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

•To understand SIF emission, surface heterogeneity needs to be quantified.•The cable-driven FluoCat is a robust instrument for SIF studies at field scale.•The sunlit vegetation fractional cover is a main structural driver of SIF.•Sensor synergies are key to provide deep understanding of the SIF spectrum. With the upcoming Fluorescence Explorer (FLEX) satellite mission from the European Space Agency, vegetation fluorescence (650–780 nm) will become available at 300x300 m resolution. Calibration and validation strategies of the fluorescence (F) signal remain however challenging, due to (1) the radiometric subtlety of the signal, (2) the multiple entangled drivers of the signal in space and in time, and (3) the need of a spatially representative acquisition, considering the previous two points. To tackle these challenges, the present work introduces the FluoCat, a cable-suspended system for the proximal sensing indirect measurement of solar-induced fluorescence, mounted across an agricultural field, covering a 60-m transect. On board the FluoCat are mounted: a high-spectral resolution Piccolo Doppio dual spectrometer system, a MAIA-S2 multispectral camera and a TeAx Thermal Capture Fusion camera, which can be triggered simultaneously according to a pre-set protocol. In order to test the system, two protocols were evaluated, a point-wise protocol, stopping at a pre-determined points to acquire the measurements, and the swiping protocol, acquiring measurements while in movement along the transect. Taking as a reference the values obtained with the swiping protocol, which captures the higher spatial variability, it was found that to achieve an averaged mean absolute percentage error (MAPE) below 2 % within between the spectral range of 500–800 nm, it is required a minimum of 6 sampling points to characterize the spectral variability of the 40-m melon crop transect. Further, by combining the fluorescence products of the Piccolo system normalized by PAR (NormF687, NormF760) and the fractional cover of sunlit vegetation (FVC Sunlit) obtained from the MAIA, we developed a multi-sensor product, i.e., the ‘sunlit green F’ for both retrieved bands. This synergy product improved the estimation of the effective surface fluorescence flux, with the leaf fluorescence emission as reference, by reducing the errors from 36 % to 18 % (band 687 nm); and from 24 % to 6 % (band 760 nm).