The continuous and automated monitoring of canopy phenology is of increasing scientific interest for the multiple implications of vegetation dynamics on ecosystem carbon and energy fluxes. For this ...purpose we evaluated the applicability of digital camera imagery for monitoring and modeling phenology and physiology of a subalpine grassland over the 2009 and 2010 growing seasons. We tested the relationships between color indices (i.e. the algebraic combinations of RGB brightness levels) tracking canopy greenness extracted from repeated digital images against field measurements of green and total biomass, leaf area index (LAI), greenness visual estimation, vegetation indices computed from continuous spectroradiometric measurements and CO₂ fluxes observed with the eddy covariance technique. A strong relationship was found between canopy greenness and (i) structural parameters (i.e., LAI) and (ii) canopy photosynthesis (i.e. Gross Primary Production; GPP). Color indices were also well correlated with vegetation indices typically used for monitoring landscape phenology from satellite, suggesting that digital repeat photography provides high-quality ground data for evaluation of satellite phenology products. We demonstrate that by using canopy greenness we can refine phenological models (Growing Season Index, GSI) by describing canopy development and considering the role of ecological factors (e.g., snow, temperature and photoperiod) controlling grassland phenology. Moreover, we show that canopy greenness combined with radiation use efficiency (RUE) obtained from spectral indices related to photochemistry (i.e., scaled Photochemical Reflectance Index) or meteorology (i.e., MOD17 RUE) can be used to predict daily GPP. Building on previous work that has demonstrated that seasonal variation in the structure and function of plant canopies can be quantified using digital camera imagery, we have highlighted the potential use of these data for the development and parameterization of phenological and RUE models, and thus point toward an extension of the proposed methodologies to the dataset collected within PhenoCam Network.
The most recent efforts to provide remote sensing (RS) estimates of plant function rely on the combination of Radiative Transfer Models (RTM) and Soil-Vegetation-Atmosphere Transfer (SVAT) models, ...such as the Soil-Canopy Observation Photosynthesis and Energy fluxes (SCOPE) model. In this work we used ground spectroradiometric and chamber-based CO2 flux measurements in a nutrient manipulated Mediterranean grassland in order to: 1) develop a multiple-constraint inversion approach of SCOPE able to retrieve vegetation biochemical, structural as well as key functional traits, such as chlorophyll concentration (Cab), leaf area index (LAI), maximum carboxylation rate (Vcmax) and the Ball-Berry sensitivity parameter (m); and 2) compare the potential of the of gross primary production (GPP) and sun-induced fluorescence (SIF), together with up-welling Thermal Infrared (TIR) radiance and optical reflectance factors (RF), to estimate such parameters. The performance of the proposed inversion method as well as of the different sets of constraints was assessed with contemporary measurements of water and heat fluxes and leaf nitrogen content, using pattern-oriented model evaluation.
The multiple-constraint inversion approach proposed together with the combination of optical RF and diel GPP and TIR data provided reliable estimates of parameters, and improved predicted water and heat fluxes. The addition of SIF to this scheme slightly improved the estimation of m. Parameter estimates were coherent with the variability imposed by the fertilization and the seasonality of the grassland. Results revealed that fertilization had an impact on Vcmax, while no significant differences were found for m. The combination of RF, SIF and diel TIR data weakly constrained functional traits. Approaches not including GPP failed to estimate LAI; however GPP overestimated Cab in the dry period. These problems might be related to the presence of high fractions of senescent leaves in the grassland. The proposed inversion approach together with pattern-oriented model evaluation open new perspectives for the retrieval of plant functional traits relevant for land surface models, and can be utilized at various research sites where hyperspectral remote sensing imagery and eddy covariance flux measurements are simultaneously taken.
•Novel SCOPE model inversion approach provides reliable plant functional traits.•GPP results a better constraint of plant functional traits than monochromatic SIF.•Retrieved parameters respond to spatial, temporal and nutrient-induced variability.•Vcmax and Ball-Berry slope evaluated against leaf nitrogen and evaporative fraction.•Pattern-oriented model evaluation enhances model and inversion performance analysis.
