Terrestrial Carbon Balance Research Articles

Satellite Observations of the Tropical Terrestrial Carbon Balance and Interactions With the Water Cycle During the 21st Century

AbstractA constellation of satellites is now in orbit providing information about terrestrial carbon and water storage and fluxes. These combined observations show that the tropical biosphere has changed significantly in the last 2 decades from the combined effects of climate variability and land use. Large areas of forest have been cleared in both wet and dry forests, increasing the source of carbon to the atmosphere. Concomitantly, tropical fire emissions have declined, at least until 2016, from changes in land‐use practices and rainfall, increasing the net carbon sink. Measurements of carbon stocks and fluxes from disturbance and recovery and of vegetation photosynthesis show significant regional variability of net biosphere exchange and gross primary productivity across the tropics and are tied to seasonal and interannual changes in water fluxes and storage. Comparison of satellite based estimates of evapotranspiration, photosynthesis, and the deuterium content of water vapor with patterns of total water storage and rainfall demonstrate the presence of vegetation‐atmosphere interactions and feedback mechanisms across tropical forests. However, these observations of stocks, fluxes and inferred interactions between them do not point unambiguously to either positive or negative feedbacks in carbon and water exchanges. These ambiguities highlight the need for assimilation of these new measurements with Earth System models for a consistent assessment of process interactions, along with focused field campaigns that integrate ground, aircraft and satellite measurements, to quantify the controlling carbon and water processes and their feedback mechanisms.

Restoration Thinning in a Drought‐Prone Idaho Forest Creates a Persistent Carbon Deficit

AbstractWestern US forests represent a carbon sink that contributes to meeting regional and global greenhouse gas targets. Forest thinning is being implemented as a strategy for reducing forest vulnerability to disturbance, including mortality from fire, insects, and drought, as well as protecting human communities. However, the terrestrial carbon balance impacts of thinning remain uncertain across regions, spatiotemporal scales, and treatment types. Continuous and in situ long‐term measurements of partial harvest impacts to stand‐scale carbon and water cycle dynamics are nonetheless rare. Here, we examine post‐thinning carbon and water flux impacts in a young ponderosa pine forest in Northern Idaho. We examine in situ stock and flux impacts during the 3 years after treatment as well as simulate the forest sector carbon balance through 2050, including on and off‐site net emissions. During the observation period, increases in tree‐scale net primary production (NPP) and water use persistence through summer drought did not overcome the impacts of density reduction, leading to 45% annual reductions of NPP. Growth duration remained constrained by summer drought in control and thinned stands. Ecosystem model and life cycle assessment estimates demonstrated a net forest sector carbon deficit relative to control stands of 27.0 Mg C ha−1 in 2050 due to emissions from dead biomass pools despite increases to net ecosystem production. Our results demonstrate dynamics resulting in carbon losses from forest thinning, providing a baseline with which to inform landscape‐scale modeling and assess tradeoffs between harvest losses and potential gains from management practices.

Reexamine China’s terrestrial ecosystem carbon balance under land use-type and climate change

Land use and climate change can strongly affect the terrestrial ecosystem carbon balance. However, there is a lack of clarity for existing studies that investigate total carbon balance for China’s terrestrial carbon balance (carbon storage and Net Ecosystem Productivity (NEP)). In this study, based on large data and the improved NEP model, we examined land use and climate change during 2000–2015 in China, calculated terrestrial ecosystem carbon storage change caused by land use change, and carbon sink/source variation under climate change; we found that during 2000–2015, 3.05 % of China’s land area had land use type changes and caused 32.97 Tg of carbon storage loss, consisting of 10.4 Tg from vegetation carbon storage and 22.57 Tg from soil organic carbon (SOC) loss. Built-up land occupying ecological land was the most obvious land transfer type, especially for grassland degeneration. Both temperature and precipitation showed decreasing trends throughout China. Mean annual NEP showed a carbon sink value of 41.73 g C.m−2.yr−1, and the NEPs of carbon sinks were mostly distributed in South and Midland China and partly in Northeast China. There were obvious regional differences and the carbon balance showed that North China and Northwest China were regions of net carbon sources. The other four regions were net carbon sinks. Land use changes caused carbon storage loss in all regions, NEPs in North China and Northwest China were carbon sources, while in the other regions were carbon sinks. All NEPs exhibited an increasing trend during 2000–2015, except for Mid-South China. Finally, according to regional carbon balance characters, different policy implications were drawn which can serve for the formulation of territorial spatial planning. Land exploitation should be limited and environmental conservation is needed in North and Northwest China. The temperature in Southwest China has been increasing continuously and merits attention. The control of built-up land expansion in other regions should be strengthened.

