Ground-Based Optical Measurements at European Flux Sites: A Review of Methods, Instruments and Current Controversies

Balzarolo, Manuela; Anderson, Karen; Nichol, Caroline; Rossini, Micol; Vescovo, L.; Arriga, Nicola; Wohlfahrt, Georg; Calvet, Jean‐Christophe; Carrara, Arnaud; Cerasoli, Sofia; Cogliati, Sergio; Daumard, F.; Eklundh, Lars; Elbers, J.A.; Evrendilek, Fatih; Handcock, R.N.; Kaduk, Jörg; Klumpp, Katja; Longdoz, Bernard; Matteucci, Giorgio; Meroni, Michele; Montagnani, Leonardo; Ourcival, Jean‐Marc; Sánchez‐Cañete, Enrique P.; Pontailler, Jean-Yves; Juszczak, Radosław; Scholes, Bob; Martín, M. Pilar

doi:10.3390/s110807954

Cited by 89 publications

(58 citation statements)

References 55 publications

(73 reference statements)

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“…2016, 8,260 3 of 25 possibility to relate remote sensing measurements with carbon fluxes as a foundation for modeling and upscaling [25]. Networks such as SpecNet and EUROSPEC have been exploring the possibility of combining flux and spectral reflectance measurements for a more complete assessment of ecosystem function [26][27][28]. These collaborations have increased the availability of both optical and flux data, allowing for improved models linking remote sensing measurements and carbon fluxes.…”

Section: Introductionmentioning

confidence: 99%

Monitoring Grassland Seasonal Carbon Dynamics, by Integrating MODIS NDVI, Proximal Optical Sampling, and Eddy Covariance Measurements

et al. 2016

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This study evaluated the seasonal productivity of a prairie grassland (Mattheis Ranch, in Alberta, Canada) using a combination of remote sensing, eddy covariance, and field sampling collected in 2012-2013. A primary objective was to evaluate different ways of parameterizing the light-use efficiency (LUE) model for assessing net ecosystem fluxes at two sites with contrasting productivity. Three variations on the NDVI (Normalized Difference Vegetation Index), differing by formula and footprint, were derived: (1) a narrow-band NDVI (NDVI 680,800 , derived from mobile field spectrometer readings); (2) a broad-band proxy NDVI (derived from an automated optical phenology station consisting of broad-band radiometers); and (3) a satellite NDVI (derived from MODIS AQUA and TERRA sensors). Harvested biomass, net CO 2 flux, and NDVI values were compared to provide a basis for assessing seasonal ecosystem productivity and gap filling of tower flux data. All three NDVIs provided good estimates of dry green biomass and were able to clearly show seasonal changes in vegetation growth and senescence, confirming their utility as metrics of productivity. When relating fluxes and optical measurements, temporal aggregation periods were considered to determine the impact of aggregation on model accuracy. NDVI values from the different methods were also calibrated against f APAR green (the fraction of photosynthetically active radiation absorbed by green vegetation) values to parameterize the APAR green (absorbed PAR) term of the LUE (light use efficiency) model for comparison with measured fluxes. While efficiency was assumed to be constant in the model, this analysis revealed hysteresis in the seasonal relationships between fluxes and optical measurements, suggesting a slight change in efficiency between the first and second half of the growing season. Consequently, the best results were obtained by splitting the data into two stages, a greening phase and a senescence phase, and applying separate fits to these two periods. By incorporating the dynamic irradiance regime, the model based on APAR green rather than NDVI best captured the high variability of the fluxes and provided a more realistic depiction of missing fluxes. The strong correlations between these optical measurements and independently measured fluxes demonstrate the utility of integrating optical with flux measurements for gap filling, and provide a foundation for using remote sensing to extrapolate from the flux tower to larger regions (upscaling) for regional analysis of net carbon uptake by grassland ecosystems.

