Article
Computer Science, Information Systems
Zekun Xu, Yu Wang, Guihou Sun, Yuehong Chen, Qiang Ma, Xiaoxiang Zhang
Summary: In this study, a geographically weighted stacking ensemble learning approach was developed to generate gridded GDP data. The approach takes into account the geographical properties of input variables and locally fuses the predictions of three base models using geographically weighted regression. The results of a case study in China showed that the proposed approach outperformed traditional methods in GDP downscaling. Therefore, this method provides a valuable option for generating gridded GDP data and has significant implications for other applications.
ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION
(2023)
Article
Agronomy
Yaxian Chen, Ziqi Lin, Xu Chen, Yangyang Liu, Jinshi Jian, Wei Zhang, Peidong Han, Zijun Wang
Summary: Grassland NPP in northern Shaanxi showed a significant increasing trend from 2000 to 2020, with high-coverage grasslands experiencing a higher rate of increase compared to medium and low-coverage grasslands. Most grasslands exhibited unstable growth and high NPP fluctuation. Rainfall and radiation were identified as the dominant factors affecting NPP, while temperature had a suppressing effect on NPP increase. Policies like returning farmland to grassland had a positive impact on grassland recovery and regional ecosystem health.
Article
Environmental Sciences
Pengfei Han, Qixiang Cai, Tomohiro Oda, Ning Zeng, Yuli Shan, Xiaohui Lin, Di Liu
Summary: The outbreak of COVID-19 has led to significant reductions in GDP, power generation, industrial activity, and transport volume in China, resulting in a decrease in CO2 emissions. The reduction was mainly contributed by the secondary industry, with Hubei province being the largest contributor. Additionally, changes in transportation also played a significant role in reducing emissions.
SCIENCE OF THE TOTAL ENVIRONMENT
(2021)
Article
Agronomy
Zewei Yue, Zhao Li, Guirui Yu, Zhi Chen, Peili Shi, Yunfeng Qiao, Kun Du, Chao Tian, Fenghua Zhao, Peifang Leng, Zhaoxin Li, Hefa Cheng, Gang Chen, Fadong Li
Summary: This study characterized the CO2 fluxes of a winter wheat-summer maize rotation cropland in different growing periods and identified the driving factors using long-term monitoring data. Leaf area index (LAI), photosynthetically active radiation (PAR), and soil water content (SWC) were found to be important drivers of CO2 fluxes in both wheat and maize seasons. The findings provide valuable insights into the carbon cycle of cropland ecosystems under climate change.
AGRICULTURAL AND FOREST METEOROLOGY
(2023)
Article
Environmental Sciences
Fangfang Kang, Xuejian Li, Huaqiang Du, Fangjie Mao, Guomo Zhou, Yanxin Xu, Zihao Huang, Jiayi Ji, Jingyi Wang
Summary: The study found that from 2001 to 2018, bamboo forests in China showed fluctuating increasing trends in GPP and NPP, with stronger spatial distribution characteristics in the south and east. More than 50% of the area had positive effects on GPP and NPP from average annual precipitation, while negative effects were observed from average annual minimum and maximum temperatures.
Article
Environmental Sciences
Hui Guo, Sien Li, Fuk-Ling Wong, Shujing Qin, Yahui Wang, Danni Yang, Hon-Ming Lam
Summary: This study investigates the effects of drip irrigation on carbon flux in arid regions of northwestern China from 2014 to 2018. Results show that the carbon flux in maize fields exhibited seasonal patterns, with leaf area index being the main driver of seasonal variation. The study also found that the drip-irrigated maize field acted as a carbon source after harvest, influenced by environmental and vegetation factors.
CARBON BALANCE AND MANAGEMENT
(2021)
Article
Oceanography
Jens M. Nielsen, Noel A. Pelland, Shaun W. Bell, Michael W. Lomas, Lisa B. Eisner, Phyllis Stabeno, Colleen Harpold, Scott Stalin, Calvin W. Mordy
Summary: This study quantifies primary production rates in the southeastern Bering Sea from 2016 to 2019 and finds that the majority of gross primary production (GPP) and net community production occur during the spring phytoplankton bloom. After the bloom, the water column experiences low GPP and net biological carbon consumption. Phytoplankton growth rates are commonly suppressed in late summer due to nitrogen limitation. This research provides important insights into seasonal variations of biogeochemical cycles, phytoplankton community growth rates, and carbon availability in the southeastern Bering Sea using high-temporal-resolution measurements.
