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Spatial–temporal dynamics of aboveground biomass (AGB) and forest area affect the carbon cycle, climate and biodiversity in the Brazilian Amazon. Here we investigate interannual changes in AGB and forest area by analysing satellite-based annual AGB and forest area datasets. We found that the gross forest area loss was larger in 2019 than in 2015, possibly due to recent loosening of forest protection policies. However, the net AGB loss was three times smaller in 2019 than in 2015. During 2010–2019, the Brazilian Amazon had a cumulative gross loss of 4.45 Pg C against a gross gain of 3.78 Pg C, resulting in a net AGB loss of 0.67 Pg C. Forest degradation (73%) contributed three times more to the gross AGB loss than deforestation (27%), given that the areal extent of degradation exceeds that of deforestation. This indicates that forest degradation has become the largest process driving carbon loss and should become a higher policy priority.Carbon loss from forests occurs through deforestation or the degradation of existing forest. The loss of forest area in the Brazilian Amazon was higher in 2019 than following drought and an El Niño event in 2015, yet degradation drove three times more biomass loss than deforestation from 2010 to 2019.

Salinization is a threat to global agricultural and soil resource allocation. Current investigations of global soil salinity are limited to coarse spatial resolution of the available datasets (>250 m) and semiqualitative classification rules (five ranks). Based on these two limitations, we proposed a framework to quantitatively estimate global soil salt content in five climate regions at 10 m by integrating Sentinel-1/2 remotely sensed images, climate, parent material, terrain data, and machine learning. In hyper-arid and arid region, models established using Sentinel-2 and other geospatial data showed the highest accuracy with R 2 of 0.85 and 0.62, respectively. In semi-arid, dry sub-humid, and humid regions, models performed best using Sentinel-1, Sentinel-2, and other geospatial data with R 2 of 0.87, 0.80, and 0.87, respectively. The accuracy of the global models is considerable with field validation in Iran and Xinjiang, and compared with digitized salinity maps in California, Brazil, Turkey, South Africa, and Shandong. The proportion of extremely saline soils in Europe is 10.21%, followed by South America (5.91%), Oceania (5.80%), North America (4.05%), Asia (1.19%), and Africa (1.11%). Climatic conditions, groundwater, and salinity index are key covariates in global soil salinity estimation. Use of radar data improves estimation accuracy in wet regions. The map of global soil salinity at 10 m provides a detailed, high-precision basis for soil property investigation and resource management.

Soil carbon changes are difficult to measure globally, and global models are poorly constrained. Here, we propose a framework to map annual changes in soil carbon and litter (SOCL) as the difference between the net land CO 2 flux from atmospheric inversions and satellite-based maps of biomass changes. We show that SOCL accumulated globally at a rate of about 0.34 ± 0.30 ( ± 1 sigma) billion tonnes of carbon per year (PgC yr − 1 ) during 2011-2020. The largest SOCL sink is found in boreal regions (0.93 ± 0.45 PgC yr − 1 in total) particularly in undisturbed peatlands and managed forests. The largest losses occur in the dry tropics (−0.50 ± 0.47 PgC yr − 1 ) and correspond with agricultural expansion from land use change, cropland management and grazing. By contrast, forests in the wet tropics act as a net soil carbon sink (0.32 ± 0.35 PgC yr − 1 ). Our findings highlight the large mitigation opportunities in the dry tropics to restore agricultural soil carbon. Soil carbon loss from human agriculture-related land use in the dry tropics largely offsets gains in northern lands, leading to a small net sink of 0.34 ± 0.30 PgC yr −1 at a global scale.

The African continent is facing one of the driest periods in the past three decades as well as continued deforestation. These disturbances threaten vegetation carbon (C) stocks and highlight the need for improved capabilities of monitoring large-scale aboveground carbon stock dynamics. Here we use a satellite dataset based on vegetation optical depth derived from low-frequency passive microwaves (L-VOD) to quantify annual aboveground biomass-carbon changes in sub-Saharan Africa between 2010 and 2016. L-VOD is shown not to saturate over densely vegetated areas. The overall net change in drylands (53% of the land area) was −0.05 petagrams of C per year (Pg C yr −1 ) associated with drying trends, and a net change of −0.02 Pg C yr −1 was observed in humid areas. These trends reflect a high inter-annual variability with a very dry year in 2015 (net change, −0.69 Pg C) with about half of the gross losses occurring in drylands. This study demonstrates, first, the applicability of L-VOD to monitor the dynamics of carbon loss and gain due to weather variations, and second, the importance of the highly dynamic and vulnerable carbon pool of dryland savannahs for the global carbon balance, despite the relatively low carbon stock per unit area. Low-frequency passive microwave data (L-VOD) allow quantification of biomass change in sub-Saharan Africa between 2010 and 2016, revealing climate-induced carbon losses, particularly in drylands.

