王成导师研究小组的论文在SCI期刊International Journal of Remote Sensing上发表

2022年9月19日，以云南师范大学云南省高校资源与环境遥感重点实验室2018级博士研究生施骏骋为第一作者、王成研究员为第二作者、王金亮教授、习晓环副研究员为通讯作者的论文“Study on the LAI and FPAR inversion of maize from airborne LiDAR and hyperspectral data”在SCI/SCIE期刊International Journal of Remote Sensing（中科院SCI期刊分区2021年12月最新基础版三区、2021年12月最新升级版三区；2021年IF 3.531）上线发表（https://doi.org/10.1080/01431161.2022.2121187）。

玉米是一种重要的谷类作物，在农业中占有重要地位。叶面积指数(LAI)和吸收光合有效辐射分数(FPAR)是与作物光合作用、蒸腾作用和呼吸作用密切相关的重要植被冠层结构参数，对作物产量估算具有重要意义。高光谱数据可以获得作物的光谱特征参数，LiDAR数据可以获得垂直结构指数。前者侧重横向特征信息，后者侧重纵向特征信息。本文采用随机森林方法对玉米LAI和FPAR进行反演建模，测试并分析了高光谱数据分辨率、LiDAR数据密度和扫描角度对反演精度的影响。结果表明，随机森林反演模型具有良好的精度和稳定性(LAI: 0.53<R²<0.78, 0.094<RMSE<0.158, 0.009<MSE<0.025, 0.074<MAE<0.136; FPAR: 0.323<R²<0.594 , 0.09<RMSE<0.261, 0.008<MSE<0.068, 0.068<MAE<0.212)。利用高光谱数据反演LAI时，分辨率为1 m时精度最高( R²=0.742, RMSE=0.141, MSE=0.02, MAE=0.119 )。利用LiDAR数据反演LAI时，密度为30%时精度最高( R²=0.78, RMSE=0.115, MSE=0.013, MAE=0.094 )。利用高光谱数据反演FPAR时，分辨率为0.128 m时精度最高( R²=0.594, RMSE=0.09, MSE=0.008, MAE=0.068)。利用激光雷达数据反演FPAR时，密度为20%时反演精度最高(R²=0.525, RMSE=0.141, MSE=0.02, MAE=0.113)。

该论文得到了国家自然基金面上项目“融合机载全波形LiDAR与高光谱数据的玉米FPAR反演机理与方法研究”的资助。

这是施骏骋博士期间发表的第二篇SCI/SCIE学术论文（详见录1）。在此，让我们恭喜施骏骋同学！希望他再接再厉！也热烈祝贺团队取好成绩！

 论文相关信息

标题：Study on the LAI and FPAR inversion of maize from airborne LiDAR and hyperspectral data

作者：Juncheng Shi ^{a, b, c}, Cheng Wang ^d, Jinliang Wang ^{a, b, c*},Xiaohuan Xi ^d*, Xuebo Yang ^d, Xue Ding ^{a, b, c*}

作者单位：

a Department of Geography, Yunnan Normal University, Kunming, China;

b Key Laboratory of Resources and Environmental Remote Sensing for Universities in Yunnan, Yunnan Normal University, Kunming, Yunnan,China;

c Center for Geospatial Information Engineering and Technology of Yunnan Province, Yunnan Normal University, Kunming, Yunnan, China;

d Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China

出版物：International Journal of Remote Sensing

摘要：

Maize is an important cereal crop and plays an important role in agriculture. The leaf area index (LAI) and the fraction of absorbed photosynthetically active radiation (FPAR) are important vegetation canopy structure parameters that are closely related to crop photosynthesis, transpiration and respiration and are of great significance for crop yield estimation. Hyperspectral data can obtain the spectral characteristic parameters of crops, and LiDAR data can obtain the vertical structure index. The former focuses on horizontal characteristic information, and the latter focuses on vertical characteristic information. In this paper, random forest method is used to carry out the inversion modeling of maize LAI and FPAR, and the effects of hyperspectral data resolution, LiDAR data density and scanning angle on the inversion accuracy are tested and analyzed. The results show that there is good accuracy and stability of the random forest inversion model (LAI: 0.53<R²<0.78, 0.094<RMSE<0.158, 0.009<MSE<0.025, 0.074<MAE<0.136; FPAR: 0.323<R²<0.594 , 0.09<RMSE<0.261, 0.008<MSE<0.068, 0.068<MAE<0.212). When using Hyperspectral data to invert LAI, the accuracy is the highest when the resolution is 1 meter( R²=0.742, RMSE=0.141, MSE=0.02, MAE=0.119 ). When using LiDAR data to invert LAI, the accuracy is the highest when the density is 30% ( R²=0.78, RMSE=0.115, MSE=0.013, MAE=0.094 ). When using Hyperspectral data to invert FPAR, the accuracy is the highest when the resolution is 0.128 meter( R²=0.594, RMSE=0.09, MSE=0.008, MAE=0.068). When using LiDAR data to invert FPAR, the accuracy is the highest when the density is 20% (R²=0.525, RMSE=0.141, MSE=0.02, MAE=0.113).

关键词：Airborne LiDAR data; Hyperspectral image; LAI; FPAR; Vegetation parameter inversion

附录1施骏骋博士期间发表SCI论文

[2]Juncheng Shi, Cheng Wang, Jinliang Wang*, Xiaohuan Xi*, Xuebo Yang, and Xue Ding*. Study on the LAI and FPAR inversion of maize from airborne LiDAR and hyperspectral data[J] INTERNATIONAL JOURNAL OF REMOTE SENSING, 2022, VOL. 43, NO. 13, 4793-4809  https://doi.org/10.1080/01431161.2022.2121187  （SCI/SCIE, 三区，2021年 IF 3.532）

[1]Juncheng Shi, Cheng Wang, Xiaohuan Xi*, Xuebo Yang, Jinliang Wang & Xue Ding. Retrieving fPAR of maize canopy using artificial neural networks with airborne LiDAR and hyperspectral data[J], Remote Sensing Letters, 2020,11:11, 1002-1011   DOI: 10.1080/2150704X.2020.1807647 （SC/SCIE, 四区，2021年IF 2.369）

（供稿：云南省高校资源与环境遥感重点实验室）