Using Sentinel-2-Based Metrics to Characterize the Spatial Heterogeneity of FLEX Sun-Induced Chlorophyll Fluorescence on Sub-Pixel Scale

Jantol, Nela; Prikaziuk, Egor; Celesti, Marco; Hernandez-Sequeira, Itza; Tomelleri, Enrico; Pacheco-Labrador, Javier; Van Wittenberghe, Shari; Pla, Filiberto; Bandopadhyay, Subhajit; Koren, Gerbrand; Siegmann, Bastian; Legović, Tarzan; Kutnjak, Hrvoje; Cendrero-Mateo, M. Pilar (2023) Using Sentinel-2-Based Metrics to Characterize the Spatial Heterogeneity of FLEX Sun-Induced Chlorophyll Fluorescence on Sub-Pixel Scale. Remote Sensing, 15 (19). ISSN 2072-4292

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Abstract

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 chlorophyll content or vegetation structure in a fluorescence pixel, increases the challenge in retrieving and quantifying SIF. High spatial resolution Sentinel-2 (S2) data (20 m) can be used to better characterize the intrapixel heterogeneity of SIF and potentially extend the application of satellite-derived SIF to heterogeneous areas. In the context of the COST Action Optical synergies for spatiotemporal SENsing of Scalable ECOphysiological traits (SENSECO), in which this study was conducted, we proposed direct (i.e., spatial heterogeneity coefficient, standard deviation, normalized entropy, ensemble decision trees) and patch mosaic (i.e., local Moran’s I) approaches to characterize the spatial heterogeneity of SIF collected at 760 and 687 nm (SIF760 and SIF687, respectively) and to correlate it with the spatial heterogeneity of selected S2 derivatives. We used HyPlant airborne imagery acquired over an agricultural area in Braccagni (Italy) to emulate S2-like top-of-the-canopy reflectance and SIF imagery at different spatial resolutions (i.e., 300, 20, and 5 m). The ensemble decision trees method characterized FLEX intrapixel heterogeneity best (R2 > 0.9 for all predictors with respect to SIF760 and SIF687). Nevertheless, the standard deviation and spatial heterogeneity coefficient using k-means clustering scene classification also provided acceptable results. In particular, the near-infrared reflectance of terrestrial vegetation (NIRv) index accounted for most of the spatial heterogeneity of SIF760 in all applied methods (R2 = 0.76 with the standard deviation method; R2 = 0.63 with the spatial heterogeneity 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 uncertainties in fluorescence retrieval at 687 nm and the low signal-to-noise ratio in the red spectral region. Our study shows the potential of the proposed methods to be implemented as part of the FLEX ground segment processing chain to quantify the intrapixel heterogeneity of a FLEX pixel and/or as a quality flag to determine the reliability of the retrieved fluorescence.

Item Type:	Article
Uncontrolled Keywords:	spatial heterogeneity; vegetation indices; biophysical traits; SIF; hyperspectral sensor; Sentinel-2; FLEX; Braccagni
Subjects:	NATURAL SCIENCES BIOTECHNICAL SCIENCES INTERDISCIPLINARY AREAS OF KNOWLEDGE
Divisions:	Division for Marine and Enviromental Research
Projects:	Project title Project leader Project code Project type Optical synergies for spatiotemporal sensing of scalable ecophysiological traints (SENSECO)- Davorka Hackenberger Kutuzović; Željka Lončarić CA17134 COST Novi koncept praćenja terena koji se osniva na Orlu, CLC+ Tarzan Legović DOK-2020-01-9841 HRZZ
Depositing User:	Ivana Vuglec
Date Deposited:	13 Apr 2026 11:00
URI:	http://fulir.irb.hr/id/eprint/11665
DOI:	10.3390/rs15194835

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