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During the last decade, advances in the remote sensing of greenhouse gas (GHG) concentrations by the Greenhouse Gases Observing SATellite-1 (GOSAT-1), GOSAT-2, and Orbiting Carbon Observatory-2 (OCO-2) have produced finer-resolution atmospheric carbon dioxide (CO2) datasets. These data are applicable for a top-down approach towards the verification of anthropogenic CO2 emissions from megacities and updating of the inventory. However, great uncertainties regarding natural CO2 flux estimates remain when back-casting CO2 emissions from concentration data, making accurate disaggregation of urban CO2 sources difficult. For this study, we used Moderate Resolution Imaging Spectroradiometer (MODIS) land products, meso-scale meteorological data, SoilGrids250 m soil profile data, and sub-daily soil moisture datasets to calculate hourly photosynthetic CO2 uptake and biogenic CO2 emissions with 500 m resolution for the Kantō Plain, Japan, at the center of which is the Tokyo metropolis. Our hourly integrated modeling results obtained for the period 2010–2018 suggest that, collectively, the vegetated land within the Greater Tokyo Area served as a daytime carbon sink year-round, where the hourly integrated net atmospheric CO2 removal was up to 14.15 ± 4.24% of hourly integrated anthropogenic emissions in winter and up to 55.42 ± 10.39% in summer. At night, plants and soil in the Greater Tokyo Area were natural carbon sources, with hourly integrated biogenic CO2 emissions equivalent to 2.27 ± 0.11%–4.97 ± 1.17% of the anthropogenic emissions in winter and 13.71 ± 2.44%–23.62 ± 3.13% in summer. Between January and July, the hourly integrated biogenic CO2 emissions of the Greater Tokyo Area increased sixfold, whereas the amplitude of the midday hourly integrated photosynthetic CO2 uptake was enhanced by nearly five times and could offset up to 79.04 ± 12.31% of the hourly integrated anthropogenic CO2 emissions in summer. The gridded hourly photosynthetic CO2 uptake and biogenic respiration estimates not only provide reference data for the estimation of total natural CO2 removal in our study area, but also supply prior input values for the disaggregation of anthropogenic CO2 emissions and biogenic CO2 fluxes when applying top-down approaches to update the megacity’s CO2 emissions inventory. The latter contribution allows unprecedented amounts of GOSAT and ground measurement data regarding CO2 concentration to be analyzed in inverse modeling of anthropogenic CO2 emissions from Tokyo and the Kantō Plain.

Sandy cropland ecosystems are major terrestrial ecosystems in semi-arid regions of northern China’s Horqin Sandy Land, where they play an important role in the regional carbon balance. Continuous observation of the CO 2 flux was conducted from 2014 to 2018 using the eddy covariance technique in a sandy maize cropland ecosystem in the Horqin Sandy Land. We analyzed carbon fluxes (the net ecosystem exchange ( NEE ) of CO 2 , ecosystem respiration ( R eco ), and the gross primary productivity ( GPP ) and their responses to environmental factors at different temporal scales using Random Forest models and correlation analysis. We found that the sandy cropland was a carbon sink, with an annual mean NEE of –124.4 g C m −2 yr −1 . However, after accounting for carbon exports and imports, the cropland became a net carbon source, with net biome production ranging from –501.1 to –266.7 g C m −2 yr −1 . At a daily scale, the Random Forest algorithm revealed that photosynthetic photon flux density, soil temperature, and soil moisture were the main drivers for variation of GPP , R eco , and NEE at different integration periods. At a monthly scale, GPP and R eco increased with increasing leaf area index ( LAI ), so the maize ecosystem’s carbon sequestration capacity increased with increasing LAI . At an annual scale, water availability (precipitation and irrigation) played a dominant role in explaining inter-annual variability of GPP and R eco . Affected by climate (e.g., precipitation) and field management (e.g., cultivation, irrigation), carbon fluxes differed greatly between years in the maize system.

