FluoSpec 2—An Automated Field Spectroscopy System to Monitor Canopy Solar-Induced Fluorescence

Abstract

Accurate estimation of terrestrial photosynthesis has broad scientific and societal impacts. Measurements of photosynthesis can be used to assess plant health, quantify crop yield, and determine the largest CO2 flux in the carbon cycle. Long-term and continuous monitoring of vegetation optical properties can provide valuable information about plant physiology. Recent developments of the remote sensing of solar-induced chlorophyll fluorescence (SIF) and vegetation spectroscopy have shown promising results in using this information to quantify plant photosynthetic activities and stresses at the ecosystem scale. However, there are few automated systems that allow for unattended observations over months to years. Here we present FluoSpec 2, an automated system for collecting irradiance and canopy radiance that has been deployed in various ecosystems in the past years. The instrument design, calibration, and tests are recorded in detail. We discuss the future directions of this field spectroscopy system. A network of SIF sensors, FluoNet, is established to measure the diurnal and seasonal variations of SIF in several ecosystems. Automated systems such as FluoSpec 2 can provide unique information on ecosystem functioning and provide important support to the satellite remote sensing of canopy photosynthesis.

Varying Contributions of Drivers to the Relationship Between Canopy Photosynthesis and Far‐Red Sun‐Induced Fluorescence for Two Maize Sites at Different Temporal Scales

“…The relative importance analysis conducted in this study confirmed that PAR in is one of the primary drivers to the strong positive GPP:SIF relationship and supported our hypothesis that the drivers vary at different temporal scales. At a time scale with small variations in canopy structure (indicated by fPAR) and physiology (indicated by LUE and F 760,y ), for example, the VT and R1–R3 stages of the maize fields in this study, PAR in has a dominant influence on the GPP:F 760 relationship, which has been well acknowledged in the SIF community (Goulas et al, ; Miao et al, ; K. Yang, Ryu, et al, ; X. Yang, Shi, et al, ). The LUE:F 760,y relationship exhibited either a negative pattern or no clear pattern (Figure and Table ), which explained some of the deviations from a strong linear pattern in the GPP:F 760 relationship.…”

“…The GPP:SIF relationship was reported to be consistently positive in previous studies, and our current study at the two maize sites confirmed the positive pattern. The LUE:SIF y relationship was different from the GPP:SIF relationship and exhibited various patterns from different studies (Damm et al, ; Miao et al, ; Wieneke et al, ; K. Yang, Ryu, et al, ; X. Yang, Shi, et al, ; Yang et al, ). Although the mechanism of the various LUE:SIF y relationship is still in debate, our current study indicated that the LUE:SIF y relationship varied at different temporal scales and at different growth crop stages.…”

“…The actual irradiance and radiance signals are obtained by subtracting the dark signals from the original measurements. Details of FluoSpec2 system have been published previously (Miao et al, ; K. Yang, Ryu, et al, ; X. Yang, Shi, et al, ).…”

“…As a result, the GPP:SIF relationship is determined by the variations in each variable and the contributions of these variables to that relationship. Previous studies found that APAR is the primary driver that leads to the linear GPP:SIF relationship (Rossini et al, ; K. Yang, Ryu, et al, ; X. Yang, Shi, et al, ; Yang et al, ; Zhang et al, ). Some studies also suggested that LUE and SIF y contribute to the relationship and a diverse LUE:SIF y relationship implies a potentially diverse GPP:SIF relationship (Badgley et al, ; Miao et al, ; Wieneke et al, ).…”

“…fPAR, which is determined by canopy structure, is relatively stable at the instantaneous scale but varies significantly between individual growth stages (Chen, ; Ogutu & Dash, ). LUE and SIF y could respond rapidly to the change in PAR in while also varying significantly with the physiological change at the seasonal scale (Kergoat et al, ; Miao et al, ; Turner et al, ; K. Yang, Ryu, et al, ; X. Yang, Shi, et al, ). In consequence, we would expect that contributions from these variables to GPP and SIF could vary at different time scales.…”