256 research outputs found
A new approach for estimating northern peatland gross primary productivity using a satellite-sensor-derived chlorophyll index
Carbon flux models that are largely driven by remotely sensed data can be used to estimate gross primary productivity (GPP) over large areas, but despite the importance of peatland ecosystems in the global carbon cycle, relatively little attention has been given to determining their success in these ecosystems. This paper is the first to explore the potential of chlorophyll-based vegetation index models for estimating peatland GPP from satellite data. Using several years of carbon flux data from contrasting peatlands, we explored the relationships between the MERIS terrestrial chlorophyll index (MTCI) and GPP, and determined whether the inclusion of environmental variables such as PAR and temperature, thought to be important determinants of peatland carbon flux, improved upon direct relationships. To place our results in context, we compared the newly developed GPP models with the MODIS (Moderate Resolution Imaging Spectrometer) GPP product. Our results show that simple MTCI-based models can be used for estimates of interannual and intra-annual variability in peatland GPP. The MTCI is a good indicator of GPP and compares favorably with more complex products derived from the MODIS sensor on a site-specific basis. The incorporation of MTCI into a light use efficiency type model, by means of partitioning the fraction of photosynthetic material within a plant canopy, shows most promise for peatland GPP estimation, outperforming all other models. Our results demonstrate that satellite data specifically related to vegetation chlorophyll content may ultimately facilitate improved quantification of peatland carbon flux dynamics
Synergetic Exploitation of the Sentinel-2 Missions for Validating the Sentinel-3 Ocean and Land Color Instrument Terrestrial Chlorophyll Index Over a Vineyard Dominated Mediterranean Environment
[EN] Continuity to the Medium Resolution Imaging Spectrometer (MERIS) Terrestrial Chlorophyll Index (MTCI) will be provided by the Ocean and Land Color Instrument (OLCI) on-board the Sentinel-3 missions. To ensure its utility in a wide range of scientific and operational applications, validation efforts are required. In the past, direct validation has been constrained by the need for costly airborne hyperspectral data acquisitions, due to the lack of freely available high spatial resolution imagery incorporating appropriate spectral bands. The Multispectral Instrument (MSI) on-board the Sentinel-2 missions now offers a promising alternative. We explored the synergetic use of MSI data for validation of the OLCI Terrestrial Chlorophyll Index (OTCI) over the Valencia Anchor Station, a large agricultural site in the Valencian Community, Spain. Using empirical and machine learning techniques applied to MSI data, in situ measurements were upscaled to the moderate spatial resolution of the OTCI. An RMSECV of 0.09 g.m(-2) (NRMSECV = 20.93%) was achieved, highlighting the valuable information MSI data can provide when used in synergy with OLCI data for land product validation. Good agreement between the OTCI and upscaled in situ measurements was observed (r = 0.77, p < 0.01), providing increased confidence to users of the product over vineyard dominated Mediterranean environments.This work was supported in part by the European Space Agency and European Commission through the Sentinel-3 Mission Performance Centre.Brown, LA.; Dash, J.; LidĂłn, A.; Lopez-Baeza, E.; Dransfeld, S. (2019). Synergetic Exploitation of the Sentinel-2 Missions for Validating the Sentinel-3 Ocean and Land Color Instrument Terrestrial Chlorophyll Index Over a Vineyard Dominated Mediterranean Environment. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 12(7):2244-2251. https://doi.org/10.1109/JSTARS.2019.28999982244225112
Validation and application of the MERIS Terrestrial Chlorophyll Index.