JOURNAL OF GEOPHYSICAL RESEARCH-OCEANS
(2023)
Article
Multidisciplinary Sciences
Renping Zhang, Jing Guo, Gang Yin
Summary: The study found that in most regions of Xinjiang, the start of the growing season was earlier and the end of the growing season was delayed, leading to an increase in the length of the growing season. The changes in grassland NPP and length of the growing season were related to precipitation levels and regional temperatures.
Article
Biodiversity Conservation
Yanni Zhao, Jian Peng, Zihan Ding, Sijing Qiu, Xuebang Liu, Jiansheng Wu, Jeroen Meersmans
Summary: There is inconsistency between grassland greenness and productivity in China, with the relative increase rate of productivity higher than that of greenness. Temperature and precipitation are the main contributing factors to grassland growth change.
ECOLOGICAL INDICATORS
(2022)
Article
Biodiversity Conservation
Chan Zuo, Junbang Wang, Xiujuan Zhang, Alan E. Watson
Summary: This study explores the long-term trend and periodic oscillations in vegetation changes in China's terrestrial ecosystem. The results show a periodic oscillation of close to 2.79 years and a long-term increasing trend in vegetation growth. The study also identifies warming minimum air temperature as a vital contributor to these changes, along with precipitation, maximum air temperature, and solar shortwave radiation.
ECOLOGICAL INDICATORS
(2023)
Article
Engineering, Multidisciplinary
Esra Karatas Akgul, Wasim Jamshed, Kottakkaran Sooppy Nisar, S. K. Elagan, Nawal A. Alshehri
Summary: This paper investigates GDP models with four different fractional derivatives, finding solutions using the Sumudu transform and proving its efficiency through theoretical outcomes and applications. The simulations are demonstrated with figures, showing the agreement of different fractional derivatives on the model.
ALEXANDRIA ENGINEERING JOURNAL
(2022)
Article
Engineering, Electrical & Electronic
Yuehong Chen, Guohao Wu, Yong Ge, Zekun Xu
Summary: This article develops a novel convolutional neural network based GDP downscaling approach (GDPnet) to transform the statistical GDP data into GDP grids by integrating various geospatial big data. Experimental results show that GDPnet has high predictive power and accuracy, and it is also faster compared to the existing Resautonet.
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
(2022)
Article
Agronomy
Ning Chen, Changchun Song, Xiaofeng Xu, Xianwei Wang, Nan Cong, Peipei Jiang, Jiaxing Zu, Li Sun, Yanyu Song, Yunjiang Zuo, Jianzhao Liu, Tao Zhang, Mingjie Xu, Peng Jiang, Zhipeng Wang, Ke Huang
Summary: Atmospheric water demand, characterized as vapor pressure deficit (VPD), has been identified as a critical driver of ecosystem function, affecting plant mortality, wildfires, and carbon loss. The study found divergent impacts of VPD on gross primary productivity (GPP) among grassland, shrubland, and forest ecosystems, with factors such as soil water content, temperature, and radiation playing important roles in regulating these impacts. Ecosystems with drier environmental conditions and poorer soil water-holding capacity, like grassland, were more susceptible to negative VPD impacts, highlighting the need for comprehensive consideration of divergent VPD impacts to accurately assess climate impacts on ecosystem function.
AGRICULTURAL AND FOREST METEOROLOGY
(2021)
Article
Plant Sciences
Ziqiang Du, Xuejia Liu, Zhitao Wu, Hong Zhang, Jie Zhao
Summary: This study explores the effects of climatic factors on forest NPP in China and finds an overall increase in NPP over the years. The varying responses of NPP to different climatic factors highlight the importance of understanding these dynamics for effective forest management.
Article
Environmental Sciences
Jiajia Liu, Tao Zhou, Hui Luo, Xia Liu, Peixin Yu, Yajie Zhang, Peifang Zhou
Summary: This study found that the impact of water factors on GPP varies spatially, with water-limited regions in the northern and northwestern parts of China showing a stronger correlation. Past water conditions also influence current GPP, and different periods of water deficits have varying effects on GPP.
Article
Soil Science
Nannan Wang, Xinhao Zhu, Yunjiang Zuo, Jianzhao Liu, Fenghui Yuan, Ziyu Guo, Lihua Zhang, Ying Sun, Chao Gong, Dufa Guo, Changchun Song, Xiaofeng Xu
Summary: This study examined the microbial processes involved in the transition of wetlands to cropland, which results in a reduction of methane emissions. The results showed that wetland conversion to cropland turns methane from a source to a sink, with significant decreases in methane-related genes and increases in methane oxidation marker genes.
