Estimating local-scale forest GPP in Northern Europe using Sentinel-2: Model comparisons with LUE, APAR, the plant phenology index, and a light response function

Junttila, Sofia; Ardö, Jonas; Cai, Zhanzhang; Jin, Hongxiao; Kljun, Natascha; Klemedtsson, Leif; Krasnova, Alisa; Lange, Holger; Lindroth, Anders; Mölder, Meelis; Noe, Steffen M.; Tagesson, Torbern; Vestin, Patrik; Weslien, Per; Eklundh, Lars

doi:10.1016/j.srs.2022.100075

Cited by 2 publications

(1 citation statement)

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“…Although Landsat can provide observations at 30 m resolution, its 16-day revisit period limits the quality of the time series and is more susceptible to cloud and rain [31]. In a recent study, Sentinel-2 data were applied to monitor vegetation phenology in Nordic forests [32]. Their high temporal-spatial resolution (10 m, 5-day) data can provide a richer, more detailed portrayal to discern landscape heterogeneity [33].…”

Section: Introductionmentioning

confidence: 99%

Comparing Different Spatial Resolutions and Indices for Retrieving Land Surface Phenology for Deciduous Broadleaf Forests

et al. 2023

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Deciduous broadleaf forests (DBF) are an extremely widespread vegetation type in the global ecosystem and an indicator of global environmental change; thus, they require accurate phenological monitoring. However, there is still a lack of systematic understanding of the sensitivity of phenological retrievals for DBF in terms of different spatial resolution data and proxy indices. In this study, 79 globally distributed DBF PhenoCam Network sites (total 314 site-years, 2013–2018) were used as the reference data (based on green chromaticity coordinates, GCC). Different spatial resolutions (30 m Landsat and Sentinel-2 data, and 500 m MCD43A4 data) and satellite remote sensing vegetation indices (normalized difference vegetation index, NDVI; enhanced vegetation index, EVI; and near-infrared reflectance of vegetation, NIRV) were compared to find the most suitable data and indices for DBF phenological retrievals. The results showed that: (1) for different spatial resolutions, both 30 m Landsat–Sentinel-2 data and 500 m MODIS data accurately captured (R2 > 0.8) DBF phenological metrics (i.e., the start of the growing season, SOS, and the end of the growing season, EOS), which are associated with the comparatively homogeneous landscape pattern of DBF; (2) for SOS, the NIRv index was closer to GCC than EVI and NDVI, and it showed a slight advantage over EVI and a significant advantage over NDVI. However, for EOS, NDVI performed best, outperforming EVI and NIRv; and (3) for different phenological metrics, the 30 m data showed a significant advantage for detecting SOS relative to the 500 m data, while the 500 m MCD43A4 outperformed the 30 m data for EOS. This was because of the differences between the wavebands used for GCC and for the satellite remote sensing vegetation indices calculations, as well as the different sensitivity of spatial resolution data to bare soil. This study provides a reference for preferred data and indices for broad scale accurate monitoring of DBF phenology.

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Section: Introductionmentioning

confidence: 99%