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ABSTRACT Cover crops, a promising strategy to increase soil organic carbon (SOC) storage in croplands and mitigate climate change, have typically been shown to benefit soil carbon (C) storage from increased plant C inputs. However, input‐driven C benefits may be augmented by the reduction of C outputs induced by cover crops, a process that has been tested by individual studies but has not yet been synthesized. Here we quantified the impact of cover crops on organic C loss via soil erosion (SOC erosion) and revealed the geographical variability at the global scale. We analyzed the field data from 152 paired control and cover crop treatments from 57 published studies worldwide using meta‐analysis and machine learning. The meta‐analysis results showed that cover crops widely reduced SOC erosion by an average of 68% on an annual basis, while they increased SOC stock by 14% (0–15 cm). The absolute SOC erosion reduction ranged from 0 to 18.0 Mg C⁻¹ ha⁻¹ year⁻¹and showed no correlation with the SOC stock change that varied from −8.07 to 22.6 Mg C⁻¹ ha⁻¹ year⁻¹at 0–15 cm depth, indicating the latter more likely related to plant C inputs. The magnitude of SOC erosion reduction was dominantly determined by topographic slope. The global map generated by machine learning showed the relative effectiveness of SOC erosion reduction mainly occurred in temperate regions, including central Europe, central‐east China, and Southern South America. Our results highlight that cover crop‐induced erosion reduction can augment SOC stock to provide additive C benefits, especially in sloping and temperate croplands, for mitigating climate change. 

Abstract. Forests in Europe experienced record-breaking dry conditions during the summer of 2022. The direction in which various forest types respond to climate extremes during their growing season is contingent upon an array of internal and external factors. These factors include the extent and severity of the extreme conditions and the tree ecophysiological characteristics adapted to environmental cues, which exhibit significant regional variations. In this study, we aimed to (1) quantify the extent and severity of the extreme soil and atmospheric dryness in 2022 in comparison to the two most extreme years in the past (2003 and 2018), (2) quantify the response of different forest types to atmospheric and soil dryness in terms of canopy browning and photosynthesis, and (3) relate the functional characteristics of the forests to the emerging responses observed remotely at the canopy level. For this purpose, we used spatial meteorological datasets between 2000 and 2022 to identify conditions with extreme soil and atmospheric dryness. We used the near-infrared reflectance of vegetation (NIRv), derived from the Moderate Resolution Imaging Spectroradiometer (MODIS), and the global OCO-2 solar-induced fluorescence (GOSIF) as an observational proxy for ecosystem gross productivity to quantify the response of forests at the canopy level. In summer 2022, southern regions of Europe experienced exceptionally pronounced atmospheric and soil dryness. These extreme conditions resulted in a 30 % more widespread decline in GOSIF across forests compared to the drought of 2018 and 60 % more widespread decline compared to the drought of 2003. Although the atmospheric and soil drought scores were more extensive and severe (indicated by a larger observed maximum z score) in 2018 compared to 2022, the negative impact on forests, as indicated by declined GOSIF, was significantly larger in 2022. Different forest types were affected to varying degrees by the extreme conditions in 2022. Deciduous broadleaf forests were the most negatively impacted due to the extent and severity of the drought within their distribution range. In contrast, areas dominated by evergreen needleleaf forest (ENF) in northern Europe experienced a positive soil moisture (SM) anomaly and minimal negative vapour pressure deficit (VPD) in 2022. These conditions led to enhanced canopy greening and stronger solar-induced fluorescence (SIF) signals, benefiting from the warming. The higher degree of canopy damage in 2022, despite less extreme conditions, highlights the evident vulnerability of European forests to future droughts. 

Abstract There are twenty experimental forest and range sites (EFRs) across the southeastern United States that are currently maintained by the USDA Forest Service (Forest Service) to conduct forest ecosystem research for addressing ecosystem management challenges. The overall objective of this study was to use multiple gridded datasets to assess the extent to which the twenty EFRs represent the climate, ecosystem structure, and ecosystem functions of southeastern forests. The EFRs represent the large variability of climate conditions across the region relatively well, but we identified small representation gaps. The representativeness of ecosystem structure by these EFRs can be improved by establishing EFRs in forests with relatively low tree cover, leaf area index, or tree canopy height. The current EFRs also represent the forest ecosystem functions of the region relatively well, although areas with intermediate and low aboveground biomass and water yield are not well represented. The trends in climate, ecosystem structure, and ecosystem functions were generally consistent between the region and the EFRs. Our study indicates that the current EFRs represent the region relatively well, but establishing additional EFRs in specific areas within the region could help more completely assess how southeastern forests respond to climate change, disturbance, and management practices. Study Implications: This study across the experimental forests and ranges (EFRs) and the southeastern forest region fills the knowledge gap regarding climate, ecosystem structure, and ecosystem functions of EFRs in the context of the broader southeastern forest region. Understanding ecosystem functions and structures across the EFR network can help the Southern Research Station to address new research questions. Our study indicates that the current EFRs represent the climate, ecosystem structure, and ecosystem functions of southeastern forests well. However, establishing additional EFRs in certain regions could help more completely assess how southeastern forests respond to climate change, disturbance, and management practices. 

