Modelling methane emissions from Arctic tundra wetlands : effects of fractional wetland maps

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

Early growing season anomalies in vegetation activity determine the large-scale climate-vegetation coupling in Europe

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 large-scale 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

In-situ start and end of growing season dates of major European crop types from France and Bulgaria at a field level

Crop phenology data offer crucial information for crop yield estimation, agricultural management, and assessment of agroecosystems. Such information becomes more important in the context of increasing year-to-year climatic variability. The dataset provides in-situ crop phenology data (first leaves emergence and harvest date) of major European crops (wheat, corn, sunflower, rapeseed) from seventeen field study sites in Bulgaria and two in France. Additional information such as the sowing date, area of each site, coordinates, method and equipment used for phenophase data estimation, and photos of the France sites are also provided. The georeferenced ground-truth dataset provides a solid base for a better understanding of crop growth and can be used to validate the retrieval of phenological stages from remote sensing data

In-situ start and end of growing season dates of major European crop types from France and Bulgaria at a field level

Crop phenology data offer crucial information for crop yield estimation, agricultural management, and assessment of agroecosystems. Such information becomes more important in the context of increasing year-to-year climatic variability. The dataset provides in-situ crop phenology data (first leaves emergence and harvest date) of major European crops (wheat, corn, sunflower, rapeseed) from seventeen field study sites in Bulgaria and two in France. Additional information such as the sowing date, area of each site, coordinates, method and equipment used for phenophase data estimation, and photos of the France sites are also provided. The georeferenced ground-truth dataset provides a solid base for a better understanding of crop growth and can be used to validate the retrieval of phenological stages from remote sensing data.This publication is the result of the Action CA17134 SENSECO (Opticalsynergies for spatiotemporal sensing of scalable ecophysiological traits) funded by COST (European Cooperation in Science and Technology, www.cost.eu, accessed on March 17, 2023). We thank Tiphaine Tallec for the in-situ data of the French sites, mainly funded by the Institut National des Sciences de l'Univers (INSU) through the ICOS ERIC and the OSR SW observatory (https://osr.cesbio.cnrs.fr/). Facilities and staff are funded and supported by the Observatory Midi-Pyrenean, the University Paul Sabatier of Toulouse 3, CNRS (Centre National de la Recherche Scientifique), CNES (Centre National d'Etude Spatial) and IRD (Institut de Recherche pour le Développement). We further thank Dessislava Ganeva for the in-situ data of the Bulgarian sites. SB was by partially supported by Generalitat Valenciana SEJIGENT program (SEJIGENT/2021/001) and European Union NextGenerationEU (ZAMBRANO 21-04)

Higher vegetation sensitivity to meteorological drought in autumn than spring across European biomes

Europe has experienced severe drought events in recent decades, posing challenges to understand vegetation responses due to diverse vegetation distribution, varying growth stages, different drought characteristics, and concurrent hydroclimatic factors. To analyze vegetation response to meteorological drought, we employed multiple vegetation indicators across European biomes. Our findings reveal that vegetation sensitivity to drought increases as the canopy develops throughout the year, with sensitivities from −0.01 in spring to 0.28 in autumn and drought-susceptible areas from 18.5 to 57.8% in Europe. Soil water shortage exacerbates vegetation-drought sensitivity temporally, while its spatial impact is limited. Vegetation-drought sensitivity strongly correlates with vapor pressure deficit and partially with atmospheric CO2 concentration. These results highlight the spatiotemporal variations in vegetation-drought sensitivities and the influence of hydroclimatic factors. The findings enhance our understanding of vegetation response to drought and the impact of concurrent hydroclimatic factors, providing valuable sub-seasonal information for water management and drought preparedness

Reviews and syntheses: Remotely sensed optical time series for monitoring vegetation productivity

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

Reviews and syntheses:Remotely sensed optical time series for monitoring vegetation productivity

International audienceAbstract. 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 e.g., 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 (DVM). 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 products or modelled estimates. 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 also enhances the accuracy of vegetation productivity monitoring

Vegetation Observation in the Big Data Era : Sentinel-2 data for mapping the seasonality of land vegetation

