Diurnal and Seasonal Solar Induced Chlorophyll Fluorescence and Photosynthesis in a Boreal Scots Pine Canopy

Solar induced chlorophyll fluorescence has been shown to be increasingly an useful proxy for the estimation of gross primary productivity (GPP), at a range of spatial scales. Here, we explore the seasonality in a continuous time series of canopy solar induced fluorescence (hereafter SiF) and its relation to canopy gross primary production (GPP), canopy light use efficiency (LUE), and direct estimates of leaf level photochemical efficiency in an evergreen canopy. SiF was calculated using infilling in two bands from the incoming and reflected radiance using a pair of Ocean Optics USB2000+ spectrometers operated in a dual field of view mode, sampling at a 30 min time step using custom written automated software, from early spring through until autumn in 2011. The optical system was mounted on a tower of 18 m height adjacent to an eddy covariance system, to observe a boreal forest ecosystem dominated by Scots pine. (Pinus sylvestris) A Walz MONITORING-PAM, multi fluorimeter system, was simultaneously mounted within the canopy adjacent to the footprint sampled by the optical system. Following correction of the SiF data for O2 and structural effects, SiF, SiF yield, LUE, the photochemicsl reflectance index (PRI), and the normalized difference vegetation index (NDVI) exhibited a seasonal pattern that followed GPP sampled by the eddy covariance system. Due to the complexities of solar azimuth and zenith angle (SZA) over the season on the SiF signal, correlations between SiF, SiF yield, GPP, and LUE were assessed on SZ

Diurnal and Seasonal Variations in Chlorophyll Fluorescence Associated with Photosynthesis at Leaf and Canopy Scales

There is a critical need for sensitive remote sensing approaches to monitor the parameters governing photosynthesis, at the temporal scales relevant to their natural dynamics. The photochemical reflectance index (PRI) and chlorophyll fluorescence (F) offer a strong potential for monitoring photosynthesis at local, regional, and global scales, however the relationships between photosynthesis and solar induced F (SIF) on diurnal and seasonal scales are not fully understood. This study examines how the fine spatial and temporal scale SIF observations relate to leaf level chlorophyll fluorescence metrics (i.e., PSII yield, YII and electron transport rate, ETR), canopy gross primary productivity (GPP), and PRI. The results contribute to enhancing the understanding of how SIF can be used to monitor canopy photosynthesis. This effort captured the seasonal and diurnal variation in GPP, reflectance, F, and SIF in the O2A (SIFA) and O2B (SIFB) atmospheric bands for corn (Zea mays L.) at a study site in Greenbelt, MD. Positive linear relationships of SIF to canopy GPP and to leaf ETR were documented, corroborating published reports. Our findings demonstrate that canopy SIF metrics are able to capture the dynamics in photosynthesis at both leaf and canopy levels, and show that the relationship between GPP and SIF metrics differs depending on the light conditions (i.e., above or below saturation level for photosynthesis). The sum of SIFA and SIFB (SIFA+B), as well as the SIFA+B yield, captured the dynamics in GPP and light use efficiency, suggesting the importance of including SIFB in monitoring photosynthetic function. Further efforts are required to determine if these findings will scale successfully to airborne and satellite levels, and to document the effects of data uncertainties on the scaling

Automatic Identification and Monitoring of Plant Diseases Using Unmanned Aerial Vehicles: A Review

Disease diagnosis is one of the major tasks for increasing food production in agriculture. Although precision agriculture (PA) takes less time and provides a more precise application of agricultural activities, the detection of disease using an Unmanned Aerial System (UAS) is a challenging task. Several Unmanned Aerial Vehicles (UAVs) and sensors have been used for this purpose. The UAVs’ platforms and their peripherals have their own limitations in accurately diagnosing plant diseases. Several types of image processing software are available for vignetting and orthorectification. The training and validation of datasets are important characteristics of data analysis. Currently, different algorithms and architectures of machine learning models are used to classify and detect plant diseases. These models help in image segmentation and feature extractions to interpret results. Researchers also use the values of vegetative indices, such as Normalized Difference Vegetative Index (NDVI), Crop Water Stress Index (CWSI), etc., acquired from different multispectral and hyperspectral sensors to fit into the statistical models to deliver results. There are still various drifts in the automatic detection of plant diseases as imaging sensors are limited by their own spectral bandwidth, resolution, background noise of the image, etc. The future of crop health monitoring using UAVs should include a gimble consisting of multiple sensors, large datasets for training and validation, the development of site-specific irradiance systems, and so on. This review briefly highlights the advantages of automatic detection of plant diseases to the growers

