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| Hauptseite > Publikationsdatenbank > Using Sentinel-2-Based Metrics to Characterize the Spatial Heterogeneity of FLEX Sun-Induced Chlorophyll Fluorescence on Sub-Pixel Scale > print |
| Hauptseite > Publikationsdatenbank > Using Sentinel-2-Based Metrics to Characterize the Spatial Heterogeneity of FLEX Sun-Induced Chlorophyll Fluorescence on Sub-Pixel Scale > print |
| 001 | 1017059 | ||
| 005 | 20240223132826.0 | ||
| 024 | 7 | _ | |a 10.3390/rs15194835 |2 doi |
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| 037 | _ | _ | |a FZJ-2023-03901 |
| 041 | _ | _ | |a English |
| 082 | _ | _ | |a 620 |
| 100 | 1 | _ | |a Jantol, Nela |0 P:(DE-HGF)0 |b 0 |e Corresponding author |
| 245 | _ | _ | |a Using Sentinel-2-Based Metrics to Characterize the Spatial Heterogeneity of FLEX Sun-Induced Chlorophyll Fluorescence on Sub-Pixel Scale |
| 260 | _ | _ | |a Basel |c 2023 |b MDPI |
| 336 | 7 | _ | |a article |2 DRIVER |
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| 336 | 7 | _ | |a Journal Article |b journal |m journal |0 PUB:(DE-HGF)16 |s 1700147791_13604 |2 PUB:(DE-HGF) |
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| 520 | _ | _ | |a Current and upcoming Sun‑Induced chlorophyll Fluorescence (SIF) satellite products(e.g., GOME, TROPOMI, OCO, FLEX) have medium‑to‑coarse spatial resolutions (i.e., 0.3–80 km)and integrate radiances from different sources into a single ground surface unit (i.e., pixel). However,intrapixel heterogeneity, i.e., different soil and vegetation fractional cover and/or different chlorophyllcontent or vegetation structure in a fluorescence pixel, increases the challenge in retrievingand quantifying SIF. High spatial resolution Sentinel‑2 (S2) data (20 m) can be used to better characterizethe intrapixel heterogeneity of SIF and potentially extend the application of satellite‑derivedSIF to heterogeneous areas. In the context of the COST Action Optical synergies for spatiotemporalSENsing of Scalable ECOphysiological traits (SENSECO), in which this study was conducted, weproposed direct (i.e., spatial heterogeneity coefficient, standard deviation, normalized entropy, ensembledecision trees) and patch mosaic (i.e., local Moran’s I) approaches to characterize the spatialheterogeneity of SIF collected at 760 and 687 nm (SIF760 and SIF687, respectively) and to correlateit with the spatial heterogeneity of selected S2 derivatives. We used HyPlant airborne imagery acquiredover an agricultural area in Braccagni (Italy) to emulate S2‑like top‑of‑the‑canopy reflectanceand SIF imagery at different spatial resolutions (i.e., 300, 20, and 5 m). The ensemble decision treesmethod characterized FLEX intrapixel heterogeneity best (R2 > 0.9 for all predictors with respect toSIF760 and SIF687). Nevertheless, the standard deviation and spatial heterogeneity coefficient using kmeansclustering scene classification also provided acceptable results. In particular, the near‑infraredreflectance of terrestrial vegetation (NIRv) index accounted for most of the spatial heterogeneity ofSIF760 in all applied methods (R2 = 0.76 with the standard deviation method; R2 = 0.63 with the spatialheterogeneity coefficient method using a scene classification map with 15 classes). The models developed for SIF687 did not perform as well as those for SIF760, possibly due to the uncertaintiesin fluorescence retrieval at 687 nm and the low signal‑to‑noise ratio in the red spectral region. Ourstudy shows the potential of the proposed methods to be implemented as part of the FLEX groundsegment processing chain to quantify the intrapixel heterogeneity of a FLEX pixel and/or as a qualityflag to determine the reliability of the retrieved fluorescence. |
| 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 |
| 588 | _ | _ | |a Dataset connected to CrossRef, Journals: juser.fz-juelich.de |
| 700 | 1 | _ | |a Prikaziuk, Egor |0 P:(DE-HGF)0 |b 1 |e Corresponding author |
| 700 | 1 | _ | |a Celesti, Marco |0 0000-0001-7249-7106 |b 2 |
| 700 | 1 | _ | |a Hernandez-Sequeira, Itza |0 0000-0002-1623-9337 |b 3 |
| 700 | 1 | _ | |a Tomelleri, Enrico |0 0000-0001-6546-6459 |b 4 |
| 700 | 1 | _ | |a Pacheco-Labrador, Javier |0 0000-0003-3401-7081 |b 5 |
| 700 | 1 | _ | |a Van Wittenberghe, Shari |0 0000-0002-5699-0352 |b 6 |
| 700 | 1 | _ | |a Pla, Filiberto |0 0000-0003-0054-3489 |b 7 |
| 700 | 1 | _ | |a Bandopadhyay, Subhajit |0 0000-0002-8657-3488 |b 8 |
| 700 | 1 | _ | |a Koren, Gerbrand |0 0000-0002-2275-0713 |b 9 |
| 700 | 1 | _ | |a Siegmann, Bastian |0 P:(DE-Juel1)172711 |b 10 |
| 700 | 1 | _ | |a Legović, Tarzan |0 P:(DE-HGF)0 |b 11 |
| 700 | 1 | _ | |a Kutnjak, Hrvoje |0 P:(DE-HGF)0 |b 12 |
| 700 | 1 | _ | |a Cendrero-Mateo, M. Pilar |0 0000-0001-5887-7890 |b 13 |
| 773 | _ | _ | |a 10.3390/rs15194835 |g Vol. 15, no. 19, p. 4835 - |0 PERI:(DE-600)2513863-7 |n 19 |p 4835 - |t Remote sensing |v 15 |y 2023 |x 2072-4292 |
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