Shuaibing Liu: Plant Physiology First Author

Shuaibing Liu, co-first author of “Estimating leaf area index using unmanned aerial vehicle data: Shallow vs. deep machine learning algorithms”

Current Position: Ph.D Candidate, School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, China

Education: B.E. & M.E in School of Surveying and Land Information Engineering, Henan Polytechnic University, Henan, China.

Non-scientific Interests: Guitar, badminton.

Brief bio: I completed my B.S. & M.E at the Henan Polytechnic University in 2016 & 2019. After graduation, I studied for a Ph.D in the school of remote sensing information engineering, Wuhan university, majoring in electronics and information. My main research direction is the field crop phenotypic monitoring and growth estimation research on the UAV high-throughput phenotyping platform. Under the direction of Prof. Jin, we have constructed a multi-source remote sensing field phenotypic observation platform for UAV. The visible, multispectral and thermal infrared UAV images were constructed into multimodal data sets, and the maize LAI was estimated in different machine learning models. The effects of field environmental factors and maize canopy reproduction on the evaluation of LAI should also be considered. We found that the maize canopy thermal information provided by thermal infrared images could improve the accuracy of LAI estimation. This work provided new insights and ideas for monitoring crop growth by multi-source remote sensing in the field.

 

姓名：刘帅兵

职位：武汉大学遥感信息工程学院，在读博士生

学历：河南理工大学测绘与国土信息工程学院，本科/硕士

兴趣爱好：吉他，羽毛球，

个人简介：我在河南理工大学分别于2016年和2019年取得工学学士学位和工程硕士学位。现为武汉大学遥感信息工程学院在读博士生，专业方向为电子与信息，致力于无人机高通量表型平台田间作物表型性状监测及长势估测研究。在金秀良老师的指导下，我们构建了无人机多源遥感田间表型观测平台。将可见光、多光谱及热红外无人机影像构建多模态数据集，在不同的模型中对玉米LAI进行估算。同时还要考虑田间环境因素以及玉米冠层生殖状况对评估LAI的影响。我们发现，热红外影像提供的热信息可以提高估测LAI的精度，这是以往研究未涉及到的。我们的工作为田间多源遥感监测作物长势提供了新的证据和思路，这也是我们之后要继续探究的。希望我们的研究能为人们更深入地了解无人机遥感的应用及研究。