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 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.

Vegetation productivity is a critical indicator of global ecosystem health and is impacted by human activities and climate change. A wide range of optical sensing platforms, from ground-based to airborne and satellite, provide spatially continuous information on terrestrial vegetation status and functioning. As optical Earth observation (EO) data are usually routinely acquired, vegetation can be monitored repeatedly over time, reflecting seasonal vegetation patterns and trends in vegetation productivity metrics. Such metrics include gross primary productivity, net primary productivity, biomass, or yield. To summarize current knowledge, in this paper we systematically reviewed time series (TS) literature for assessing state-of-the-art vegetation productivity monitoring approaches for different ecosystems based on optical remote sensing (RS) data. As the integration of solar-induced fluorescence (SIF) data in vegetation productivity processing chains has emerged as a promising source, we also include this relatively recent sensor modality. We define three methodological categories to derive productivity metrics from remotely sensed TS of vegetation indices or quantitative traits: (i) trend analysis and anomaly detection, (ii) land surface phenology, and (iii) integration and assimilation of TS-derived metrics into statistical and process-based dynamic vegetation models (DVMs). Although the majority of used TS data streams originate from data acquired from satellite platforms, TS data from aircraft and unoccupied aerial vehicles have found their way into productivity monitoring studies. To facilitate processing, we provide a list of common toolboxes for inferring productivity metrics and information from TS data. We further discuss validation strategies of the RS data derived productivity metrics: (1) using in situ measured data, such as yield; (2) sensor networks of distinct sensors, including spectroradiometers, flux towers, or phenological cameras; and (3) inter-comparison of different productivity metrics. Finally, we address current challenges and propose a conceptual framework for productivity metrics derivation, including fully integrated DVMs and radiative transfer models here labelled as “Digital Twin”. This novel framework meets the requirements of multiple ecosystems and enables both an improved understanding of vegetation temporal dynamics in response to climate and environmental drivers and enhances the accuracy of vegetation productivity monitoring.

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 dry plot. The degree of wetness determined, directly or indirectly, the relative contribution of each GHG to the total budgets. Faster establishment of vegetation on the wet plot reduced total emissions of CO₂ as compared to the dry plot but this was partially offset by higher CH₄ emissions. Waterlogging following clear-cutting likely caused both plots to switch from sinks to sources of CH₄. In addition, there were periods with N₂O uptake at the wet plot, although both plots were net sources of N₂O on an annual basis. We observed clear diel patters in CO₂, CH₄ and N₂O fluxes during the growing season at both plots, with the exception of CH₄ at the dry plot. The total three-year carbon budgets were 4107 gCO₂-equivalent m⁻² and 5274 gCO₂-equivalent m⁻² at the wet and the dry plots, respectively. CO₂ contributed 91.8% to the total carbon budget at the wet plot and 98.2% at the dry plot. For the only full year with N₂O measurements, the total GHG budgets were 1069.9 gCO₂-eqvivalents m⁻² and 1695.7 gCO₂-eqvivalents m⁻² at the wet and dry plot, respectively. At the wet plot, CH₄ contributed 3.7%, while N₂O contributed 7.3%. At the dry plot, CH₄ and N₂O contributed 1.5% and 7.6%, respectively. Our results emphasize the importance of considering the effects of the three GHGs on the climate for any forest management policy aiming at enhancing the mitigation potential of forests.