FluoCat: A cable-suspended multi-sensor system for the vegetation SIF Cal/Val monitoring and estimation of effective sunlit surface fluorescence

Moncholi-Estornell, Adrián; Wittenberghe, Shari Van; Cendrero-Mateo, Maria Pilar; Alonso, Luis; Jiménez, Marcos; Urrego, Patricia; Arthur, Alasdair Mac; Moreno, José

doi:10.1016/j.jag.2022.103147

Cited by 4 publications

(5 citation statements)

References 49 publications

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“…A multisensor cable-suspended motorized platform (UAV Works, Valencia, Spain) called FluoCat (Figure 1B) was used to monitor the transects in the vegetation area. This platform was specifically designed to measure solar-induced fluorescence and provide additional data for the interpretation of fluorescence emission [36]. It is equipped with the Piccolo Doppio dual spectrometer system (GeoSciences, University of Edinburgh, Edinburgh, UK), a MAIAS2 multispectral camera with the ILS irradiance sensor module (SAL Engineering, Russi, Italy), and a TeAx Thermal Capture Fusion camera (TeAx Technology, Wilnsdorf, Germany), as shown in Figure 2.…”

Section: Multisensor Setup and Transect Sampling Protocolmentioning

confidence: 99%

“…The fibre optics were connected to two spectroradiometers, one covering the entire VNIR spectral range from 400 to 1180 nm, with a spectral resolution of ~1.5 nm, and another (called FLUO) covering the spectral range from 640 to 790 nm, with a spectral resolution of ~0.3 nm. The TOC surface reflectance was calculated for both radiometers using the signal captured from the upwelling and downwelling channels as described in [36]. The VNIR measurement was used to spectrally parameterise the vegetation, and the FLUO was used to retrieve the SIF.…”

Section: Multisensor Setup and Transect Sampling Protocolmentioning

confidence: 99%

“…Based on the MAIA's reflectance images and following the protocol described in [36], we classified each MAIA's pixel as soil, sunlit, or shaded vegetation fraction. To define the area where the Maia and Piccolo measurements overlapped, the Piccolo FOV was first projected onto the MAIA surface images.…”

Section: Sunlit Fractional Vegetation Cover Based On Maia Multispectr...mentioning

confidence: 99%

“…Based on the MAIA FVC total and a reflectance band 7 threshold, a second mask was created to discriminate between sunlit and shaded leaves, resulting in the reference MAIA FVC sunlit and MAIA FVC shaded. For further details see [36].…”

Section: Sunlit Fractional Vegetation Cover Based On Maia Multispectr...mentioning

confidence: 99%

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Enhancing Solar-Induced Fluorescence Interpretation: Quantifying Fractional Sunlit Vegetation Cover Using Linear Spectral Unmixing

Moncholi-Estornell,

Cendrero-Mateo,

Antala

et al. 2023

Remote Sensing

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Solar-induced chlorophyll fluorescence (SIF) is closely related to plant photosynthetic activity and has been used in different studies as a proxy for vegetation health status. However, in order to use SIF as a relevant indicator of plant physiological stress, it is necessary to accurately quantify the amount of light absorbed by the photosynthetic plant pigments, called the absorbed photosynthetically active radiation (APAR). The ratio between fluorescence emission and light absorption (i.e., SIF and APAR) is known as the fluorescence quantum efficiency (FQE). In this work, simultaneous measurements of SIF and reflected radiance were performed both at the leaf and canopy levels for Salvia farinacea and Datura stramonium plants. With the aim of disentangling the proportion of sunlit and shaded absorbed PAR, an ad hoc experimental setup was designed to provide a wide range of fraction vegetation cover (FVC) canopy settings. A linear spectral unmixing method was proposed to estimate the contribution of soil, sunlit, and shaded vegetation from the total reflectance spectrum measured at the canopy level. Later, the retrieved sunlit FVC (FVCsunlit) was used to estimate the (dominant) green APAR flux, and this was combined with the integral of the spectrally resolved fluorescence to calculate the FQE. The results of this study demonstrated that under no-stress conditions and independently of the FVC, similar FQE values were observed when SIF was properly normalised by the green APAR flux. The results obtained showed that the reflectance spectra retrieved using a linear unmixing method had a maximum RMSE of less than 0.03 along the spectrum. The FVCsunlit evaluation showed an RMSE of 14% with an R2 of 0.84. Moreover, the FQE values obtained at the top of the canopy (TOC) were found statistically comparable to the reference values at the leaf level. These results support further efforts to improve the interpretation of fluorescence based on field spectroscopy and the further upscaling to imaging spectroscopy at airborne and satellite levels.

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Section: Multisensor Setup and Transect Sampling Protocolmentioning

confidence: 99%

Section: Multisensor Setup and Transect Sampling Protocolmentioning

confidence: 99%

Section: Sunlit Fractional Vegetation Cover Based On Maia Multispectr...mentioning

confidence: 99%

Section: Sunlit Fractional Vegetation Cover Based On Maia Multispectr...mentioning

confidence: 99%

See 2 more Smart Citations

Enhancing Solar-Induced Fluorescence Interpretation: Quantifying Fractional Sunlit Vegetation Cover Using Linear Spectral Unmixing

Moncholi-Estornell,

Cendrero-Mateo,

Antala

et al. 2023

Remote Sensing

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“…Moreover, Tagliabue et al [27] showed that in mixed forests, spatial heterogeneity plays a crucial role in controlling the relationship between far-red SIF and GPP. Moncholi-Estornell et al [28] showed that normalizing the SIF signal emitted from the top of the canopy by the fractional cover of sunlit vegetation improves the estimation of the effective fluorescence flux, reducing the error from 36% to 18% (red fluorescence) and from 24% to 6% (far-red fluorescence), respectively. These studies show how an inaccurate characterization of the spatial heterogeneity of a fluorescence pixel can lead to errors in the estimation of the fluorescence signal.…”

Section: Introductionmentioning

confidence: 99%

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

Jantol,

Prikaziuk,

Celesti

et al. 2023

Remote Sensing

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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.

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Imaging Spatial Heterogeneity of Solar-Induced Chlorophyll Fluorescence (SIF) with Very High Spatial Resolution Drone Imagery

Bendig,

Siegmann,

Kneer

et al. 2023

IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium

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FluoCat: A cable-suspended multi-sensor system for the vegetation SIF Cal/Val monitoring and estimation of effective sunlit surface fluorescence

Cited by 4 publications

References 49 publications

Enhancing Solar-Induced Fluorescence Interpretation: Quantifying Fractional Sunlit Vegetation Cover Using Linear Spectral Unmixing

Enhancing Solar-Induced Fluorescence Interpretation: Quantifying Fractional Sunlit Vegetation Cover Using Linear Spectral Unmixing

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

Imaging Spatial Heterogeneity of Solar-Induced Chlorophyll Fluorescence (SIF) with Very High Spatial Resolution Drone Imagery

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