Observations of vegetation phenology at regional-to-global scales provide important information r... more Observations of vegetation phenology at regional-to-global scales provide important information regarding seasonal variation in the fluxes of energy, carbon, and water between the biosphere and the atmosphere. Numerous algorithms have been developed to estimate phenological transition dates using time series of remotely sensed spectral vegetation indices. A key challenge, however, is that different algorithms provide inconsistent results. This study provides a comprehensive comparison of start of season (SOS) and end of season (EOS) phenological transition dates estimated from 500 m MODIS data based on two widely used sources of such data: the TIMESAT program and the MODIS Global Land Cover Dynamics (MLCD) product. Specifically, we evaluate the impact of land cover class, criteria used to identify SOS and EOS, and fitting algorithm (local versus global) on the transition dates estimated from time series of MODIS enhanced vegetation index (EVI). Satellite-derived transition dates fro...
The high-resolution Sentinel-2 data potentially enable the estimation of gross primary productivi... more The high-resolution Sentinel-2 data potentially enable the estimation of gross primary productivity (GPP) at finer spatial resolution by better capturing the spatial variation in a heterogeneous landscapes. This study investigates the potential of 10 m resolution reflectance from the Sentinel-2 Multispectral Instrument to improve the accuracy of GPP estimation across Nordic vegetation types, compared with the 250 m and 500 m resolution reflectance from the Moderate Resolution Imaging Spectroradiometer (MODIS). We applied linear regression models with inputs of two-band enhanced vegetation index (EVI2) derived from Sentinel-2 and MODIS reflectance, respectively, together with various environmental drivers to estimate daily GPP at eight Nordic eddy covariance (EC) flux tower sites. Compared with the GPP from EC measurements, the accuracies of modelled GPP were generally high (R2 = 0.84 for Sentinel-2; R2 = 0.83 for MODIS), and the differences between Sentinel-2 and MODIS were minimal....
Time series from Landsat and Sentinel-2 satellites have great potential for modeling vegetation s... more Time series from Landsat and Sentinel-2 satellites have great potential for modeling vegetation seasonality. However, irregular time sampling and frequent data loss due to clouds, snow, and short growing seasons, makes this modeling a challenge. We describe a new method for modeling seasonal vegetation index dynamics from satellite time series data. The method is based on box constrained separable least squares fits to logistic model functions combined with seasonal shape priors. To enable robust estimates, we extract a base level (i.e., the minimum dormant season value) from the frequency distribution of clear-sky vegetation index values. A seasonal shape prior is computed from several years of data, and in the final fits local parameters are box constrained. More specifically, if enough data values exist in a certain time period, the corresponding local parameters determining the shape of the model function over this period are relaxed and allowed to vary freely. If there are no o...
The climate-vegetation coupling exerts a strong control on terrestrial carbon budgets and will af... more The climate-vegetation coupling exerts a strong control on terrestrial carbon budgets and will affect the future evolution of global climate under continued anthropogenic forcing. Nonetheless, the effects of climatic conditions on such coupling at specific times in the growing season remain poorly understood. We quantify the climate-vegetation coupling in Europe over 1982-2014 at multiple spatial and temporal scales, by decomposing sub-seasonal anomalies of vegetation greenness using a grid-wise definition of the growing season. We base our analysis on long-term vegetation indices (Normalized Difference Vegetation Index and two-band Enhanced Vegetation Index), growing conditions (including 2m temperature, downwards surface solar radiation, and root-zone soil moisture), and multiple teleconnection indices that reflect the large-scale climatic conditions over Europe. We find that the largescale climate-vegetation coupling during the first two months of the growing season largely determines the full-year coupling. The North Atlantic Oscillation and Scandinavian Pattern phases one-to-two months before the start of the growing season are the dominant and contrasting drivers of the early growing season climate-vegetation coupling over large parts of boreal and temperate Europe. The East Atlantic Pattern several months in advance of the growing season exerts a strong control on the temperate belt and the Mediterranean region. The strong role of early growing season anomalies in vegetative activity within the growing season emphasizes the importance of a grid-wise definition of the growing season when studying the large-scale climate-vegetation coupling in Europe. Plain Language Summary Climate and terrestrial ecosystems interact and affect the global climate. Such a climate-vegetation relationship can be effectively quantified by using satellites to measure how leafy and active the vegetation is, and numerical indices reflecting large-scale climate patterns over a given region. Previous studies generally focused on changes in mean vegetation indices over the full growing season, which is usually defined by a fixed range of astronomical months for large geographical regions. This overlooks the fact that growing seasons differ in space and vegetation responds differently to the climate in different growing season periods. In this study, we explore how vegetation and climate interact within a growing season, here defined specifically for the local conditions. We find that there are strong relationships between the large-scale climate patterns and vegetation indices during the first two months of the growing season. Our findings highlight the important role of the vegetation activity during the early growing season for the year-to-year vegetation changes in Europe. Hence, for a better understanding of the climate-vegetation relationships, it is necessary to consider the spatial differences in the growing season, in particular for large geographical regions. WU ET AL.
