Modeling Winter Wheat Leaf Area Index and Canopy Water Content With Three Different Approaches Using Sentinel-2 Multispectral Instrument Data

Abstract

Leaf area index (LAI) and canopy water content (CWC) are important variables for monitoring crop growth and drought, which can be estimated from remotely sensed data. The goal of this study was to evaluate the suitability of the Sentinel-2 multispectral instrument (S2 MSI) data for winter wheat LAI and CWC estimation with three different inversion approaches in the main farming region in North China. During the winter wheat key growth stages in 2017, 22 fields, each with five independent samples, the total number of sample plot is 110, were designed for experimental measurements. In this study, the LAI and CWC were retrieved separately using empirical models through different spectral indices, neural network (NN) algorithms, and lookup table (LUT) methods based on the PROSAIL model. The accuracies of the estimated LAI and CWC were assessed through in situ measurements. The results show that the LUT inversion approach was more suitable for LAI and CWC estimation than the spectral index-based empirical model or the NN algorithm. With the LUT approach, LAI was obtained with a root mean square error (RMSE) of $\text{0.43}\,{\text{m}}^{\text{2}} \cdot {\text{m}}^{-\text{2}}$ and a relative RMSE (RRMSE) of 11% using seven S2 MSI bands, and CWC was obtained with an RMSE of $\text{0.41}\,{\text{kg}} \cdot {\text{m}}^{-\text{2}}$ , and an RRMSE of 32% using five S2 MSI bands. In all the three methods, S2 MSI was sensitive to LAI variation and able to reach higher accuracies when red edge bands were used. However, CWC inversion was still a challenge using S2 MSI data.