Peatlands play an important role in the global carbon cycle as they contain a large soil carbon stock. However, current climate change could potentially shift peatlands from being carbon sinks to carbon sources. Remote sensing methods provide an opportunity to monitor carbon dioxide (CO2) exchange in peatland ecosystems at large scales under these changing conditions. In this study, we developed empirical models of the CO2 balance (net ecosystem exchange, NEE), gross primary production (GPP), and ecosystem respiration (ER) that could be used for upscaling CO2 fluxes with remotely sensed data. Two to three years of eddy covariance (EC) data from five peatlands in Sweden and Finland were compared to modelled NEE, GPP and ER based on vegetation indices from 10 m resolution Sentinel-2 MSI and land surface temperature from 1 km resolution MODIS data. To ensure a precise match between the EC data and the Sentinel-2 observations, a footprint model was applied to derive footprint-weighted daily means of the vegetation indices. Average model parameters for all sites were acquired with a leave-one-out-cross-validation procedure. Both the GPP and the ER models gave high agreement with the EC-derived fluxes (R2 = 0.70 and 0.56, NRMSE = 14% and 15%, respectively). The performance of the NEE model was weaker (average R2 = 0.36 and NRMSE = 13%). Our findings demonstrate that using optical and thermal satellite sensor data is a feasible method for upscaling the GPP and ER of northern boreal peatlands, although further studies are needed to investigate the sources of the unexplained spatial and temporal variation of the CO2 fluxes.

Peatlands are important sinks for atmospheric carbon (C), yet the role of plant functional types (PFTs) for C sequestration under climatic perturbations is still unclear. A plant-removal experiment was used to study the importance of vascular PFTs for the net ecosystem CO 2 exchange (NEE) during (i.e., resistance) and after (i.e., recovery) an experimental drought. The removal of PFTs caused a decrease of NEE, but the rate differed between microhabitats (i.e., hummocks and lawns) and the type of PFTs. Ericoid removal had a large effect on NEE in hummocks, while the graminoids played a major role in the lawns. The removal of PFTs did not affect the resistance or the recovery after the experimental drought. We argue that the response of Sphagnum mosses (the only PFT present in all treatments) to drought is dominant over that of coexisting PFTs. However, we observed that the moment in time when the system switched from C sink to C source during the drought was controlled by the vascular PFTs. In the light of climate change, the shifts in species composition or even the loss of certain PFTs are expected to strongly affect the future C dynamics in response to environmental stress.

Arid and semi-arid ecosystems dominated by shrubby species are an important component in the global carbon cycle but are largely under-represented in studies of the effect of climate change on carbon flux. This study synthesizes data from longterm eddy covariance measurements and experiments to assess how changes in ecosystem composition, driven by precipitation patterns, affect inter-annual variability of carbon flux and their components in a halophyte desert community dominated by deep-rooted shrubs (phreatophytes, which depend on groundwater as their primary water source). Our results demonstrated that the carbon balance of this community responded strongly to precipitation variations. Both pregrowing season precipitation and growing season precipitation frequency significantly affected interannual variations in ecosystem carbon flux. Heavy pre-growing season precipitation (November–April, mostly as snow) increased annual net ecosystem carbon exchange, by facilitating the growth and carbon assimilation of shallow-rooted annual plants, which used spring and summer precipitation to increase community productivity. Sufficient pre-growing season precipitation led to more germination and growth of shallow-rooted annual plants. When followed by high-frequency growing season precipitation, community productivity of this desert ecosystem was lifted to the level of grassland or forest ecosystems. The long-term observations and experimental results confirmed that precipitation patterns and the herbaceous component were dominant drivers of the carbon dynamics in this phreatophyte-dominated desert ecosystem. This study illustrates the importance of inter-annual variations in climate and ecosystem composition for the carbon flux in arid and semiarid ecosystems. It also highlights the important effect of changing frequency and seasonal pattern of precipitation on the regional and global carbon cycle in the coming decades.