Abstract
Climate change is expected to increase both the frequency and the intensity of climate extremes, consequently increasing the risk of forest role transition from carbon sequestration to ...carbon emission. These changes are occurring more rapidly in the Alps, with important consequences for tree species adapted to strong climate seasonality and short growing season. In this study, we aimed at investigating the responses of a high-altitude
Larix decidua
Mill. forest to heat and drought, by coupling ecosystem- and tree-level measurements. From 2012 to 2018, ecosystem carbon and water fluxes (i.e. gross primary production, net ecosystem exchange, and evapotranspiration) were measured by means of the eddy covariance technique, together with the monitoring of canopy development (i.e. larch phenology and normalized difference vegetation index). From 2015 to 2017 we carried out additional observations at the tree level, including stem growth and its duration, direct phenological observations, sap flow, and tree water deficit. Results showed that the warm spells in 2015 and 2017 caused an advance of the phenological development and, thus, of the seasonal trajectories of many processes, at both tree and ecosystem level. However, we did not observe any significant quantitative changes regarding ecosystem gas exchanges during extreme years. In contrast, in 2017 we found a reduction of 17% in larch stem growth and a contraction of 45% of the stem growth period. The growing season in 2017 was indeed characterized by different drought events and by the highest water deficit during the study years. Due to its multi-level approach, our study provided evidence of the independence between C-source (i.e. photosynthesis) and C-sink (i.e. tree stem growth) processes in a subalpine larch forest.
The coupling of radiative transfer, energy balance, and photosynthesis models has brought new opportunities to characterize vegetation functional properties from space. However, these models do not ...accurately represent processes in ecosystems characterized by mixtures of green vegetation and senescent plant material (SPM), in particular grasslands. These inaccuracies limit the retrieval of vegetation biophysical and functional properties. Green and senesced plants feature contrasting spectral properties and carry out different functions that current coupled models do not represent separately. Besides, senescent pigments' absorption features change as SPM decomposes, and neither is this process well parameterized in radiative transfer models. This manuscript aims at overcoming these limitations. On the one hand, we have developed senSCOPE, a version of the Soil-Canopy Observation of Photosynthesis and Energy fluxes (SCOPE) that separately represents light interaction and physiology of green and senesced leaves. On the other, we have characterized new specific absorption coefficients of senescent pigments (Ks) from optical measurements of SPM from a Mediterranean grassland. Sensitivity analyses revealed that compared to SCOPE, senSCOPE 1) predicts variables that respond more linearly to the faction of green leaf area; and 2) keeps high levels of absorbed photosynthetically active radiation in the green leaves, which leads to significant differences in leaf photosynthesis, non-photochemical quenching, and transpiration. Moreover, we compared SCOPE vs. senSCOPE's capability to provide estimates of functional and biophysical parameters of vegetation. We assimilated different combinations of reflectance factors (R), chlorophyll sun-induced fluorescence radiance in the O2-A band (F760), gross primary production (GPP), and thermal radiance (Lt) measured in a Mediterranean grassland. Besides, we compared the role of three different sets of Ks coefficients in the inversion of senSCOPE, two estimated from SPM. The performance of the inversions was assessed using field data and a pattern-oriented model evaluation approach. Unlike SCOPE, senSCOPE provided unbiased estimates of chlorophyll content (Cab) during the dry season. The use of SPM-specific Ks improved the representation of R in the near-infrared wavelengths; and, consequently, the estimation of leaf area index (LAI). Compared with field LAI, the coefficient of determination R2 increased from ~0.4 to ~0.6, depending on the inversion constraints. Compared with SCOPE, the new model and coefficients together reduced the root mean squared error between observed and modeled R (~40%), F760 (~30%), and GPP (~5%). Both models failed to represent Lt; in this case, senSCOPE featured larger uncertainties. The modeling approach we propose improves the simulation and retrieval of vegetation properties and function in grasslands. Further work is needed to test the applicability of senSCOPE in different ecosystems, improve the simulation of the thermal spectral domain, and better characterize the optical parameters of SPM. To do so, new databases of SPM optical and biophysical properties should be produced.
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•New version of SCOPE accounts for senesced non-photosynthetic leaves in canopy.•Separated light transfer and energy balance change physiology and spectral signals.•Optical properties of senesced material characterized from Mediterranean species.•senSCOPE improves the retrieval of vegetation traits in semi-arid grassland.
