Upscaling field-measured seasonal ground vegetation patterns with Sentinel-2 images in boreal ecosystems

• Published in: International Journal of Remote Sensing
• Main Authors: Pang, Yuwen, Räsänen, Aleksi, Juselius, Teemu, Aurela, Mika, Juutinen, Sari, Väliranta, Minna, Virtanen, Tarmo
• Other Authors: Ecosystems and Environment Research Programme, Environmental Change Research Unit (ECRU), Helsinki Institute of Sustainability Science (HELSUS), Tarmo Virtanen / Principal Investigator
• Format: Article in Journal/Newspaper
• Language: English
• Published: Taylor & Francis 2023
• Subjects: 11831 Plant biology; 222 Other engineering and technologies; Northern Finland
• Online Access: http://hdl.handle.net/10138/563984

Aboveground biomass (AGB) and leaf area index (LAI) are key variables of ecosystem processes and functioning. Knowledge is lacking on how well the seasonal patterns of ground vegetation AGB and LAI can be detected by satellite images in boreal ecosystems. We conducted field measurements between May and September during one growing season to investigate the seasonal development of ground vegetation AGB and LAI of seven plant functional types (PFTs) across seven vegetation types (VTs) within three peatland and forest study areas in northern Finland. We upscaled field-measured AGB and LAI with Sentinel-2 (S2) imagery by applying random forest (RF) regressions. Field-measured AGB peaked around the first week of August and, in most cases, one to two weeks later than LAI. Regarding PFTs, deciduous vascular plants had clear unimodal seasonal patterns, while the AGB and LAI of evergreen vegetation and mosses remained steady over the season. Remote sensing regression models explained 24.2–50.2% of the AGB (RMSE: 78.8–198.7 g m−2) and 48.5–56.1% of the LAI (RMSE: 0.207–0.497 m2 m−2) across sites. Peatland-dominant sites and VTs had a higher prediction accuracy. S2-predicted peak dates of AGB and LAI were one to three weeks earlier than the field-based ones. Our findings suggest that boreal ground vegetation seasonality varies among PFTs and VTs and that S2 time series data can be applied to monitor its spatiotemporal patterns, especially in treeless regions. Peer reviewed