Periodic Reporting for period 1 - RESOLVE (Remote sensing of photosynthetic traits for high latitude plant productivity modelling)
Reporting period: 2018-10-01 to 2020-09-30
- Background to the research problem
The arctic is experiencing unprecedented climate change, with land surface temperatures in northern regions increasing at double the global average rate. However, the impacts of these climate-induced changes on vegetation productivity and species distribution, and the impacts that any changes may have on the terrestrial carbon sink is highly uncertain. Attempts to accurately model vegetation productivity are crucial to understanding the extent and implications of a changing climate. Such predictions are complicated in high latitude regions, because there is no comparable analogue in current climates or in recent geological records. This uncertainty is exacerbated by the sparsity of field observations, due primarily to a lack of accessibility and a lack of long term monitoring sites. The use of remote sensing satellite data offers an opportunity, both to investigate trends over large spatial extents and track to historical changes through an available archive of legacy satellite data. A remote sensing-based approach is crucial for detecting changes in vegetation productivity and understanding the implications for atmospheric CO2 levels within the complex northern ecosystems.
- Why is this work important?
Plants take up a large proportion of CO2 from the atmosphere through photosynthesis. Any climate-induced changes in photosynthesis could either modulate or amplify increasing atmospheric CO2 concentrations. It is therefore imperative that the exchange of CO2 between plants and the atmosphere is accurately quantified. Recent developments in remote sensing methods and satellite technologies have opened up exciting new opportunities to improve modelled plant photosynthesis over large areas. These include an increase in the number of optical narrowband satellite sensors that measure reflected radiance in red-edge wavelengths (~705-740 nm), which have improved our ability to spatially map leaf chlorophyll content; a key component of plants’ photosynthetic machinery, over regional to global scales. A second key advance, is the use of several satellite sensors that were originally designed for atmospheric research in measuring the extremely small fluorescence signal emitted by plants. Advances in both solar induced fluorescence and leaf chlorophyll satellite retrieval methods may allow a more refined approach for targeting more precisely how vegetation function is changing across arctic plant communities, and improve estimates of the terrestrial carbon budget, under current and future climate scenarios. However, in order to understand these measurements at the satellite scale, we must first link remote sensing measurements to plant variables and processes using ground experiments, at the leaf level.
The overall objectives were to:
1) investigate how arctic-boreal vegetation physiology has changed over decadal time-frames at the biome scale;
2) determine the the main environmental drivers that are affecting vegetation productivity across the Arctic and subarctic regions?
The arctic is experiencing unprecedented climate change, with land surface temperatures in northern regions increasing at double the global average rate. However, the impacts of these climate-induced changes on vegetation productivity and species distribution, and the impacts that any changes may have on the terrestrial carbon sink is highly uncertain. Attempts to accurately model vegetation productivity are crucial to understanding the extent and implications of a changing climate. Such predictions are complicated in high latitude regions, because there is no comparable analogue in current climates or in recent geological records. This uncertainty is exacerbated by the sparsity of field observations, due primarily to a lack of accessibility and a lack of long term monitoring sites. The use of remote sensing satellite data offers an opportunity, both to investigate trends over large spatial extents and track to historical changes through an available archive of legacy satellite data. A remote sensing-based approach is crucial for detecting changes in vegetation productivity and understanding the implications for atmospheric CO2 levels within the complex northern ecosystems.
- Why is this work important?
Plants take up a large proportion of CO2 from the atmosphere through photosynthesis. Any climate-induced changes in photosynthesis could either modulate or amplify increasing atmospheric CO2 concentrations. It is therefore imperative that the exchange of CO2 between plants and the atmosphere is accurately quantified. Recent developments in remote sensing methods and satellite technologies have opened up exciting new opportunities to improve modelled plant photosynthesis over large areas. These include an increase in the number of optical narrowband satellite sensors that measure reflected radiance in red-edge wavelengths (~705-740 nm), which have improved our ability to spatially map leaf chlorophyll content; a key component of plants’ photosynthetic machinery, over regional to global scales. A second key advance, is the use of several satellite sensors that were originally designed for atmospheric research in measuring the extremely small fluorescence signal emitted by plants. Advances in both solar induced fluorescence and leaf chlorophyll satellite retrieval methods may allow a more refined approach for targeting more precisely how vegetation function is changing across arctic plant communities, and improve estimates of the terrestrial carbon budget, under current and future climate scenarios. However, in order to understand these measurements at the satellite scale, we must first link remote sensing measurements to plant variables and processes using ground experiments, at the leaf level.
The overall objectives were to:
1) investigate how arctic-boreal vegetation physiology has changed over decadal time-frames at the biome scale;
2) determine the the main environmental drivers that are affecting vegetation productivity across the Arctic and subarctic regions?