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Section: Introductionmentioning

confidence: 99%

Monitoring Grassland Seasonal Carbon Dynamics, by Integrating MODIS NDVI, Proximal Optical Sampling, and Eddy Covariance Measurements

et al. 2016

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“…In addition, in situ observations of spectral reflectance are increasingly applied to link surface measurements of energy and carbon fluxes with Earth observations (Gamon et al 2006). The measurements of hyper-spectral surface reflectance at eddy covariance sites is an important step for the spatial extrapolation of in situ measurements with remote sensing retrievals from satellite platforms (Balzarolo et al 2011) and to parameterize light use efficiency models for the up-scaling of gross primary productivity (GPP) (Sims et al 2006;Meroni et al 2009;Meroni et al 2011;Peñuelas et al 2011;Rossini et al 2012).…”

Section: Introductionmentioning

confidence: 99%

Geometry of the hemispherical radiometric footprint over plant canopies

Marcolla

Cescatti

2017

Theor Appl Climatol

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Radiometric measurements of hemispherical surface reflectance and long-wave irradiance are required to quantify the broadband albedo and the outgoing thermal radiation. These observations are typically integrated with eddy covariance measurements of sensible and latent heat fluxes to characterize the surface energy budget. While the aerodynamic footprint has been widely investigated, the geometry of the hemispherical radiometric footprint over plant canopies has been rarely tackled. In the present work, the size and shape of the hemispherical radiometric footprint are formalized for a bare surface and in presence of a vegetation cover. For this purpose, four idealized canopies are analyzed and the dependency of the radiometric footprint on leaf area index and canopy height is explored. Besides, the radiometric footprint is compared with the aerodynamic footprint in conditions of neutral stability. It was observed that almost 100% of the hemispherical radiometric signal originates within a distance of a few radiometer heights, while only about 50-80% of the cumulative aerodynamic signal is generated within a distance of about 20 sensor heights. In order to achieve comparable extensions of the footprint areas, hemispherical radiometric measurements should therefore be taken about 6-15 times higher than turbulent flux ones, depending on the vegetation type. The analysis also highlights that the size of the radiative footprint decreases at increasing leaf area index, whereas the aerodynamic footprint shows an opposite behavior. For the abovementioned reasons, this work may support the interpretation of energy flux measurements and the optimal design of eddy covariance stations located in heterogeneous sites.

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“…Currently, several research groups have developed different automatic devices to collect canopy spectral properties (Leuning et al, 2006;Hilker et al, 2007;Nakaji et al, 2007Nakaji et al, , 2008Daumard et al, 2010;Hilker et al, 2010;Ide et al, 2010;Balzarolo et al, 2011;Meroni et al, 2011) for the purpose of gaining new insights into the quantification and monitoring of plant photosynthesis on a temporal scale. Such devices are generally operated automatically for long periods in the sampling area of flux towers.…”

Section: Introductionmentioning

confidence: 99%

Remote sensing-based estimation of gross primary production in a subalpine grassland

et al. 2012

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Abstract. 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 (CO 2 ) 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 IPAR g ), biochemical (chlorophyll concentration) and ecophysiological (green light-use efficiency, LUE g ) canopy variables. In this study, the normalized difference vegetation index (NDVI) was the index best correlated with LAI and f IPAR g (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 (PRI 551 ), computed as (R 531 − R 551 )/(R 531 + R 551 ) with LUE g (r = 0.64).Subsequently, these VIs were used to estimate GPP using different modelling solutions based on Monteith's lightuse efficiency model describing the GPP as driven by the photosynthetically active radiation absorbed by green vegetation (APAR g ) and by the efficiency (ε) 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 LUE g 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.

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Ground-Based Optical Measurements at European Flux Sites: A Review of Methods, Instruments and Current Controversies

Cited by 89 publications

References 55 publications

Monitoring Grassland Seasonal Carbon Dynamics, by Integrating MODIS NDVI, Proximal Optical Sampling, and Eddy Covariance Measurements

Monitoring Grassland Seasonal Carbon Dynamics, by Integrating MODIS NDVI, Proximal Optical Sampling, and Eddy Covariance Measurements

Geometry of the hemispherical radiometric footprint over plant canopies

Remote sensing-based estimation of gross primary production in a subalpine grassland

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