Evaluating the response of the land carbon sink to the anomalies in temperature and drought imposed by El Niño events provides insights into the present-day carbon cycle and its climate-driven variability. It is also a necessary step to build confidence in terrestrial ecosystems models' response to the warming and drying stresses expected in the future over many continents, and particularly in the tropics. Here we present an in-depth analysis of the response of the terrestrial carbon cycle to the 2015/2016 El Niño that imposed extreme warming and dry conditions in the tropics and other sensitive regions. First, we provide a synthesis of the spatio-temporal evolution of anomalies in net land–atmosphere CO2 fluxes estimated by two in situ measurements based on atmospheric inversions and 16 land-surface models (LSMs) from TRENDYv6. Simulated changes in ecosystem productivity, decomposition rates and fire emissions are also investigated. Inversions and LSMs generally agree on the decrease and subsequent recovery of the land sink in response to the onset, peak and demise of El Niño conditions and point to the decreased strength of the land carbon sink: by 0.4–0.7 PgC yr−1 (inversions) and by 1.0 PgC yr−1 (LSMs) during 2015/2016. LSM simulations indicate that a decrease in productivity, rather than increase in respiration, dominated the net biome productivity anomalies in response to ENSO throughout the tropics, mainly associated with prolonged drought conditions. This article is part of a discussion meeting issue ‘The impact of the 2015/2016 El Niño on the terrestrial tropical carbon cycle: patterns, mechanisms and implications’.

Northern ecosystems (≥ 30° N) have been accumulating vegetation biomass carbon in recent decades, but increasing droughts and wildfires threaten this carbon sink. Here, we analyse annual changes in live vegetation biomass in northern ecosystems using low-frequency microwave satellite observations at 25 km spatial resolution from 2010 to 2022. We find that live biomass carbon stocks have undergone a reversal from a positive to a negative trend during the study period with 2016 marking the turning point. During 2016–2022, live biomass carbon stocks decreased at a rate of − 0.20 − 0.26 − 0.11 PgC yr −1 across northern ecosystems, primarily in temperate biomes ( − 0.26 − 0.33 − 0.17 PgC yr −1 ). The annual mean gross loss of 4% of live biomass carbon in this region during 2016-2022 reflects high interannual variability, with significant losses associated with droughts and a further drop of − 0.60 − 0.75 − 0.47 PgC in the very dry year of 2022. Our findings highlight the vulnerability of live biomass carbon stocks to emerging climate-induced disturbances in northern ecosystems, challenging the sustainability of the current large terrestrial carbon sink in this key region for the global carbon balance. Drought, fires, and human activities have reversed the accumulation of live biomass carbon in the Northern Hemisphere, with total biomass shifting from a positive to a negative trend around 2016.

Land-based mitigation is essential in reducing net carbon emissions. Yet, the attribution of carbon fluxes remains highly uncertain, in particular for the forest-rich region of Eastern Europe (incl. Western Russia). Here we integrate various data sources to show that Eastern Europe accounted for an above-ground biomass carbon sink of ~0.41 gigatons of carbon per year over the period 2010–2019, that is 78% of the entire European carbon sink. We find that this carbon sink is declining, mainly driven by changes in land use and land management, but also by increasing natural disturbances. Based on a random forest model, we show that land use and management changes are main drivers of the declining carbon sink in Eastern Europe, although soil moisture variability is also important. Specifically, the saturation effect of tree regrowth in abandoned agricultural areas, combined with increasing wood harvest removals, particularly in European Russia, contributed to the decrease in the Eastern European carbon sink.