Leaching of dissolved organic carbon from mineral soils plays a significant role in the terrestrial carbon balance.

The leaching of dissolved organic carbon (DOC) from soils to the river network is an overlooked component of the terrestrial soil C budget. Measurements of DOC concentrations in soil, runoff and drainage are scarce and their spatial distribution highly skewed towards industrialized countries. The contribution of terrestrial DOC leaching to the global‐scale C balance of terrestrial ecosystems thus remains poorly constrained. Here, using a process based, integrative, modelling approach to upscale from existing observations, we estimate a global terrestrial DOC leaching flux of 0.28 ± 0.07 Gt C year−1 which is conservative, as it only includes the contribution of mineral soils. Our results suggest that globally about 15% of the terrestrial Net Ecosystem Productivity (NEP, calculated as the difference between Net Primary Production and soil respiration) is exported to aquatic systems as leached DOC. In the tropical rainforest, the leached fraction of terrestrial NEP even reaches 22%. Furthermore, we simulated spatial‐temporal trends in DOC leaching from soil to the river networks from 1860 to 2010. We estimated a global increase in terrestrial DOC inputs to river network of 35 Tg C year−1 (14%) from 1860 to 2010. Despite their low global contribution to the DOC leaching flux, boreal regions have the highest relative increase (28%) while tropics have the lowest relative increase (9%) over the historical period (1860s compared to 2000s). The results from our observationally constrained model approach demonstrate that DOC leaching is a significant flux in the terrestrial C budget at regional and global scales.

Satellite Monitoring of Global Surface Soil Organic Carbon Dynamics Using the SMAP Level 4 Carbon Product

AbstractSoil organic carbon (SOC) is an important metric of soil health and the terrestrial carbon balance. Short‐term climate variations affect SOC through changes in temperature and moisture, which control vegetation growth and soil decomposition. We evaluated a satellite data‐driven carbon model, operating under the NASA Soil Moisture Active‐Passive (SMAP) mission, as a means of monitoring global surface SOC dynamics. The SMAP Level 4 Carbon (L4C) product estimates a daily global carbon budget including surface (0‐ to 5‐cm depth) SOC. We found that the L4C mean latitudinal SOC distribution is generally consistent with alternative assessments from static soil inventory records and dynamic global vegetation models (r ≥ 0.89). Within forest systems, based on inventory data, L4C SOC is most similar in magnitude to litterfall but is correlated with coarse woody debris ( ) and total SOC ( ). L4C SOC is sensitive to seasonal and annual climate variability, with mean residence times that range from 1.5 years in the wet tropics to 17 years in the cold tundra. Incorporating soil moisture retrievals from the SMAP L‐band (1.4 GHz) microwave radiometer within the L4C algorithm provides enhanced soil moisture sensitivity under low‐to‐moderate vegetation cover (<5 kg m−2 vegetation water content). The L‐band soil moisture had the greatest impact on the L4C carbon budget in semiarid regions, which span almost 60% of the globe and account for substantial variability in the terrestrial carbon sink. The L4C operational product enables prognostic investigations into effects of recent climate trends and anomalies (e.g., droughts and pluvials) on shallow soil carbon dynamics.

