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Forest age is crucial for both carbon cycle modelling and effective forest management. Remote sensing provides crucial data for large-scale forest age mapping, but existing products often suffer from low spatial resolutions (typically 1,000 m), making them unsuitable for most forest stands in China, which are generally smaller than this threshold. Recent studies generated static forest age products for 2019 (CAFA V1.0) (Shang et al., 2023a) and 2020 (Cheng et al., 2024) at a 30-m spatial resolution. However, their low temporal resolution limits their applicability for tracking multi-year forest carbon changes. This study aims to generate China's annual forest age dataset (CAFA V2.0) at a 30-m resolution from 1986 to 2022, utilizing forest disturbance monitoring and machine learning techniques. Forest disturbance monitoring, which typically has lower uncertainty compared to machine learning approaches, is primarily employed to update annual forest age. The modified COLD (mCOLD) algorithm, which incorporates spatial information and bidirectional monitoring, was used for forest disturbance monitoring. For undisturbed forests, forest age was estimated using machine learning models trained separately for different regions and forest cover types, with inputs including forest height, vegetation indices, climate, terrain, and soil data. Additionally, adjustments were made for underestimations in the Northeast and Southwest regions identified in CAFA V1.0 using additional reference age samples and region-specific and forest type-specific models. Validation, using a randomly selected 30 % of two reference datasets, indicated that the mapped age of disturbed forest exhibited a small error of ±2.48 years, while the mapped age of undisturbed forest from 1986 to 2022 had a larger error of ±7.91 years. The generated 30 m annual forest age dataset can facilitate forest carbon cycle modelling in China, offering valuable insights for national forest management practices. The CAFA V2.0 dataset is publicly available at https://doi.org/10.6084/m9.figshare.24464170 (Shang et al., 2023b). [ABSTRACT FROM AUTHOR]

As the largest carbon reservoir within terrestrial ecosystems, forest ecosystems play a major role as carbon sinks in the global carbon cycle. There are still some uncertainties regarding the responses of different carbon fluxes to environmental changes in cold temperate climate forest ecosystems. Here, 14 cold temperate forest flux sites for at least ten years were investigated, including carbon fluxes and environmental variables such as temperature, precipitation, shortwave radiation, and vapor pressure deficit. By calculating the Spearman correlation coefficient, there was a congruence between photosynthetic productivity (i.e., gross primary productivity, GPP) and carbon sequestration (i.e., net ecosystem productivity, NEP) at thirteen forest sites, and at one forest site, GPP and NEP were decoupled. Annual GPP and NEP displayed a consistent trend when temperature and precipitation had significantly opposite trends and when temperature had a significantly positive correlation with VPD. But when VPD was significantly negatively correlated with both temperature and SW in spring and when temperature was negatively correlated with both SW and VPD in summer, a decoupling of GPP and NEP occurred. The impacts of various environmental factors on the annual carbon fluxes were calculated for each year and season using the path analysis method. At forest sites with consistent trends in GPP and NEP, annual, spring, and summer temperatures had significant positive correlations with GPP and ecosystem respiration (RE). While at the decoupled forest site, environmental factors had a stronger effect on RE, which then contributed to the observed decoupling of GPP and NEP. Finally, the Partial Least Squares method was used to analyze the relative contribution of each environmental factor to annual carbon fluxes. The results revealed that temperature and summer precipitation were the key environmental factors affecting forest ecosystems. This study provides important insights into the different responses of carbon fluxes in forest ecosystems undergoing environmental changes. [ABSTRACT FROM AUTHOR]

Leaf chlorophyll content (LCC) is a prominent plant physiological trait and a proxy for leaf photosynthetic capacity. The acquisition of LCC data over large spatial and temporal scales facilitates vegetation growth monitoring and terrestrial carbon cycle modeling. In this study, a global 500 m LCC weekly dataset (GLCC) was produced from ENVISAT MERIS and Sentinel-3 OLCI satellite data using a physical radiative transfer modeling approach that considers the influence of canopy structure and soil background. Firstly, five look-up-tables (LUTs) were generated using PROSPECT-D+4-Scale and PROSAIL-D models for woody and non-woody plants. For the four LUTs applicable to woody plants, each LUT contains three sub-LUTs corresponding to three types of crown height. The one LUT applicable to non-woody vegetation type includes 25 sub-LUTs corresponding to five kinds of canopy structures and five kinds of soil backgrounds. The final retrieval was considered the aggregation of the LCC inversion results of all sub-LUTs for each plant function type (PFT). Then, the GLCC dataset was generated and validated using field measurements, yielding an overall accuracy of R2 = 0.41 and RMSE = 8.94 μg cm−2. Finally, the GLCC dataset presented acceptable consistency with the existing MERIS LCC dataset. OLCI, as the successor to MERIS data, was used for the first time to co-produce LCC data from 2003–2012 to 2018–2020 in conjunction with MERIS data. This new GLCC dataset spanning nearly 20 years will provide a valuable opportunity to analyze variations in vegetation dynamics. [ABSTRACT FROM AUTHOR]

