Different chemical compositions of soil organic carbon(SOC)affect its persistence and whether it signifi-cantly differs between natural forests and plantations remains unclear.By synthesizing 234 observations of SOC c...Different chemical compositions of soil organic carbon(SOC)affect its persistence and whether it signifi-cantly differs between natural forests and plantations remains unclear.By synthesizing 234 observations of SOC chemical compositions,we evaluated global patterns of concentra-tion,individual chemical composition(alkyl C,O-alkyl C,aromatic C,and carbonyl C),and their distribution even-ness.Our results indicate a notably higher SOC,a markedly larger proportion of recalcitrant alkyl C,and lower easily decomposed carbonyl C proportion in natural forests.How-ever,SOC chemical compositions were appreciably more evenly distributed in plantations.Based on the assumed con-ceptual index of SOC chemical composition evenness,we deduced that,compared to natural forests,plantations may have higher possible resistance to SOC decomposition under disturbances.In tropical regions,SOC levels,recalcitrant SOC chemical composition,and their distributed evenness were significantly higher in natural forests,indicating that SOC has higher chemical stability and possible resistance to decomposition.Climate factors had minor effects on alkyl C in forests globally,while they notably affected SOC chemi-cal composition in tropical forests.This could contribute to the differences in chemical compositions and their distrib-uted evenness between plantations and natural stands.展开更多
Among hyperspectral imaging technologies, interferometric spectral imaging is widely used in remote sening due to advantages of large luminous flux and high resolution. However, with complicated mechanism, interferome...Among hyperspectral imaging technologies, interferometric spectral imaging is widely used in remote sening due to advantages of large luminous flux and high resolution. However, with complicated mechanism, interferometric imaging faces the impact of multi-stage degradation. Most exsiting interferometric spectrum reconstruction methods are based on tradition model-based framework with multiple steps, showing poor efficiency and restricted performance. Thus, we propose an interferometric spectrum reconstruction method based on degradation synthesis and deep learning.Firstly, based on imaging mechanism, we proposed an mathematical model of interferometric imaging to analyse the degradation components as noises and trends during imaging. The model consists of three stages, namely instrument degradation, sensing degradation, and signal-independent degradation process. Then, we designed calibration-based method to estimate parameters in the model, of which the results are used for synthesizing realistic dataset for learning-based algorithms.In addition, we proposed a dual-stage interferogram spectrum reconstruction framework, which supports pre-training and integration of denoising DNNs. Experiments exhibits the reliability of our degradation model and synthesized data, and the effectiveness of the proposed reconstruction method.展开更多
基金supported by the National Natural Science Foundation of China(Grants 31971463,31930078)the National Key R&D Program of China(Grant 2021YFD2200402)the Chinese Academy of Forestry(Grant CAFYBB2020ZA001).
文摘Different chemical compositions of soil organic carbon(SOC)affect its persistence and whether it signifi-cantly differs between natural forests and plantations remains unclear.By synthesizing 234 observations of SOC chemical compositions,we evaluated global patterns of concentra-tion,individual chemical composition(alkyl C,O-alkyl C,aromatic C,and carbonyl C),and their distribution even-ness.Our results indicate a notably higher SOC,a markedly larger proportion of recalcitrant alkyl C,and lower easily decomposed carbonyl C proportion in natural forests.How-ever,SOC chemical compositions were appreciably more evenly distributed in plantations.Based on the assumed con-ceptual index of SOC chemical composition evenness,we deduced that,compared to natural forests,plantations may have higher possible resistance to SOC decomposition under disturbances.In tropical regions,SOC levels,recalcitrant SOC chemical composition,and their distributed evenness were significantly higher in natural forests,indicating that SOC has higher chemical stability and possible resistance to decomposition.Climate factors had minor effects on alkyl C in forests globally,while they notably affected SOC chemi-cal composition in tropical forests.This could contribute to the differences in chemical compositions and their distrib-uted evenness between plantations and natural stands.
文摘Among hyperspectral imaging technologies, interferometric spectral imaging is widely used in remote sening due to advantages of large luminous flux and high resolution. However, with complicated mechanism, interferometric imaging faces the impact of multi-stage degradation. Most exsiting interferometric spectrum reconstruction methods are based on tradition model-based framework with multiple steps, showing poor efficiency and restricted performance. Thus, we propose an interferometric spectrum reconstruction method based on degradation synthesis and deep learning.Firstly, based on imaging mechanism, we proposed an mathematical model of interferometric imaging to analyse the degradation components as noises and trends during imaging. The model consists of three stages, namely instrument degradation, sensing degradation, and signal-independent degradation process. Then, we designed calibration-based method to estimate parameters in the model, of which the results are used for synthesizing realistic dataset for learning-based algorithms.In addition, we proposed a dual-stage interferogram spectrum reconstruction framework, which supports pre-training and integration of denoising DNNs. Experiments exhibits the reliability of our degradation model and synthesized data, and the effectiveness of the proposed reconstruction method.