Collar Properties and Measurement Time Confer Minimal Bias Overall on Annual Soil Respiration Estimates in a Global Database

AbstractMeasuring the soil‐to‐atmosphere carbon dioxide (CO2) flux (soil respiration, RS) is important to understanding terrestrial carbon balance and to forecasting climate change. Such measurements are frequently made using measurement collars permanently inserted into the soil surface. However, differences in measurement duration and frequency, as well as collar properties, may lead to biases in the estimation of annual RS. Using a newly updated global RS database (SRDB‐V5), we investigated the annual RS bias associated with five methodological factors: collar height, collar coverage area, collar insertion depth, measurement duration, and measurement frequency. We found that annual RS was negatively correlated with collar insertion depth, consistent with the idea that collar insertion cuts roots and thus reduces RS. Annual RS was also negatively related with collar height and collar coverage area, perhaps because uniform head‐space mixing is difficult to achieve in larger volume chambers; however, these effects were quantitatively small (bias of ~2% to 10% of mean RS). We found no correlation of measurement duration or measurement frequency with annual RS. These findings suggest that variation in RS methodology generally introduces minimal bias overall. Therefore, compilations of minimally adjusted annual RS measurements provide a reliable resource for synthesis studies, global annual RS modeling, and investigation of how soil carbon responds to climate change.

Simulating Erosion-Induced Soil and Carbon Delivery From Uplands to Rivers in a Global Land Surface Model.

Global water erosion strongly affects the terrestrial carbon balance. However, this process is currently ignored by most global land surface models (LSMs) that are used to project the responses of terrestrial carbon storage to climate and land use changes. One of the main obstacles to implement erosion processes in LSMs is the high spatial resolution needed to accurately represent the effect of topography on soil erosion and sediment delivery to rivers. In this study, we present an upscaling scheme for including erosion‐induced lateral soil organic carbon (SOC) movements into the ORCHIDEE LSM. This upscaling scheme integrates information from high‐resolution (3″) topographic and soil erodibility data into a LSM forcing file at 0.5° spatial resolution. Evaluation of our model for the Rhine catchment indicates that it reproduces well the observed spatial and temporal (both seasonal and interannual) variations in river runoff and the sediment delivery from uplands to the river network. Although the average annual lateral SOC flux from uplands to the Rhine River network only amounts to 0.5% of the annual net primary production and 0.01% of the total SOC stock in the whole catchment, SOC loss caused by soil erosion over a long period (e.g., thousands of years) has the potential to cause a 12% reduction in the simulated equilibrium SOC stocks. Overall, this study presents a promising approach for including the erosion‐induced lateral carbon flux from the land to aquatic systems into LSMs and highlights the important role of erosion processes in the terrestrial carbon balance.

The role of climate, foliar stoichiometry and plant diversity on ecosystem carbon balance.

Global change is affecting terrestrial carbon (C) balances. The effect of climate on ecosystem C balance has been largely explored, but the roles of other concurrently changing factors, such as diversity and nutrient availability, remain elusive. We used eddy-covariance C-flux measurements from 62 ecosystems from which we compiled information on climate, ecosystem type, stand age, species abundance and foliar concentrations of N and P of the main species, to assess their importance in the ecosystem C balance. Climate and productivity were the main determinants of ecosystem C balance and its stability. In P-rich sites, increasing N was related to increased gross primary production and respiration and vice versa, but reduced net C uptake. Our analyses did not provide evidence for a strong relation between ecosystem diversity and their productivity and stability. Nonetheless, these results suggest that nutrient imbalances and, potentially, diversity loss may alter future global C balance.

Photoperiod Explains the Asynchronization Between Vegetation Carbon Phenology and Vegetation Greenness Phenology