Article
Environmental Sciences
Yi Zhang, Dengsheng Lu, Xiandie Jiang, Yunhe Li, Dengqiu Li
Summary: In this study, the 3-PG model was optimized and calibrated using survey and UAV lidar data at the sample plot scale and applied at the forest sub-compartment scale. The results show that both survey forests age data and remote-sensing-derived forest age data can accurately estimate eucalyptus plantation parameters. The simulation results based on remote-sensed forest age data are significantly better than the ones based on survey data, providing an important reference for future studies using remote sensing-derived forest age data in large spatial scales.
Article
Ecology
Xiaomin Wang, Shanyun Wang, Yuanhe Yang, Hanqin Tian, Mike S. M. Jetten, Changchun Song, Guibing Zhu
Summary: Since the start of the Anthropocene, northern seasonally frozen peatlands have been warming rapidly, resulting in increased nitrogen mineralization and substantial emissions of nitrous oxide (N2O) during the thawing periods in spring. Heterotrophic bacterial and fungal denitrification were identified as the main sources of N2O in frozen peatland profiles. Thawing significantly stimulates the expression of genes encoding N2O-producing protein complexes, leading to high N2O emissions during the spring thawing period.
Article
Computer Science, Information Systems
Yuan Liu, Chuang Zhang, Yu Yan, Xin Zhou, Zhihong Tian, Jie Zhang
Summary: This study proposes a semi-centralized trust management system architecture based on blockchain to support various applications and services with massive IoT devices. The IoT devices are centralized organized by cloud servers, which maintain a rating data ledger within each domain using the proposed rotation-based consensus protocol. A computational trust model is proposed to identify and mitigate the influence of malicious devices by aggregating direct and indirect trust information. Simulation experiments and comparisons with classical models demonstrate the effectiveness of the proposed trust model in identifying and mitigating the influence of malicious devices.
IEEE TRANSACTIONS ON SERVICES COMPUTING
(2023)
Article
Remote Sensing
Ruoqi Wang, Guiying Li, Yagang Lu, Dengsheng Lu
Summary: This research compared the advantages of using object-based GSV modeling approach with traditional grid-based approaches for poplar GSV estimation. The results showed that the object-based approach was more accurate in estimating GSV and solving the mixed plot problem in the striped forest distribution areas.
GEO-SPATIAL INFORMATION SCIENCE
(2023)
Article
Agronomy
Chengcheng Gang, Hao Shi, Hanqin Tian, Shufen Pan, Naiqing Pan, Rongting Xu, Zhuonan Wang, Zihao Bian, Yongfa You, Yuanzhi Yao
Summary: The impact of land use and land cover change (LULCC) on soil organic carbon (SOC) stock is uncertain and largely unknown due to the choice of land use datasets. Using a process-based model, this study investigated the global SOC changes driven by six LULCC datasets and found significant differences in SOC loss estimates. These differences were mainly attributed to changes in vegetation net primary production in boreal and temperate forests and were more pronounced in low latitude regions. The accuracy of LULCC data is crucial for determining the global carbon budget, highlighting the need for harmonizing satellite observations and inventory data.
AGRICULTURAL AND FOREST METEOROLOGY
(2023)
Article
Environmental Sciences
Cynthia Nevison, Xin Lan, Doug Worthy, Hanqin Tian
Summary: Based on the inversion results, the estimation of nitrous oxide emissions in Canada for the period of 2011-2015 is uncertain, and the overall net flux is not significantly different from zero. The emissions in Canadian cropland, mainly located in Alberta, Saskatchewan, and Manitoba, are better resolved with a total flux estimated at 0.08 +/- 0.08 Tg N/yr. The addition of 4 new Canadian sites to the inversion improves the uncertainty, but it remains large due to the low signal to background ratio at all Canadian N2O measurement sites.
ATMOSPHERIC ENVIRONMENT
(2023)
Article
Environmental Sciences
Chaoqun Lu, Jien Zhang, Bo Yi, Ignacio Calderon, Hongli Feng, Ruiqing Miao, David Hennessy, Shufen Pan, Hanqin Tian
Summary: Increasing demands for food and biofuels have resulted in cascading effects on cropland expansions, fertilizer use, and riverine nitrogen loads. However, the trade-off between riverine nitrogen pollution and crop production is not well understood due to the lack of predictive understanding of ecological processes across land and water. A new concept called riverine nitrogen footprint (RNF) is proposed to quantify changes in nitrogen loads with respect to crop production gain. The study highlights the importance of developing a food-energy-water nexus indicator to examine region-specific trade-offs between crop production and nitrogen loads for achieving nutrient mitigation goals while sustaining economic gains.