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Anomalies in vegetation activity in the early growing season determine the climate-vegetation coupling in Europe
<p> Recent accelerating global warming with increasing climate variability ... more <p> Recent accelerating global warming with increasing climate variability exerts a strong impact on terrestrial carbon budgets, but the ecosystem response to the changing climate and the overall climate-vegetation coupling remain largely unclear during different stages of the growing season. The timing of growing seasons can be modulated by different environmental conditions (e.g., thermal and hydrological changes) and affect the overall interpretation of regional climate-vegetation coupling. Here, we analyse the climate-vegetation coupling for Europe during 1982–2014 using a grid-wise definition of the growing season period based on remote sensing data. We quantify sub-seasonal anomalies of vegetation greenness from long-term vegetation indices (Normalized Difference Vegetation Index and two-band Enhanced Vegetation Index), and their relationships with corresponding local growing conditions (2m temperature, downwards surface solar radiation and root-zone soil moisture); and with multiple climate variability indices that reflect the large-scale climatic conditions over Europe. We find that early growing season anomalies in vegetation greenness tend to be large during the first two months of the growing season and that the coupling of these anomalies with large-scale climate largely determines the full-year climate-vegetation coupling. The North Atlantic Oscillation (NAO) and Scandinavian Pattern (SCA) phases evaluated one to two months before the start of growing season are the dominant drivers of the early growing season climate-vegetation coupling over large parts of boreal and temperate Europe. However, the sign of the effect of these indices on vegetation greenness is opposite. The East Atlantic Pattern (EA) evaluated several months in advance of the growing season is instead a main controlling factor on the temperate belt and the Mediterranean region. These findings highlight the importance of accounting for the spatial heterogeneity of growing season periods using location-specific definitions when studying large-scale land-atmosphere interactions.</p>
The 2015 Paris Agreement encourages stakeholders to implement sustainable forest management polic... more The 2015 Paris Agreement encourages stakeholders to implement sustainable forest management policies to mitigate anthropogenic emissions of greenhouse gases (GHG). The net effects of forest management on the climate and the environment are, however, still not completely understood, partially as a result of a lack of long-term measurements of GHG fluxes in managed forests. During the period 2010–2013, we simultaneously measured carbon dioxide (CO2), methane (CH4) and nitrous oxide (N2O) fluxes using the flux-gradient technique at two clear-cut plots of different degrees of wetness, located in central Sweden. The measurements started approx. one year after clear-cutting, directly following soil scarification and planting. The study focused on robust inter-plot comparisons, spatial and temporal dynamics of GHG fluxes, and the determination of the global warming potential of a clear-cut boreal forest. The clear-cutting resulted in significant emissions of GHGs at both the wet and the dr...
The 2015 Paris Agreement encourages stakeholders to implement sustainable forest management polic... more The 2015 Paris Agreement encourages stakeholders to implement sustainable forest management policies to mitigate anthropogenic emissions of greenhouse gases (GHG). The net effects of forest management on the climate and the environment are, however, still not completely understood, partially as a result of a lack of long-term measurements of GHG fluxes in managed forests. During the period 2010–2013, we simultaneously measured carbon dioxide (CO2), methane (CH4) and nitrous oxide (N2O) fluxes using the flux-gradient technique at two clear-cut plots of different degrees of wetness, located in central Sweden. The measurements started approx. one year after clear-cutting, directly following soil scarification and planting. The study focused on robust inter-plot comparisons, spatial and temporal dynamics of GHG fluxes, and the determination of the global warming potential of a clear-cut boreal forest. The clear-cutting resulted in significant emissions of GHGs at both the wet and the dr...
Many time-series smoothing methods can be used for reducing noise and extracting plant phenologic... more Many time-series smoothing methods can be used for reducing noise and extracting plant phenological parameters from remotely-sensed data, but there is still no conclusive evidence in favor of one method over others. Here we use moderate-resolution imaging spectroradiometer (MODIS) derived normalized difference vegetation index (NDVI) to investigate five smoothing methods: Savitzky-Golay fitting (SG), locally weighted regression scatterplot smoothing (LO), spline smoothing (SP), asymmetric Gaussian function fitting (AG), and double logistic function fitting (DL). We use ground tower measured NDVI (10 sites) and gross primary productivity (GPP, 4 sites) to evaluate the smoothed satellite-derived NDVI time-series, and elevation data to evaluate phenology parameters derived from smoothed NDVI. The results indicate that all smoothing methods can reduce noise and improve signal quality, but that no single method always performs better than others. Overall, the local filtering methods (SG and LO) can generate very accurate results if smoothing parameters are optimally calibrated. If local calibration cannot be performed, cross validation is a way to automatically determine the smoothing parameter. However, this method may in some cases generate poor fits, and when calibration is not possible the function fitting methods (AG and DL) provide the most robust description of the seasonal dynamics.
