检索结果-内蒙古大学图书馆

SSRN 2024年

作者： Zhou, Yuwei Biswas, Asim Hong, Yongsheng Chen, Songchao Hu, Bifeng Shi, Zhou Guo, Yan Li, Shuo Key Laboratory for Geographical Process Analysis & Simulation of Hubei Province College of Urban & Environmental Sciences Central China Normal University Wuhan430079 China School of Environmental Sciences University of Guelph ONN1G2W1 Canada State Key Laboratory of Soil and Sustainable Agriculture Institute of Soil Science Chinese Academy of Sciences Nanjing210008 China Institute of Applied Remote Sensing and Information Technology College of Environmental and Resource Sciences Zhejiang University Hangzhou310058 China ZJU-Hangzhou Global Scientific and Technological Innovation Center Hangzhou311200 China Department of Land Resource Management School of Tourism and Urban Management Jiangxi University of Finance and Economics Nanchang330013 China Institute of Agricultural Information Technology Henan Academy of Agricultural Sciences Zhengzhou450002 China Key Laboratory of Huang-Huai-Hai Smart Agricultural Technology Ministry of Agriculture and Rural Affairs Zhengzhou450002 China

Soil visible-near-infrared (vis–NIR) spectroscopy offers a rapid, uncontaminated, and cost-efficient method for estimating physicochemical properties such as soil organic carbon (SOC). The development of soil spectral libraries (SSLs) and localized modeling methods has significantly improved the selection of appropriate modeling sets from SSLs for soil analysis. Nevertheless, most studies assume that the SSLs sufficiently cover the target samples for prediction. This study challenges this assumption by investigating the feasibility of using an SSL to predict SOC accurately in a local area when the dataset to be predicted (n = 156,800) vastly exceeds the SSL capacity (n = 3755). We utilized 1-meter-deep intact soil profiles and employed spectral similarity calculations and continuum-removal localized modeling to construct a Local subset from the SSL, with a Global subset serving as a baseline for comparison. The effectiveness of partial least-squares regression (PLSR) and random forest (RF) algorithms in establishing quantitative relationships between spectra and SOC content was evaluated. Our results demonstrated that the Local model, with significantly fewer samples (n = 1116), achieved higher predictive accuracy than the Global model. Both Global (R2 = 0.80, RMSE = 0.74) and Local (R2 = 0.83, RMSE = 0.75) models, developed using the RF algorithm, not only exhibited excellent accuracy but also enabled detailed and cost-effective characterization of the spatial distribution of SOC. Thus, leveraging SSLs enhances the cost-efficiency and predictive capacity of vis–NIR spectral analysis, particularly in handling large datasets at a local scale, underscoring the value of localized approaches in soil spectroscopy. © 2024, The Authors. All rights reserved.

关键词： Hyperspectral imaging

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High Nitrogen Fertilizer Input Enhanced the Microbial Network Complexity in the Paddy Soil

SSRN

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SSRN 2023年

作者： Chen, Yanan Li, Yan Qiu, Tianyi He, Haoran Liu, Ji Duan, Chengjiao Cui, Yongxing Fang, Linchuan The Research Center of Soil and Water Conservation and Ecological Environment Chinese Academy of Sciences Ministry of Education Shaanxi Yangling712100 China State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau Institute of Soil and Water Conservation CAS and MWR Yangling712100 China University of Chinese Academy of Sciences Beijing100049 China Institute of Environment Resource and Soil Fertilizer Zhejiang Academy of Agricultural Sciences Hangzhou310021 China College of Natural Resources and Environment Northwest A&F University Yangling712100 China Hubei Province Key Laboratory for Geographical Process Analysis and Simulation Central China Normal University Wuhan430079 China Department of Ecohydrology Leibniz Institute of Freshwater Ecology and Inland Fisheries Berlin12587 Germany Sino-French Institute for Earth System Science College of Urban and Environmental Sciences Peking University Beijing100871 China Wuhan University of Technology China

Nitrogen (N) fertilization drives the composition and function of soil microbial communities, which are crucial for regulating soil biogeochemical cycling and maintaining ecosystem stability. The influences of N fertilizer on soil microbial composition and diversity have been widely explored, while it is commonly overlooked that ecosystem processes are carried out via complex webs of co-occurrence networks among microbiome members. Here, we examined the effects of five N fertilization levels (0, 135, 180, 225, and 360 kg N ha-1) on microbial co-occurrence networks and key functional taxa such as ammonia-oxidizers in paddy soils. The results showed that N addition altered microbial community composition, which were positively related to soil total N and available phosphorus (P) contents. The abundance of ammonia-oxidizing archaea (AOA) significantly decreased after N addition, whereas ammonia-oxidizing bacteria (AOB) increased in N360 treatment. Compared with low-N group (N0 and N135), the high-N group (N225 and N360) shaped more complex microbial webs and thus improved the stability of the microbial community. Partial least squares path modelling further revealed that N fertilizer had a higher effect on microbial network complexity in the high-N group (0.83) than the low-N group (0.49). Although there were more positive links across all microbial networks, the proportion of negative links significantly increased in the high-N networks, suggesting that excess N addition aggravated the competition among microbial species. Disentangling these interactions between microbial communities and N fertilization advances our understanding of biogeochemical processes in paddy soils and their effects on nutrient supply to rice production. Our findings highlighted that highly N-enriched paddy soils have more stable microbial networks and can better sustain soil ecological functions to cope with the ongoing environmental changes. © 2023, The Authors. All rights reserved.