Abstract The global forest carbon stocks represent the amount of carbon stored in woody vegetation and are important for quantifying the ability of the global forests to sequester atmospheric CO₂and to provide ecosystem services (e.g., timber) under climate change. The forest ecosystem carbon pool estimates are highly variable and poorly quantified in areas lacking forest inventory estimates. Here, we compare and analyze aboveground biomass (AGB) estimates from five satellite‐based global data sets and nine dynamic global vegetation models (DVGMs). We find that across the data sets, mean AGB exhibits the largest variability around the tropical area. In addition, AGB shows a similar latitudinal trend but large variability among the data sets. Satellite‐based AGB estimates are lower than those simulated by DVGMs. The divergence among the satellite‐based AGB estimates can be driven by the methodology, input satellite products, and the forested areas used to estimate AGB. The modeled NPP, autotrophic respiration, and carbon allocation mostly drive the variability of AGB simulated by DGVMs. The future availability of a high‐quality global forest area map is anticipated to improve AGB estimate accuracy and to reduce the discrepancies among different satellite‐ and model‐based AGB estimates. We suggest the carbon‐modeling community reexamine the methodology used to estimate AGB and forested areas for a more robust global forest carbon stock estimation. 

Developing the capacity to monitor species diversity worldwide is of great importance in halting biodiversity loss. To this end, remote sensing plays a unique role. In this study, we evaluate the potential of Global Ecosystem Dynamics Investigation (GEDI) data, combined with conventional satellite optical imagery and climate reanalysis data, to predict in situ alpha diversity (Species richness, Simpson index, and Shannon index) among tree species. Data from Sentinel-2 optical imagery, ERA-5 climate data, SRTM-DEM imagery, and simulated GEDI data were selected for the characterization of diversity in four study areas. The integration of ancillary data can improve biodiversity metrics predictions. Random Forest (RF) regression models were suitable for estimating tree species diversity indices from remote sensing variables. From these models, we generated diversity index maps for the entire Cerrado using all GEDI data available in orbit. For all models, the structural metric Foliage Height Diversity (FHD) was selected; the Renormalized Difference Vegetation Index (RDVI) was also selected in all species diversity models. For the Shannon model, two GEDI variables were selected. Overall, the models indicated performances for species diversity ranging from (R2 = 0.24 to 0.56). In terms of RMSE%, the Shannon model had the lowest value among the diversity indices (31.98%). Our results suggested that the developed models are valuable tools for assessing species diversity in tropical savanna ecosystems, although each model can be chosen based on the objectives of a given study, the target amount of performance/error, and the availability of data. 

La Niña climate anomalies have historically been associated with substantial reductions in the atmospheric CO₂growth rate. However, the 2021 La Niña exhibited a unique near-neutral impact on the CO₂growth rate. In this study, we investigate the underlying mechanisms by using an ensemble of net CO₂fluxes constrained by CO₂observations from the Orbiting Carbon Observatory-2 in conjunction with estimates of gross primary production and fire carbon emissions. Our analysis reveals that the close-to-normal atmospheric CO₂growth rate in 2021 was the result of the compensation between increased net carbon uptake over the tropics and reduced net carbon uptake over the Northern Hemisphere mid-latitudes. Specifically, we identify that the extreme drought and warm anomalies in Europe and Asia reduced the net carbon uptake and offset 72% of the increased net carbon uptake over the tropics in 2021. This study contributes to our broader understanding of how regional processes can shape the trajectory of atmospheric CO₂concentration under climate change. 

Geostationary satellite reveals the asymmetrical impact of heatwaves on plant diurnal photosynthesis at the continental scale. 

Abstract Earlier snowmelt, warmer temperatures and herbivory are among the factors that influence high-latitude tundra productivity near the town of Utqiaġvik in northern Alaska. However, our understanding of the potential interactions between these factors is limited. MODIS observations provide cover fractions of vegetation, snow, standing water, and soil, and fractional absorption of photosynthetically active radiation by canopy chlorophyll (fAPAR_chl) per pixel. Here, we evaluated a recent time-period (2001–2014) that the tundra experienced large interannual variability in vegetation productivity metrics (i.e. fAPAR_chland APAR_chl), which was explainable by both abiotic and biotic factors. We found earlier snowmelt to increase soil and vegetation cover, and productivity in June, while warmer temperatures significantly increased monthly productivity. However, abiotic factors failed to explain stark decreases in productivity during August of 2008, which coincided with a severe lemming outbreak. MODIS observations found this tundra ecosystem to completely recover two years later, resulting in elevated productivity. This study highlights the potential roles of both climate and herbivory in modulating the interannual variability of remotely retrieved plant productivity metrics in Arctic coastal tundra ecosystems.