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The accurate estimation of anthropogenic carbon emissions is of great significance for understanding the global carbon cycle and guides the setting and implementation of global climate policy and CO2 emission-reduction goals. This study built a data-driven stacked random forest regression model for estimating gridded global fossil fuel CO2 emissions. The driving variables include the annual features of column-averaged CO2 dry-air mole fraction (XCO2) anomalies based on their ecofloristic zone, night-time light data from the Visible Infrared Imaging Radiometer Suite (VIIRS), terrestrial carbon fluxes, and vegetation parameters. A two-layer stacked random forest regression model was built to fit 1° gridded inventory of open-source data inventory for anthropogenic CO2 (ODIAC). Then, the model was trained using the 2014–2018 dataset to estimate emissions in 2019, which provided a higher accuracy compared with a single-layer model with an R2 of 0.766 and an RMSE of 0.359. The predicted gridded emissions are consistent with Global Carbon Grid at 1° scale with an R2 of 0.665, and the national total emissions provided a higher R2 at 0.977 with the Global Carbon Project (GCP) data, as compared to the ODIAC (R2 = 0.956) data, in European countries. This study demonstrates that data-driven random forest regression models are capable of estimating anthropogenic CO2 emissions at a grid scale. [ABSTRACT FROM AUTHOR]

Alpine meadow ecosystems are extremely vulnerable to climate change and serve an essential function in terrestrial carbon sinks. Accurately estimating their gross primary productivity (GPP) is essential for understanding the global carbon cycle. Solar-induced chlorophyll fluorescence (SIF), as a companion product directly related to plant photosynthesis process, has become an attractive pathway for estimating GPP accurately. To date, the quantitative SIF-GPP relationship in terrestrial ecosystems is not yet clear. Especially, red SIF and far-red SIF present differences in their ability to track GPP under different environmental conditions. In this study, we investigated the performance of SIF at both red and far-red band in monitoring the GPP of an alpine meadow ecosystem based on continuous tower-based observations in 2019 and 2020. The results show that the canopy red SIF ( SIF Red ) and far-red SIF ( SIF Far-red ) were both strongly correlated with GPP. SIF Red was comparable to SIF Far-red for monitoring GPP based on comparisons of both half-hourly averaged and daily averaged datasets. Moreover, the relationship between SIF Red and GPP was linearly correlated, while the relationship between SIF Far-red and GPP tended to be nonlinear. At a diurnal scale, dramatic changes in photosynthetically active radiation (PAR), air temperature (Ta), and vapor pressure deficit (VPD) all had effects on the slope of the linear fitted line with zero intercept for SIF Red -GPP and SIF Far-red -GPP, and the effect on the slope of the linear fitted line with zero intercept for SIF Far-red -GPP was obviously stronger than that for SIF Red -GPP. PAR was the dominant factor among the three environmental factors in determining the diurnal variation of the slope of SIF-GPP. At a seasonal scale, the SIF Far-red /GPP was susceptible to PAR, Ta, and VPD, while the SIF Red /GPP remained relatively stable at different levels of Ta and VPD, and it was only weakly affected by PAR, suggesting that SIF Red was more consistent than SIF Far-red with GPP in response to seasonal variations in environmental factors. These results indicate that SIF Red has more potential than SIF Far-red for monitoring the GPP of alpine meadow ecosystems and can also assist researchers in gaining a more comprehensive understanding of the diversity of SIF-GPP relationships in different ecosystems. [ABSTRACT FROM AUTHOR]