关键词： Nitrogen fertilizers

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Slope Position Mediates the Co-Utilization of Phosphorus by Plants and Microbes Through Rhizosphere processes in a Phosphorus-Limited Forest

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SSRN 2022年

作者： Qiu, Tianyi Yu, Jialuo He, Liyuan Liu, Ji Cui, Qingliang Cui, Yongxing Duan, Chengjiao Zhao, Shuling Wang, Yunqiang Fang, Linchuan College of Natural Resources and Environment Northwest A&F University Shaanxi Yangling712100 China Key Laboratory of Ecosystem Network Observation and Modelling Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Beijing100101 China Biology Department San Diego State University San DiegoCA United States Hubei Province Key Laboratory for Geographical Process Analysis and Simulation Central China Normal University Wuhan430079 China State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau Institute of Soil and Water Conservation CAS and MWR Yangling712100 China Sino-French Institute for Earth System Science College of Urban and Environmental Sciences Peking University Beijing100871 China State Key Laboratory of Loess and Quaternary Geology Institute of Earth Environment Chinese Academy of Sciences Xi’an710061 China CAS Center for Excellence in Quaternary Science and Global Change Chinese Academy of Sciences Xi’an710061 China

Phosphorus (P) limitation hinders ecological restoration in vulnerable ecosystems, particularly in severely eroded forests. Slope position and rhizosphere effect (RE) are two critical drivers of biological P utilization, but their unknown potential interaction limits the improvement of ecological restoration under P limitation. To bridge this gap, we investigated the interaction on plant–microbe P utilization by quantifying the REs at different slope positions (top, middle, and bottom) in a typical artificial forest ( Robinia pseudoacacia ) of the Loess Plateau, China. The results showed that: (1) most of the positive REs increased by 1.5 to 5.6 times with decreasing slope position, which reversed the resource distribution trend along the slope. (2) Plant and microbial P limitation exhibited contrasting patterns due to nutrient competition (standardized slope = –0.746, P © 2022, The Authors. All rights reserved.

关键词： Phosphorus

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Adaptablity of Six Global Drought Indices Over China

Adaptablity of Six Global Drought Indices Over China

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Jing Lu Li Jia Jie Zhou Chaolei Zheng Guangcheng Hu State Key Laboratory of Remote Sensing Science Chinese Academy of Sciences Beijing China Key Laboratory for Geographical Process Analysis & Simulation of Hubei Province Central China Normal University Wuhan China

This study quantitatively evaluated the performance of six global drought indices (the self-calibrating Palmer Drought Severity Index -scPDSI, the Standardized Precipitation-Evapotranspiration Index - SPEI, the Global Precipitation Climatology Centre Drought Index - GPCC_DI, the Multivariate Standardized Drought Index - MSDI, the Standardized Soil Moisture Index - SSI, and the Standardized Precipitation Index - SPI) over China using the drought records from the Emergency Events Database (EM-DAT) by developing two indicators of the monitored drought area percentage (MDAP) and the monitored drought period percentage (MDPP). The results showed that scPDSI, SPEI, GPCC_DI, and MSDI can capture drought events in China from 1980 to 2015 better than SPI and SSI, with MDAP of ~80% and MDPP of ~70% at optimal timescales, among which SPEI and MSDI is slightly better than scPDSI and GPCC_DI.

关键词： Indexes Monitoring Meteorology Spatial resolution Remote sensing Earth

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A NEW METHOD FOR NOISE REMOVAL IN NPP-VIIRS MONTHLY NIGHTTIME LIGHT IMAGERY OVER THE SAHEL REGION

A NEW METHOD FOR NOISE REMOVAL IN NPP-VIIRS MONTHLY NIGHTTIM...

