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International audience; Evergreen conifer forests are the most prevalent land cover type in North America. Seasonal changes in the color of evergreen forest canopies have been documented with near-surface remote sensing, but the physiological mechanisms underlying these changes, and the implications for photosynthetic uptake, have not been fully elucidated. Here, we integrate on-the-ground phenological observations, leaf-level physiological measurements, near surface hyperspectral remote sensing and digital camera imagery, towerbased CO 2 flux measurements, and a predictive model to simulate seasonal canopy color dynamics. We show that seasonal changes in canopy color occur independently of new leaf production, but track changes in chlorophyll fluorescence, the photochemical reflectance index, and leaf pigmentation. We demonstrate that at winter-dormant sites, seasonal changes in canopy color can be used to predict the onset of canopy-level photosynthesis in spring, and its cessation in autumn. Finally, we parameterize a simple temperature-based model to predict the seasonal cycle of canopy greenness, and we show that the model successfully simulates interannual variation in the timing of changes in canopy color. These results provide mechanistic insight into the factors driving seasonal changes in evergreen canopy color and provide opportunities to monitor and model seasonal variation in photosynthetic activity using color-based vegetation indices.

Arctic warming is affecting snow cover and soil hydrology, with consequences for carbon sequestration in tundra ecosystems. The scarcity of observations in the Arctic has limited our understanding of the impact of covarying environmental drivers on the carbon balance of tundra ecosystems. In this study, we address some of these uncertainties through a novel record of 119 site-years of summer data from eddy covariance towers representing dominant tundra vegetation types located on continuous permafrost in the Arctic. Here we found that earlier snowmelt was associated with more tundra net CO2 sequestration and higher gross primary productivity (GPP) only in June and July, but with lower net carbon sequestration and lower GPP in August. Although higher evapotranspiration (ET) can result in soil drying with the progression of the summer, we did not find significantly lower soil moisture with earlier snowmelt, nor evidence that water stress affected GPP in the late growing season. Our results suggest that the expected increased CO2 sequestration arising from Arctic warming and the associated increase in growing season length may not materialize if tundra ecosystems are not able to continue sequestering CO2 later in the season.

International audience; Many plant phenological events are sensitive to temperature, leading to changes in the seasonal cycle of ecosystem function as the climate warms. To evaluate the current and future implications of temperature changes for plant phenology, researchers commonly use a metric of temperature sensitivity, which quantifies the change in phenology per degree change in temperature. Here, we examine the temperature sensitivity of phenology, and highlight conditions under which the widely used days-per-degree sensitivity approach is subject to methodological issues that can generate misleading results. We identify several factors, in particular the length of the period over which temperature is integrated, and changes in the statistical characteristics of the integrated temperature, that can affect the estimated apparent sensitivity to temperature. We show how the resulting artifacts can lead to spurious differences in apparent temperature sensitivity and artificial spatial gradients. Such issues are rarely considered in analyses of the temperature sensitivity of phenology. Given the issues identified, we advocate for process-oriented modelling approaches, informed by observations and with fully characterised uncertainties, as a more robust alternative to the simple days-per-degree temperature sensitivity metric. We also suggest approaches to minimise and assess spurious influences in the days-per-degree metric.

Extreme weather causes substantial damage to livelihoods of smallholder farmers globally and are projected to become more frequent in the coming decades as a result of climate change. Index insurance can theoretically help farmers to adapt and mitigate the risks posed by extreme weather events, providing a financial safety net in the event of crop damage or harvest failure. However, uptake of index insurance in practice has lagged far behind expectations. A key reason is that many existing index insurance products suffer from high levels of basis risk, where insurance payouts correlate poorly with actual crop losses due to deficiencies in the underlying index relationship, contract structure or data used to trigger insurance payouts to farmers. In this study, we analyse to what extent the use of crop simulation models and crop phenology monitoring from satellite remote sensing can reduce basis risk in index insurance. Our approach uses a calibrated biophysical process-based crop model (APSIM) to generate a large synthetic crop yield training dataset in order to overcome lack of detailed in-situ observational yield datasets – a common limitation and source of uncertainty in traditional index insurance product design. We use this synthetic yield dataset to train a simple statistical model of crop yields as a function of meteorological and crop growth conditions that can be quantified using open-access earth observation imagery, radiative transfer models, and gridded weather products. Our approach thus provides a scalable tool for yield estimation in smallholder environments, which leverages multiple complementary sources of data that to date have largely been used in isolation in the design and implementation of index insuranceWe apply our yield estimation framework to a case study of rice production in Odisha state in eastern India, an area where agriculture is exposed to significant production risks from monsoonal rainfall variability. Our results demonstrate that yield estimation accuracy improves when using meteorological and crop growth data in combination as predictors, and when accounting for the timing of critical crop development stages using satellite phenological monitoring. Validating against observed yield data from crop cutting experiments, our framework is able to explain around 54% of the variance in rice yields at the village cluster (Gram Panchayat) level that is the key spatial unit for area-yield index insurance products covering millions of smallholder farmers in India. Crucially, our modelling approach significantly outperforms vegetation index-based models that were trained directly on the observed yield data, highlighting the added value obtained from use of crop simulation models in combination with other data sources commonly used in index design.