Climate is one of the key variables driving ecosystems at local to global scales. How and to what extent vegetation responds to climate variability is a challenging topic for
global change analysis. Earth observation provides an opportunity to study temporal ecosystem dynamics, providing much needed information about the response of vegetation to environmental and climatic change at local to global scales. The European Space Agency (ESA) uses data recorded by the Medium Resolution Imaging Spectrometer (MERlS) in red I near infrared spectral bands to produce an operational
product called the MERlS Terrestrial Chlorophyll Index (MTCI). The MTCI is related to the position of the red edge in vegetation spectra and can be used to estimate the
chlorophyll content of vegetation. The MTCI therefore provides a powerful product to monitor phenology, stress and productivity. The MTCI needs full validation if it is to be embraced by the user community who require precise and consistent, spatial and temporal comparisons of vegetation condition. This research details experimental investigations into variables that may influence the
relationship between the MTCI and vegetation chlorophyll content, namely soil background and sensor view angle, vegetation type and spatial scale. Validation campaigns in the New Forest and at Brooms Barn agricultural study site reinforced the strong correlation between chlorophyll content and MTCI that was evident from laboratory spectroscopy investigations, demonstrating the suitability of the MTCI as a surrogate for field chlorophyll content measurements independent of cover type. However, this relationship was significantly weakened where the leaf area index (LAI) was low, indicating that the MTCI is sensitive to the effects of soil background. In the light of such conclusions, this project then assessed the MTCI as a tool to monitor changes in ecosystem phenology as a function of climatic variability, and the suitability of the MTCI as a surrogate measure of photosynthetic light use efficiency, to model ecosystem gross primary productivity (GPP) at various sites in North America with contrasting vegetation types. Changes in MTCI throughout the growing season
demonstrated the potential of the MTCI to estimate vegetation dynamics, characterising the temporal characteristics in both phenology and gross primary productivity
Validation and application of the MERIS Terrestrial Chlorophyll Index
Climate is one of the key variables driving ecosystems at local to global scales. How and to what extent vegetation responds to climate variability is a challenging topic for global change analysis. Earth observation provides an opportunity to study temporal ecosystem dynamics, providing much needed information about the response of vegetation to environmental and climatic change at local to global scales. The European Space Agency (ESA) uses data recorded by the Medium Resolution Imaging Spectrometer (MERlS) in red I near infrared spectral bands to produce an operational product called the MERlS Terrestrial Chlorophyll Index (MTCI). The MTCI is related to the position of the red edge in vegetation spectra and can be used to estimate the chlorophyll content of vegetation. The MTCI therefore provides a powerful product to monitor phenology, stress and productivity. The MTCI needs full validation if it is to be embraced by the user community who require precise and consistent, spatial and temporal comparisons of vegetation condition. This research details experimental investigations into variables that may influence the relationship between the MTCI and vegetation chlorophyll content, namely soil background and sensor view angle, vegetation type and spatial scale. Validation campaigns in the New Forest and at Brooms Barn agricultural study site reinforced the strong correlation between chlorophyll content and MTCI that was evident from laboratory spectroscopy investigations, demonstrating the suitability of the MTCI as a surrogate for field chlorophyll content measurements independent of cover type. However, this relationship was significantly weakened where the leaf area index (LAI) was low, indicating that the MTCI is sensitive to the effects of soil background. In the light of such conclusions, this project then assessed the MTCI as a tool to monitor changes in ecosystem phenology as a function of climatic variability, and the suitability of the MTCI as a surrogate measure of photosynthetic light use efficiency, to model ecosystem gross primary productivity (GPP) at various sites in North America with contrasting vegetation types. Changes in MTCI throughout the growing season demonstrated the potential of the MTCI to estimate vegetation dynamics, characterising the temporal characteristics in both phenology and gross primary productivity.EThOS - Electronic Theses Online ServiceGBUnited Kingdo
Remote sensing-based estimation of gross primary production in a subalpine grassland
This study investigates the performances in a terrestrial ecosystem of gross primary production (GPP) estimation of a suite of spectral vegetation indexes (VIs) that can be computed from currently orbiting platforms. Vegetation indexes were computed from near-surface field spectroscopy measurements collected using an automatic system designed for high temporal frequency acquisition of spectral
measurements in the visible near-infrared region. Spectral observations were collected for two consecutive years in Italy in a subalpine grassland equipped with an eddy covariance (EC) flux tower that provides continuous measurements of net ecosystem carbon dioxide (CO2) exchange (NEE) and the derived GPP.