ENVIRONMENTAL RESEARCH LETTERS
(2023)
Article
Environmental Sciences
Liyuan He, Nicolas Viovy, Xiaofeng Xu
Summary: This study investigates the macroecology of bacteria and fungi by integrating a microbial-explicit model with measured fungal and bacterial biomass carbon. The study finds stronger biogeographic patterns of fungi compared to bacteria in the United States, and similar latitudinal trends in turnover rates. However, the component fluxes (carbon assimilation, respiration, and necromass production) show distinct patterns between bacteria and fungi, with vegetation productivity and mean annual temperature being dominant factors. Understanding fungal and bacterial macroecology is important for linking microbial metabolism and soil biogeochemical processes.
GLOBAL BIOGEOCHEMICAL CYCLES
(2023)
Article
Biodiversity Conservation
Yongxing Ren, Dehua Mao, Zongming Wang, Zicheng Yu, Xiaofeng Xu, Yanan Huang, Yanbiao Xi, Ling Luo, Mingming Jia, Kaishan Song, Xiaoyan Li
Summary: This study used machine learning methods to estimate the organic carbon storage in wetlands in China and investigated its changes over time. The results showed that the decrease in wetland area and climate change led to a decrease in organic carbon storage. The estimates provide important insights into the future changes in wetland carbon storage.
GLOBAL CHANGE BIOLOGY
(2023)
Article
Biodiversity Conservation
Zihao Bian, Hanqin Tian, Shufen Pan, Hao Shi, Chaoqun Lu, Christopher Anderson, Wei-Jun Cai, Charles S. Hopkinson, Dubravko Justic, Latif Kalin, Steven Lohrenz, Steven Mcnulty, Naiqing Pan, Ge Sun, Zhuonan Wang, Yuanzhi Yao, Yongfa You
Summary: Human-induced nitrogen-phosphorus imbalance can impact the structure and functioning of aquatic ecosystems. The study suggests that different release rates of soil legacy nutrients contribute to the decreasing N:P loading ratio. The findings underscore the importance of controlling nitrogen loading and integrating soil legacy phosphorus into nutrient management strategies.
GLOBAL CHANGE BIOLOGY
(2023)
Article
Environmental Sciences
Zihao Bian, Ge Sun, Steven McNulty, Shufen Pan, Hanqin Tian
Summary: This study improved a distributed regional land surface model to evaluate the impacts of climate and land use changes on soil erosion and sediment yield in the Mississippi River Basin (MRB) over the past century. The results showed that despite no significant increase in annual precipitation and runoff, sediment yield significantly increased during 1980-2018, mainly driven by intensified extreme precipitation. Land use change played a critical role in determining sediment yield in the early 20th century, while climate variability became the dominant driver in recent decades. Extreme climate is increasingly affecting soil erosion and sedimentation, emphasizing the need to revisit existing Best Management Practices for water quality in the MRB.
WATER RESOURCES RESEARCH
(2023)
Article
Environmental Sciences
Yongpeng Ye, Dengsheng Lu, Zuohang Wu, Kuo Liao, Mingxing Zhou, Kai Jian, Dengqiu Li
Summary: This study developed a framework based on multi-source high-resolution satellite images to analyze vertical characteristics of mountainous vegetation distribution. The results showed distinct differentiation of vegetation types along elevation gradients in Wuyishan National Park, with significant differences in distribution patterns under different human protection levels.
Article
Environmental Sciences
Linchao Li, Bin Wang, Puyu Feng, Jonas Jagermeyr, Senthold Asseng, Christoph Mueller, Ian Macadam, De Li Liu, Cathy Waters, Yajie Zhang, Qinsi He, Yu Shi, Shang Chen, Xiaowei Guo, Yi Li, Jianqiang He, Hao Feng, Guijun Yang, Hanqin Tian, Qiang Yu
Summary: This study investigates the influence of ensemble configurations on crop yield projections and modeling uncertainty. The findings suggest that specific ensemble compositions and sizes can effectively capture modeling uncertainty and represent the full ensemble. The contribution of individual crop models to the overall uncertainty varies by region and crop type, emphasizing the importance of considering specific models in local-scale applications.
COMMUNICATIONS EARTH & ENVIRONMENT
(2023)
Article
Computer Science, Artificial Intelligence
Yixin Liu, Ming Jin, Shirui Pan, Chuan Zhou, Yu Zheng, Feng Xia, Philip S. Yu
Summary: Deep learning on graphs is a rapidly growing field, however, most works have focused on (semi-) supervised learning, leading to issues such as heavy label reliance and insufficient generalization. To address these problems, self-supervised learning (SSL) has emerged as a promising learning paradigm for graph data. In this review, we provide a comprehensive overview of existing SSL approaches for graphs, including a unified framework, different categories of approaches, applications, datasets, benchmarks, and challenges for future research in this field.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2023)
