女同做爱

纪委信箱
教师简介
麻金继
 作者:李小文 编辑:李小文  预审:陈健  发布日期:2017年06月24日 浏览次数:

麻金继,男,19752月,安徽当涂人;二级教授,博. Retrieval of Water Cloud Droplet Size from Multi-Angle Satellite Imagery. Atmospheric Environment, 2025: 121342.

Ø Shen, F. , Lin, X. , Chen, H. , Ma, J.* , Shi, K. , & Cao, W. . City-level total and sub-category energy intensity estimation using machine learning. International Journal of Digital Earth, 2025, 18(1): 2463947.

Ø Haifeng Xu, Bing Dong, Zhili Xu, Jinji Ma*, Fei Shen. Study on habitat suitability and ecological network of rare cranes in Poyang Lake National Nature Reserve. Ecological Indicators, 2025, 174: 113480.

Ø Haifeng Xu, Wenhui Luo, Jinji Ma*, Shijie Zhao, Rui Qian, Bing Dong,. Spatiotemporal evolution and risk thresholds of PM2.5 components in China from the human health perspective. Environmental Pollution, 2025, 373: 126194.

Ø Haifeng Xu, Wenhui Luo, Jinji Ma*, Bing Dong, Cheng Wan, Shijie Zhao, Cheng Dai, Rui Qian, . Construction and analysis of atmospheric visibility and fog-haze datasets in China (2001−2023) based on machine learning models. Atmospheric Research, 2025, 322: 108160.

Ø Cheng Wan, Haifeng Xu, Wenhui Luo, Jinji Ma*,. Estimation of regional PM2.5 concentration in China based on fine-mode aerosol optical thickness (AODf) and study of influencing factors. Atmospheric Environment, 2025, 344: 121026.

Ø Haifeng Xu, Jinji Ma*, Wenhui Luo, Cheng Wan, Zheng Qiang Li. Research on the distribution and influencing factors of fine mode aerosol optical depth optical depth (AODf) in China. Atmospheric Environment, 334,2024:120721.

Ø Guo, J., Ma J*Li, Z., and Hong, J. Building a top-down method based on machine learning for evaluating energy intensity at a fine scale. Energy 255, 2022:124505.

Ø Li, J., Ma J*, Ye, X. A Batch Pixel-Based Algorithm to Composite Landsat Time Series Images. Remote Sensing 14, no.172022:4252.

Ø Lin, X., Ma J*, Chen, H., Shen, F., Ahmad, S., and Li, Z. Carbon Emissions Estimation and Spatiotemporal Analysis of China at City Level Based on Multi‐Dimensional Data and Machine Learning. , , , 343–354, 2019.

Ø Sifeng Zhu, Xingfeng Chen, Li Liu, Lili Qie, Zhengqiang Li, Jinji Ma*, Shule Ge, Jin Hong, Xin Li,Hailiang Gao, Evaluation of radiometric performance of MODIS visible bands using the Rayleigh scattering method. J. Appl. Remote Sens. 2019,13(1), 018503.

Ø Ma Jinji*, Wu hao, Chao Wang, etc. Multiyear satellite and surface observations of cloud fraction over China, Journal of Geophysical Research: Atmospheres, Vol.119:7655-7666,2014.

Ø Chao Wang, Qiming Liu, Na Ying, Xianhua Wang, Jinji Ma*, Air quality evaluation on an urban scale based on MODIS satellite images, Atmospheric Research, Vol.132-133, 22-34, 2013.

Ø Jinji Ma*, Shizhi Yang, Xian bing Wang, Yanli Qiao. Atmospheric correction: computing atmospheric diffuse transmittance, Atmospheric Research, Vol.80, NO.1, 1-14, 2006.

Ø 王宇瑶, 麻金继*, 李婧晗, 洪津, 李正强.云偏振遥感综述.遥感学报,2022,26(05),852-872.

Ø 张洪海,高一博,李超,麻金继*,方雪静,熊伟. 针对SHS探测仪的中高层OH自由基临边观测仿真研究. 光谱学与光谱分析, 2017, 37(9): 2685-2691.

Ø 麻金继*,李素文.降低 DOAS 系统探测限的新型反演算法研究,光学学报,Vol.29(9),2051-2054,2009.

Ø 麻金继*,乔延利,杨世植;利用MODIS图像反演中国近海海域的气溶胶光学特性,光学学报,Vol.29(8), 2039-2045,2009.

Ø 麻金继*,乔延利,杨世植;基于 MODIS 图像反演海岸带气溶胶光学特性,武汉大学学报(信息科学版), Vol.34(7)842-8462009.

Ø Ma jinji*, Li Suwen. Retrieving Model of Differential Optical Absorption Spectroscopy Based On M-Estimator Robust Regression, Acta Photonic Sinica, Vol.38(8), 2035-2039, 2009.

Ø 麻金继*,陶安,王家成,杨世植.基于 MODIS 图像海岸带二类水的提取,武汉大学学报(信息科学版),Vol.32,第 1 期,78-802007.

Ø 麻金继*,杨世植;利用 MODIS 图像反演海岸与海岛的地物光谱反射率,武汉大学学报(信息科学版),Vol.30,第 9 期,791-7952005.


2. 发明专利:

Ø 一种基于偏振图像的污染云分类识别方法,ZL201810424849.X,发明专利;已授权

Ø 一种基于偏振图像的云检测方法,ZL201810074212.2,发明专利;已授权。

3. 专著:

Ø 麻金继,王春林,洪津,李正强著,多源卫星云遥感,202101月,科学出版社

Ø 麻金继,熊伟,叶松著,中高层大气OH自由基探测原理与方法,201912月,科学出版社。

Ø 麻金继,梁栋栋编著;三维测绘新技术,20185月,科学出版社。

Ø 凌善金,梁栋栋,麻金继编著;新编地图学,2017年,科学出版社。

4. 软件著作:

Ø 可复垦资源自动查找系统V1.0

Ø 违法用地监控系统V1.0;

Ø 基于GF-5DPC图像的大气污染物动态监测系统V1.0

Ø 淮河流域安徽段生态环境动态评价系统V1.0

Ø 偏振图像的云识别系统 V1.0

Ø 一种基于SHS中高层OH浓度反演系统V1.0

Ø 地理信息政务系统 V1.0

Ø 基于天地图的便民地图服务系统 V1.0

Ø 数字城市用户管理审核系统 V1.0

Ø 天地图地理信息公共服务平台 V1.0

Ø 基于天地图的安卓端地图服务系统 V1.0

5. 获奖

Ø 2021年度获安徽省科技进步一等奖(排名第四);20223

Ø 2023年度安徽省教学成果二等奖(排名第一);20246