While numerous studies report shifts in vegetation phenology, in this regard eddy covariance (EC) data, despite its continuous high-frequency observations, still requires further exploration. ...Furthermore, there is no general consensus on optimal methodologies for data smoothing and extracting phenological transition dates (PTDs). Here, we revisit existing methodologies and present new prospects to investigate phenological changes in gross primary productivity (GPP) from EC measurements. First, we present a smoothing technique of GPP time series through the derivative of its smoothed annual cumulative sum. Second, we calculate PTDs and their trends from a commonly used threshold method that identifies days with a fixed percentage of the annual maximum GPP. A systematic analysis is performed for various thresholds ranging from 0.1 to 0.7. Lastly, we examine the relation of PTDs trends to trends in GPP across the years on a weekly basis. Results from 47 EC sites with long time series (> 10 years) show that advancing trends in start of season (SOS) are strongest at lower thresholds but for the end of season (EOS) at higher thresholds. Moreover, the trends are variable at different thresholds for individual vegetation types and individual sites, outlining reasonable concerns on using a single threshold value. Relationship of trends in PTDs and weekly GPP reveal association of advanced SOS and delayed EOS to increase in immediate primary productivity, but not to the trends in overall seasonal productivity. Drawing on these analyses, we emphasise on abstaining from subjective choices and investigating relationship of PTDs trend to finer temporal trends of GPP. Our study examines existing methodological challenges and presents approaches that optimize the use of EC data in identifying vegetation phenological changes and their relation to carbon uptake.
The input of mineral dust from arid regions impacts snow
optical properties. The induced albedo reduction generally alters the
melting dynamics of the snowpack, resulting in earlier snowmelt. In this
...paper, we evaluate the impact of dust depositions on the melting dynamics of
snowpack at a high-elevation site (2160 m) in the European Alps (Torgnon,
Aosta Valley, Italy) during three hydrological years (2013–2016). These
years were characterized by several Saharan dust events that deposited
significant amounts of mineral dust in the European Alps. We quantify the
shortening of the snow season due to dust deposition by comparing observed snow
depths and those simulated with the Crocus model accounting, or not, for the
impact of impurities. The model was run and tested using meteorological data
from an automated weather station. We propose the use of repeated digital
images for tracking dust deposition and resurfacing in the snowpack. The
good agreement between model prediction and digital images allowed us to
propose the use of an RGB index (i.e. snow darkening index – SDI) for
monitoring dust on snow using images from a digital camera. We also present
a geochemical characterization of dust reaching the Alpine chain during
spring in 2014. Elements found in dust were classified as a function of
their origin and compared with Saharan sources. A strong enrichment in Fe
was observed in snow containing Saharan dust. In our case study, the
comparison between modelling results and observations showed that impurities
deposited in snow anticipated the disappearance of snow up to 38 d a out of
a total 7 months of typical snow duration. This happened for the season
2015–2016 that was characterized by a strong dust deposition event. During
the other seasons considered here (2013–2014 and 2014–2015), the snow
melt-out date was 18 and 11 d earlier, respectively. We conclude that the
effect of the Saharan dust is expected to reduce snow cover duration through
the snow-albedo feedback. This process is known to have a series of further
hydrological and phenological feedback effects that should be characterized
in future research.
Remote sensing (RS) increasingly seeks to produce global‐coverage maps of plant functional diversity (PFD) across scales. PFD can be quantified with metrics assessing field or RS data dissimilarity. ...However, their comparison suffers from the lack of normalization approaches that (1) correct for differences in the number and correlation of traits and spectral variables and (2) do not require comparing all the available samples to estimate the maximum trait's dissimilarity (unfeasible in RS).
We propose a generalizable normalization (GN) based on the maximum potential dissimilarity for the traits and spectral data considered and compare it to more traditional approaches (e.g. the maximum dissimilarity within datasets). To do so, we simulated plant communities with radiative transfer models and compared RS‐based diversity measurements across spatial scales (α‐ and β‐diversity components). Specifically, we assessed the capability of different normalization approaches (GN, local, none) to provide PFD estimates comparable between (1) RS and plant traits and (2) estimates from different RS missions.
Unlike the other approaches, GN provides diversity component estimates that are directly comparable between field data and RS missions with different spectral configurations by removing the effect of differences in the number of traits or bands and the maximum dissimilarity across datasets.