Photosynthesis in Amazon rainforests is essential to the carbon and water cycle on a global scale, but its seasonal dynamics remain less understood. Using clear sky satellite products combined with in situ ground observations, we investigated the seasonal dynamics of photosynthesis in this unique region as well as the contribution of moisture to these dynamics. We confirmed the unique dry season increase in photosynthesis for most parts of the study region as well as small positive photosynthetic anomalies in relatively dryer years, such as El Niño years. Taking into account the contribution of increased light and new leaf flushing in dry seasons to the seasonal dynamics of photosynthesis, we found significant negative correlations between moisture variables (especially soil moisture) and photosynthesis in Amazon rainforests, especially for humid ecosystems such as primary moist forests and for humid periods such as wet seasons. Excessive moisture contributes to the seasonal dynamics of photosynthesis by changing the value of the peak and the position of the minimum values, but this process was not well‐documented in previous studies. Our study highlights the need to consider the negative impacts of excessive moisture on photosynthesis in wet ecosystems, and further research is required to support the determination of future changes in photosynthesis in different regions. Plain Language Summary: The dynamics of photosynthesis in Amazon rainforests and their response to environmental variables is important to the global change studies. Based on observations from satellite and flux towers, we confirmed the negative correlation between photosynthesis and environmental moisture in wettest ecosystems during wet periods for the study region. Among the moisture‐related indicators mentioned in our research, soil moisture in the deep underground showed the strongest negative correlation with SIF for the study region. The negative impact of excessive moisture modifies the seasonal changes in photosynthesis, and the alleviation of it in dry seasons could make the dry season increase more significant. Our findings indicated that the moisture also contributes to the dynamics of photosynthesis in Amazon rainforests. In addition to the impact of water shortage, the negative impacts of excessive moisture on photosynthesis should also be considered for wettest ecosystems. Key Points: Photosynthetic dynamics in Amazon rainforests were captured using satellite‐based observations of solar‐induced chlorophyll fluorescenceA negative correlation between photosynthesis and moisture was found in the preserved moist forest ecosystems during the wet seasonExcessive moisture stress contributes to the seasonal dynamics of photosynthesis in the Amazon rainforests [ABSTRACT FROM AUTHOR]

Land-cover plays an important role in the Earth's energy balance, the hydrological cycle, and the carbon cycle. Therefore, it is important to evaluate the current global land-cover (GLC) products and to understand the differences between these products so that they can be used effectively in different applications. In this study, three 30-m GLC products, namely GlobeLand30-2010, GLC_FCS30-2015, and FROM_GLC30-2015, were evaluated in terms of areal consistency and spatial consistency using the Land Use/Cover Area frame statistical Survey (LUCAS) reference dataset over the European Union (EU). Given the limitations of the traditional confusion matrix used in accuracy assessment, we adjusted the confusion matrices from sample counts by accounting for the class proportions of the map and reported the standard errors of the descriptive accuracy measures in the accuracy assessment. The results revealed the following. (1) The overall accuracy of the GlobeLand30-2010 product was the highest at 88.90 ± 0.68%; this was followed by GLC_FCS30-2015 (84.33 ± 0.80%) and FROM_GLC2015 (65.31 ± 1.0%). (2) The consistency between the GLC_FCS30-2015 and GlobeLand30-2010 is higher than the consistency between other products, with an area correlation coefficient of 0.930 and a proportion of consistent pixels of 52.41%, respectively. (3) Across the area of the EU, the dominant land-cover types such as forest and cropland are the most consistent across the three products, whereas the spatial consistency for bare land, grassland, shrubland, and wetland is relatively low. (4) The proportion of pixels for which the consistency is low accounts for less than 16.17% of pixels, whereas the proportion of pixels for which the consistency is high accounts for about 39.12%. The disagreement between these products primarily occurs in transitional zones with mixed land cover types or in mountain areas. Overall, the GlobeLand30 and GLC-FCS30 products were found to be the most consistent and to have good classification accuracy in the EU, with the disagreement between the three 30-m GLC products mainly occurring in heterogeneous regions. [ABSTRACT FROM AUTHOR]

