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| 001 | 1041486 | ||
| 005 | 20250804202236.0 | ||
| 024 | 7 | _ | |a 10.1016/j.rse.2025.114739 |2 doi |
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| 100 | 1 | _ | |a Krämer, Julie |0 P:(DE-HGF)0 |b 0 |e Corresponding author |
| 245 | _ | _ | |a Downscaling the full-spectrum solar-induced fluorescence emission signal of a mixed crop canopy to the photosystem level using the hybrid approach |
| 260 | _ | _ | |a Amsterdam [u.a.] |c 2025 |b Elsevier Science |
| 336 | 7 | _ | |a article |2 DRIVER |
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| 520 | _ | _ | |a Remote sensing of hyperspectral vegetation reflectance and solar-induced chlorophyll fluorescence (SIF) isessential for evaluating crop functionality and photosynthetic performance. While primarily applied in monocultures,these tools show promise in diverse cropping systems, enhancing ecological intensification. Plant-plantinteractions in such systems can influence key physiological processes, such as photosynthesis, making SIF avaluable tool for evaluating how crop diversity affects photosynthetic function and productivity. However,detecting SIF in diverse stands remains challenging due to uncertainties in light re-absorption and scattering. Toaddress these challenges, we propose a hybrid model inversion framework that combines canopy observationswith physical modeling to derive leaf biochemical, canopy structural variables, and SIF spectra at leaf andphotosystem levels. This approach employs a machine learning retrieval algorithm (MLRA), trained on syntheticspectra from radiative transfer model (RTM) simulations, to quantify re-absorption and scattering effects. Usingthe SpecFit retrieval algorithm, the temporal evolution of full-spectrum SIF at the canopy level can be derived. Todownscale SIF to the photosystem level and retrieve its quantum yield, we corrected the canopy SIF spectrum forre-absorption and scattering effects calculated from TOC reflectance. Spectral measurements were gathered fromfield experiments conducted over three years, covering various growth stages of cereal and legume monocropsand their mixture. Our method accurately predicts important leaf biochemical and canopy structural variables,such as leaf area (LAI, R2 = 0.75) and leaf chlorophyll content (LCC, R2 = 0.91), and shows a general highretrieval performance for light absorption (fAPARChl, R2 = 0.99 for the internal model validation). We confirmedthe reliability of our method in modeling re-absorption and scattering processes by comparing canopy SIFdownscaled to the leaf level with independent leaf-level SIF measurements. While the results show a goodprediction accuracy in terms of fluorescence magnitude at the leaf level, we did not find a strong agreement ofcorresponding leaf and canopy measurements at the single plot level. |
| 536 | _ | _ | |a 2173 - Agro-biogeosystems: controls, feedbacks and impact (POF4-217) |0 G:(DE-HGF)POF4-2173 |c POF4-217 |f POF IV |x 0 |
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| 700 | 1 | _ | |a Siegmann, Bastian |0 P:(DE-Juel1)172711 |b 1 |
| 700 | 1 | _ | |a Castro, Antony Oswaldo |0 P:(DE-Juel1)190393 |b 2 |
| 700 | 1 | _ | |a Muller, Onno |0 P:(DE-Juel1)161185 |b 3 |
| 700 | 1 | _ | |a Pude, Ralf |0 P:(DE-HGF)0 |b 4 |
| 700 | 1 | _ | |a Döring, Thomas |0 P:(DE-HGF)0 |b 5 |
| 700 | 1 | _ | |a Rascher, Uwe |0 P:(DE-Juel1)129388 |b 6 |
| 773 | _ | _ | |a 10.1016/j.rse.2025.114739 |g Vol. 324, p. 114739 - |0 PERI:(DE-600)1498713-2 |p 114739 - |t Remote sensing of environment |v 324 |y 2025 |x 0034-4257 |
| 856 | 4 | _ | |u https://juser.fz-juelich.de/record/1041486/files/1-s2.0-S0034425725001439-main.pdf |y OpenAccess |
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