Our understanding of plant responses to climate change, and their feedbacks to the climate system, rely heavily on consistent long-term observations. Yet, measuring tropical plant species is particularly demanding and results in a lack of spatial and temporal coverage to build relationships between forest dynamics and climatology in the central Congo Basin. Here dendrochronology and wood chemical analyses might provide important ecophysiological information addressing this knowledge gap, especially in tropical forests where the lack of a pronounced seasonality often makes it difficult to discern variability in xylem cell size and density. Conventional optical dendrochronology measurements therefore have strong limitations within these ecosystems, however chemical and elemental analysis can provide additional information. For example, seasonal fluctuations in the carbon (δ13C) and oxygen (δ18O) stable isotope composition of cellulose is linked to variations in local climate and changes in physiological function. A fewstudies have shown that intra-annual and inter-annual variations in the content of calcium [Ca] in tropical tree rings can be used to age tropical trees without rings, constraining estimates of biomass accumulation. Other studies have linked calcium accumulation in different plant organs to the rate of transpiration in trees making it a powerful predictor of inter-annual variability in rainfall for monsoon regions and a strong novel proxy for drought stress.Here we present the first results of X-ray Fluorescence (XRF) measurements conducted at the SOLEIL synchrotron and analysis of [Ca] and [Sr], combined with ancillary data such as anatomical, stable isotope and climatological measurements for three tropical tree species. Most species show variability in calcium corresponding to previous proxy measurements, corroborating previous results and showing the potential of non-destructive XRF measurements of wood samples in support of ecophysiological research. The potential of high throughput scanning, in contrast to stable isotope measurements, opens possibilities to gather data on the large scale required to understand diverse tropical forest ecosystems and their responses to (drought) disturbances.

International audience; Vegetation phenology obtained from time series of remote sensing data is relevant for a range of ecological applications. The freely available Sentinel-2 imagery at a 10 m spatial resolution with a similar to 5-day repeat cycle provides an opportunity to map vegetation phenology at an unprecedented fine spatial scale. To facilitate the production of a Europe-wide Copernicus Land Monitoring Sentinel-2 based phenology dataset, we design and evaluate a framework based on a comprehensive set of ground observations, including eddy covariance gross primary production (GPP), PhenoCam green chromatic coordinate (GCC), and phenology phases from the Pan-European Phenological database (PEP725). We test three vegetation indices (VI) - the normalized difference vegetation index (NDVI), the two-band enhanced vegetation index (EVI2), and the plant phenology index (PPI) - regarding their capability to track the seasonal trajectories of GPP and GCC and their performance in reflecting spatial variabilities of the corresponding GPP and GCC phenometrics, i.e., start of season (SOS) and end of season (EOS). We find that for GPP phenology, PPI performs the best, in particular for evergreen coniferous forest areas where the seasonal variations in leaf area are small and snow is prevalent during wintertime. Results are inconclusive for GCC phenology, for which no index is consistently better than the others. When comparing to PEP725 phenology phases, PPI and EVI2 perform better than NDVI regarding the spatial correlation and consistency (i.e., lower standard deviation). We also link VI phenometrics at various amplitude thresholds to the PEP725 phenophases and find that PPI SOS at 25% and PPI EOS at 15% provide the best matches with the ground-observed phenological stages. Finally, we demonstrate that applying bidirectional reflectance distribution function correction to Sentinel-2 reflectance is a step that can be excluded for phenology mapping in Europe.

International audience; The CONterminous United States (CONUS) presents a large range of climate conditions and biomes where terrestrial primary productivity and its inter-annual variability are controlled regionally by rainfall and/or temperature. Here, the response of ecosystem productivity to those climate variables was investigated across different biomes from 2010 to 2018 using three climate datasets of precipitation, air temperature or drought severity, combined with several proxies of ecosystem productivity: a remote sensing product of aboveground biomass, an net primary productivity (NPP) remote sensing product, an NPP model-based product and four gross primary productivity products. We used an asymmetry index (AI) where positive AI indicates a greater increase of ecosystem productivity in wet years compared to the decline in dry years, and negative AI indicates a greater decline of ecosystem productivity in dry years compared to the increase in wet years. We found consistent spatial patterns of AI across the CONUS for the different products, with negative asymmetries over the Great Plains and positive asymmetries over the southwestern CONUS. Shrubs and, to a lesser extent, evergreen forests show a persistent positive asymmetry, whilst (natural) grasslands appear to have transitioned from positive to negative anomalies during the last decade. The general tendency of dominant negative asymmetry response for ecosystem productivity across the CONUS appears to be influenced by the negative asymmetry of precipitation anomalies. AI was found to be a function of mean rainfall: more positive AIs were found in dry areas where plants are adapted to drought and take advantage of rainfall pulses, and more negative AIs were found in wet areas, with a threshold delineating the two regimes corresponding to a mean annual rainfall of 200–400 mm/year.

