Forest 2200 nm/660 nm reflectance seasonality for improved satellite-image atmospheric correction

Mait Lang; Lea Hallik; Allan Sims

doi:10.2478/fsmu-2025-0001

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Forest 2200 nm/660 nm reflectance seasonality for improved satellite-image atmospheric correction

Forestry Studies

Volume 82 (2025): Issue 1 (December 2025)

By: Mait Lang, Lea Hallik and Allan Sims

Open Access

|Mar 2026

Abstract

A widely used algorithm, Sen2Cor, for the atmospheric correction of Sentinel-2 MSI images assumes a constant ratio of red (0.6 μm) and near-infrared (2.2 μm) spectral reflectance, taking ρ_2.2/ρ_0.6 = 2. In fact, however, this ratio exhibits substantial dependence on vegetation phenology. The discrepancy produces errors in predicted surface reflectance. We analyse the reflectance of 92,230 forest stands, using full time series of Landsat-8/9 OLI and Sentinel-2 MSI images taken over Estonia. We find the ρ_2.2/ρ_0.6 value for evergreen needleleaf forests to be rather stable over time, with a mean value around 2.25. In broadleaf deciduous forests, on the other hand, we find ρ_2.2/ρ_0.6 to increase rapidly in the spring, to remain on average close to 3 or greater, and then to decrease in the autumn. We offer parametric and tabulated models of ρ_2.2/ρ_0.6 for eventual integration with Sen2Cor.

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Articles in this issue

DOI: https://doi.org/10.2478/fsmu-2025-0001 | Journal eISSN: 1736-8723 | Journal ISSN: 1406-9954

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Language: English

Page range: 1 - 19

Submitted on: Feb 6, 2025

Accepted on: Aug 12, 2025

Published on: Mar 23, 2026

Published by: Estonian University of Life Sciences

In partnership with: Paradigm Publishing Services

Publication frequency: 2 issues per year

Keywords:

Landsat,

Related subjects:

Ecology,

© 2026 Mait Lang, Lea Hallik, Allan Sims, published by Estonian University of Life Sciences
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Volume 82 (2025): Issue 1 (December 2025)