Can Vegetation Indices Serve as Proxies for Potential Sun-Induced Fluorescence (SIF)? A Fuzzy Simulation Approach on Airborne Imaging Spectroscopy Data

Bandopadhyay, Subhajit; Rastogi, Anshu; Cogliati, Sergio; Rascher, Uwe; Gąbka, Maciej; Juszczak, Radosław

doi:10.3390/rs13132545

Cited by 11 publications

(3 citation statements)

References 86 publications

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“…Whereas the differences in VIs and LAI between CL and CR during the summer were observed to be significant, indicating the importance of hydrological regimes and fluctuations of water table depth for peatland vegetation (Supplementary Table S2) [18]. The observation of various VIs is considered an efficient method for detecting the changes in the plant physiology at the ecosystem level, but VIs have their limitations; therefore, other indices such as SIF have been developed that can better present the physiological status of the vegetations [15,36]. In this study, NDVI, EVI, and NIRv showed a seasonal pattern for both the studied sites and was observed to be highly correlated with modeled LAI, but the correlation with measured LAI was observed to be poor (Figure 5), which can be again explained through the non-consideration of mosses and part of the litter interference in measured LAI.…”

Section: Discussionmentioning

confidence: 99%

Exploring the Potential of SCOPE Model for Detection of Leaf Area Index and Sun-Induced Fluorescence of Peatland Canopy

et al. 2022

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The study of peatland is challenging due to the water saturation and evergreen mixed vegetation that ranges from simple forms of plants such as mosses to higher forms of plants such as cranberries, grasses, etc. The changing water level through the growing season makes the peatland vegetation very dynamic. In this work, we have used ground-level remote-sensing signals to understand the dynamic nature of peatland vegetation. We have also estimated the leaf area index (LAI) and Sun-Induced fluorescence (SIF) through the Soil Canopy Observation of Photosynthesis and Energy fluxes (SCOPE) model. The estimated LAI and SIF were compared with the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Near-Infrared Reflectance of vegetation (NIRv), and measured SIF. The modeled LAI was observed to be significantly correlated with NDVI, EVI, and NIRv, whereas a good correlation was observed between measured and modeled SIF. Along with showing the dynamic behavior of peatland vegetation, the study indicates that SCOPE in its inverted form can be used to estimate reflectance-based LAI for peatland, which can be more reliable to present biomass and productivity of peatland ecosystem in comparison to transmittance-based LAI measurement for such ecosystem. The good correlation between measured and modeled SIF at 760 nm indicates that a reliable SIF value can be estimated through the SCOPE model for a complex ecosystem such as peatland, which can be very helpful in the absence of high-resolution hyperspectral data (usually used for SIF measurements).

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Section: Discussionmentioning

confidence: 99%

Exploring the Potential of SCOPE Model for Detection of Leaf Area Index and Sun-Induced Fluorescence of Peatland Canopy

et al. 2022

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“…Although aerial thermal data has been previously applied in peatland research (Kopeć et al, 2016), further research into the potential use of airborne thermal data in assessing peatland vegetation conditions is recommended. To date, there have been few works in Poland using spectral data in peatland monitoring (Bandopadhyay et al, 2021(Bandopadhyay et al, , 2019. However, none has attempted to collate palaeoecological, dendrochronological, and remote sensing data.…”

Section: Current Condition Of the Peatland Vs Remote Sensing And Dend...mentioning

confidence: 99%

Integrating palaeoecology and dendrochronology to explore the impact of climate and forest management on a peatland in Scots pine monoculture

Bąk,

Lamentowicz,

Kołaczek

et al. 2024

Preprint

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Abstract. Assessing the scale, rate and consequences of climate change, manifested primarily by rising average air temperatures and altered precipitation regimes, is a critical challenge in contemporary scientific research. These changes are accompanied by various anomalies and extreme events that negatively impact ecosystems worldwide. Monoculture forests, including Scots pine (Pinus sylvestris L.) monocultures, are particularly vulnerable to these changes due to their homogeneous structure and simplified ecosystem linkages compared to mixed forests, making them more sensitive to extreme events such as insect outbreaks, droughts, fires and strong winds. In the context of global warming, forest fires are becoming extremely dangerous, and the risk of their occurrence increases as average temperatures rise. The situation becomes even more dramatic when fire enters areas of peatlands, as these ecosystems effectively withdraw carbon from the rapid carbon cycle and store it for up to thousands of years. Consequently, peatlands become emitters of carbon dioxide into the atmosphere. In this study, we aim to trace the last 300 years of historical development of a peatland situated in a Scots pine monoculture. Our focus is on the Okoniny peatland located within the Tuchola Pinewoods in northern Poland, one of the country's largest forest complexes. We delved into the phase when the peatland's surroundings transitioned from a mixed forest to a pine monoculture and investigated the impact of changes in forest management on the peatland vegetation and hydrology. Our reconstructions are based on a multi-proxy approach using: pollen, plant macrofossils, micro- and macrocharcoal and testate amoebae. We combine the peatland palaeoecological record with the dendrochronology of Pinus sylvestris to compare the response of these two archives. Our results show that a change in forest management and progressive climate warming affected the development of the peatland. We note an increase in acidity over the analyzed period and a decrease in the water table over the last few decades that led to the lake-peatland transition. These changes progressed with the strongest agricultural activity in the area in the 19th century. However, the 20th century was a period of continuous decline in agriculture and an increase in the dominance of Scots pine in the landscape as the effect of afforestation. Dendroclimatic data indicate a negative effect of temperature on Scots pine and pressure from summer rainfall deficiency. Additional remote sensing analysis, using hyperspectral, LiDAR and thermal airborne data, provided information about the current condition of the peatland vegetation. With the application of spectral indices and the analysis of land surface temperature, spatial variations in peatland drying have been identified. Considering the context of forest management and the protection of valuable ecosystems in monocultural forests, the conclusions are relevant for peatland and forest ecology, palaeoecology and forestry.

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“…Membership values computed from VI 20 or BT 20 variables were combined using the fuzzy overlay operator GAMMA since it has been reported to offer a balance between over and underestimation of fluorescence radiance [80,81]. µ_γ (x) = 〖[µ_SUM (x)]〗^γ * 〖[µ_PRODUCT (x)]〗^(1 − γ) Then a linear model was fit using the integrated membership values as a predictor of SIF λ : (F_λ) = b_0 + b_1 µ_γ (x) Fuzzy modelling was applied to 5 and 20 m spatial resolution data, and then these maps were gridded to 300 m pixels.…”

Section: Normalized Entropymentioning

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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Can Vegetation Indices Serve as Proxies for Potential Sun-Induced Fluorescence (SIF)? A Fuzzy Simulation Approach on Airborne Imaging Spectroscopy Data

Cited by 11 publications

References 86 publications

Exploring the Potential of SCOPE Model for Detection of Leaf Area Index and Sun-Induced Fluorescence of Peatland Canopy

Exploring the Potential of SCOPE Model for Detection of Leaf Area Index and Sun-Induced Fluorescence of Peatland Canopy

Integrating palaeoecology and dendrochronology to explore the impact of climate and forest management on a peatland in Scots pine monoculture

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

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