As one of the most important terrestrial carbon sinks, forests play a critical role in regulating global and regional carbon budgets (Houghton & Nassikas, 2018;Le Noë et al., 2021). Previous studies have estimated a global forest sink of 2.4 Pg C yr −1 for 1990 to 2007 (Pan et al., 2011), which contributes a large fraction of the entire terrestrial sink globally (Friedlingstein et al., 2020). The Chinese government has committed to reaching carbon neutrality by 2060 (NDRC, 2021), which means the various ecosystem carbon sinks and carbon capture and storage need to offset all fossil fuel CO 2 emissions (Rogelj et al., 2015). In China, forest ecosystems also dominate the terrestrial carbon sink, contributing 80% of terrestrial carbon sinks and 39% of terrestrial carbon stocks while providing a least 2.97 Pg C of potential carbon sequestration for 2010 to 2030 (Fang et al., 2018;Tang et al., 2018). In addition, the identification of forest types is also important for simulating carbon sinks because ecosystem models set different model parameters for various forest types (Houghton et al., 1983). For example, the leaf turnover rate

The copyright of data is a key point that needs to be solved in spatial data infrastructure for data sharing. In this paper, we propose a decentralized digital rights management model of spatial data, which can provide a novel way of solving the existing copyright management problem or other problems in spatial data infrastructure for data sharing. An Ethereum smart contract is used in this model to realize spatial data digital rights management function. The InterPlanetary File System is utilized as external data storage for storing spatial data in the decentralized file system to avoid data destruction that is caused by a single point of failure. There is no central server in the model architecture, which has a completely decentralized nature and it makes spatial data rights management not dependent on third-party trust institutions. We designed three spatial data copyright management algorithms, developed a prototype system to implement and test the model, used the smart contract security verification tool to check code vulnerabilities, and, finally, discussed the usability, scalability, efficiency, performance, and security of the proposed model. The result indicates that the proposed model not only has diversified functions of copyright management compared with previous studies on the blockchain-based digital rights management, but it can also solve the existing problems in traditional spatial data infrastructure for data sharing due to its characteristics of complete decentralization, mass orientation, immediacy, and high security.Research are examples of such platforms. Digital rights management (DRM) refers to the techniques that are used by publishers to control the rights of protected objects [4]. Subsequently, the process of publishing spatial science data can be described as applying digital rights management to data sharing to solve the copyright problem of spatial science data. Since existing scientific data publishing schemes all use traditional digital rights management technology, these schemes have both their own shortcomings and shortcomings in traditional digital rights management technology, mainly including:(1) They are aimed at scientific data produced in scientific research papers, and non-scientists are basically unable to participate, which limits the scope of data sharing; (2) The application process is cumbersome in traditional digital rights management, the copyright registration takes a long time, and the review process is expensive. Moreover, the publication of the paper needs periodic review in spatial data publishing schemes, which increases the time cost of data sharing; (3) At present, most of the scientific data publishing systems are centralized, thus, if the server in the system fails, it will cause temporary or permanent inability to access services and data, which will cause immeasurable losses to both publishers and users; and, (4) The centralized digital rights management system has the risk of illegal tampering with copyright registration information that is caused by in...

Vegetation gross primary production (GPP) is the largest terrestrial carbon flux and plays an important role in regulating the carbon sink. Current terrestrial ecosystem models (TEMs) are indispensable tools for evaluating and predicting GPP. However, to which degree the TEMs can capture the interannual variability (IAV) of GPP remains unclear. With large data sets of remote sensing, in situ observations, and predictions of TEMs at a global scale, this study found that the current TEMs substantially underestimate the GPP IAV in comparison to observations at global flux towers. Our results also showed the larger underestimations of IAV in GPP at nonforest ecosystem types than forest types, especially in arid and semiarid grassland and shrubland. One cause of the underestimation is that the IAV in GPP predicted by models is strongly dependent on canopy structure, that is, leaf area index (LAI), and the models underestimate the changes of canopy physiology responding to climate change. On the other hand, the simulated interannual variations of LAI are much less than the observed. Our results highlight the importance of improving TEMs by precisely characterizing the contribution of canopy physiological changes on the IAV in GPP and of clarifying the reason for the underestimated IAV in LAI. With these efforts, it may be possible to accurately predict the IAV in GPP and the stability of the global carbon sink in the context of global climate change.

Forests are the largest terrestrial ecosystem carbon pool and provide the most important nature-based climate mitigation pathway. Compared with belowground biomass (BGB) and soil carbon, aboveground biomass (AGB) is more sensitive to human disturbance and climate change. Therefore, accurate forest AGB mapping will help us better assess the mitigation potential of forests against climate change. Here, we developed six models to estimate national forest AGB using six machine learning algorithms based on 52,415 spaceborne Light Detection and Ranging (LiDAR) footprints and 22 environmental features for China in 2007. The results showed that the ensemble model generated by the stacking algorithm performed best with a determination coefficient (R2) of 0.76 and a root mean square error (RMSE) of 22.40 Mg/ha. The verifications at pixel level (R2 = 0.78, RMSE = 16.08 Mg/ha) and provincial level (R2 = 0.53, RMSE = 14.05 Mg/ha) indicated the accuracy of the estimated forest AGB map is satisfactory. The forest AGB density of China was estimated to be 53.16 ± 1.63 Mg/ha, with a total of 11.00 ± 0.34 Pg. Net primary productivity (NPP), normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), average annual rainfall, and annual temperature anomaly are the five most important environmental factors for forest AGB estimation. The forest AGB map we produced is expected to reduce the uncertainty of forest carbon source and sink estimations.