Terrestrial ecosystems play a major role in the interannual variability of the global carbon budget representing a substantial sink equivalent to about one-third of current anthropogenic CO2 emissions (Le Quéré et al., 2018). Therefore, it is vital to understand how plants and vegetation respond to the impacts of climate extremes as this impacts the productivity terrestrial ecosystems. The conterminous United States (CONUS) represent a diverse range of climate conditions and ecosystems where productivity and its interannual variability are controlled regionally by rainfall and/or temperature. The responses of ecosystem productivity to wet and dry years have been previously investigated over the CONUS using annual aboveground net primary productivity (ANPP) data from multi-site observations (Knapp and Smith, 2001). From this previous study, a positive asymmetry of ANPP in response to rainfall anomalies was found at individual sites (i.e. an increase of ANPP in wet years was greater than a decline in dry years). Here, we evaluate the asymmetry of ecosystem productivity to rainfall over the entire CONUS from 2010 to 2018 using multiple data streams including: the Global Unified Gauge-Based precipitation data, the GRIDMET surface meteorological data (maximum temperature, minimum temperature, precipitation accumulation, and Palmer Drought Severity Index), the SMOS satellite L-Vegetation optical depth product, CO2 fluxes (net ecosystem exchange (NEE) & gross primary productivity (GPP)) derived from eddy covariance measurements, MODIS ANPP product, Fluxnet GPP at site scale, and three different GPP products from observation-driven models. We address the following two questions: (1) How does ecosystem productivity across the CONUS respond to rainfall anomalies during the period 2010-2018? (2) Does the evidence for positive asymmetry previously observed using site studies hold true across the entire CONUS? For this, we define an asymmetry index (AI) where positive AI indicate a greater increase of productivity in wet years compared to the decline in dry years, and negative AI indicate a greater decline of productivity in dry years compared to the increases in wet years. We find that the spatial patterns of AI across the CONUS are similar amongst the different products and exhibit more pronounced negative asymmetries over the Great Plains and the west north central region whilst positive asymmetries are observed over the southwestern USA during the 2010-2018 period. While the “shrubland” biome shows a persistent positive asymmetry during the period, the “grasslands” biome appears to have switched from positive (observed by Knapp and Smith, 2001) to negative anomalies during the last decade. The observed asymmetry of the different GPP products is reflected by the negative asymmetry of the precipitation anomalies (skewness of precipitation annual anomalies), which we conclude is the primary driver of negative asymmetry across the US continental surface.ReferencesKnapp, A.K., Smith, M.D., 2001. Variation Among Biomes in Temporal Dynamics of Aboveground Primary Production. Science (80-. ). 291, 481. https://doi.org/10.1126/science.291.5503.481Le Quéré, C., Andrew, R.M., Friedlingstein, P., Sitch, S., et al., 2018. Global Carbon Budget 2018. Earth Syst. Sci. Data 10, 2141–2194. https://doi.org/10.5194/essd-10-2141-2018

Temperate and boreal conifer forests are dormant for many months during the cold season. Climate change is altering the winter environment, with increased temperature, altered precipitation, and earlier snowmelt in many locations. If significant enough, these changes may alter patterns of dormancy and activity of evergreens. Here we studied the factors limiting photosynthetic activity of a high-elevation subalpine forest that has undergone substantial warming in recent decades. We tested the hypothesis that this warming has been significant enough to allow photosynthesis during sunny warm days in winter. Using thermal imagery, we found that foliage in winter was sometimes near the temperature optimum for photosynthesis, but no net carbon gain occurred for most of the cold season. Water transport was limited by blockage of sap transport by frozen boles, but not by frozen soils. Foliar carotenoid content was much higher during winter, driven largely by increases in the pool size of the photoprotective xanthophyll cycle. There was no seasonal change in chlorophyll or lutein content. Net carbon uptake began only as boles thawed, with no difference in timing among tree species, and the spring increase in canopy-level photosynthetic capacity occurred before sap transport was detected. The seasonality of gross primary productivity (GPP) was strongly linked to seasonality of xanthophyll cycle deepoxidation state in all species. Seasonality of GPP was detectable with two metrics of canopy color – the Green Chromatic Coordinate and Green-Red Vegetation Index (a proxy for the newly proposed MODIS-based chlorophyll/carotenoid index or CCI). Both indices were significantly correlated with GPP. Together these results indicate the potential for airborne or near-surface remote sensing of leaf color to serve as a metric of photosynthetic activity in evergreen forests, and to monitor physiological changes associated with the progression in and out of winter dormancy.

Time series of vegetation indices (e.g. normalized difference vegetation index [NDVI]) and color indices (e.g. green chromatic coordinate [GCC]) based on radiometric measurements are now available at different spatial and temporal scales ranging from weekly satellite observations to sub-hourly in situ measurements by means of near-surface remote sensing (e.g. spectral sensors or digital cameras). In situ measurements are essential for providing validation data for satellite-derived vegetation indices. In this study we used a recently developed method to calculate NDVI from near-infrared (NIR) enabled digital cameras (NDVIC) at 17 sites (for a total of 74 year-sites) encompassing six plant functional types (PFT) from the PhenoCam network. The seasonality of NDVIC was comparable to both NDVI measured by ground spectral sensors and by the moderate resolution imaging spectroradiometer (MODIS). We calculated site- and PFT-specific scaling factors to correct NDVIC values and recommend the use of site-specific NDVI from MODIS in order to scale NDVIC. We also compared GCC extracted from red-green-blue images to NDVIC and found PFT-dependent systematic differences in their seasonalities. During senescence, NDVIC lags behind GCC in deciduous broad-leaf forests and grasslands, suggesting that GCC is more sensitive to changes in leaf color and NDVIC is more sensitive to changes in leaf area. In evergreen forests, NDVIC peaks later than GCC in spring, probably tracking the processes of shoot elongation and new needle formation. Both GCC and NDVIC can be used as validation tools for the MODIS Land Cover Dynamics Product (MCD12Q2) for deciduous broad-leaf spring phenology, whereas NDVIC is more comparable than GCC with autumn phenology derived from MODIS. For evergreen forests, we found a poor relationship between MCD12Q2 and camera-derived phenology, highlighting the need for more work to better characterize the seasonality of both canopy structure and leaf biochemistry in those ecosystems. Our results demonstrate that NDVIC is in excellent agreement with NDVI obtained from spectral measurements, and that NDVIC and GCC can complement each other in describing ecosystem phenology. Additionally, NDVIC allows the detection of structural changes in the canopy that cannot be detected by visible-wavelength imagery.