Time series from Landsat and Sentinel-2 satellites have great potential for modeling vegetation s... more Time series from Landsat and Sentinel-2 satellites have great potential for modeling vegetation seasonality. However, irregular time sampling and frequent data loss due to clouds, snow, and short growing seasons, makes this modeling a challenge. We describe a new method for modeling seasonal vegetation index dynamics from satellite time series data. The method is based on box constrained separable least squares fits to logistic model functions combined with seasonal shape priors. To enable robust estimates, we extract a base level (i.e., the minimum dormant season value) from the frequency distribution of clear-sky vegetation index values. A seasonal shape prior is computed from several years of data, and in the final fits local parameters are box constrained. More specifically, if enough data values exist in a certain time period, the corresponding local parameters determining the shape of the model function over this period are relaxed and allowed to vary freely. If there are no observations in a period, the corresponding local parameters are locked to the parameters of the shape prior. The method is flexible enough to model interannual variations, yet robust enough when data are sparse. We test the method with Landsat, Sentinel-2, and MODIS data over a forested site in Sweden, demonstrating the feasibility and potential of the method for operational modeling of growing seasons.
Many time-series smoothing methods can be used for reducing noise and extracting plant phenologic... more Many time-series smoothing methods can be used for reducing noise and extracting plant phenological parameters from remotely-sensed data, but there is still no conclusive evidence in favor of one method over others. Here we use moderate-resolution imaging spectroradiometer (MODIS) derived normalized difference vegetation index (NDVI) to investigate five smoothing methods: Savitzky-Golay fitting (SG), locally weighted regression scatterplot smoothing (LO), spline smoothing (SP), asymmetric Gaussian function fitting (AG), and double logistic function fitting (DL). We use ground tower measured NDVI (10 sites) and gross primary productivity (GPP, 4 sites) to evaluate the smoothed satellite-derived NDVI time-series, and elevation data to evaluate phenology parameters derived from smoothed NDVI. The results indicate that all smoothing methods can reduce noise and improve signal quality, but that no single method always performs better than others. Overall, the local filtering methods (SG and LO) can generate very accurate results if smoothing parameters are optimally calibrated. If local calibration cannot be performed, cross validation is a way to automatically determine the smoothing parameter. However, this method may in some cases generate poor fits, and when calibration is not possible the function fitting methods (AG and DL) provide the most robust description of the seasonal dynamics.
The Arctic tundra has been considered as one of the most sensitive areas to global climate change... more The Arctic tundra has been considered as one of the most sensitive areas to global climate change. One impact of global warming is that permafrost thawing could result in more waterlogged and anaerobic conditions, and consequently an increasing release of methane (CH4) to the atmosphere. These potential CH4 emissions can further amplify global warming. Therefore, it is important to assess the quantity of CH4 emissions from Arctic tundra wetlands and their sensitivity to climate change. Process-based CH4 modelling is commonly used to estimate CH4 emissions using single-source fractional wetland maps; however, it is not clear how the difference among multisource of fractional wetland maps affects CH4 estimations. In this study LPJ-GUESS WHyMe was applied to simulate CH4 emissions of Arctic tundra between 1961 and 2009 by using multisource fractional wetland maps, and their quantitative and qualitative differences in estimating CH4 emissions from these fractional wetland maps was compared. Parameter sensitivity tests and a parameter optimization for the model were performed before the model was applied to Arctic tundra. The CH4/CO2 production ratio under anaerobic conditions (CH4/CO2) and fraction of available oxygen used for methane oxidation (foxid) were identified as the most important model parameters in estimating total CH4 fluxes of Arctic tundra in the period 1961-2009. The regional simulation using multisource fractional wetland maps showed that the uncertainties of CH4 emissions in Arctic tundra caused by fractional wetland maps were larger than that due to parameter uncertainty. However, the temporal variability of CH4 emissions in Arctic tundra is not significantly different when using different fractional wetland maps. For different transport pathways of CH4 emissions, diffusion was determined as the dominant pathway for methane transport from wetland to the atmosphere in Arctic tundra. CH4 fluxes in Arctic tundra are more sensitive to soil temperature at 25 cm if the water table position is above the soil surface.
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