Using satellite remote sensing data for observing vegetation seasonality is an important approach to estimate phenology and carbon uptake of land vegetation. The successful launch of Sentinel-2B in 2017 initiated full operation of the Sentinel-2 twin satellites, and they now provide 10 - 60 m spatial resolution satellite data at 5 days temporal resolution worldwide, releasing approximately 3.2 TB of image data per day. With Sentinel-2's huge amount of high spatial resolution and high temporal resolution data, Earth observation is facing new opportunities and challenges. To adapt to the characteristics of Sentinel-2 MSI data, the existing time-series analysis methods used for vegetation seasonality studies with regular time step data (e.g., from the MODIS sensor) require modification and improvements. In this thesis, a new time-series analysis method, based on the currently available methods, was developed for estimating vegetation seasonality from high spatial resolution Sentinel-2 data. The new method is applied to Sentinel-2 data to estimate vegetation phenology and photosynthetic carbon uptake, and the outputs are evaluated based on ground reference data and compared to MODIS products. By comparing with ground reference data (in-situ NDVI time-series, flux tower GPP time-series, and elevation), function fitting methods (e.g., double logistic function fitting) provide the most robust description of the seasonal dynamics for MODIS NDVI time-series among five tested smoothing methods. Based on this finding, we developed box constrained separable least squares fits to double logistic functions with seasonal shape priors, and tested the robustness of the method on six years of simulated Sentinel-2 data by use of MODIS data. The results show that the new method is flexible enough to simulate interannual variations and robust enough when data are sparse. The box constrained function fitting method applied to Sentinel-2 MSI 2-band Enhanced Vegetation Index (EVI2) data was further used to estimate vegetation phenology and gross primary productivity (GPP) across diverse Nordic vegetation types. The results indicate that daily EVI2 time-series derived from Sentinel-2 is more accurate than from MODIS, with an RMSE of 0.08 for Sentinel-2 and 0.13 for MODIS versus the ground spectral data. With reference to the dates of greenness rising estimated from digital cameras, the dates estimated from Sentinel-2 (RMSE: 8.1 days) are closer than those from MODIS (RMSE: 14.4 days). Sentinel-2 data also generate more phenological details along elevation gradients and land cover variations than MODIS. However, Sentinel-2 does not show any advantage in estimating GPP, when comparing with data from flux towers. The average error between the modelled GPP from Sentinel-2 EVI2 and the GPP derived from flux tower data was similar to that from MODIS. This result partly reflects inabilities in the flux tower data to resolve variation at the same high resolution as Sentinel-2, and further studies will be required to fully evaluate the capability of the sensor in this respect.In conclusion, the new method, box constrained separable least squares fits to double logistic functions with seasonal shape priors, is useful and computationally efficient for robustly reconstructing daily vegetation index time-series and estimating vegetation phenology from Sentinel-2 data. In addition, by applying the new method to Sentinel-2 data is useful for describing the spatial variation of GPP in the footprint area, although Sentinel-2 did not show improvements in estimating GPP compared with MODIS data. The developed time-series methods will be implemented in a subsequent version of the TIMESAT software package for processing of irregular time step data

Modelling Daily Gross Primary Productivity with Sentinel-2 Data in the Nordic Region–Comparison with Data from MODIS

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. This demonstrates the general consistency in GPP estimates based on the two satellite sensor systems at the Nordic regional scale. On the other hand, the model accuracy did not improve by using the higher spatial-resolution Sentinel-2 data. More analyses of different model formulations, more tests of remotely sensed indices and biophysical parameters, and analyses across a wider range of geographical locations and times will be required to achieve improved GPP estimations from Sentinel-2 satellite data

Improving neural network classification of indigenous forest in New Zealand with phenological features

Accurate and up-to-date land cover maps inform and support effective management and policy decisions. Describing phenological changes in spectral response using time-series data may help to distinguish vegetation types, thereby allowing for more specificity within vegetation classification. In this research, we test this by classifying indigenous forest vegetation in New Zealand, using PlanetScope (PS) and Sentinel-2 (S-2) satellite time-series data. The study was undertaken in a podocarp forest in New Zealand's central north island, which was classified into nine land cover classes. Phenological features, based on S-2 imagery, were extracted, including the enhanced vegetation index (EVI), enhanced vegetation index 2 (EVI2) and normalised difference vegetation index (NDVI). Google Earth Engine (GEE) harmonic analysis and TIMESAT double logistic fitting function were used to extract phenological features. Pixel-based classifications were performed using a Neural Network on six different scenarios. The accuracy of the classification scenarios was determined and the importance score for each feature was evaluated. Using only the fused PS and S-2 bands, the land cover in the study area was classified with 90.1% accuracy. Adding phenological features increased the classification accuracy to 93.1%. When combined with VIs, texture features, and a digital terrain model, the addition of phenological features increased the classification accuracy to 96.6%. Including GEE-generated phenological features resulted in better classification accuracies than TIMESAT features. In terms of feature importance evaluation, EVI2- and NDVI-generated phenological features all had high scores; the effectiveness of EVI features could potentially have been limited by the quality of the blue band. The results demonstrate that it is possible to produce a more accurate classification of New Zealand's native vegetation by using phenological features. This method offers important cost-savings as the platforms for phenological analysis are free to use