The Mediterranean-type oak/grass savanna of California is composed of widely spaced oak trees with understory grasses. These savanna regions are interspersed with large areas of more open grasslands. The ability of remotely sensed data (with various spatial resolutions) to monitor the phenology in these water-limited oak/grass savannas and open grasslands is explored over the 2012–2015 timeframe using data from Landsat (30 m), the MODerate resolution Imaging Spectroradiometer (MODIS – gridded 500 m), and the Visible Infrared Imaging Radiometer Suite (VIIRS – gridded 500 m) data. Vegetation phenology detected from near-ground level, webcam based PhenoCam imagery from two sites in the Ameriflux Network (long-term flux measurement network of the Americas) (Tonzi Ranch and Vaira Ranch) is upscaled, using a National Agriculture Imagery Program (NAIP) aerial image (1 m), to evaluate the detection of vegetation phenology of these savannas and grasslands with the satellite data. Results show that the Normalized Difference Vegetation Index (NDVI) time series observed from the satellite sensors are all strongly correlated with the PhenoCam NDVI values from Tonzi Ranch (R 2 > 0.67) and Vaira Ranch (R 2 > 0.81). However, the different viewing geometries and spatial coverage of the PhenoCams and the various satellite sensors may cause differences in the absolute phenological transition dates. Analysis of frequency histograms of phenological dates illustrate that the phenological dates in the relatively homogeneous open grasslands are consistent across the different spatial resolutions, in contrast, the relatively heterogeneous oak/grass savannas display has somewhat later greenup, maturity, and dormancy dates at 30 m resolution than at 500 m scale due to the different phenological cycles exhibited by the overstory trees and the understory grasses. In addition, phenologies derived from the MODIS view angle corrected reflectance (Nadir BRDF-Adjusted Reflectance – NBAR) and the newly developed VIIRS NBAR are shown to provide comparable phenological dates (majority absolute bias ≤2 days) in this area.

Monitoring vegetation phenology is critical for quantifying climate change impacts on ecosystems. We present an extensive dataset of 1783 site-years of phenological data derived from PhenoCam network imagery from 393 digital cameras, situated from tropics to tundra across a wide range of plant functional types, biomes, and climates. Most cameras are located in North America. Every half hour, cameras upload images to the PhenoCam server. Images are displayed in near-real time and provisional data products, including timeseries of the Green Chromatic Coordinate (Gcc), are made publicly available through the project web page (https://phenocam.sr.unh.edu/webcam/gallery/). Processing is conducted separately for each plant functional type in the camera field of view. The PhenoCam Dataset v2.0, described here, has been fully processed and curated, including outlier detection and expert inspection, to ensure high quality data. This dataset can be used to validate satellite data products, to evaluate predictions of land surface models, to interpret the seasonality of ecosystem-scale CO2 and H2O flux data, and to study climate change impacts on the terrestrial biosphere., Measurement(s)plant phenological trait • plant greennessTechnology Type(s)digital cameraSample Characteristic - OrganismEmbryophytaSample Characteristic - Environmentterrestrial biomeSample Characteristic - LocationEarth (planet) Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.9913694

Extreme weather events cause considerable damage to the livelihoods of smallholder farmers globally. Whilst index insurance can help farmers cope with the financial consequences of extreme weather, a major challenge for index insurance is basis risk, where insurance payouts correlate poorly with actual crop losses. We analyse to what extent the use of crop simulation models and crop phenology monitoring can reduce basis risk in index insurance. Using a biophysical process-based crop model (Agricultural Production System sIMulator (APSIM)) applied for rice producers in Odisha, India, we simulate a synthetic yield dataset to train non-parametric statistical models to predict rice yields as a function of meteorological and phenological conditions. We find that the performance of statistical yield models depends on whether meteorological or phenological conditions are used as predictors and whether one aggregates these predictors by season or crop growth stage. Validating the preferred statistical model with observed yield data, we find that the model explains around 54% of the variance in rice yields at the village cluster (Gram Panchayat) level, outperforming vegetation index-based models that were trained directly on the observed yield data. Our methods and findings can guide efforts to design smart phenology-based index insurance and target yield monitoring resources in smallholder farming environments.

The interacting effects of temperature and precipitation changes on grasslands remain hard to quantify. Research now indicates widespread and consistent increases in North American grassland productivity under climate change despite greater aridity.

© 2018 John Wiley & Sons Ltd Aim: Large tropical trees form the interface between ground and airborne observations, offering a unique opportunity to capture forest properties remotely and to investigate their variations on broad scales. However, despite rapid development of metrics to characterize the forest canopy from remotely sensed data, a gap remains between aerial and field inventories. To close this gap, we propose a new pan-tropical model to predict plot-level forest structure properties and biomass from only the largest trees. Location: Pan-tropical. Time period: Early 21st century. Major taxa studied: Woody plants. Methods: Using a dataset of 867 plots distributed among 118 sites across the tropics, we tested the prediction of the quadratic mean diameter, basal area, Lorey's height, community wood density and aboveground biomass (AGB) from the ith largest trees. Results: Measuring the largest trees in tropical forests enables unbiased predictions of plot- and site-level forest structure. The 20 largest trees per hectare predicted quadratic mean diameter, basal area, Lorey's height, community wood density and AGB with 12, 16, 4, 4 and 17.7% of relative error, respectively. Most of the remaining error in biomass prediction is driven by differences in the proportion of total biomass held in medium-sized trees (50–70 cm diameter at breast height), which shows some continental dependency, with American tropical forests presenting the highest proportion of total biomass in these intermediate-diameter classes relative to other continents. Main conclusions: Our approach provides new information on tropical forest structure and can be used to generate accurate field estimates of tropical forest carbon stocks to support the calibration and validation of current and forthcoming space missions. It will reduce the cost of field inventories and contribute to scientific understanding of tropical forest ecosystems and response to climate change.

