Prof. Muhammad Bilal
 | Prof. Muhammad Bilal ("Distinguished Professor" Jiangsu Provincial Department of Education; Top 2% of World Scientists in Meteorology and Atmospheric Sciences based on a single year (2019) by Stanford University, USA) Henan Polytechnic University, China Web: https://www.researchgate.net/profile/Muhammad-Bilal-428 Prof. Bilal received the Ph.D. in Photogrammetry and Remote Sensing from the Department of Land Surveying & Geoinformatics (LSGI), the Hong Kong Polytechnic University (PolyU: 65th QS World Ranking), Hong Kong, through the prestigious Hong Kong Ph.D. Fellow Scheme (2010/2011). Prof. Bilal has the honor to be the first Pakistani (out of 300 applicants from Pakistan) who was selected for the Hong Kong Ph.D. Fellowship in the pioneer batch of 2010. Currently, Bilal am serving as a Professor in the School of Surveying and Land Information Engineering at the Henan Polytechnic University, Jiaozuo, China. From October 2017 to September 2022, he served as a Professor at Nanjing Nanjing University of Information Science and Technology, Nanjing, China, and from 2014 to 2017, he worked as a Postdoctoral Fellow at PolyU (HK). In October 2018, the Jiangsu Provincial Department of Education conferred Bilal the special title of “Distinguished Professor” based on his outstanding research achievements, and in October 2020, he ranked in the Top 2% of World Scientists in Meteorology and Atmospheric Sciences based on a single year (2019) by Stanford University, USA. Prof. Bilal have published more than 120 research articles in top SCI (E) journals (IF > 600; Citations: >3250; H-Index: 34) and he is also a certified “top peer-reviewer” by Publons – Web of Science with 290 reviews for 64 scientific journals. He has devised the following innovative methods: (1) Simplified Aerosol Retrieval Algorithm (SARA), (2) Simplified Merge Scheme (SMS), (3)Simplified and Robust Surface Reflectance Estimation Method (SREM), and (4) AEROsol generic classification using a novel Satellite remote sensing Approach (AEROSA). Muhammad Bilal在香港理工大学（理大：QS世界排名第65位）土地测量与地理信息学系（LSGI）通过著名的香港博士研究生计划（2010/2011）获得摄影测量与遥感专业的博士学位。并从巴基斯坦的300名申请者中成为2010年第一批入选香港博士生奖学金的首位巴基斯坦人。目前，在中国焦作的河南工业大学测量与土地信息工程学院任职教授。2014年至2017年，在理大（香港）担任博士后研究员；2017年10月至2022年9月，在南京信息工程大学担任教授；2018年10月，江苏省教育厅基于其卓越研究成就，授予其 "特聘教授 "的特殊称号；2020年10月，被美国斯坦福大学按单年（2019年）排名，进入世界气象学和大气科学科学家的前2%。Bilal教授在顶级SCI（E）期刊上发表了120多篇研究文章（IF>600；引用次数：>3250；H指数：34），也是Publons - Web of Science认证的 "顶级同行评审员"，对64种科学期刊进行了290次评审。他设计了以下创新方法：（1）简化气溶胶反演算法（SARA），（2）简化合并方案（SMS），（3）简化和鲁棒的表面反射率估计方法（SREM），和（4）使用新型卫星遥感方法（AEROSA）的AEROsol通用分类。 |
Speech Title & Abstract
Title: SEMARA: Integration of SREM and SARA Algorithms for Aerosol Optical Depth Retrievals from Multi-Resolution Remote Sensing Sensors
Abstract:
A new approach, SEMARA: an integration of the Simplified and Robust Surface Reflectance Estimation Method (SREM) and the Simplified Aerosol Retrievals Algorithm, is introduced to retrieve aerosol optical depth (AOD) at 550 nm from multi-resolution remote sensing sensors, e.g., Landsat 8 OLI at 30 m spatial resolution, MODIS at 500 m resolution, and VIIRS at 750 m resolution. The SEMARA approach integrated (i) the SREM method, which is used to estimate surface reflectance without incorporating water vapor, ozone, and aerosol information, and (ii) the SARA algorithm, which uses the surface reflectance estimated by the SREM method, AOD as input from AERONET site, and does not depend on the simulated look-up table. The SEMARA results were validated against AERONET AOD, and the results were assessed using Pearson’s correlation coefficient (r), root mean squared error (RMSE), relative mean bias (RMB), and expected error (EE = ± 0.05 ± 20%). The validation results show that SEMARA AOD correlates well with AERONET AOD with high correlation coefficients (r), small RMSE, small RMB, and a higher percentage of retrievals within the EE for Landsat 8 OLI, MODIS, and VIIRS. The results suggest that the SEMARA approach can retrieve AOD with high accuracy and small errors from Multi-Resolution Remote Sensing Sensors.
报告题目：SEMARA：基于多分辨率遥感传感器反演气溶胶光学厚度的SREM和SARA算法集成
摘要：
本文介绍的SEMARA算法集成了简化和稳健的表面反射率估计法（SREM）和简化气溶胶反演算法(SARA)，可通过多分辨率遥感传感器（如30米空间分辨率的陆地卫星8号陆地成像仪、500米分辨率的中分辨率成像光谱仪、750米分辨率的可见光红外成像辐射仪），进行 550纳米波段的气溶胶光学厚度（AOD）反演。其中，SREM算法用于估计地表反射率，而不纳入水蒸气、臭氧和气溶胶信息；SARA算法使用AOD SREM算法估计的地表反射率作为气溶胶观测网（AERONET）站点的输入，并且不依赖于模拟查找表。本文利用AERONET的气溶胶光学厚度验证SEMARA算法所得结果，并使用皮尔逊相关系数(r)、均方根误差(RMSE)、相对平均偏差(RMB)和预期误差(EE=0.05±20%）对结果进行评估。验证结果表明，SEMAR气溶胶光学厚度与AERONET气溶胶光学厚度有很好的相关性，相关系数高、均方根误差小、相对平均偏差小，且陆地卫星8号陆地成像仪、中分辨率成像光谱仪和可见光红外成像辐射仪结果均显示SEMARA气溶胶光学厚度在预期误差范围内具有较高的反演百分比。以上结果表明，SEMARA算法可通过多分辨率遥感传感器反演气溶胶光学厚度，且精度高、误差小。