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| Home > Publications database > Analyzing Daily Estimation of Forest Gross Primary Production Based on Harmonized Landsat-8 and Sentinel-2 Product Using SCOPE Process-Based Model > print |
| 001 | 888056 | ||
| 005 | 20210130010802.0 | ||
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| 100 | 1 | _ | |a Raj, Rahul |0 0000-0002-7871-6629 |b 0 |e Corresponding author |
| 245 | _ | _ | |a Analyzing Daily Estimation of Forest Gross Primary Production Based on Harmonized Landsat-8 and Sentinel-2 Product Using SCOPE Process-Based Model |
| 260 | _ | _ | |a Basel |c 2020 |b MDPI |
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| 520 | _ | _ | |a 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. |
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| 700 | 1 | _ | |a Bayat, Bagher |0 P:(DE-Juel1)177038 |b 1 |
| 700 | 1 | _ | |a Lukeš, Petr |0 P:(DE-HGF)0 |b 2 |
| 700 | 1 | _ | |a Šigut, Ladislav |0 0000-0003-1951-4100 |b 3 |
| 700 | 1 | _ | |a Homolová, Lucie |0 0000-0001-7455-2834 |b 4 |
| 773 | _ | _ | |a 10.3390/rs12223773 |g Vol. 12, no. 22, p. 3773 - |0 PERI:(DE-600)2513863-7 |n 22 |p 3773 - |t Remote sensing |v 12 |y 2020 |x 2072-4292 |
| 856 | 4 | _ | |u https://juser.fz-juelich.de/record/888056/files/remotesensing-12-03773-v2.pdf |y OpenAccess |
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