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IEEE International Geoscience and Remote Sensing Symposium

作者： Xiaotian Yuan Li Jia Jie Zhou Massimo Menenti Qiting Chen Institute of Remote Sensing and Digital Earth Chinese Academy of Science Key Laboratory for Geographical Process Analysis & Simulation of Hubei Province Central China Normal University Wuhan China

The monthly nighttime light data observed by the Visible Infrared Imaging Radiometer Suite (VIIRS) sensor onboard the National Polar-orbiting Partnership (NPP) satellite are useful to assess human activities. However, noise in the VIIRS data and unstable power supply in many areas often limit its application in detecting anthropic activities. This study aims to develop a new method (i.e., Patches De-noising Method, PDM) to remove noise in the VIIRS monthly nighttime light data towards better information on anthropic activities. The new synthesized data were examined in the Sahel region where the intensity of nighttime light is often affected by unstable power supply. The results showed that the new data could capture more settlements correctly compared with the annual data released by the National Centers for Environmental Information (NCEI) of the National Oceanic and Atmospheric Administration (NOAA). This suggests that the Patches De-noising Method is a useful way to remove noise in the NPP-VIIRS monthly nighttime light data.

关键词： Biomass Noise measurement Earth Noise reduction Power supplies Spatial resolution Google

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Correction to: Spatial pattern of the effects of human activities on the land surface of China and their spatial relationship with the natural environment

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Environment, Development and Sustainability 2021年第12期24卷 14421-14421页

作者： Hu, Shuling Yu, Bin Luo, Shen Zhuo, Rongrong Key Laboratory of Hubei Province Analysis and Simulation of Geographic Process Central China Normal University Wuhan China College of Tourism and Culture Industry Guizhou University Guiyang China

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Globally increased cropland soil exposure to climate extremes in recent decades

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Nature Communications 2025年第1期16卷 1-10页

作者： Feng, Luwei Wang, Yumiao Fensholt, Rasmus Tong, Xiaoye Tagesson, Torbern Zhang, Xiaoxin Ardö, Jonas Zhou, Jie Shao, Wenxuan Dou, Yujie Sang, Yirong Tian, Feng Hubei Key Laboratory of Quantitative Remote Sensing of Land and Atmosphere School of Remote Sensing and Information Engineering Wuhan University Wuhan China Perception and Effectiveness Assessment for Carbon-neutrality Efforts Engineering Research Center of Ministry of Education Institute for Carbon Neutrality Wuhan University Wuhan China Hubei Luojia Laboratory Wuhan University Wuhan China State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan University Wuhan China Department of Geography and Spatial Information Techniques Ningbo University Ningbo China Department of Geosciences and Natural Resource Management University of Copenhagen Copenhagen Denmark Department of Physical Geography and Ecosystem Science Lund University Lund Sweden Future Urbanity & Sustainable Environment (FUSE) Lab Division of Landscape Architecture Faculty of Architecture The University of Hong Kong Hong Kong Special Administrative Region Hong Kong Key Laboratory for Geographical Process Analysis & Simulation of Hubei Province College of Urban and Environmental Sciences Central China Normal University Wuhan China

Soil quality is fundamental to nutrient-rich food production and the sustainability of terrestrial ecosystems. However, inappropriate agricultural practices often lead to persistent soil exposure to air and sunlight, which increases soil organic matter losses and erosion risks, particularly under climate extremes. Here, we provide a satellite-based mapping of daily soil exposure occurrence across global croplands from 2001 to 2022 and evaluate the associated degradation risks caused by extreme climate events. We find that while 57% of global croplands experienced a reduction in soil exposure duration in the past two decades, 86% are increasingly subjected to climate extremes. The areas exposed to increasing climate extremes tend to have higher soil organic carbon levels, indicating an intensified degradation risk of global nutrient-rich cropland soils. Our study offers spatio-temporally explicit insights into global cropland soil exposure and its vulnerability to climate extremes, providing evidence to support improvements in sustainable agriculture practices. © The Author(s) 2025.

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A differentiability-based processes and parameters learning hydrologic model for advancing runoff prediction and process understanding

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Journal of Hydrology 2025年 661卷

作者： Chunxiao Zhang Heng Li Yuqian Hu Dingtao Shen Bingli Xu Min Chen Wenhao Chu Rongrong Li School of Information Engineering China University of Geosciences Beijing Beijing China Hebei Key Laboratory of Geospatial Digital Twin and Collaborative Optimization China University of Geosciences Beijing Beijing China Chinese Academy of Surveying and Mapping Beijing China Key Laboratory for Geographical Process Analysis & Simulation of Hubei Province Central China Normal University Wuhan China College of Urban and Environmental Sciences Central China Normal University Wuhan China Department of Information and Communication Academy of Army Armored Forces Beijing China Key Laboratory of Virtual Geographic Environment (Ministry of Education of PRC) Nanjing Normal University Nanjing Jiangsu China International Research Center of Big Data for Sustainable Development Goals Beijing China Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application Nanjing Jiangsu China Institute of Space and Earth Information Science The Chinese University of Hong Kong Shatin New Territories Hong Kong China