Therefore, GN enables the separated analysis of RS images from different sensors to produce comparable global‐coverage cartography. We suggest GN is necessary to validate RS approaches and develop interpretable maps of PFD using different RS missions.
Resumen
La teledetección busca producir cartografía global de la diversidad funcional de la vegetación (DFV) a diferentes escalas espaciales. La DFV puede cuantificarse mediante métricas que evalúan la disimilaridad de datos de campo o de teledetección. Sin embargo, su comparación sufre de la falta de métodos de normalización que (1) corrijan las diferencias en el número y la correlación de aspectos funcionales o variables espectrales y (2) no requiera comparar todas las muestras disponibles para determinar la disimilaridad máxima (lo que no es plausible en teledetección).
Proponemos una Normalización Generalizable (NG) basada en la disimilaridad máxima potencial para aspectos funcionales y datos espectrales, y la comparamos con métodos más tradicionales (como la máxima disimilaridad del conjunto de datos). Para ello, simulamos comunidades vegetales con modelos de transferencia radiativa y comparamos las métricas derivadas mediante teledetección a diferentes escalas espaciales (componentes α y β de la diversidad). En concreto, evaluamos la capacidad de los diferentes métodos de normalización (NG, local y ninguno) para proveer estimaciones de DFV comparables entre (1) variables espectrales y aspectos funcionales de las plantas, y (2) estimaciones de diferentes misiones satelitales.
A diferencia de los otros métodos, NG produce estimaciones de los componentes de la diversidad que son directamente comparables entre datos de campo y datos satelitales de misiones con diferente configuración espectral, eliminando el efecto de las diferencias en el número de aspectos o bandas espectrales y la máxima disimilaridad entre conjuntos de datos.
Por tanto, NG posibilita analizar separadamente imágenes de diferentes sensores para producir cartografías globales comparables. Consideramos que la NG es necesaria para validar métodos de teledetección y desarrollar mapas interpretables de DFV combinando diferentes misiones de teledetección.
Abstract Land-surface phenology is a widely used indicator of how terrestrial ecosystems respond to environmental change. The spatial variability of this plant functional trait has also been ...advocated as an indicator of the functional composition of ecosystems. However, a global-scale assessment of spatial patterns in the spatial heterogeneity of forest phenology is currently lacking. To address this knowledge gap, we developed an index based on satellite retrievals and used it to quantify phenological diversity across global forest biomes. We show that there is considerable variation in phenological diversity among biomes, with the highest overall levels occurring in arid and temperate regions. An analysis of the drivers of the spatial patterns revealed that temperature-related factors primarily determine the variation in phenological diversity. Notably, temperature seasonality and mean annual temperature emerged as the most significant variables in explaining this global-scale variability. Furthermore, an assessment of temporal changes over an 18-year period revealed strong climate-driven shifts of phenological diversity in boreal and arid regions, suggesting that there may be an ongoing widespread homogenisation of land surface phenology within forest ecosystems. Our findings ultimately contribute to the development of a novel Essential Biodiversity Variable, which may enable scientists and practitioners to quantify the functional composition of ecosystems at unprecedented spatial and temporal scales.
Plant functional diversity (FD) is an important component of biodiversity. Evidence shows that FD strongly determines ecosystem functioning and stability and also regulates various ecosystem services ...that underpin human well-being. Given the importance of FD, it is critical to monitor its variations in an explicit manner across space and time, a highly demanding task that cannot be resolved solely by field data. Today, high hopes are placed on satellite-based observations to complement field plot data. The promise is that multiscale monitoring of plant FD, ecosystem functioning, and their services is now possible at global scales in near real-time. However, non-trivial scale challenges remain to be overcome before plant ecology can capitalize on the latest advances in Earth Observation (EO). Here, we articulate the existing scale challenges in linking field and satellite data and further elaborated in detail how to address these challenges via the latest innovations in optical and radar sensor technologies and image analysis algorithms. Addressing these challenges not only requires novel remote sensing theories and algorithms but also urges more effective communication between remote sensing scientists and field ecologists to foster mutual understanding of the existing challenges. Only through a collaborative approach can we achieve the global plant functional diversity monitoring goal.
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