSE < 0.48 µg/cm2) compared to weighted canopy chlorophyll (RMSE > 0.48 µg/cm2) across all seasons. We also developed a generalized model that explained 52.57–67.82% variation in canopy traits across the three seasons. Using the most accurate Random Forest model for each season, we demonstrated the capability of Sentinel-2 data to map seasonal dynamics of canopy traits across the park. Results presented in this study revealed that canopy trait expression can have a profound effect on modelling the accuracy of canopy traits using satellite imagery throughout the growing seasons. These findings have implications on model accuracy when monitoring the dynamics of ecosystem functions, processes and services.

KW - ITC-ISI-JOURNAL-ARTICLE

UR - https://ezproxy2.utwente.nl/login?url=https://library.itc.utwente.nl/login/2019/isi/darvishzadeh_acc.pdf

U2 - 10.1016/j.isprsjprs.2019.09.005

DO - 10.1016/j.isprsjprs.2019.09.005

M3 - Article

VL - 157

SP - 108

EP - 123

JO - ISPRS journal of photogrammetry and remote sensing

JF - ISPRS journal of photogrammetry and remote sensing

SN - 0924-2716

ER -