Rangelands provide significant environmental benefits through many ecosystem services, which may include soil organic carbon (SOC) sequestration. However, quantifying SOC stocks and monitoring carbon (C) fluxes in rangelands are challenging due to the considerable spatial and temporal variability tied to rangeland C dynamics as well as limited data availability. We developed the Rangeland Carbon Tracking and Management (RCTM) system to track long‐term changes in SOC and ecosystem C fluxes by leveraging remote sensing inputs and environmental variable data sets with algorithms representing terrestrial C‐cycle processes. Bayesian calibration was conducted using quality‐controlled C flux data sets obtained from 61 Ameriflux and NEON flux tower sites from Western and Midwestern US rangelands to parameterize the model according to dominant vegetation classes (perennial and/or annual grass, grass‐shrub mixture, and grass‐tree mixture). The resulting RCTM system produced higher model accuracy for estimating annual cumulative gross primary productivity (GPP) (R2 > 0.6, RMSE <390 g C m−2) relative to net ecosystem exchange of CO2 (NEE) (R2 > 0.4, RMSE <180 g C m−2). Model performance in estimating rangeland C fluxes varied by season and vegetation type. The RCTM captured the spatial variability of SOC stocks with R2 = 0.6 when validated against SOC measurements across 13 NEON sites. Model simulations indicated slightly enhanced SOC stocks for the flux tower sites during the past decade, which is mainly driven by an increase in precipitation. Future efforts to refine the RCTM system will benefit from long‐term network‐based monitoring of vegetation biomass, C fluxes, and SOC stocks. Plain Language Summary Rangelands play a crucial role in providing various ecosystem services, including potential climate change mitigation through increased soil organic carbon (SOC) storage. Accurate estimates of changes in carbon (C) storage are challenging due to the heterogeneous nature of rangelands and the limited availability of field observations. In this work, we leveraged remote sensing observations, tower‐based C flux measurements from over 60 rangeland sites in the Western and Midwestern US, and other environmental data sets to build the process‐based Rangeland Carbon Tracking and Management (RCTM) modeling system. The RCTM system is designed to simulate the past 20 years of rangeland C dynamics and is regionally calibrated. The RCTM system performs well in estimating spatial and temporal rangeland C fluxes as well as spatial SOC storage. Model simulation results revealed increased SOC storage and rangeland productivity driven by annual precipitation patterns. The RCTM system developed by this work can be used to generate accurate spatial and temporal estimates of SOC storage and C fluxes at fine spatial (30 m) and temporal (every 5 days) resolutions, and is well‐suited for informing rangeland C management strategies and improving broad‐scale policy making. Key Points The Rangeland Carbon Tracking and Monitoring System was calibrated to simulate vegetation type‐specific rangeland C dynamics Regional variability in carbon fluxes and soil organic carbon is well represented by a remote sensing‐driven process modeling approach Soil organic carbon stocks in Western and Midwestern US rangelands increased over the past 20 years due to increased precipitation

Lawns as a landcover change substantially alter evapotranspiration, CO2, and energy exchanges and are of rising importance considering their spatial extent. We contrast eddy covariance (EC) flux measurements collected in the Denver, Colorado, USA metropolitan area in 2011 and 2012 over a lawn and a xeric tallgrass prairie. Close linkages between seasonal vegetation development, energy fluxes, and net ecosystem exchange (NEE) of CO2 were found. Irrigation of the lawn modified energy and CO2 fluxes and greatly contributed to differences observed between sites. Due to greater water inputs (precipitation + irrigation) at the lawn in this semi-arid climate, energy partitioning at the lawn was dominated by latent heat (LE) flux. As a result, evapotranspiration (ET) of the lawn was more than double that of tallgrass prairie (2011: 639(±17) mm vs. 302(±9) mm; 2012: 584(±15) mm vs. 265(±7) mm). NEE for the lawn was characterized by a longer growing season, higher daily net uptake of CO2, and growing season NEE that was also more than twice that of the prairie (2011: −173(±23) g C m−2 vs. -81(±10) g C m−2; 2012: −73(±22) g C m−2 vs. -21(±8) g C m−2). During the drought year (2012), temperature and water stress greatly influenced the direction and magnitude of CO2 flux at both sites. The results suggest that lawns in Denver can function as carbon sinks and conditionally contribute to the mitigation of carbon emissions - directly by CO2 uptake and indirectly through effects of evaporative cooling on microclimate and energy use.