Vegetation phenology controls the seasonality of many ecosystem processes, as well as numerous biosphere-atmosphere feedbacks. Phenology is also highly sensitive to climate change and variability. Here we present a series of datasets, together consisting of almost 750 years of observations, characterizing vegetation phenology in diverse ecosystems across North America. Our data are derived from conventional, visible-wavelength, automated digital camera imagery collected through the PhenoCam network. For each archived image, we extracted RGB (red, green, blue) colour channel information, with means and other statistics calculated across a region-of-interest (ROI) delineating a specific vegetation type. From the high-frequency (typically, 30 min) imagery, we derived time series characterizing vegetation colour, including “canopy greenness”, processed to 1- and 3-day intervals. For ecosystems with one or more annual cycles of vegetation activity, we provide estimates, with uncertainties, for the start of the “greenness rising” and end of the “greenness falling” stages. The database can be used for phenological model validation and development, evaluation of satellite remote sensing data products, benchmarking earth system models, and studies of climate change impacts on terrestrial ecosystems.

International audience; In deciduous forests, spring leaf phenology controls the onset of numerous ecosystem functions. While most studies have focused on a single annual spring event, such as budburst, ecosystem functions like photosynthesis and transpiration increase gradually after budburst, as leaves grow to their mature size. Here, we examine the "velocity of green-up," or duration between budburst and leaf maturity, in deciduous forest ecosystems of eastern North America. We use a diverse data set that includes 301 site-years of phenocam data across a range of sites, as well as 22 years of direct ground observations of individual trees and 3 years of fine-scale high-frequency aerial photography, both from Harvard Forest. We find a significant association between later start of spring and faster green-up: - 0.47 ± 0.04 (slope ± 1 SE) days change in length of green-up for every day later start of spring within phenocam sites, - 0.31 ± 0.06 days/day for trees under direct observation, and - 1.61 ± 0.08 days/day spatially across fine-scale landscape units. To explore the climatic drivers of spring leaf development, we fit degree-day models to the observational data from Harvard Forest. We find that the default phenology parameters of the ecosystem model PnET make biased predictions of leaf initiation (39 days early) and maturity (13 days late) for red oak, while the optimized model has biases of 1 day or less. Springtime productivity predictions using optimized parameters are closer to results driven by observational data (within 1%) than those of the default parameterization (17% difference). Our study advances empirical understanding of the link between early and late spring phenophases and demonstrates that accurately modeling these transitions is important for simulating seasonal variation in ecosystem productivity.

Phenology is a valuable diagnostic of ecosystem health, and has applications to environmental monitoring and management. Here, we conduct an intercomparison analysis using phenological transition dates derived from near-surface PhenoCam imagery and MODIS satellite remote sensing. We used approximately 600 site-years of data, from 128 camera sites covering a wide range of vegetation types and climate zones. During both “greenness rising” and “greenness falling” transition phases, we found generally good agreement between PhenoCam and MODIS transition dates for agricultural, deciduous forest, and grassland sites, provided that the vegetation in the camera field of view was representative of the broader landscape. The correlation between PhenoCam and MODIS transition dates was poor for evergreen forest sites. We discuss potential reasons (including sub-pixel spatial heterogeneity, flexibility of the transition date extraction method, vegetation index sensitivity in evergreen systems, and PhenoCam geolocation uncertainty) for varying agreement between time series of vegetation indices derived from PhenoCam and MODIS imagery. This analysis increases our confidence in the ability of satellite remote sensing to accurately characterize seasonal dynamics in a range of ecosystems, and provides a basis for interpreting those dynamics in the context of tangible phenological changes occurring on the ground.

International audience; 1. Phenology is a first-order control on productivity and mediates the biophysical environment by altering albedo, surface roughness length and evapotranspiration. Accurate and transparent modelling of vegetation phenology is therefore key in understanding feedbacks between the biosphere and the climate system. 2. Here, we present the PHENOR R package and modelling framework. The framework leverages measurements of vegetation phenology from four common phenology observation datasets, the PhenoCam network, the USA National Phenology Network (USA-NPN), the Pan European Phenology Project (PEP725), MODIS phenology (MCD12Q2) combined with (global) retrospective and projected climate data. 3. We show an example analysis, using the PHENOR modelling framework, which quickly and easily compares 20 included spring phenology models for three plant functional types. An analysis of model skill using the root mean squared (RMSE) error shows little or no difference regardless of model structure, corroborating previous studies. We argue that addressing this issue will require novel model development combined with easy data assimilation as facilitated by our framework. 4. In conclusion, we hope the PHENOR phenology modelling framework in the R language and environment for statistical computing will facilitate reproducibility and community driven phenology model development, in order to increase their overall predictive power, and leverage an ever growing number of phenology data products.

