Abstract
Vegetation top-of-canopy reflectance contains valuable information for estimating vegetation biochemical and structural properties, and canopy photosynthesis (gross primary production (GPP)). Satellite images allow studying temporal variations in vegetation properties and photosynthesis. The National Aeronautics and Space Administration (NASA) has produced a harmonized Landsat-8 and Sentinel-2 (HLS) data set to improve temporal coverage. In this study, we aimed to explore the potential and investigate the information content of the HLS data set using the Soil Canopy Observation of Photosynthesis and Energy fluxes (SCOPE) model to retrieve the temporal variations in vegetation properties, followed by the GPP simulations during the 2016 growing season of an evergreen Norway spruce dominated forest stand. We optimized the optical radiative transfer routine of the SCOPE model to retrieve vegetation properties such as leaf area index and leaf chlorophyll, water, and dry matter contents. The results indicated percentage differences less than 30% between the retrieved and measured vegetation properties. Additionally, we compared the retrievals from HLS data with those from hyperspectral airborne data for the same site, showing that HLS data preserve a considerable amount of information about the vegetation properties. Time series of vegetation properties, retrieved from HLS data, served as the SCOPE inputs for the time series of GPP simulations. The SCOPE model reproduced the temporal cycle of local flux tower measurements of GPP, as indicated by the high Nash–Sutcliffe efficiency value (>0.5). However, GPP simulations did not significantly change when we ran the SCOPE model with constant vegetation properties during the growing season. This might be attributed to the low variability in the vegetation properties of the evergreen forest stand within a vegetation season. We further observed that the temporal variation in maximum carboxylation capacity had a pronounced effect on GPP simulations. We focused on an evergreen forest stand. Further studies should investigate the potential of HLS data across different forest types, such as deciduous stand.
Highlights
Vegetation is an essential component of the terrestrial ecosystems that interacts with the atmosphere through the carbon and water cycles
The present analysis showed the potential of the newly produced multispectral harmonized Landsat-8 and Sentinel-2 (HLS) data to retrieve time series of vegetation properties, which were further used as inputs to the Soil Canopy Observation, Photochemistry and Energy fluxes (SCOPE) model to simulate the time series of gross primary production GPPSIM
The study led to the following conclusions: 1. HLS data can provide the dense time series of surface reflectance at the desired locations because it combines data from two existing satellites: Landsat-8 and Sentinel-2
Summary
Vegetation is an essential component of the terrestrial ecosystems that interacts with the atmosphere through the carbon and water cycles. Data-driven approaches establish an empirical model between ground measurements of GPP (e.g., GPP partitioned from flux tower measurements of net ecosystem exchange [6]) and other explanatory variables that are commonly derived from remote sensing data (satellite images). Environmental stressors may affect the photosynthesis rate even during the early stages without the appearance of any significant visual symptoms at the leaf (e.g., leaf color change) or canopy levels (e.g., leaf area index change). This creates challenges to relating the maximum photosynthesis capacity to maximum vegetation greenness. The majority of VIs are derived at the leaf level (e.g., PRI as a proxy for the leaf conversion efficiency of absorbed light); further upscaling from the leaf to the canopy level is not always feasible due to the additional confounding effects of the canopy structure on VIs
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