Burned landscapes present several challenges to quantifying landscape carbon balance. Fire scars are composed of a mosaic of patches that differ in burn severity, which may influence postfire carbon budgets through damage to vegetation and carbon stocks. We deployed three eddy covariance towers along a burn severity gradient (i.e., severely burned, moderately burned, and unburned tundra) to monitor postfire net ecosystem exchange of CO 2 (NEE) within the large 2007 Anaktuvuk River fire scar in Alaska, USA, during the summer of 2008. Remote sensing data from the MODerate resolution Imaging Spectroradiometer (MODIS) was used to assess the spatial representativeness of the tower sites and parameterize a NEE model that was used to scale tower measurements to the landscape. The tower sites had similar vegetation and reflectance properties prior to the Anaktuvuk River fire and represented the range of surface conditions observed within the fire scar during the 2008 summer. Burn severity influenced a variety of surface properties, including residual organic matter, plant mortality, and vegetation recovery, which in turn determined postfire NEE. Carbon sequestration decreased with increased burn severity and was largely controlled by decreases in canopy photosynthesis. The MODIS two-band enhanced vegetation index (EVI2) monitored the seasonal course of surface greenness and explained 86%% of the variability in NEE across the burn severity gradient. We demonstrate that understanding the relationship between burn severity, surface reflectance, and NEE is critical for estimating the overall postfire carbon balance of the Anaktuvuk River fire scar.

Plant phenology timings, such as spring green-up and autumn senescence, are essential state information characterizing biological responses and terrestrial carbon cycles. Current efforts for the in situ reflectance measurements are not enough to obtain the exact interpretation of how seasonal spectral signature responds to phenological stages in boreal evergreen needleleaf forests. This study shows the first in situ continuous measurements of canopy scale (overstory + understory) and understory spectral reflectance and vegetation index in an open boreal forest in interior Alaska. Two visible and near infrared spectroradiometer systems were installed at the top of the observation tower and the forest understory, and spectral reflectance measurements were performed in 10 min intervals from early spring to late autumn. We found that canopy scale normalized difference vegetation index (NDVI) varied with the solar zenith angle. On the other hand, NDVI of understory plants was less sensitive to the solar zenith angle. Due to the influence of the solar geometry, the annual maximum canopy NDVI observed in the morning satellite overpass time (10–11 am) shifted to the spring direction compared with the standardized NDVI by the fixed solar zenith angle range (60−70°). We also found that the in situ NDVI time-series had a month-long high NDVI plateau in autumn, which was completely out of photosynthetically active periods when compared with eddy covariance net ecosystem exchange measurements. The result suggests that the onset of an autumn high NDVI plateau is likely to be the end of the growing season. In this way, our spectral measurements can serve as baseline information for the development and validation of satellite-based phenology algorithms in the northern high latitudes.

QUESTION: There are large variations in ecosystem carbon (C) exchange in desert ecosystems; however, few studies have examined the effects of community phenological staging on seasonal variations of ecosystem C exchange. We asked whether factors that control temporal changes in net ecosystem C exchange (NEE) vary with an obvious community transition from spring annuals to summer annuals and perennials in a temperate desert steppe. LOCATION: South margin of the Gurbantunggute Desert, northwestern China. METHODS: Ecosystem C and water exchange were measured regularly using closed static chambers and analysed at daily and seasonal intervals. Soil moisture and temperature, photosynthetically active radiation (PAR) and plant biomass were also investigated. RESULTS: Soil temperature had a dominant influence on C release into the atmosphere from the ecosystem during the snowmelting phase (mid‐March to early April). In the spring annual dominant phase (mid‐April to early June), the diurnal pattern of NEE was consistent with the pattern of PAR. The ecosystem became a weak C resource (0.16 ± 0.03 μmol CO₂ m⁻²·s⁻¹) and NEE was positively correlated with community biomass during this phase. In the summer annual and perennial dominant phase (late June to late September), NEE showed relatively large C release (0.74 ± 0.03 μmol CO₂ m⁻²·s⁻¹) and was negatively correlated with soil temperature. CONCLUSION: Our results indicate that the primary abiotic factor controlling NEE varies throughout the year, and NEE is determined by the interaction of a plant functional group with precipitation and temperature.