AbstractVegetation carbon phenology is a key indicator of vegetation actual photosynthesis activity and regulates terrestrial ecosystem carbon balance. A comprehensive understanding of the structural difference of carbon phenology and greenness phenology is still lacking. This study evaluated the structural difference of vegetation greenness phenology and carbon phenology on 95 eddy covariance flux sites (637 site‐years). Vegetation greenness phenology is extracted from remote sensing vegetation indices (i.e., enhanced vegetation index [EVI] and normalized difference vegetation index [NDVI]) of Moderate Resolution Imaging Spectroradiometer satellites; carbon phenology is extracted from daily gross primary production estimates at flux sites. We found that there exists remarkable asynchronization between vegetation greenness phenology and carbon phenology on the 95 flux sites. Generally, the asynchronization is more prevalent in the withering season, with the bias about 18.7 ± 13.0 days for EVI and about 29.8 ± 14.4 days for NDVI. In comparison, in the green‐up season, the bias is only about −1.6 ± 12.9 days for EVI and −6.0 ± 13.8 days for NDVI. We found that photoperiod is the dominant factor controlling the asynchronization and the decrease of photosynthetic capacity in the withering season, other than photosynthetically active radiation or temperature. Our study also reveals that EVI × PC and NDVI × PC are good proxies of vegetation carbon phenology, where PC is the photoperiod constraint and is quantified as . Using PC in the withering season reduces the bias of EVI from 18.7 ± 13.0 days to −2.4 ± 11.2 days and the bias of NDVI from 29.8 ± 14.4 days to −1.1 ± 12.7 days.

Comparison of traditional ground-based observations and digital remote sensing of phenological transitions in a floodplain forest

Phenological observations are important as indicators of global warming and as estimation tools for the terrestrial carbon balance in vulnerable ecosystems, such as the last fragments of floodplain forests in the Czechia. The aim of this paper was to compare ground-based phenological observations of three dominant species (European hornbeam, English oak and narrow-leaved ash) in this ecosystem, with the seasonal trajectory of the greenness index (Gcc) and thresholds extracted from images taken by phenocameras located on a meteorological mast. The average annual air temperature in the studied years 2014–2017 was 1 °C higher than the long-term average, and the precipitation deficit reached ⅓ of annual rainfall. We found a high proportion of above-average warm days in the warmest part of the growing season. Above-average air temperatures significantly accelerated the onset of budbreak in ash. Yet a higher proportion of above-average air temperatures prolonged the period between budbreak to fully developed leaf area, especially in ash and oak. In 2017, rapid cooling after exceptionally warm temperatures at the onset of spring had a detrimental effect on the stand productivity and showed a marked effect on the phenological shifts. The period when leaf area developed was in the range of DOY 66-286 for hornbeam, DOY 79-329 for oak and DOY 88-321 for ash in 2014–2017. The seasonal trajectory of Gcc showed differences between tree species that corresponded to the dynamics of the onset of phenophases observed in the field. According to image analyses, the phenophase of greenup and maturity for hornbeam and ash had minimal uncertainty. In contrast, the uncertainty was high in the determination of phenophases for oak. Our observations show that the modern method of phenological observation by phenocameras is suitable for mixed forests, but classical ground-based observations by a phenologist are still crucial in order to verify the results.

Comparison of forest above-ground biomass from dynamic global vegetation models with spatially explicit remotely sensed observation-based estimates.

Gaps in our current understanding and quantification of biomass carbon stocks, particularly in tropics, lead to large uncertainty in future projections of the terrestrial carbon balance. We use the recently published GlobBiomass data set of forest above-ground biomass (AGB) density for the year 2010, obtained from multiple remote sensing and in situ observations at 100m spatial resolution to evaluate AGB estimated by nine dynamic global vegetation models (DGVMs). The global total forest AGB of the nine DGVMs is 365±66Pg C, the spread corresponding to the standard deviation between models, compared to 275Pg C with an uncertainty of ~13.5% from GlobBiomass. Model-data discrepancy in total forest AGB can be attributed to their discrepancies in the AGB density and/or forest area. While DGVMs represent the global spatial gradients of AGB density reasonably well, they only have modest ability to reproduce the regional spatial gradients of AGB density at scales below 1000km. The 95th percentile of AGB density (AGB95 ) in tropics can be considered as the potential maximum of AGB density which can be reached for a given annual precipitation. GlobBiomass data show local deficits of AGB density compared to the AGB95 , particularly in transitional and/or wet regions in tropics. We hypothesize that local human disturbances cause more AGB density deficits from GlobBiomass than from DGVMs, which rarely represent human disturbances. We then analyse empirical relationships between AGB density deficits and forest cover changes, population density, burned areas and livestock density. Regression analysis indicated that more than 40% of the spatial variance of AGB density deficits in South America and Africa can be explained; in Southeast Asia, these factors explain only ~25%. This result suggests TRENDY v6 DGVMs tend to underestimate biomass loss from diverse and widespread anthropogenic disturbances, and as a result overestimate turnover time in AGB.

