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首页> 外文期刊>Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of >Modeling Winter Wheat Leaf Area Index and Canopy Water Content With Three Different Approaches Using Sentinel-2 Multispectral Instrument Data
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Modeling Winter Wheat Leaf Area Index and Canopy Water Content With Three Different Approaches Using Sentinel-2 Multispectral Instrument Data

机译：使用Sentinel-2多光谱仪器数据建模冬小麦叶面积指数和冠层水上的三种不同方法

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摘要

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 0.43m(2).m(-2) and a relative RMSE (RRMSE) of 11% using seven S2MSI bands, and CWC was obtained with an RMSE of 0.41 kg.m(-2), and an RRMSE of 32% using five S2 MSI bands. In all the three methods, S2MSI 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.
机译：叶面积指数（LAI）和冠层水含量（CWC）是监测作物生长和干旱的重要变量，可以从远程感测数据估算。本研究的目的是评估冬小麦赖莱及CWC估计的Sentinel-2多光谱仪器（S2 MSI）数据以及华北主要农业地区的三种不同的反演方法的适用性。在2017年冬小麦重点生长阶段，22个田地，每种具有五个独立样品，样品图总数为110，设计用于实验测量。在本研究中，通过基于扶手模型的不同光谱索引，神经网络（NN）算法和查找表（LUT）方法单独使用实证模型来检索LAI和CWC。通过原位测量评估估计的LAI和CWC的准确性。结果表明，LUT反转方法更适合LAI和CWC估计，而不是基于光谱索引的经验模型或NN算法。利用LUT方法，使用0.43m（2）°M（-2）的根均方误差（RMSE）获得LAI，使用七个S2MSI带的相对RMSE（RRMSE）和11％的相对RMSE（RRMSE），并用CWC获得RMSE为0.41 kg.m（-2），使用五个S2 MSI带的RRMSE为32％。在所有三种方法中，S2MSI对LAI变化敏感，并且当使用红色边缘带时能够达到更高的准确性。但是，CWC反转仍然是使用S2 MSI数据的挑战。

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