Assessing vegetation function with imaging spectroscopy

Healthy vegetation function supports diverse biological communities and ecosystem processes, and provides crops, forest products, forage, and countless other benefits. Vegetation function can be assessed by examining dynamic processes and by evaluating plant traits, which themselves are dynamic. Using both trait-based and process-based approaches, spectroscopy can assess vegetation function at multiple scales using a variety of sensors and platforms ranging from proximal to airborne and satellite measurements. Since spectroscopic data are defined by the instruments and platforms available, along with their corresponding spatial, temporal and spectral scales, and since these scales may not always match those of the function of interest, consideration of scale is a necessary focus. For a full understanding of vegetation processes, combined (multi-scale) sampling methods using empirical and theoretical approaches are required, along with improved informatics

Disentangling Changes in the Spectral Shape of Chlorophyll Fluorescence : Implications for Remote Sensing of Photosynthesis

Novel satellite measurements of solar-induced chlorophyll fluorescence (SIF) can improve our understanding of global photosynthesis; however, little is known about how to interpret the controls on its spectral variability. To address this, we disentangle simultaneous drivers of fluorescence spectra by coupling active and passive fluorescence measurements with photosynthesis. We show empirical and mechanistic evidence for where, why, and to what extent leaf fluorescence spectra change. Three distinct components explain more than 95% of the variance in leaf fluorescence spectra under both steady-state and changing illumination conditions. A single spectral shape of fluorescence explains 84% of the variance across a wide range of species. The magnitude of this shape responds to absorbed light and photosynthetic up/down regulation; meanwhile, chlorophyll concentration and nonphotochemical quenching control 9% and 3% of the remaining spectral variance, respectively. The spectral shape of fluorescence is remarkably stable where most current satellite retrievals occur (far-red, >740nm), and dynamic downregulation of photosynthesis reduces fluorescence magnitude similarly across the 670- to 850-nm range. We conduct an exploratory analysis of hourly red and far-red canopy SIF in soybean, which shows a subtle change in red:far-red fluorescence coincident with photosynthetic downregulation but is overshadowed by longer-term changes in canopy chlorophyll and structure. Based on our leaf and canopy analysis, caution should be taken when attributing large changes in the spectral shape of remotely sensed SIF to plant stress, particularly if data acquisition is temporally sparse. Ultimately, changes in SIF magnitude at wavelengths greater than 740 nm alone may prove sufficient for tracking photosynthetic dynamics. Plain Language Summary Satellite remote sensing provides a global picture of photosynthetic activity-allowing us to see when, where, and how much CO2 plants are assimilating. To do this, satellites measure a small emission of energy from the plants called chlorophyll fluorescence. However, this measurement is typically made across a narrow wavelength range, while the emission spectrum (650-850 nm) is quite dynamic. We show where, why, and to what extent leaf fluorescence spectra change across a diverse range of species and conditions, ultimately informing canopy remote sensing measurements. Results suggest that wavelengths currently used by satellites are stable enough to track the downregulation of photosynthesis resulting from stress, while spectral shape changes respond more strongly to dynamics in canopy structure and chlorophyll concentration.Peer reviewe

Passive direct measurement of sun-induced chlorophyll fluorescence spectrum from in vivo leaves

The fluorescence of chlorophyll in vegetation is a weak signal emitted between 650 and 850 nm that is mixed with the much more intense light reflected by the leaf, which is why active methods are commonly used (through the additional contribution of controlled artificial light) or using indirect measurements instead. So, the measurement is provided just in relative units in the first case, or the accuracy of the estimate in the second case is uncertain without proper direct validation. The Thesis presents a new device, called FluoWat, for passive measurement that allows direct measurement of the fluorescence emission of leaves in vivo under natural conditions in the field with sunlight. And it is part of the activities supporting the preparation of ESA’s FLEX mission for the global monitoring of vegetation fluorescence. The device consists of a small dark chamber implemented as a clip, so that the leaf can be housed inside without damaging it, with an opening to illuminate the sample by pointing at the sun, and a sliding filter holder with a low-pass filter that blocks sunlight in the same spectral range as fluorescence is emitted while allowing the excitation light to pass through, then a spectroradiometer connected to the clip measures the fluorescence spectrum without interference from sunlight. In addition, it is possible to measure the reflectance and transmittance factors of the leaf, which allows determining the absorptance, necessary to determine the photosynthetically active radiation (PAR) that has been absorbed (APAR). An essential parameter to properly interpret the fluorescence signal in relation to photosynthesis. Similarly, the reflectance and transmittance spectra in the visible range make it possible to determine the degree of photoprotection of the leaf and/or its chlorophyll content. A sensitivity analysis of different factors likely to affect the measurement has been carried out, such as the residual light that passes through the filter, or the effect of transients on fluorescence emission, among others. Processing methods have been developed to mitigate their effects on the fluorescence measurement, increasing the accuracy of the results. Finally, a series of experiments are presented in which the system is put to the test and that illustrate how, with the measurements provided by this new device, a better understanding of the dynamics of fluorescence emission while the vegetation adapts to different illumination changes, levels of stress and changing environmental conditions