Differentiable parameterized learning (dPL) represents a cutting-edge advancement in synergizing process-based models (PBMs) and machine learning to improve both the model prediction and interpretability. However, dPL mainly focuses on refining parameters within a predefined process-based structure, which limits its performance and process understanding to the constraints of existing PBM frameworks. To address this limitation, we propose an extended framework: Differentiable process and Parameter Learnings (dP2Ls). dP2Ls incorporate regionalized neural networks (NNs) to simultaneously improve both the modeling of process variables and parameter learnings. Within dP2Ls, the EXP-HYDRO model serves as a differentiable physical backbone, where the potential evapotranspiration (PET) process is modeled using a regionalized long short-term memory (LSTM), while the dPL strategy is independently applied to learning hydrological parameters. Experiments with runoff prediction in ungauged catchments across the contiguous United States revealed that: (1) The median Nash-Sutcliffe efficiency (NSE) of regionalized dP2Ls in ungauged catchments surpassed that of all models using NNs for either process learning or parameter learning and approached the LSTM model (the median NSE differs by only 0.021); (2) Regionalized dP2Ls enhanced the interpretability of some intermediate variables by jointly learning the process variables and parameters, resulting in the correlation coefficients of snowpack and ET fitting the estimation products increased by 0.022 and 0.026, respectively; (3) The embedded regionalized LSTM outputs more interpretable PET than EXP-HYDRO, demonstrating the capability of NNs to improve traditional process understanding. In summary, this study demonstrates the advantages of dP2Ls in regionalized modeling, overcoming the limitations of traditional process-based structures through flexible NN configurations, and providing a reliable pathway for the future in-depth diagno

关键词： Catchments

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The inflection point about COVID-19 may have passed

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Science Bulletin 2020年第11期65卷 865-867,M0003页

作者： Chaolin Gu Jie Zhu Yifei Sun Kai Zhou Jiang Gu School of Architecture Tsinghua UniversityBeijing 100085China Jiangsu Branch of China Academy of Urban Planning and Design Nanjing 210000China Department of Geography and Environmental Studies California State University NorthridgeInternational Institute for Innovation and Development(I3D)NorthridgeCA 91326USA School of Architecture Hunan UniversityChangsha 4W082China Hubei Provincial Key Laboratory for Geographical Process Analysis and Simulation Academy of Wuhan Metropolitan AreaCollege of Urban and Environmental ScienceCentral China Normal UniversityWuhan 430079China

In an era of globalization and informatisation,a Public Health Emergency of International Concern(PHEIC)imposes a huge impact on everyone’s life[1],as what the COVID-19 has demonstrated at the *** China’s nationwide quarantine and extremely strict epidemic prevention measures,it becomes increasingly critical for China and the world to know the evolving trend,though unclear yet,for the sake of the global *** is mainly due to the following two important reasons[2].First of all,because of the quarantine,a sudden shutdown of manufacturing in China(now the second largest economy of the world)would break the global supply chain,with the potential to eliminate China’s current advantageous ***,many small enterprises in the service industry are facing death or life situation with the spreading disease,and many will bankrupt[3].When the inflection point of the COVID-19 epidemic appears suddenly becomes a critical issue for China and the world.

关键词： nationwide everyone suddenly

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Coexistence under hierarchical resource exploitation: The role of R*-preemption tradeoff

arXiv

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arXiv 2019年

作者： Qi, Man DeMalach, Niv Zhang, Hailin Sun, Tao Key Laboratory of Geographical Process Analysis & Simulation of Hubei Province College of Urban and Environmental Sciences Central China Normal University Wuhan430079 China State Key Laboratory of Water Environment Simulation School of Environment Beijing Normal University Beijing100875 China Institute of Plant Sciences and Genetics in Agriculture Faculty of Agriculture Food and Environment The Hebrew University of Jerusalem Rehovot Israel

Resource competition theory predicts coexistence and exclusion patterns based on species' R*s, the minimum resource values required for a species to persist. A central assumption of the theory is that all species have equal access to resources. However, many systems are characterized by preemption exploitation, where some species deplete resources before their competitors can access them (e.g., asymmetric light competition, contest competition among animals). We hypothesize that coexistence under preemption requires an R*-preemption tradeoff, i.e., the species with the priority access should have a higher R* (lower 'efficiency'). Thus, we developed an extension of resource competition theory to investigate partial and total preemption (in the latter, the preemptor is unaffected by species with lower preemption rank). We found that an R*-preemption tradeoff is a necessary condition for coexistence in all models. Moreover, under total preemption, the tradeoff alone is sufficient for coexistence. In contrast, under partial preemption, more conditions are needed, which restricts the parameter space of coexistence. Finally, we discussed the implications of our finding for seemingly distinct tradeoffs, which we view as special cases of R*-preemption tradeoff. These tradeoffs include the digger-grazer, the competition-colonization, and tradeoffs related to light competition between trees and understories. Copyright © 2019, The Authors. All rights reserved.

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