Shifts in vegetation phenology are a key example of the biological effects of climate change1–3. However, there is substantial uncertainty about whether these temperature-driven trends will continue, or whether other factors—for example, photoperiod—will become more important as warming exceeds the bounds of historical variability4,5. Here we use phenological transition dates derived from digital repeat photography6 to show that experimental whole-ecosystem warming treatments7 of up to +9 °C linearly correlate with a delayed autumn green-down and advanced spring green-up of the dominant woody species in a boreal Picea–Sphagnum bog. Results were confirmed by direct observation of both vegetative and reproductive phenology of these and other bog plant species, and by multiple years of observations. There was little evidence that the observed responses were constrained by photoperiod. Our results indicate a likely extension of the period of vegetation activity by 1–2 weeks under a ‘CO2 stabilization’ climate scenario (+2.6 ± 0.7 °C), and 3–6 weeks under a ‘high-CO2 emission’ scenario (+5.9 ± 1.1 °C), by the end of the twenty-first century. We also observed severe tissue mortality in the warmest enclosures after a severe spring frost event. Failure to cue to photoperiod resulted in precocious green-up and a premature loss of frost hardiness8, which suggests that vulnerability to spring frost damage will increase in a warmer world9,10. Vegetation strategies that have evolved to balance tradeoffs associated with phenological temperature tracking may be optimal under historical climates, but these strategies may not be optimized for future climate regimes. These in situ experimental results are of particular importance because boreal forests have both a circumpolar distribution and a key role in the global carbon cycle11. Experimental whole-ecosystem warming treatments of a Picea–Sphagnum peat bog reveal the likely phenological consequences of future temperature increases that exceed those of historical climate regimes.

International audience; AbstractKey messageTree heights in the central Congo Basin are overestimated using best-available height-diameter models. These errors are propagated into the estimation of aboveground biomass and canopy height, causing significant bias when used for calibration of remote sensing products in this region.ContextTree height-diameter models are important components of estimating aboveground biomass (AGB) and calibrating remote sensing products in tropical forests.AimsFor a data-poor area of the central Congo Basin, we quantified height-diameter model performance of local, regional and pan-tropical models for their use in estimating AGB and canopy height.MethodsAt three old-growth forest sites, we assessed the bias introduced in height estimation by regional and pan-tropical height-diameter models. We developed an optimal local model with site-level randomizations accounted for by using a mixed-effects modeling approach. We quantified the error propagation of modeled heights for estimating AGB and canopy height.ResultsRegional and pan-tropical height-diameter models produced a significant overestimation in tree height, propagating into significant overestimations of AGB and Lorey’s height. The pan-tropical model accounting for climatic drivers performed better than the regional models. We present a local height-diameter model which produced nonsignificant errors for AGB and canopy height estimations at our study area.ConclusionThe application of general models at our study area introduced bias in tree height estimations and the derived stand-level variables. Improved delimitation of regions in tropical Africa with similar forest structure is needed to produce models fit for calibrating remote sensing products.

In the spring of 2010, temperatures averaged ~3 °C above the long-term mean (March–May) across the northeastern United States. However, in mid-to-late spring, much of this region experienced a severe frost event. The spring of 2010 therefore provides a case study on how future spring temperature extremes may affect northeastern forest ecosystems. We assessed the response of three northern hardwood tree species (sugar maple, American beech, yellow birch) to these anomalous temperature patterns using several different data sources and addressed four main questions: (1) Along an elevational gradient, how was each species affected by the late spring frost? (2) How did differences in phenological growth strategy influence their response? (3) How did the late spring frost affect ecosystem productivity within the study domain? (4) What are the potential long-term impacts of spring frost events on forest community ecology? Our results show that all species exhibited early leaf development triggered by the warm spring. However, yellow birch and American beech have more conservative growth strategies and were largely unaffected by the late spring frost. In contrast, sugar maples responded strongly to warmer temperatures and experienced widespread frost damage that resulted in leaf loss and delayed canopy development. Late spring frost events may therefore provide a competitive advantage for yellow birch and American beech at the expense of sugar maple. Results from satellite remote sensing confirm that frost damage was widespread throughout the region at higher elevations (>500 m). The frost event is estimated to have reduced gross ecosystem productivity by 70–153 g C m−2, or 7–14% of the annual gross productivity (1061 ± 82 g C m−2) across 8753 km2 of high-elevation forest. We conclude that frost events following leaf out, which are expected to become more common with climate change, may influence both forest composition and ecosystem productivity.

Green leaf phenology is known to be sensitive to climate variation. Phenology is also important because it exerts significant control on terrestrial carbon cycling and sequestration. High-quality measurements of green leaf phenology are therefore increasingly important for understanding the effects of climate change on ecosystem function and biosphere–atmosphere interactions. In this paper, we compare “near-surface” and satellite remote sensing-based observations of vegetation phenology at four deciduous forest sites. Specifically, we addressed three questions related to how observations of plant phenology measured by red–green–blue (RGB) cameras mounted on towers above forest canopies are related to measurements of phenology acquired by moderate resolution sensors on satellites. First, how are estimated phenophase transition dates — or the observable stages in the life cycle of plants — influenced by the choice of vegetation index (VI) measured by remote sensing? Second, are VIs and phenological metrics derived from near-surface and satellite remote sensing comparable, and what is the nature and magnitude of covariation between near-surface and satellite-remote sensing-based estimates of phenology at seasonal and interannual time scales? Third, does near-surface remote sensing data provide a basis for validating satellite-derived land surface phenology products and what are the requirements for achieving this goal? Our study provides substantial support for future efforts linking satellite and near-surface remote sensing. We show significant agreement between phenological time series and metrics derived from these two data sources. However, issues of scale and representation strongly influence the relationship between near surface and satellite remote sensing measures of phenology. In particular, intra- and interannual correlation between time series from each source are dependent on how representative the camera FOV is of the regional landscape. Further, our results show that the specific VI used to monitor phenology exerts substantial influence on satellite VI derived phenological metrics, and by extension, how they compare to VI time series and metrics obtained from near-surface remote sensing. These results improve understanding of how near-surface and satellite remote sensing complement each other. However, more work is required to develop formal protocols for evaluating, calibrating and validating satellite remote sensing phenology products using near surface remote sensing at a regional to continental scale.