Evaluating solar-induced fluorescence across spatial and temporal scales to monitor primary productivity

Solar-induced chlorophyll fluorescence (SIF) has been widely cited in carbon cycling studies as a proxy for photosynthesis, and SIF data are commonly incorporated into terrestrial primary productivity models. Though satellite-based SIF products show close relationships with gross primary productivity (GPP), this is not universally true at intermediate scales. A meta-analysis of the tower-based and airborne SIF literature revealed that mean SIF retrievals from unstressed vegetation span three orders of magnitude. While reporting on spectrometer calibration procedures, hardware characterizations, and associated corrections is inconsistent, laboratory and field experiments show that these factors may contribute to significant uncertainty in SIF retrievals. Additionally, there remain ongoing questions regarding the interpretation of SIF data made across spatial scales and the link between satellite SIF retrievals and primary productivity on the ground. Chlorophyll fluorescence originates from dynamic energy partitioning at the leaf level and does not exhibit a uniformly linear relationship with photosynthesis at finer scales. As a standalone metric, SIF measured at the tower scale was not found to track changes in carbon assimilation following stomatal closure induced in deciduous woody tree branches. This lack of relationship may be explained by alternative energy partitioning pathways, such as thermal energy dissipation mediated by xanthophyll cycle pigments; the activity of these pigments can be tracked using the photochemical reflectance index (PRI). Gradual, phenological changes in energy partitioning are observed as changes in the slope of the SIF-PRI relationship over the course of a season. Along with high frequency effects such as wind-mediated changes in leaf orientation and reflectance, and rapid changes in sky condition due to clouds, PRI offers crucial insights needed to link SIF to leaf physiology. While SIF offers tremendous promise for improving the characterization of terrestrial carbon exchange, and a fuller understanding of the boundaries on its utility and interpretation as a biophysical phenomenon will help to create more reliable models of global productivity

The 2013 FLEX—US Airborne Campaign at the Parker Tract Loblolly Pine Plantation in North Carolina, USA82

The first European Space Agency (ESA) and NASA collaboration in an airborne campaign to support ESA’s FLuorescence EXplorer (FLEX) mission was conducted in North Carolina, USA during September–October 2013 (FLEX-US 2013) at the Parker Tract Loblolly Pine (LP) Plantation (Plymouth, NC, USA). This campaign combined two unique airborne instrument packages to obtain simultaneous observations of solar-induced fluorescence (SIF), LiDAR-based canopy structural information, visible through shortwave infrared (VSWIR) reflectance spectra, and surface temperature, to advance vegetation studies of carbon cycle dynamics and ecosystem health. We obtained statistically significant results for fluorescence, canopy temperature, and tower fluxes from data collected at four times of day over two consecutive autumn days across an age class chronosequence. Both the red fluorescence (F685) and far-red fluorescence (F740) radiances had highest values at mid-day, but their fluorescence yields exhibited different diurnal responses across LP age classes. The diurnal trends for F685 varied with forest canopy temperature difference (canopy minus air), having a stronger daily amplitude change for young vs. old canopies. The Photochemical Reflectance Index (PRI) was positively correlated with this temperature variable over the diurnal cycle. Tower measurements from mature loblolly stand showed the red/far-red fluorescence ratio was linearly related to canopy light use efficiency (LUE) over the diurnal cycle, but performed even better for the combined morning/afternoon (without midday) observations. This study demonstrates the importance of diurnal observations for interpretation of fluorescence dynamics, the need for red fluorescence to understand canopy physiological processes, and the benefits of combining fluorescence, reflectance, and structure information to clarify canopy function versus structure characteristics for a coniferous fores