Predicting Grassland Leaf Area Index in the Meadow Steppes of Northern China: A Comparative Study of Regression Approaches and Hybrid Geostatistical Methods

文献类型： 外文期刊

第一作者： Li, Zhenwang

作者： Li, Zhenwang;Tang, Huan;Yang, Fan;Chen, Baorui;Wang, Xu;Xin, Xiaoping;Wang, Jianghao;Ge, Yong;Huang, Chengquan

作者机构：

关键词： leaf area index;grassland;predict;geostatistics;regression;remote sensing

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： 2016 年 8 卷 8 期

页码：

收录情况： SCI

摘要： Leaf area index (LAI) is a key parameter used to describe vegetation structures and is widely used in ecosystem biophysical process and vegetation productivity models. Many algorithms have been developed for the estimation of LAI based on remote sensing images. Our goal was to produce accurate and timely predictions of grassland LAI for the meadow steppes of northern China. Here, we compare the predictive power of regression approaches and hybrid geostatistical methods using Chinese Huanjing (HJ) satellite charge coupled device (CCD) data. The regression methods evaluated include partial least squares regression (PLSR), artificial neural networks (ANNs) and random forests (RFs). The two hybrid geostatistical methods were regression kriging (RK) and random forests residuals kriging (RFRK). The predictions were validated for different grassland types and different growing stages, and their performances were also examined by adding several groups of vegetation indices (VIs). The two hybrid geostatistical models (RK and RFRK) yielded the most accurate predictions (root mean squared error (RMSE) = 0.21 m(2)/m(2) and 0.23 m(2)/m(2) for RK and RFRK, respectively), followed by the RF model (RMSE = 0.27 m(2)/m(2)), which was the most accurate among the regression models. These three models also exhibited the best temporal performance across the duration of the growing season. The PLSR and ANN models were less accurate (RMSE = 0.33 m(2)/m(2) and 0.35 m(2)/m(2) for ANN and PLSR, respectively), and the PLSR model performed the worst (exhibiting varied temporal performance and unreliable prediction accuracy that was susceptible to ground conditions). By adding VIs to the predictor variables, the predictions of the PLSR and ANN models were obviously improved (RMSE improved from 0.35 m(2)/m(2) to 0.28 m(2)/m(2) for PLSR and from 0.33 m(2)/m(2) to 0.28 m(2)/m(2) for ANN); the RF and RFRK models did not generate more accurate predictions and the performance of the RK model declined (RMSE decreased from 0.21 m(2)/m(2) to 0.32 m(2)/m(2)).

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