Different VIs were calculated based on ESA-MERIS and NASA-MODIS spectral bands and correlated with biophysical (Leaf area index, LAI; fraction of photosynthetically active radiation intercepted by green vegetation, f IPARg), biochemical (chlorophyll concentration) and ecophysiological (green light-use efficiency, LUEg) canopy variables. In this study, the normalized difference vegetation index (NDVI)
was the index best correlated with LAI and f IPARg (r = 0.90 and 0.95, respectively), the MERIS terrestrial chlorophyll index (MTCI) with leaf chlorophyll content (r = 0.91) and the photochemical reflectance index (PRI551), computed as (R531 âR551)/(R531 +R551) with LUEg (r = 0.64).
Subsequently, these VIs were used to estimate GPP using different modelling solutions based on Monteithâs lightuse efficiency model describing the GPP as driven by the photosynthetically active radiation absorbed by green vegetation (APARg) and by the efficiency (") with which plants use the absorbed radiation to fix carbon via photosynthesis.
Results show that GPP can be successfully modelled with a combination of VIs and meteorological data or VIs only.
Vegetation indexes designed to be more sensitive to chlorophyll content explained most of the variability in GPP in the ecosystem investigated, characterised by a strong seasonal dynamic of GPP. Accuracy in GPP estimation slightly improves when taking into account high frequency modulations of GPP driven by incident PAR or modelling LUEg with the PRI in model formulation. Similar results were obtained for both measured daily VIs and VIs obtained as 16-day composite time series and then downscaled from the compositing period to daily scale (resampled data). However, the use of resampled data rather than measured daily input data decreases the accuracy of the total GPP estimation on an annual basis.JRC.H.4-Monitoring Agricultural Resource
Reviews and syntheses: Systematic Earth observations for use in terrestrial carbon cycle data assimilation systems
The global carbon cycle is an important component of the Earth system and it interacts with the hydrology, energy and nutrient cycles as well as ecosystem dynamics. A better understanding of the global carbon cycle is required for improved projections of climate change including corresponding changes in water and food resources and for the verification of measures to reduce anthropogenic greenhouse gas emissions. An improved understanding of the carbon cycle can be achieved by data assimilation systems, which integrate observations relevant to the carbon cycle into coupled carbon, water, energy and nutrient models. Hence, the ingredients for such systems are a carbon cycle model, an algorithm for the assimilation and systematic and well error-characterised observations relevant to the carbon cycle. Relevant observations for assimilation include various in situ measurements in the atmosphere (e.g. concentrations of CO2 and other gases) and on land (e.g. fluxes of carbon water and energy, carbon stocks) as well as remote sensing observations (e.g. atmospheric composition, vegetation and surface properties). We briefly review the different existing data assimilation techniques and contrast them to model benchmarking and evaluation efforts (which also rely on observations). A common requirement for all assimilation techniques is a full description of the observational data properties. Uncertainty estimates of the observations are as important as the observations themselves because they similarly determine the outcome of such assimilation systems. Hence, this article reviews the requirements of data assimilation systems on observations and provides a non-exhaustive overview of current observations and their uncertainties for use in terrestrial carbon cycle data assimilation. We report on progress since the review of model-data synthesis in terrestrial carbon observations by Raupach et al.(2005), emphasising the rapid advance in relevant space-based observations
Scientific and technical challenges in remote sensing of plant canopy reflectance and fluorescence
State-of-the-art optical remote sensing of vegetation canopies is reviewed here to stimulate support from laboratory and field plant research. This overview of recent satellite spectral sensors and the methods used to retrieve remotely quantitative biophysical and biochemical characteristics of vegetation canopies shows that there have been substantial advances in optical remote sensing over the past few decades. Nevertheless, adaptation and transfer of currently available fluorometric methods aboard air- and space-borne platforms can help to eliminate errors and uncertainties in recent remote sensing data interpretation. With this perspective, red and blue-green fluorescence emission as measured in the laboratory and field is reviewed. Remotely sensed plant fluorescence signals have the potential to facilitate a better understanding of vegetation photosynthetic dynamics and primary production on a large scale. The review summarizes several scientific challenges that still need to be resolved to achieve operational fluorescence based remote sensing approache
New Methods for Measurements of Photosynthesis from Space
Our ability to close the Earth's carbon budget and predict feedbacks in a warming climate
depends critically on knowing where, when, and how carbon dioxide (CO2) is exchanged
between the land and atmosphere. In particular, determining the rate of carbon fixation by
the Earth's biosphere (commonly referred to as gross primary productivity, or GPP) and the
dependence of this productivity on climate is a central goal. Historically, GPP has been
inferred from spectral imagery of the land and ocean. Assessment of GPP from the color of
the land and ocean requires, however, additional knowledge of the types of plants in the
scene, their regulatory mechanisms, and climate variables such as soil moistureâjust the
independent variables of interest!