Enhanced regional terrestrial carbon uptake over Korea revealed by atmospheric CO2 measurements from 1999 to 2017.

Understanding changes in terrestrial carbon balance is important to improve our knowledge of the regional carbon cycle and climate change. However, evaluating regional changes in the terrestrial carbon balance is challenging due to the lack of surface flux measurements. This study reveals that the terrestrial carbon uptake over the Republic of Korea has been enhanced from 1999 to 2017 by analyzing long-term atmospheric CO2 concentration measurements at the Anmyeondo Station (36.53°N, 126.32°E) located in the western coast. The influence of terrestrial carbon flux on atmospheric CO2 concentrations (ΔCO2 ) is estimated from the difference of CO2 concentrations that were influenced by the land sector (through easterly winds) and the Yellow Sea sector (through westerly winds). We find a significant trend in ΔCO2 of -4.75ppm per decade (p<.05) during the vegetation growing season (May through October), suggesting that the regional terrestrial carbon uptake has increased relative to the surrounding ocean areas. Combined analysis with satellite measured normalized difference vegetation index and gross primary production shows that the enhanced carbon uptake is associated with significant nationwide increases in vegetation and its production. Process-based terrestrial model and inverse model simulations estimate that regional terrestrial carbon uptake increases by up to 18.9 and 8.0TgC for the study period, accounting for 13.4% and 5.7% of the average annual domestic carbon emissions, respectively. Atmospheric chemical transport model simulations indicate that the enhanced terrestrial carbon sink is the primary reason for the observed ΔCO2 trend rather than anthropogenic emissions and atmospheric circulation changes. Our results highlight the fact that atmospheric CO2 measurements could open up the possibility of detecting regional changes in the terrestrial carbon cycle even where anthropogenic emissions are not negligible.

Carbon Flux Variability From a Relatively Simple Ecosystem Model With Assimilated Data Is Consistent With Terrestrial Biosphere Model Estimates

AbstractModeling the net carbon balance is challenging due to the knowledge gaps in the variability and processes controlling gross carbon fluxes. Terrestrial carbon cycle modeling is susceptible to several sources of bias, including meteorological uncertainty, model structural uncertainty, and model parametric uncertainty. To determine the impact of these uncertainties, we compare three model‐derived representations of the global terrestrial carbon balance across 1997–2009: (1) observation‐constrained model‐data fusion (CARBon Data Model FraMEwork, CARDAMOM), (2) the reanalysis‐driven Trends in Net Land‐Atmosphere Carbon Exchange (TRENDY) land biosphere model ensemble, and (3) the Coupled Model Intercomparison Project 5 (CMIP5) Earth System Model ensemble. We consider the spread in carbon cycle simulations attributable primarily to parametric uncertainty (CARDAMOM), structural uncertainty (TRENDY), and combined structural and simulated meteorological uncertainty (CMIP5). We find that the spread across the CARDAMOM ensemble long‐term mean—produced by parameter uncertainty—is larger than the spread of TRENDY and CMIP5 for net biosphere exchange (NBE) but similar for gross primary productivity (GPP). The carbon flux dynamics of CARDAMOM compares to models in TRENDY as well as models in TRENDY compare to each other in many regions for NBE seasonal (nine of 12), NBE interannual (11 of 12), and GPP seasonal variability (7 of 12), although not for GPP interannual variability (2 of 12). The simple model structure of CARDAMOM and systematic assimilation of observations is sufficient to produce carbon dynamics within the range of more complex models. These results are encouraging for the use of model‐data fusion products with empirically estimated uncertainty for global carbon cycle studies.

