Enhancing Chlorophyll Content Estimation With Sentinel-2 Imagery: A Fusion Of Deep Learning And Biophysical Models

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F86652079%3A_____%2F24%3A00601484" target="_blank" >RIV/86652079:_____/24:00601484 - isvavai.cz</a>

Result on the web

<a href="https://ieeexplore.ieee.org/xpl/conhome/10640349/proceeding" target="_blank" >https://ieeexplore.ieee.org/xpl/conhome/10640349/proceeding</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/IGARSS53475.2024" target="_blank" >10.1109/IGARSS53475.2024</a>

Result language

angličtina

Original language name

Enhancing Chlorophyll Content Estimation With Sentinel-2 Imagery: A Fusion Of Deep Learning And Biophysical Models

Original language description

Understanding the status of plant traits associated with photosynthesis is essential to gain insights into the carbon dynamics of forest ecosystems and their interconnectedness with global climate change processes. Chlorophyll is a key biochemical component as it is integral to the photosynthetic mechanism. Chlorophyll content (Cab) is therefore widely used to track vegetation dynamics and assess the health status of canopies. In this paper, we present an innovative approach to accurately estimate chlorophyll content at the canopy level by integrating radiative transfer models (RTMs) with deep learning algorithms. Our methodology utilizes a U-Net deep learning model trained with Sentinel-2 (SE2) imagery and the chlorophyll content obtained from resampled SE2 reflectance simulations conducted with the Pro4SAIL model. We evaluated the resulting Cab predictions against leaf chlorophyll content measured in Lanžhot forest, Czechia, during six field campaigns in 2019 and 2020. Our models effectively captured the complexities of forest canopies in Lanžhot forest, where they achieved an RMSE of 8.4 µg/cm2 and MAE of 6.22 µg/cm2. The findings highlight the potential of integrating deep learning algorithms with RTMs and Sentinel-2 time series for chlorophyll content retrieval. The approach not only reduces processing time but also eliminates bottlenecks associated with extrapolating plant trait inversions from RTMs to satellite images. The proposed method significantly enhances the feasibility of integrating RTMs into near real-time monitoring systems. This integration holds promise to the track biochemical effects of biotic (e.g., bark beetle attacks) and abiotic forest disturbances (e.g., drought). By leveraging the synergy of RTMs, deep learning algorithms, and Sentinel2 imagery, this work addresses the challenges of timely and accurate monitoring in dynamic forest ecosystems.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

—

OECD FORD branch

20705 - Remote sensing

Project

<a href="/en/project/LM2023048" target="_blank" >LM2023048: Czech Carbon Observation System</a><br>

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2024

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

ISBN

979-8-3503-6032-5

ISSN

2153-7003

e-ISSN

—

Number of pages

9400

Pages from-to

4486-4489

Publisher name

IEEE Geoscience and Remote Sensing Society (GRSS)

Place of publication

Athény

Event location

Athény

Event date

Jul 7, 2024

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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