Leaf chlorophyll content (LCC) is a pivotal parameter in the monitoring of agriculture and carbon cycle modeling at regional and global scales. ENVISAT MERIS and Sentinel-3 OLCI data are suitable for use in the global monitoring of LCC because of their spectral specifications (covering red-edge bands), wide field of view and short revisit times. Generally, remote sensing approaches for LCC retrieval consist of statistically- and physically-based models. The physical approaches for LCC estimation require the use of radiative transfer models (RTMs), which are more robust and transferrable than empirical models. However, the operational retrieval of LCC at large scales is affected by the large variability in canopy structures and soil backgrounds. In this study, we proposed an improved look-up-table (LUT) approach to retrieve LCC by combining multiple canopy structures and soil backgrounds to deal with the ill-posed inversion problem caused by the lack of prior knowledge on canopy structure and soil-background reflectance. Firstly, the PROSAIL-D model was used to simulate canopy spectra with diverse imaging gometrics, canopy structures, soil backgrounds and leaf biochemical contents, and the canopy spectra were resampled according to the spectral response functions of ENVISAT MERIS and Sentinel-3 OLCI instruments. Then, an LUT that included 25 sub-LUTs corresponding to five types of canopy structure and five types of soil background was generated for LCC estimation. The mean of the best eight solutions, rather than the single best solution with the smallest RMSE value, was selected as the retrieval of each sub-LUT. The final inversion result was obtained by calculating the mean value of the 25 sub-LUTs. Finally, the improved LUT approach was tested using simulations, field measurements and ENVISAT MERIS satellite data. A simulation using spectral bands from the MERIS and Sentinel-3 OLCI simulation datasets yielded an R2 value of 0.81 and an RMSE value of 10.1 μg cm−2. Validation performed well with field-measured canopy spectra and MERIS imagery giving RMSE values of 9.9 μg cm−2 for wheat and 9.6 μg cm−2 for soybean using canopy spectra and 8.6 μg cm−2 for soybean using MERIS data. The comparison with traditional chlorophyll-sensitive indices showed that our improved LUT approach gave the best performance for all cases. Therefore, these promising results are directly applicable to the use of ENVISAT MERIS and Sentinel-3 OLCI data for monitoring of crop LCC at a regional or global scale. [ABSTRACT FROM AUTHOR]

Measuring forest aboveground biomass (AGB) at local to regional scales is critical to understanding their role in regional and global carbon cycles. The Three-North Shelterbelt Forest Program (TNSFP) is the largest ecological restoration project in the world, and has been ongoing for over 40 years. In this study, we developed models to estimate the planted forest aboveground biomass (PF_AGB) for Yulin, a typical area in the project. Surface reflectances in the study area from 1978 to 2013 were obtained from Landsat series images, and integrated forest z-scores were constructed to measure afforestation and the stand age of planted forest. Normalized difference vegetation index (NDVI) was combined with stand age to develop an initial model to estimate PF_AGB. We then developed additional models that added environment variables to our initial model, including climatic factors (average temperature, total precipitation, and total sunshine duration) and a topography factor (slope). The model which combined the total precipitation and slope greatly improved the accuracy of PF_AGB estimation compared to the initial model, indicating that the environmental variables related to water distribution indirectly affected the growth of the planted forest and the resulting AGB. Afforestation in the study area occurred mainly in the early 1980s and early 21st century, and the PF_AGB in 2003 was 2.3 times than that of 1998, since the fourth term TNSFP started in 2000. The PF_AGB in 2013 was about 3.33 times of that in 2003 because many young trees matured. The leave-one-out cross-validation (LOOCV) approach showed that our estimated PF_AGB had a significant correlation with field-measured data (correlation coefficient (r) = 0.89, p < 0.001, root mean square error (RMSE) = 6.79 t/ha). Our studies provided a method to estimate long time series PF_AGB using satellite repetitive measures, particularly for arid or semi-arid areas. [ABSTRACT FROM AUTHOR]

• The spatial and temporal patterns of China's carbon changes at the pixel scale were provided. • China's aboveground biomass carbon (AGC) stock showed an increase in recent years. • The carbon sink rate of China's forests peaked in middle-aged forests and then decreased with increasing forest age. • The Grain for Green Program contributed most to the increase of aboveground carbon sinks in China. China's terrestrial ecosystems are pivotal in upholding the global carbon balance, with their recognized capacity for carbon sequestration holding significant importance on a global scale, but there are still challenges in accurately monitoring its spatial-temporal patterns. SMOSMAP-IB L - VOD, a recently developed low-frequency L - band Vegetation Optical Depth (L-VOD) product, was employed to monitor the fluctuations in aboveground vegetation carbon stock over China between 2015 and 2021, and to investigate the influence of forest age and ecological restoration projects on China's carbon sink capacity. The carbon sink of above-ground vegetation in China was + 169. 98 + 135.59 + 179.44 Tg C yr-1 during 2015–2021. Southwest and northeast China's forests exhibit higher carbon sink capacity, contrasting with lower values per unit area are mainly found in northwestern China. The forest served as the foremost contributor to the carbon sink of above-ground vegetation in China, although their carbon density peaked in the middle-aged forests and then gradually decreased as the age of the forest grew. Furthermore, carbon sink areas were observed in most regions where ecological restoration projects were implemented, indicating that the execution of afforestation and ecological restoration initiatives has proven effective in carbon sequestration, creating a sink in these areas. Our results provide an improved foundation for formulating new afforestation policies in the future. [ABSTRACT FROM AUTHOR]