Abstract
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 m2/m2 and 0.23 m2/m2 for RK and RFRK, respectively), followed by the RF model (RMSE = 0.27 m2/m2), 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 m2/m2 and 0.35 m2/m2 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 m2/m2 to 0.28 m2/m2 for PLSR and from 0.33 m2/m2 to 0.28 m2/m2 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 m2/m2 to 0.32 m2/m2).
Highlights
Leaf area index (LAI) is defined as the one-sided green leaf area per unit ground area [1] and is a crucial parameter driving the biological processes of plants
This study provides useful knowledge regarding the performance of different methods for the quantitative prediction of grassland LAI to guide their applications in ecosystem modeling
The aim of partial least squares regression (PLSR) is to build a linear model as follows: Y “ Xβε where Y is the mean-centered vector of the response variable, X is the mean-centered matrix of the predictive variables, β is the matrix of coefficients, and ε is the matrix of residuals
Summary
Leaf area index (LAI) is defined as the one-sided green leaf area per unit ground area [1] and is a crucial parameter driving the biological processes of plants. The most popular and commonly used approaches are empirical statistical methods, including simple linear regression [8], multiple linear regression [9], and partial least squares regression (PLSR) [10] These methods primarily compute the relationship between LAI and a spectral observation or a combination of spectral observations (vegetation indices, VIs) by relying on statistical or physical knowledge. Due to their empirical nature, these regression models are site and sensor specific, and their performance can be hampered by factors, such as differences in surface properties and sun position, as well as viewing geometry [11,12,13]. The non-linear relationship between remote sensing data and biogeophysical variables endows these flexible models with the ability to combine different data structure features in a non-linear manner and to conform to the requirements of different tasks [18,19]
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