In June 2019, fieldwork was carried out at Abisko, Sweden, approximately 200 km north of the Arctic Circle, over a three week period. Field measurements were conducted at three separate field sites, each representing different arctic vegetation communities, including a wetland site at the Stordalen Mire flux tower (Rubus chamaemorus, Vaccinium vitis-idaea) a deciduous broadleaf site (Betula pubescens) and dwarf shrub site (Betula nana, Salix spp.). We carried out simultaneous measurements of: i) leaf photosynthesis, ii) solar-induced chlorophyll fluorescence (SIF) which is an optical measure of light re-emitted from chlorophyll molecules, and iii) photochemical reflectance (PRI) which is a spectral vegetation index that provides information on the thermal dissipation of excess energy by leaves. These measurements, which represent the different possible pathways for light once it is absorbed by leaf, were collected at 1 minute sampling intervals, for the different Arctic plant species. Additional sampling surveys were also conducted, for leaf chlorophyll content, leaf nitrogen content, “Pulse Amplitude Modulation” (PAM) fluorescence (which measures the efficiency of photosynthetic machinery) and leaf photosynthetic capacity measurements.
The key findings from this work were that:
1) Leaf chlorophyll content shows a strong, consistent relationship with Vcmax25 across all sampled arctic plant functional types.
2) Simultaneous measurements of leaf photosynthesis, SIF and PRI showed that the SIF-photosynthesis relationship varies according to if the plant is light saturated.
There has been considerable interest in using SIF as a direct proxy for photosynthesis, with strong, linear results reported at coarse spatial and temporal scales. However, results from this study demonstrate that at minute time-steps, the relationship between SIF and leaf-level photosynthesis is non-linear, as excess light that is not used in photosynthesis is variably partitioned to both fluorescence and heat dissipation pathways. Results also showed a consistent, strong relationship between leaf chlorophyll content and the maximum leaf photosynthetic capacity, across all species and vegetation types. This finding has significant implications for improving modelled estimates of photosynthesis by using leaf chlorophyll as a proxy for photosynthetic capacity, a key parameter in terrestrial biosphere models. The results have been disseminated to the wider scientific community through a presentation at the American Geophysical Union 2019 Fall meeting, and to the general public in a series of blog posts and an article in the INTERACT Stories of Arctic Science II book in 2020.
The key findings from this work were that:
1) Leaf chlorophyll content shows a strong, consistent relationship with Vcmax25 across all sampled arctic plant functional types.
2) Simultaneous measurements of leaf photosynthesis, SIF and PRI showed that the SIF-photosynthesis relationship varies according to if the plant is light saturated.
There has been considerable interest in using SIF as a direct proxy for photosynthesis, with strong, linear results reported at coarse spatial and temporal scales. However, results from this study demonstrate that at minute time-steps, the relationship between SIF and leaf-level photosynthesis is non-linear, as excess light that is not used in photosynthesis is variably partitioned to both fluorescence and heat dissipation pathways. Results also showed a consistent, strong relationship between leaf chlorophyll content and the maximum leaf photosynthetic capacity, across all species and vegetation types. This finding has significant implications for improving modelled estimates of photosynthesis by using leaf chlorophyll as a proxy for photosynthetic capacity, a key parameter in terrestrial biosphere models. The results have been disseminated to the wider scientific community through a presentation at the American Geophysical Union 2019 Fall meeting, and to the general public in a series of blog posts and an article in the INTERACT Stories of Arctic Science II book in 2020.
Current state-of-the-art research in the monitoring of biome-wide vegetation trends across the Arctic have been focused on using satellite spectral vegetation indices such as the Normalised Difference Vegetation Index (NDVI), which is regularly used as a proxy for plant productivity. However, the NDVI signal is an amalgamation of canopy structure and function, and it is unclear if apparent NDVI trends are related to changing biomass, species composition or leaf pigment content. The initial results from this work demonstrate that it is possible to track plant physiology over short time intervals, which will provide a more direct measure of plant photosynthesis and facilitate an improved monitoring of plant-atmosphere gas exchange. A strong relationship between leaf chlorophyll content and the maximum leaf photosynthetic capacity, across all species and vegetation types has significant implications for improving modelled estimates of photosynthesis by using leaf chlorophyll as a proxy for photosynthetic capacity, a key parameter in terrestrial biosphere models.
The identification and validation of novel remote sensing methods used in this study on several different arctic vegetation types has been shown to more precisely and accurately track vegetation response to the environment has important implications to understanding the impact from climate change on vegetation and subsequent feedback mechanisms. This will likely be of of significant interest, both to academics who may incorporate these techniques to predict the likely magnitude, and spatial dependence of the consequences of global change, and the general public who may be interested more widely in climate change.
The identification and validation of novel remote sensing methods used in this study on several different arctic vegetation types has been shown to more precisely and accurately track vegetation response to the environment has important implications to understanding the impact from climate change on vegetation and subsequent feedback mechanisms. This will likely be of of significant interest, both to academics who may incorporate these techniques to predict the likely magnitude, and spatial dependence of the consequences of global change, and the general public who may be interested more widely in climate change.