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Your search keyword '"Yu, Chaoqing"' showing total 163 results
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163 results on '"Yu, Chaoqing"'

10. Managing nitrogen to restore water quality in China

Author

Yu, ChaoQing, Huang, Xiao, Chen, Han, Godfray, H. Charles J., Wright, Jonathon S., Hall, Jim W., Gong, Peng, Ni, ShaoQiang, Qiao, ShengChao, Huang, GuoRui, Xiao, YuChen, Zhang, Jie, Feng, Zhao, Ju, XiaoTang, Ciais, Philippe, Stenseth, Nils Chr., Hessen, Dag O., Sun, ZhanLi, Yu, Le, Cai, WenJia, Fu, HaoHuan, Huang, XiaoMeng, Zhang, Chi, Liu, HongBin, and Taylor, James

Published
2019
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11. The Lancet Countdown: tracking progress on health and climate change

Author

Watts, Nick, Adger, W Neil, Ayeb-Karlsson, Sonja, Bai, Yuqi, Byass, Peter, Campbell-Lendrum, Diarmid, Colbourn, Tim, Cox, Peter, Davies, Michael, Depledge, Michael, Depoux, Anneliese, Dominguez-Salas, Paula, Drummond, Paul, Ekins, Paul, Flahault, Antoine, Grace, Delia, Graham, Hilary, Haines, Andy, Hamilton, Ian, Johnson, Anne, Kelman, Ilan, Kovats, Sari, Liang, Lu, Lott, Melissa, Lowe, Robert, Luo, Yong, Mace, Georgina, Maslin, Mark, Morrissey, Karyn, Murray, Kris, Neville, Tara, Nilsson, Maria, Oreszczyn, Tadj, Parthemore, Christine, Pencheon, David, Robinson, Elizabeth, Schütte, Stefanie, Shumake-Guillemot, Joy, Vineis, Paolo, Wilkinson, Paul, Wheeler, Nicola, Xu, Bing, Yang, Jun, Yin, Yongyuan, Yu, Chaoqing, Gong, Peng, Montgomery, Hugh, and Costello, Anthony

Published
2017
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13. Combining Satellite Imagery and a Deep Learning Algorithm to Retrieve the Water Levels of Small Reservoirs.

Author

Wu, Jiarui, Huang, Xiao, Xu, Nan, Zhu, Qishuai, Zorn, Conrad, Guo, Wenzhou, Wang, Jiangnan, Wang, Beibei, Shao, Shuaibo, and Yu, Chaoqing

Subjects
MACHINE learning, REMOTE-sensing images, WATER management, CONVOLUTIONAL neural networks, DEEP learning, WATER levels
Abstract

There are an estimated 800,000 small reservoirs globally with a range of uses. Given the collective importance of these reservoirs to water resource management and wider society, it is essential that we can monitor and understand the hydrological dynamics of ungauged reservoirs, particularly in a changing climate. However, unlike large reservoirs, continuous and systematic hydrological observations of small reservoirs are often unavailable. In response, this study has developed a retrieval framework for water levels of small reservoirs using a deep learning algorithm and remotely sensed satellite data. Demonstrated at four reservoirs in California, satellite imagery from both Sentinel-1 and Sentinel-2 along with corresponding water level field measurements was collected. Post-processed images were fed into a water level inversion convolutional neural network model for water level inversion, while different combinations of these satellite images, sampling approaches for training/testing data, and attention modules were used to train the model and evaluated for accuracy. The results show that random sampling of training data coupled with Sentinel-2 satellite imagery was generally the most accurate initially. Performance is improved by incorporating a channel attention mechanism, with the average R2 increasing by 8.6% and the average RMSE and MAE decreasing by 15.5% and 36.4%, respectively. The proposed framework was further validated on three additional reservoirs in different regions. In conclusion, the retrieval framework proposed in this study provides a stable and accurate methodology for water level estimation of small reservoirs and can be a powerful tool for small reservoir monitoring over large spatial scales. [ABSTRACT FROM AUTHOR]

Published
2023
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14. Health and climate change: policy responses to protect public health