Refining benchmarks for soil organic carbon in Australia’s temperate forests

Soil organic carbon (SOC) stocks in Australia’s temperate forests have been overlooked in national soil databases and in global SOC analyses of natural ecosystems despite the importance of temperate forests to the global terrestrial carbon balance. This limits the potential to both predict change in SOC stocks in temperate Australia and to identify where and how SOC stocks can be managed to mitigate climate change. Based on data from 707 sites, we examine variations in SOC concentrations and stocks across a range of natural temperate broadleaf forests in south-eastern Australia. Comparisons with current Australia-wide databases highlight previous under-estimation of forest SOC concentrations, leading to substantial underprediction of SOC stocks in the most productive forests (e.g. this study’s mean of 207 Mg C ha−1 compared with a database mean estimate of 141 Mg C ha−1 for Tall open-forests to 30-cm soil depth). Random Forest models involving 27 environmental variables (representing climate, terrain, parent material, soil attributes, vegetation, and fire history) explained up to 79% of the variation in SOC concentrations and 77% of the variation in SOC stocks to 30-cm depth. Climate variables (precipitation, temperature) were of greatest importance to the prediction of both SOC concentrations and SOC stocks, tending to override the importance of terrain and fire-history variables at this study’s regional scale. While patterns in SOC concentrations and stocks were correlated, SOC concentrations were not a reliable proxy for SOC stocks to 10-cm depth, reiterating the importance of mass equivalent measures (i.e. soil bulk density) to assessing changes in soil carbon storage. Our study provides a timely check of the model-based estimates of SOC concentrations and stocks in Australia’s temperate forests that are currently available in nation-wide databases and improves the available information for defining benchmarks, and for identifying potential areas of SOC loss and gain, in programs that aim to mitigate climate change.

Soil carbon release responses to long-term versus short-term climatic warming in an arid ecosystem

Abstract. Climate change severely impacts the grassland carbon cycling by altering rates of litter decomposition and soil respiration (Rs), especially in arid areas. However, little is known about the Rs responses to different warming magnitudes and watering pulses in situ in desert steppes. To examine their effects on Rs, we conducted long-term moderate warming (4 years, ∼3 ∘C), short-term acute warming (1 year, ∼4 ∘C) and watering field experiments in a desert grassland of northern China. While experimental warming significantly reduced average Rs by 32.5 % and 40.8 % under long-term moderate and short-term acute warming regimes, respectively, watering pulses (fully irrigating the soil to field capacity) stimulated it substantially. This indicates that climatic warming constrains soil carbon release, which is controlled mainly by decreased soil moisture, consequently influencing soil carbon dynamics. Warming did not change the exponential relationship between Rs and soil temperature, whereas the relationship between Rs and soil moisture was better fitted to a sigmoid function. The belowground biomass, soil nutrition, and microbial biomass were not significantly affected by either long-term or short-term warming regimes, respectively. The results of this study highlight the great dependence of soil carbon emission on warming regimes of different durations and the important role of precipitation pulses during the growing season in assessing the terrestrial ecosystem carbon balance and cycle.

Analysis and prediction of vegetation dynamics under the background of climate change in Xinjiang, China.

BackgroundVegetation dynamics is defined as a significant indictor in regulating terrestrial carbon balance and climate change, and this issue is important for the evaluation of climate change. Though much work has been done concerning the correlations among vegetation dynamics, precipitation and temperature, the related questions about relationships between vegetation dynamics and other climatic factors (e.g., specific humidity, net radiation, soil moisture) have not been thoroughly considered. Understanding these questions is of primary importance in developing policies to address climate change.MethodsIn this study, the least squares regression analysis method was used to simulate the trend of vegetation dynamics based on the normalized difference vegetation index (NDVI) from 1981 to 2018. A partial correlation analysis method was used to explore the relationship between vegetation dynamics and climate change; and further,the revised greyscale model was applied to predict the future growth trend of natural vegetation.ResultsThe Mann-Kendall test results showed that th e air temperature rose sharply in 1997 and had been in a state of high fluctuations since then. Strong changes in hydrothermal conditions had major impact on vegetation dynamics in the area. Specifically, the NDVI value of natural vegetation showed an increasing trend from 1981 to 2018, and the same changes occurred in the precipitation. From 1981 to 1997, the values of natural vegetation increased at a rate of 0.0016 per year. From 1999 to 2009, the NDVI value decreased by an average rate of 0.0025 per year. From 2010 to 2018, the values began an increasing trend and reached a peak in 2017, with an average annual rate of 0.0033. The high vegetation dynamics areas were mainly concentrated in the north and south slopes of the Tianshan Mountains, the Ili River Valley and the Altay area. The greyscale prediction results showed that the annual average NDVI values of natural vegetation may present a fluctuating increasing trend. The NDVI value in 2030 is 0.0196 higher than that in 2018, with an increase of 6.18%.ConclusionsOur results indicate that: (i) the variations of climatic factors have caused a huge change in the hydrothermal conditions in Xinjiang; (ii) the vegetation dynamics in Xinjiang showed obvious volatility, and then in the end stage of the study were higher than the initial stage the vegetation dynamics in Xinjiang showed a staged increasing trend; (iii) the vegetation dynamics were affected by many factors,of which precipitation was the main reason; (iv) in the next decade, the vegetation dynamics in Xinjiang will show an increasing trend.