© 2015 by the Ecological Society of America The proliferation of digital cameras co-located with eddy covariance instrumentation provides new opportunities to better understand the relationship between canopy phenology and the seasonality of canopy photosynthesis. In this paper we analyze the abilities and limitations of canopy color metrics measured by digital repeat photography to track seasonal canopy development and photosynthesis, determine phenological transition dates, and estimate intra-annual and interannual variability in canopy photosynthesis. We used 59 site-years of camera imagery and net ecosystem exchange measurements from 17 towers spanning three plant functional types (deciduous broadleaf forest, evergreen needleleaf forest, and grassland/crops) to derive color indices and estimate gross primary productivity (GPP). GPP was strongly correlated with greenness derived from camera imagery in all three plant functional types. Specifically, the beginning of the photosynthetic period in deciduous broadleaf forest and grassland/crops and the end of the photosynthetic period in grassland/crops were both correlated with changes in greenness; changes in redness were correlated with the end of the photosynthetic period in deciduous broadleaf forest. However, it was not possible to accurately identify the beginning or ending of the photosynthetic period using camera greenness in evergreen needleleaf forest. At deciduous broadleaf sites, anomalies in integrated greenness and total GPP were significantly correlated up to 60 days after the mean onset date for the start of spring. More generally, results from this work demonstrate that digital repeat photography can be used to quantify both the duration of the photosynthetically active period as well as total GPP in deciduous broadleaf forest and grassland/crops, but that new and different approaches are required before comparable results can be achieved in evergreen needleleaf forest.

Digital repeat photography is becoming widely used for near-surface remote sensing of vegetation. Canopy greenness, which has been used extensively for phenological applications, can be readily quantified from camera images. Important questions remain, however, as to whether the observed changes in canopy greenness are directly related to changes in leaf-level traits, changes in canopy structure, or some combination thereof. We investigated relationships between canopy greenness and various metrics of canopy structure and function, using five years (2008-2012) of automated digital imagery, ground observations of phenological transitions, leaf area index (LAI) measurements, and eddy covariance estimates of gross ecosystem photosynthesis from the Harvard Forest, a temperate deciduous forest in the northeastern United States. Additionally, we sampled canopy sunlit leaves on a weekly basis throughout the growing season of 2011. We measured physiological and morphological traits including leaf size, mass (wet/dry), nitrogen content, chlorophyll fluorescence, and spectral reflectance and characterized individual leaf color with flatbed scanner imagery. Our results show that observed spring and autumn phenological transition dates are well captured by information extracted from digital repeat photography. However, spring development of both LAI and the measured physiological and morphological traits are shown to lag behind spring increases in canopy greenness, which rises very quickly to its maximum value before leaves are even half their final size. Based on the hypothesis that changes in canopy greenness represent the aggregate effect of changes in both leaf-level properties (specifically, leaf color) and changes in canopy structure (specifically, LAI), we developed a two end-member mixing model. With just a single free parameter, the model was able to reproduce the observed seasonal trajectory of canopy greenness. This analysis shows that canopy greenness is relatively insensitive to changes in LAI at high LAI levels, which we further demonstrate by assessing the impact of an ice storm on both LAI and canopy greenness. Our study provides new insights into the mechanisms driving seasonal changes in canopy greenness retrieved from digital camera imagery. The nonlinear relationship between canopy greenness and canopy LAI has important implications both for phenological research applications and for assessing responses of vegetation to disturbances.

Tropical forest soils are a significant source for the greenhouse gas N2O as well as for NO, a precursor of tropospheric ozone. However, current estimates are uncertain due to the limited number of field measurements. Furthermore, there is considerable spatial and temporal variability of N2O and NO emissions due to the variation of environmental conditions such as soil properties, vegetation characteristics and meteorology. In this study we used a process-based model (ForestDNDC-tropica) to estimate N2O and NO emissions from tropical highland forest (Nyungwe) soils in southwestern Rwanda. To extend the model inputs to regional scale, ForestDNDC-tropica was linked to an exceptionally large legacy soil dataset. There was agreement between N2O and NO measurements and the model predictions though the ForestDNDC-tropica resulted in considerable lower emissions for few sites. Low similarity was specifically found for acidic soil with high clay content and reduced metals, indicating that chemo-denitrification processes on acidic soils might be under-represented in the current ForestDNDC-tropica model. The results showed that soil bulk density and pH are the most influential factors driving spatial variations in soil N2O and NO emissions for tropical forest soils. The area investigated (1113 km2) was estimated to emit ca. 439 ± 50 t N2O-N yr−1 (2.8–5.5 kg N2O-N ha−1 yr−1) and 244 ± 16 t NO-N yr−1 (0.8–5.1 kg N ha−1 yr−1). Consistent with less detailed studies, we confirm that tropical highland rainforest soils are a major source of atmospheric N2O and NO.

Policies to reduce emissions from deforestation and forest degradation largely depend on accurate estimates of tropical forest carbon stocks. Here we present the first field-based carbon stock data for the Central Congo Basin in Yangambi, Democratic Republic of Congo. We find an average aboveground carbon stock of 162 ± 20 Mg C ha(-1) for intact old-growth forest, which is significantly lower than stocks recorded in the outer regions of the Congo Basin. The best available tree height-diameter relationships derived for Central Africa do not render accurate canopy height estimates for our study area. Aboveground carbon stocks would be overestimated by 24% if these inaccurate relationships were used. The studied forests have a lower stature compared with forests in the outer regions of the basin, which confirms remotely sensed patterns. Additionally, we find an average soil carbon stock of 111 ± 24 Mg C ha(-1), slightly influenced by the current land-use change.