Sunlight absorbed by chlorophyll in photosynthetic organisms is mostly used to drive
photosynthesis, but some can also be dissipated as heat or reâradiated at longer wavelengths
(660â800 nm). This nearâinfrared light reâemitted from illuminated plants is termed solarinduced
fluorescence (SIF), and it has been found to strongly correlate with GPP. To advance
our understanding of SIF and its relation to GPP and environmental stress at the planetary
scale, the Keck Institute for Space Studies (KISS) convened a workshopâheld in Pasadena,
California, in August 2012âto focus on a newly developed capacity to monitor chlorophyll
fluorescence from terrestrial vegetation by satellite. This revolutionary approach for
retrieving global observations of SIF promises to provide direct and spatially resolved
information on GPP, an ideal bottomâup complement to the atmospheric net CO2 exchange
inversions.
Workshop participants leveraged our efforts on previous studies and workshops related to
the European Space Agencyâs FLuorescence EXplorer (FLEX) mission concept, which had
already targeted SIF for a possible satellite mission and had developed a vibrant research
community with many important publications. These studies, mostly focused on landscape,
canopy, and leafâlevel interpretation, provided the groundâwork for the workshop, which
focused on the global carbon cycle and synergies with atmospheric net flux inversions.
Workshop participants included key members of several communities: plant physiologists
with experience using active fluorescence methods to quantify photosynthesis; ecologists
and radiative transfer experts who are studying the challenge of scaling from the leaf to
regional scales; atmospheric scientists with experience retrieving photometric information
from spaceâborne spectrometers; and carbon cycle experts who are integrating new
observations into models that describe the exchange of carbon between the atmosphere,
land and ocean. Together, the participants examined the link between âpassiveâ fluorescence
observed from orbiting spacecraft and the underlying photochemistry, plant physiology and
biogeochemistry of the land and ocean.
This report details the opportunity for forging a deep connection between scientists doing
basic research in photosynthetic mechanisms and the more applied community doing
research on the Earth System. Too often these connections have gotten lost in empiricism
associated with the coarse scale of global models. Chlorophyll fluorescence has been a major
tool for basic research in photosynthesis for nearly a century. SIF observations from space,
although sensing a large footprint, probe molecular events occurring in the leaves below.
This offers an opportunity for direct mechanistic insight that is unparalleled for studies of
biology in the Earth System.
A major focus of the workshop was to review the basic mechanisms that underlie this
phenomenon, and to explore modeling tools that have been developed to link the biophysical
and biochemical knowledge of photosynthesis with the observableâin this case, the
radiance of SIFâseen by the satellite. Discussions led to the identification of areas where
knowledge is still lacking. For example, the inability to do controlled illumination
observations from space limits the ability to fully constrain the variables that link
fluorescence and photosynthesis.