Evaluating satellite retrieved fractional snow-covered area at a high-Arctic site using terrestrial photography

The seasonal snow-cover is one of the most rapidly varying natural surface features on Earth. It strongly modulates the terrestrial water, energy, and carbon balance. Fractional snow-covered area (fSCA) is an essential snow variable that can be retrieved from multispectral satellite imagery. In this study, we evaluate fSCA retrievals from multiple sensors that are currently in polar orbit: the operational land imager (OLI) on-board Landsat 8, the multispectral instrument (MSI) on-board the Sentinel-2 satellites, and the moderate resolution imaging spectroradiometer (MODIS) on-board Terra and Aqua. We consider several retrieval algorithms that fall into three classes: thresholding of the normalized difference snow index (NDSI), regression on the NDSI, and spectral unmixing. We conduct the evaluation at a high-Arctic site in Svalbard, Norway, by comparing satellite retrieved fSCA to coincident high-resolution snow-cover maps obtained from a terrestrial automatic camera system. For the lower resolution MODIS retrievals, the regression-based retrievals outperformed the unmixing-based retrievals for all metrics but the bias. For the higher resolution sensors (OLI and MSI), retrievals based on NDSI thresholding overestimated the fSCA due to the mixed pixel problem whereas spectral unmixing retrievals provided the most reliable estimates across the board. We therefore encourage the operationalization of spectral unmixing retrievals of fSCA from both OLI and MSI.

Global satellite-driven estimates of heterotrophic respiration

Abstract. While heterotrophic respiration (Rh) makes up about a quarter of gross global terrestrial carbon fluxes, it remains among the least-observed carbon fluxes, particularly outside the midlatitudes. In situ measurements collected in the Soil Respiration Database (SRDB) number only a few hundred worldwide. Similarly, only a single data-driven wall-to-wall estimate of annual average heterotrophic respiration exists, based on bottom-up upscaling of SRDB measurements using an assumed functional form to account for climate variability. In this study, we exploit recent advances in remote sensing of terrestrial carbon fluxes to estimate global variations in heterotrophic respiration in a top-down fashion at monthly temporal resolution and 4∘×5∘ spatial resolution. We combine net ecosystem productivity estimates from atmospheric inversions of the NASA Carbon Monitoring System-Flux (CMS-Flux) with an optimally scaled gross primary productivity dataset based on satellite-observed solar-induced fluorescence variations to estimate total ecosystem respiration as a residual of the terrestrial carbon balance. The ecosystem respiration is then separated into autotrophic and heterotrophic components based on a spatially varying carbon use efficiency retrieved in a model–data fusion framework (the CARbon DAta MOdel fraMework, CARDAMOM). The resulting dataset is independent of any assumptions about how heterotrophic respiration responds to climate or substrate variations. It estimates an annual average global average heterotrophic respiration flux of 43.6±19.3 Pg C yr−1. Sensitivity and uncertainty analyses showed that the top-down Rh are more sensitive to the choice of input gross primary productivity (GPP) and net ecosystem productivity (NEP) datasets than to the assumption of a static carbon use efficiency (CUE) value, with the possible exception of the wet tropics. These top-down estimates are compared to bottom-up estimates of annual heterotrophic respiration, using new uncertainty estimates that partially account for sampling and model errors. Top-down heterotrophic respiration estimates are higher than those from bottom-up upscaling everywhere except at high latitudes and are 30 % greater overall (43.6 Pg C yr−1 vs. 33.4 Pg C yr−1). The uncertainty ranges of both methods are comparable, except poleward of 45∘ N, where bottom-up uncertainties are greater. The ratio of top-down heterotrophic to total ecosystem respiration varies seasonally by as much as 0.6 depending on season and climate, illustrating the importance of studying the drivers of autotrophic and heterotrophic respiration separately, and thus the importance of data-driven estimates of Rh such as those estimated here.