International audience; Interannual variability in biosphere-atmosphere exchange of CO2 is driven by a diverse range of biotic and abiotic factors. Replicating this variability thus represents the ‘acid test’ for terrestrial biosphere models. Although such models are commonly used to project responses to both normal and anomalous variability in climate, they are rarely tested explicitly against inter-annual variability in observations. Herein, using standardized data from the North American Carbon Program, we assess the performance of 16 terrestrial biosphere models and 3 remote sensing products against long-term measurements of biosphere-atmosphere CO2 exchange made with eddy-covariance flux towers at 11 forested sites in North America. Instead of focusing on model-data agreement we take a systematic, variability-oriented approach and show that although the models tend to reproduce the mean magnitude of the observed annual flux variability, they fail to reproduce the timing. Large biases in modeled annual means are evident for all models. Observed interannual variability is found to commonly be on the order of magnitude of the mean fluxes. None of the models consistently reproduce observed interannual variability within measurement uncertainty. Underrepresentation of variability in spring phenology, soil thaw and snowpack melting, and difficulties in reproducing the lagged response to extreme climatic events are identified as systematic errors, common to all models included in this study.

Over the last decade, remote-sensing products found their use in numerous public applications and scientific research. Frequent validation of remote-sensing products is necessary to ensure the quality and accuracy. An important step in this process is the upscaling from field measurements of leaf area index (LAI) or other biogeophysical variables to the scale of a high-resolution satellite image. Unlike other vegetation types, semi-arid woodlands are often characterized by a distinct vegetation pattern, a low vegetation cover and site variations in bare soil reflectance which influence the upscaling process. This paper focuses on the upscaling of LAI field measurements of a semi-arid woodland in northwest China and the uncertainties associated with it. The LAI measurements were scaled up using two different approaches. The first upscaling method uses an ordinary average of the LAI measurements. The alternative method uses an area-based weighted average. The vegetation characteristics showed abundant small vegetation patches which were not accurately reflected in the systematic LAI field measurements. Therefore, the alternative method which reflected the vegetation structure more accurately needs to be considered. We conclude that an area-weighted average based on the fractional green vegetation cover is to be preferred over a simple average. If possible destructive sampling of the LAI is be preferred over indirect LAI measurements to reduce the errors associated with vegetation clumping and the presence of abundant non-green biomass.

Background : African tropical rainforests are one of the most important hotspots to look for changes in the upcoming decades when it comes to C storage and release. The focus of studying C dynamics in these systems lies traditionally on living aboveground biomass. Belowground soil organic carbon stocks have received little attention and estimates of the size, controls and distribution of soil organic carbon stocks are highly uncertain. In our study on lowland rainforest in the central Congo basin, we combine both an assessment of the aboveground C stock with an assessment of the belowground C stock and analyze the latter in terms of functional pools and controlling factors. Principal Findings : Our study shows that despite similar vegetation, soil and climatic conditions, soil organic carbon stocks in an area with greater tree height (=larger aboveground carbon stock) were only half compared to an area with lower tree height (=smaller aboveground carbon stock). This suggests that substantial variability in the aboveground vs. belowground C allocation strategy and/or C turnover in two similar tropical forest systems can lead to significant differences in total soil organic C content and C fractions with important consequences for the assessment of the total C stock of the system. Conclusions/Significance : We suggest nutrient limitation, especially potassium, as the driver for aboveground versus belowground C allocation. However, other drivers such as C turnover, tree functional traits or demographic considerations cannot be excluded. We argue that large and unaccounted variability in C stocks is to be expected in African tropical rain-forests. Currently, these differences in aboveground and belowground C stocks are not adequately verified and implemented mechanistically into Earth System Models. This will, hence, introduce additional uncertainty to models and predictions of the response of C storage of the Congo basin forest to climate change and its contribution to the terrestrial C budget.

By the end of this century, mean annual temperatures in the Northeastern United States are expected to warm by 3–5 °C, which will have significant impacts on the structure and function of temperate forests in this region. To improve understanding of these impacts, we exploited two recent climate anomalies to explore how the springtime phenology of Northeastern temperate deciduous forests will respond to future climate warming. Specifically, springtime temperatures in 2010 and 2012 were the warmest on record in the Northeastern United States, with temperatures that were roughly equivalent to the lower end of warming scenarios that are projected for this region decades from now. Climate conditions in these two years therefore provide a unique empirical basis, that complements model-based studies, for improving understanding of how northeastern temperate forest phenology will change in the future. To perform our investigation, we analyzed near surface air temperatures from the United States Historical Climatology Network, time series of satellite-derived vegetation indices from NASA’s Moderate Resolution Imaging Spectroradiometer, and in situ phenological observations. Our study region encompassed the northern third of the eastern temperate forest ecoregion, extending from Pennsylvania to Canada. Springtime temperatures in 2010 and 2012 were nearly 3 °C warmer than long-term average temperatures from 1971–2000 over the region, leading to median anomalies of more than 100 growing degree days. In response, satellite and ground observations show that leaf emergence occurred up to two weeks earlier than normal, but with significant sensitivity to the specific timing of thermal forcing. These results are important for two reasons. First, they provide an empirical demonstration of the sensitivity of springtime phenology in northeastern temperate forests to future climate change that supports and complements modelbased predictions. Second, our results show that subtle differences in the character of thermal