Author

Watts, Nick, Adger, W Neil, Agnolucci, Paolo, Blackstock, Jason, Byass, Peter, Cai, Wenjia, Chaytor, Sarah, Colbourn, Tim, Collins, Mat, Cooper, Adam, Cox, Peter M, Depledge, Joanna, Drummond, Paul, Ekins, Paul, Galaz, Victor, Grace, Delia, Graham, Hilary, Grubb, Michael, Haines, Andy, Hamilton, Ian, Hunter, Alasdair, Jiang, Xujia, Li, Moxuan, Kelman, Ilan, Liang, Lu, Lott, Melissa, Lowe, Robert, Luo, Yong, Mace, Georgina, Maslin, Mark, Nilsson, Maria, Oreszczyn, Tadj, Pye, Steve, Quinn, Tara, Svensdotter, My, Venevsky, Sergey, Warner, Koko, Xu, Bing, Yang, Jun, Yin, Yongyuan, Yu, Chaoqing, Zhang, Qiang, Gong, Peng, Montgomery, Hugh, and Costello, Anthony

Published
2015
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25. Coupled modelling and sampling approaches to assess the impacts of human water management on land-sea carbon transfer

Author

Ni, Shaigiang, Huang, Xiao, Gan, Weixiu, Zorn, Conrad, Xiao, Yuchen, Huang, Guorui, Yu, Chaoqing, Cao, Jifu, Zhang, Jie, Feng, Zhao, Yu, Le, Lin, Guanghui, Silvennoinen, Hanna, non-UU output of UU-AW members, and non-UU output of UU-AW members

Subjects
Irrigation, Environmental Engineering, Stable isotope analysis, 010504 meteorology & atmospheric sciences, chemistry.chemical_element, 010501 environmental sciences, 01 natural sciences, Hydrological modeling, hydrological modeling, Carbon cycle, Land-sea carbon transfer, stable isotope analysis, Environmental Chemistry, Waste Management and Disposal, 0105 earth and related environmental sciences, Isotope analysis, Hydrology, VDP::Landbruks- og Fiskerifag: 900, business.industry, Sampling (statistics), land-use carbon transfer, field sampling, Water extraction, Pollution, Human water management, chemistry, Field sampling, Agriculture, human water management, Environmental science, business, Surface runoff, Carbon
Abstract

Land-sea riverine carbon transfer (LSRCT) is one of the key processes in the global carbon cycle. Although natural factors (e.g. climate, soil) influence LSRCT, human water management strategies have also been identified as a critical component. However, few systematic approaches quantifying the contribution of coupled natural and anthropogenic factors on LSRCT have been published. This study presents an integrated framework coupling hydrological modeling, field sampling and stable isotope analysis for the quantitative assessment of the impact of human water management practices (e.g. irrigation, dam construction) on LSRCT under different hydrological conditions. By applying this approach to the case study of the Nandu River, China, we find that carbon (C) concentrations originating from different land-uses (e.g. forest, cropland) are relatively stable and outlet C variations are mainly dominated by controlled runoff volumes rather than by input C concentrations. These results indicate that human water management practices are responsible for a reduction of ∼60% of riverine C at seasonal timescales, with an even greater reduction during drought conditions. Annual C discharges have been significantly reduced (e.g. 77 ± 5% in 2015 and 39 ± 11% in 2016) due to changes in human water extraction coupled with climate variation. In addition, isotope analysis also shows that C fluxes influenced by human activities (e.g. agriculture, aquaculture) could contribute the dominant particulate organic carbon under typical climatic conditions, as well as drought conditions. This research demonstrates the substantial effect that human water management practices have on the seasonal and annual fluxes of LSRCT, especially in such small basins. This work also shows the applicability of this integrated approach, using multiple tools to quantify the contribution of coupled anthropogenic and natural factors on LSRCT, and the general framework is believed to be feasible with limited modifications for larger basins in future research. Coupled modelling and sampling approaches to assess the impacts of human water management on land-sea carbon transfer

Published
2020

28. Stable classification with limited sample: transferring a 30-m resolution sample set collected in 2015 to mapping 10-m resolution global land cover in 2017