Soils are at the crossroads of many existential issues that humanity is currently facing. Soils are a finite resource that is under threat, mainly due to human pressure. There is an urgent need to map and monitor them at field, regional, and global scales in order to improve their management and prevent their degradation. This remains a challenge due to the high and often complex spatial variability inherent to soils. Over the last four decades, major research efforts in the field of pedometrics have led to the development of methods allowing to capture the complex nature of soils. As a result, digital soil mapping (DSM) approaches have been developed for quantifying soils in space and time. DSM and monitoring have become operational thanks to the harmonization of soil databases, advances in spatial modeling and machine learning, and the increasing availability of spatiotemporal covariates, including the exponential increase in freely available remote sensing (RS) data. The latter boosted research in DSM, allowing the mapping of soils at high resolution and assessing the changes through time. We present a review of the main contributions and developments of French (inter)national research, which has a long history in both RS and DSM. Thanks to the French SPOT satellite constellation that started in the early 1980s, the French RS and soil research communities have pioneered DSM using remote sensing. This review describes the data, tools, and methods using RS imagery to support the spatial predictions of a wide range of soil properties and discusses their pros and cons. The review demonstrates that RS data are frequently used in soil mapping (i) by considering them as a substitute for analytical measurements, or (ii) by considering them as covariates related to the controlling factors of soil formation and evolution. It further highlights the great potential of RS imagery to improve DSM, and provides an overview of the main challenges and prospects related to digital soil mapping and future sensors. This opens up broad prospects for the use of RS for DSM and natural resource monitoring.

Over the past 2 to 3 decades, Chinese forests are estimated to act as a large carbon sink, yet the magnitude and spatial patterns of this sink differ considerably among studies. Using 3 microwave (L- and X-band vegetation optical depth [VOD]) and 3 optical (normalized difference vegetation index, leaf area index, and tree cover) remote-sensing vegetation products, this study compared the estimated live woody aboveground biomass carbon (AGC) dynamics over China between 2013 and 2019. Our results showed that tree cover has the highest spatial consistency with 3 published AGC maps (mean correlation value R = 0.84), followed by L-VOD ( R = 0.83), which outperform the other VODs. An AGC estimation model was proposed to combine all indices to estimate the annual AGC dynamics in China during 2013 to 2019. The performance of the AGC estimation model was good (root mean square error = 0.05 Pg C and R 2 = 0.90 with a mean relative uncertainty of 9.8% at pixel scale [0.25°]). Results of the AGC estimation model showed that carbon uptake by the forests in China was about +0.17 Pg C year −1 from 2013 to 2019. At the regional level, provinces in southwest China including Guizhou (+22.35 Tg C year −1 ), Sichuan (+14.49 Tg C year −1 ), and Hunan (+11.42 Tg C year −1 ) provinces had the highest carbon sink rates during 2013 to 2019. Most of the carbon-sink regions have been afforested recently, implying that afforestation and ecological engineering projects have been effective means for carbon sequestration in these regions.

Aim: The aim was to examine whether recent mortality can be explained by hydraulic failure linked to water deficit. Location: Western Europe. Time period: 1986–2014. Major taxa studied: Forty-four tree species. Methods: We modelled the hydraulic safety margin (HSM) across the ranges of 44 tree species at their driest margin (n = 193,261 plots), defined as the difference between the estimated minimal soil water potential of each plot and the species water stress threshold, which corresponds to the hydraulic failure of the vascular system. Soil water potential was estimated by applying Campbell's equations on the minimal and maximal soil water contents estimated from 1979 to 2010 in the top 289 cm of soil and five soil textures across the species ranges. For each species, we modelled the amount of average mortality derived from plots of the Spanish and French National Forest Inventories to the variation in modelled hydraulic safety margin and environmental drivers across the species ranges using hurdle models. Results: We did not identify any global convergence of modelled HSM within the species distribution ranges, finding instead a rather large variability in modelled HSM for most of the studied species. Fifteen species, out of 25 for which the models were practicable, showed significantly higher mortality in populations with negative HSM in comparison to those showing positive HSM, with positive and negative interaction along the aridity index. Main conclusions: The combination of competition, average climate and modelled HSM explained average tree mortality. Most of the species presented at least one population that had already experienced a negative HSM and many other populations a positive but narrow HSM, suggesting that climate change is likely to push some populations towards a higher risk of hydraulic failure in the drier conditions projected for Western Europe.