Hyplant-Derived Sun-Induced Fluorescence—A New Opportunity to Disentangle Complex Vegetation Signals from Diverse Vegetation Types

Abstract

Hyperspectral remote sensing (RS) provides unique possibilities to monitor peatland vegetation traits and their temporal dynamics at a fine spatial scale. Peatlands provide a vital contribution to ecosystem services by their massive carbon storage and wide heterogeneity. However, monitoring, understanding, and disentangling the diverse vegetation traits from a heterogeneous landscape using complex RS signal is challenging, due to its wide biodiversity and distinctive plant species composition. In this work, we aim to demonstrate, for the first time, the large heterogeneity of peatland vegetation traits using well-established vegetation indices (VIs) and Sun-Induced Fluorescence (SIF) for describing the spatial heterogeneity of the signals which may correspond to spatial diversity of biochemical and structural traits. SIF originates from the initial reactions in photosystems and is emitted at wavelengths between 650–780 nm, with the first peak at around 687 nm and the second peak around 760 nm. We used the first HyPlant airborne data set recorded over a heterogeneous peatland area and its surrounding ecosystems (i.e., forest, grassland) in Poland. We deployed a comparative analysis of SIF and VIs obtained from differently managed and natural vegetation ecosystems, as well as from diverse small-scale peatland plant communities. Furthermore, spatial relationships between SIF and VIs from large-scale vegetation ecosystems to small-scale peatland plant communities were examined. Apart from signal variations, we observed a positive correlation between SIF and greenness-sensitive VIs, whereas a negative correlation between SIF and a VI sensitive to photosynthesis was observed for large-scale vegetation ecosystems. In general, higher values of SIF were associated with higher biomass of vascular plants (associated with higher Leaf Area Index (LAI)). SIF signals, especially SIF760, were strongly associated with the functional diversity of the peatland vegetation. At the peatland area, higher values of SIF760 were associated with plant communities of high perennials, whereas, lower values of SIF760 indicated peatland patches dominated by Sphagnum. In general, SIF760 reflected the productivity gradient on the fen peatland, from Sphagnum-dominated patches with the lowest SIF and fAPAR values indicating lowest productivity to the Carex-dominated patches with the highest SIF and fAPAR values indicating highest productivity.

Analysis of designs used in monitoring crop growth based on remote sensing methods

“…Causal relationships are not only limited to simple plant-environment interactions but also involve relationships among leaf, canopy, and soil parameters (Yue et al, 2020) or between vegetation indices and grain yield . Finally, objectives in the field of RS can also focus on assessing signal quality, evaluating, for instance, the variability of remote sensing signals related to plant communities (Bandopadhyay et al, 2019), seasons (Zarco-Tejada et al, 2016), or vegetation type .…”

“…Within the category of limitations related to research design, different studies have reported the following problems: a limited availability of plant biophysical data during the growth season (Molijn et al, 2018), a low number of variables included in the modeling of plant growth (Sofonia et al, 2019), a lack of specificity for detection methods that rely on phenotypic changes following decreases in chlorophyll concentrations and plant water potential, which can be caused by manifold stress types (Mahlein, 2016), uncertainties and error propagation associated with data processing due to a general lack of systematic analysis and calibration (Bandopadhyay et al, 2019), a lack of atmospheric data corrections, particularly for aerosol optical thickness and terrain altitude corrections (Davidson et al, 2006), the use of chemical and chlorophyll meter methods, which do not provide real-time measurements on a regional or global scale (Yu et al, 2014), the limited specificity of wavelengths related to crop/species that have been optimized for RRDIs, which cannot be extrapolated to other crops under different conditions (Yu et al, 2014), and the practice of attributing large changes in spectral shape to a given variable when data acquisition is temporally sparse (Magney et al, 2017).…”

Review of Top-of-Canopy Sun-Induced Fluorescence (SIF) Studies from Ground, UAV, Airborne to Spaceborne Observations

“…However, no significant SIF differences were observed due to water stress. In one of the recent HyPlant-related studies, Bandopadhyay et al [122] examined the sensitivity of SIF and vegetation indices from various heterogeneous ecosystems (i.e., grassland, forest, and peatland) and over peatland plant communities (i.e., Calamagrostietum neglectae, Sphagno recurvi-Eriophoretum angustifolii, Typhetum latifoliae, Cladietum marisci, etc.) using HyPlant data.…”

Linking integrative plant physiology with agronomy to sustain future plant production

“…Stereovision and laser-scanning methods offer fascinating new opportunities for detecting and classifying plant architecture features such as branching angles, stem lengths, threedimensional leaf shapes and their distribution within canopies (Guo and Xu, 2016;Paulus et al, 2013;Wahabzada et al, 2015;Lottes et al, 2018). Such knowledge is crucially important for interpreting physiology-related vegetation signals such as sun-induced fluorescence from diverse vegetation types using airborne hyperspectral measurements and applying radiative transfer modeling (Bandopadhyay et al, 2019;Schickling et al, 2016). Using appropriate sensors, unmanned aerial systems (UAS) are increasingly used for carrying out such operations and determining phenotype characteristics (Roth et al, 2018).…”