The impact of the 2009/2010 drought on vegetation growth and terrestrial carbon balance in Southwest China

The 2009/2010 drought in Southwest China was a “once in a century drought” event. This drought event had strong adverse impacts, such as water scarcity, crop failure, and economic loss, on ecosystems and the human society. Explicit representations of this drought event and associated changes in vegetation dynamics and carbon cycle, however, are still largely missed in literature. Here we used the standardized anomaly of 3-month Standard Precipitation-Evapotranspiration Index (SPEI) to characterize this 2009/2010 drought event, including its onset and end months, duration and severity. We examined the drought impacts on vegetation greenness using Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI); and assessed the terrestrial carbon flux responses to drought using data from satellite-based datasets, atmospheric inversion, one data-based model, and three ecosystem models. Our analysis showed that this “autumn-winter-spring” drought mostly suppressed vegetation growth in Yunnan, North Guangxi, Guizhou and East Sichuan. In comparisons to the same months of the reference period (2000–2015), the drought caused a large reduction in carbon uptake (4.4 ± 5 gCm−2month-1) from the decrease in gross primary production (GPP, 5.7 ± 9.5 gCm−2month-1), although ecosystem respiration (TER) also decreased by a smaller extent (1.3 ± 5.1 gCm−2month-1). Nonetheless, more than 65% of the drought-impacted area recovered in both vegetation greenness and productivity within 3 months, while about 10% of the drought-affected area failed to recover even after 6 months post the drought event. Across different vegetation types, forests and shrubs had stronger capability of drought resistance and shorter post-drought recovery time than crops and grasses. Model comparison reveals that while data from different sources generally agreed on the sign of vegetation greenness and carbon flux responses to drought, they differ significantly in the extent of such responses.

Mapping Forest Cover in Northeast China from Chinese HJ-1 Satellite Data Using an Object-Based Algorithm.

Forest plays a significant role in the global carbon budget and ecological processes. The precise mapping of forest cover can help significantly reduce uncertainties in the estimation of terrestrial carbon balance. A reliable and operational method is necessary for a rapid regional forest mapping. In this study, the goal relies on mapping forest and subcategories in Northeast China through the use of high spatio-temporal resolution HJ-1 imagery and time series vegetation indices within the context of an object-based image analysis and decision tree classification. Multi-temporal HJ-1 images obtained in a single year provide an opportunity to acquire phenology information. By analyzing the difference of spectral and phenology information between forest and non-forest, forest subcategories, decision trees using threshold values were finally proposed. The resultant forest map has a high overall accuracy of 0.91 ± 0.01 with a 95% confidence interval, based on the validation using ground truth data from field surveys. The forest map extracted from HJ-1 imagery was compared with two existing global land cover datasets: GlobCover 2009 and MCD12Q1 2009. The HJ-1-based forest area is larger than that of MCD12Q1 and GlobCover and more closely resembles the national statistics data on forest area, which accounts for more than 40% of the total area of the Northeast China. The spatial disagreement primarily occurs in the northern part of the Daxing’an Mountains, Sanjiang Plain and the southwestern part of the Songliao Plain. The compared result also indicated that the forest subcategories information from global land cover products may introduce large uncertainties for ecological modeling and these should be cautiously used in various ecological models. Given the higher spatial and temporal resolution, HJ-1-based forest products could be very useful as input to biogeochemical models (particularly carbon cycle models) that require accurate and updated estimates of forest area and type.