Another focus of the workshop explored a âtopâdownâ view of the SIF signal from space.
Early studies clearly identified a strong correlation between the strength of this signal and
our best estimate of the rate of photosynthesis (GPP) over the globe. New studies show that
this observation provides improvements over conventional reflectanceâbased remote
sensing in detecting seasonal and environmental (particularly drought related) modulation
of photosynthesis. Apparently SIF responds much more quickly and with greater dynamic
range than typical greenness indices when GPP is perturbed. However, discussions at the
workshop also identified areas where topâdown analysis seemed to be âout in frontâ of
mechanistic studies. For example, changes in SIF based on changes in canopy light
interception and the light use efficiency of the canopy, both of which occur in response to
drought, are assumed equivalent in the topâdown analysis, but the mechanistic justification
for this is still lacking from the bottomâup side.
Workshop participants considered implications of these mechanistic and empirical insights
for largeâscale models of the carbon cycle and biogeochemistry, and also made progress
toward incorporating SIF as a simulated output in land surface models used in global and
regionalâscale analysis of the carbon cycle. Comparison of remotely sensed SIF with modelsimulated
SIF may open new possibilities for model evaluation and data assimilation,
perhaps leading to better modeling tools for analysis of the other retrieval from GOSAT
satellite, atmospheric CO2 concentration. Participants also identified another application for
SIF: a linkage to the physical climate system arising from the ability to better identify
regional development of plant water stress. Decreases in transpiration over large areas of a
continent are implicated in the development and âlockingâinâ of drought conditions. These
discussions also identified areas where current land surface models need to be improved in
order to enable this research. Specifically, the radiation transport treatments need dramatic
overhauls to correctly simulate SIF.
Finally, workshop participants explored approaches for retrieval of SIF from satellite and
groundâbased sensors. The difficulty of resolving SIF from the overwhelming flux of reflected
sunlight in the spectral region where fluorescence occurs was once a major impediment to
making this measurement. Placement of very high spectral resolution spectrometers on
GOSAT (and other greenhouse gasâsensing satellites) has enabled retrievals based on infilling
of solar Fraunhofer lines, enabling accurate fluorescence measurements even in the
presence of moderately thick clouds. Perhaps the most interesting challenge here is that
there is no readily portable groundâbased instrumentation that even approaches the
capability of GOSAT and other planned greenhouse gas satellites. This strongly limits scientistsâ ability to conduct groundâbased studies to characterize the footprint of the GOSAT
measurement and to conduct studies of radiation transport needed to interpret SIF
measurement.
The workshop results represent a snapshot of the state of knowledge in this area. New
research activities have sprung from the deliberations during the workshop, with
publications to follow. The introduction of this new measurement technology to a wide slice
of the community of Earth System Scientists will help them understand how this new
technology could help solve problems in their research, address concerns about the
interpretation, identify future research needs, and elicit support of the wider community for
research needed to support this observation.
Somewhat analogous to the original discovery that vegetation indices could be derived from
satellite measurements originally intended to detect clouds, the GOSAT observations are a
rare case in which a (fortuitous) global satellite dataset becomes available before the
research community had a consolidated understanding on how (beyond an empirical
correlation) it could be applied to understanding the underlying processes. Vegetation
indices have since changed the way we see the global biosphere, and the workshop
participants envision that fluorescence can perform the next indispensable step by
complementing these measurements with independent estimates that are more indicative of
actual (as opposed to potential) photosynthesis. Apart from the potential FLEX mission, no
dedicated satellite missions are currently planned. OCOâ2 and â3 will provide much more
data than GOSAT, but will still not allow for regional studies due to the lack of mapping
capabilities. Geostationary observations may even prove most useful, as they could track
fluorescence over the course of the day and clearly identify stressârelated downâregulation of
photosynthesis. Retrieval of fluorescence on the global scale should be recognized as a
valuable tool; it can bring the same quantum leap in our understanding of the global carbon
cycle as vegetation indices once did
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