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

Authors :
Jantol, Nela
Prikaziuk, Egor
Celesti, Marco
Hernandez-Sequeira, Itza
Tomelleri, Enrico
Pacheco-Labrador, Javier
Wittenberghe, Shari Van
Pla, Filiberto
Bandopadhyay, Subhajit
Koren, Gerbrand
Siegmann, Bastian
Legović, Tarzan
Kutnjak, Hrvoje
Cendrero-Mateo, M. Pilar
Jantol, Nela
Prikaziuk, Egor
Celesti, Marco
Hernandez-Sequeira, Itza
Tomelleri, Enrico
Pacheco-Labrador, Javier
Wittenberghe, Shari Van
Pla, Filiberto
Bandopadhyay, Subhajit
Koren, Gerbrand
Siegmann, Bastian
Legović, Tarzan
Kutnjak, Hrvoje
Cendrero-Mateo, M. Pilar
Source :
Remote Sensing vol.15 (2023) nr.19 [ISSN 2072-4292]
Publication Year :
2023

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 spat

Details

Database :
OAIster
Journal :
Remote Sensing vol.15 (2023) nr.19 [ISSN 2072-4292]
Notes :
DOI: 10.3390/rs15194835, English
Publication Type :
Electronic Resource
Accession number :
edsoai.on1445831614
Document Type :
Electronic Resource