| 结合植被指数与纹理特征的玉米冠层FAPAR遥感估算研究 |
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| 引用本文： | 王思宇,聂臣巍,余汛,邵明超,王梓旭,努热曼古丽·托乎提,刘亚东,程明瀚,官云兰,金秀良.结合植被指数与纹理特征的玉米冠层FAPAR遥感估算研究[J].作物杂志,2021,37(2):183-7201. |
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| 作者姓名： | 王思宇 聂臣巍 余汛 邵明超 王梓旭 努热曼古丽·托乎提 刘亚东 程明瀚 官云兰 金秀良 |
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| 作者单位： | 1东华理工大学测绘工程学院,330013,江西南昌2中国农业科学院作物科学研究所,100081,北京3河南理工大学,454003,河南焦作4河北工程大学地球科学与工程学院,056006,河北邯郸5长安大学地球科学与资源学院,710061,陕西西安6中国地质大学（武汉）地球科学学院,430074,湖北武汉7河海大学农业工程学院,210098,江苏南京 |
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| 基金项目： | 国家自然科学基金-面上项目(42071426);中国农业科学院基本科研业务费专项院级统筹项目(Y2020YJ07);中国农业科学院科技创新工程和基本科研业务费专项项目(ICS2020YJ01BX) |
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| 摘 要： | 光合有效辐射吸收比率（fraction of absorbed photosynthetically active radiation,FAPAR）是反映作物产量的重要参数之一。无人机遥感能够快速无损地获取高分辨率植被冠层光谱信息,已成为进行物理化参数反演的重要手段。以不同播期玉米为研究对象,基于无人机搭载多光谱传感器,提取植被指数与植被纹理特征,使用偏最小二乘（partial least squares regression,PLSR）方法将二者结合反演玉米FAPAR,并与传统单独使用植被指数或植被纹理特征反演植被FAPAR的方法进行比较。结果表明：使用传统方法单独利用植被指数反演FAPAR（验证RMSE最低为7.33×10-2,rRMSE最低为8.66%）的效果比单独利用纹理特征反演FAPAR（验证RMSE最低为9.50×10-2,rRMSE最低为11.23%）的精度更高;使用PLSR方法单独利用植被指数或纹理特征估算FAPAR的效果比传统方法精度更高（植被指数与纹理特征的验证RMSE最低分别为6.77×10-2和5.24×10-2,rRMSE最低分别为8.01%和6.19%）;使用PLSR方法将植被指数与纹理特征相结合估算FAPAR（验证RMSE最低为4.72×10-2,rRMSE最低为5.57%）的效果比单独使用植被指数或纹理特征估算FAPAR的精度更高。综上,使用PLSR方法将植被指数和植被纹理特征相结合来反演玉米冠层FAPAR可行,为作物FAPAR遥感反演研究提供了新的思路。
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| 关 键 词： | FAPAR 多光谱影像 植被指数 纹理特征 PLSR |
| 收稿时间： | 2020-11-15 |
Maize Canopy FAPAR Remote Sensing Estimation Combining Vegetation Indexes and Texture Characteristics |
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| Wang Siyu,Nie Chenwei,Yu Xun,Shao Mingchao,Wang Zixu,Nuremanguli· Tuohuti,Liu Yadong,Cheng Minghan,Guan Yunlan,Jin Xiuliang.Maize Canopy FAPAR Remote Sensing Estimation Combining Vegetation Indexes and Texture Characteristics[J].Crops,2021,37(2):183-7201. |
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| Authors: | Wang Siyu Nie Chenwei Yu Xun Shao Mingchao Wang Zixu Nuremanguli· Tuohuti Liu Yadong Cheng Minghan Guan Yunlan Jin Xiuliang |
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| Abstract: | The fraction of absorbed photosynthetically active radiation (FAPAR) is one of the most important parameters reflecting the crop yields. The unmanned aerial vehicle (UAV) remote sensing, which can obtain high-resolution vegetation canopy spectral information quickly and nondestructively, has become an important method of inverting the physicochemical parameters of crops. Taking maize with different sowing dates as the research object, this research extracted vegetation indexes (VIs) and texture features based on the UAV multispectral images. After that, a partial least squares regression (PLSR) method was used to invert maize FAPAR combining the two indexes, and compared with the traditional methods using VIs or texture features alone. The results showed that the accuracy of FAPAR inversion by using VIs alone (the validated RMSE is as low as 0.0733, and the validated rRMSE is as low as 8.66%) was higher than that by using texture feature alone (the validated RMSE is as low as 0.0950, and the validated rRMSE is as low as 11.23%). Moreover, the PLSR method was more accurate than the traditional methods in estimating FAPAR by VIs or texture features alone (the validated RMSE of VIs and texture features is as low as 0.0677 and 0.0524, and the validated rRMSE is as low as 8.01% and 6.19%). The accuracy of combining VIs and texture features using the PLSR method (the validated RMSE is as low as 0.0472, and the rRMSE is as low as 5.57%) was higher than that using VIs or texture features alone. The research indicated that it is feasible to invert FAPAR of maize canopy by combining VIs and texture features using the PLSR method, which provides a new idea for crop FAPAR remote sensing inversion. |
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| Keywords: | FAPAR Multispectral images Vegetation index Texture features PLSR |
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