Author

Gong, Peng, Liu, Han, Zhang, Meinan, Li, Congcong, Wang, Jie, Huang, Huabing, Clinton, Nicholas, Ji, Luyan, Li, Wenyu, Bai, Yuqi, Chen, Bin, Xu, Bing, Zhu, Zhiliang, Yuan, Cui, Ping Suen, Hoi, Guo, Jing, Xu, Nan, Li, Weijia, Zhao, Yuanyuan, Yang, Jun, Yu, Chaoqing, Wang, Xi, Fu, Haohuan, Yu, Le, Dronova, Iryna, Hui, Fengming, Cheng, Xiao, Shi, Xueli, Xiao, Fengjin, Liu, Qiufeng, and Song, Lianchun

Published
2019
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34. Spatiotemporal dynamics of soil phosphorus and crop uptake in global cropland during the 20th century

Author

Zhang, Jie, Beusen, Arthur H.W., van Apeldoorn, Dirk F., Mogollón, José M., Yu, Chaoqing, Bouwman, Alexander F., Bio-, hydro-, and environmental geochemistry, and Geochemistry

Subjects
agricultural land, agricultural modeling, soil nutrient, nutrient uptake, soil property, crop production, fertilizer application, food security, crop yield, phosphorus, food production
Abstract

Phosphorus (P) plays a vital role in global crop production and food security. In this study, we investigate the changes in soil P pool inventories calibrated from historical countrywide crop P uptake, using a 0.5-by-0.5° spatially explicit model for the period 1900–2010. Globally, the total P pool per hectare increased rapidly between 1900 and 2010 in soils of Europe (+31 %), South America (+2 %), North America (+15 %), Asia (+17 %), and Oceania (+17 %), while it has been stable in Africa. Simulated crop P uptake is influenced by both soil properties (available P and the P retention potential) and crop characteristics (maximum uptake). Until 1950, P fertilizer application had a negligible influence on crop uptake, but recently it has become a driving factor for food production in industrialized countries and a number of transition countries like Brazil, Korea, and China. This comprehensive and spatially explicit model can be used to assess how long surplus P fertilization is needed or how long depletions of built-up surplus P can continue without affecting crop yield.

Published
2017

37. Exploring the temporal density of Landsat observations for cropland mapping: experiments from Egypt, Ethiopia, and South Africa

Author

Xu, Yidi, primary, Yu, Le, additional, Peng, Dailiang, additional, Cai, Xueliang, additional, Cheng, Yuqi, additional, Zhao, Jiyao, additional, Zhao, Yuanyuan, additional, Feng, Duole, additional, Hackman, Kwame, additional, Huang, Xiaomeng, additional, Lu, Hui, additional, Yu, Chaoqing, additional, and Gong, Peng, additional

Published
2018
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48. Spatial-temporal patterns of features selected using random forests: a case study of corn and soybeans mapping in the US.

Author

Liu, Xiaoxuan, Yu, Le, Zhong, Liheng, Hao, Pengyu, Wu, Bo, Wang, Hongshuo, Yu, Chaoqing, and Gong, Peng

Subjects
RANDOM forest algorithms, AGRICULTURAL remote sensing, REMOTE-sensing images, SOYBEAN, CORN farming
Abstract

It is not easy in remote sensing field to distinguish corn and soybean mapping for the similarity of the mixed summer crops. To understand the variations better and generate corn and soybean maps more accurately, more accurate mapping of these two crops is required. However, classifying different crops with remote sensing is not easy. Finding the discriminating features to use in a mapping classifier can lead to higher-precision mapping. In this paper, we used feature selection of random forests to analyse the most important phenological features for mapping corn and soybeans in the US Corn Belt (also called the Extended Corn Belt, ECB), and identified the spatial and temporal patterns of these features in the ECB. Results show that all the states in the ECB have very similar patterns: Temporal pattern showed that different years always reflected the same pattern. Date-related and phenology curve-related features were the most important. Also, the whole phenology curve was a prerequisite for more accurate mapping. These patterns make the variations more understandable and also support the further research to consider the most important variables for detailed classification. [ABSTRACT FROM AUTHOR]

Published
2019
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