文献类型： 外文期刊

作者： Wei, Chuanwen ¹ ; Huang, Jingfeng ¹ ; Mansaray, Lamin R. ¹ ; Li, Zhenhai ⁴ ; Liu, Weiwei ¹ ; Han, Jiahui ¹ ;

作者机构： 1.Zhejiang Univ, Inst Appl Remote Sensing & Informat Technol, Hangzhou 310058, Zhejiang, Peoples R China

2.Zhejiang Univ, Key Lab Agr Remote Sensing & Informat Syst, Hangzhou 310058, Zhejiang, Peoples R China

3.Sierra Leone Agr Res Inst, Dept Agrometeorol & Geoinformat, MLWERC, Tower Hill, Freetown 1313, Pmb, Sierra Leone

4.Beijing Acad Agr & Forestry Sci, Beijing Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China

关键词： winter oilseed rape;leaf area index;high spatial resolution;k-near neighbor;random forest;spectral vegetation index

期刊名称：REMOTE SENSING （ 影响因子：4.848； 五年影响因子：5.353 ）

ISSN： 2072-4292

年卷期： 2017 年 9 卷 5 期

页码：

收录情况： SCI

摘要： Leaf area index (LAI) is a key input in models describing biosphere processes and has widely been used in monitoring crop growth and in yield estimation. In this study, a hybrid inversion method is developed to estimate LAI values of winter oilseed rape during growth using high spatial resolution optical satellite data covering a test site located in southeast China. Based on PROSAIL (coupling of PROSPECT and SAIL) simulation datasets, nine vegetation indices (VIs) were analyzed to identify the optimal independent variables for estimating LAI values. The optimal VIs were selected using curve fitting methods and the random forest algorithm. Hybrid inversion models were then built to determine the relationships between optimal simulated VIs and LAI values (generated by the PROSAIL model) using modeling methods, including curve fitting, k-nearest neighbor (kNN), and random forest regression (RFR). Finally, the mapping and estimation of winter oilseed rape LAI using reflectance obtained from Pleiades-1A, WorldView-3, SPOT-6, and WorldView-2 were implemented using the inversion method and the LAI estimation accuracy was validated using ground-measured datasets acquired during the 2014-2015 growing season. Our study indicates that based on the estimation results derived from different datasets, RFR is the optimal modeling algorithm amidst curve fitting and kNN with R-2 > 0.954 and RMSE < 0.218. Using the optimal VIs, the remote sensing-based mapping of winter oilseed rape LAI yielded an accuracy of R-2 = 0.520 and RMSE = 0.923 (RRMSE = 93.7%). These results have demonstrated the potential operational applicability of the hybrid method proposed in this study for the mapping and retrieval of winter oilseed rape LAI values at field scales using multi-source and high spatial resolution optical remote sensing datasets. Details provided by this high resolution mapping cannot be easily discerned at coarser mapping scales and over larger spatial extents that usually employ lower resolution satellite images. Our study therefore has significant implications for field crop monitoring at local scales, providing relevant data for agronomic practices and precision agriculture.