Estimation of Wheat LAI at Middle to High Levels Using Unmanned Aerial Vehicle Narrowband Multispectral Imagery

Xia Yao,Yong Liu,Yan Zhu,Tao Cheng,Yongchao Tian,Qi Chen,Ni Wang

doi:10.3390/rs9121304

Abstract

Leaf area index (LAI) is a significant biophysical variable in the models of hydrology, climatology and crop growth. Rapid monitoring of LAI is critical in modern precision agriculture. Remote sensing (RS) on satellite, aerial and unmanned aerial vehicles (UAVs) has become a popular technique in monitoring crop LAI. Among them, UAVs are highly attractive to researchers and agriculturists. However, some of the UAVs vegetation index (VI)—derived LAI models have relatively low accuracy because of the limited number of multispectral bands, especially as they tend to saturate at the middle to high LAI levels, which are the LAI levels of high-yielding wheat crops in China. This study aims to effectively estimate wheat LAI with UAVs narrowband multispectral image (400–800 nm spectral regions, 10 cm resolution) under varying growth conditions during five critical growth stages, and to provide the potential technical support for optimizing the nitrogen fertilization. Results demonstrated that the newly developed LAI model with modified triangular vegetation index (MTVI2) has better accuracy with higher coefficient of determination (Rc2 = 0.79, Rv2 = 0.80) and lower relative root mean squared error (RRMSE = 24%), and higher sensitivity under various LAI values (from 2 to 7), which will broaden the applied range of the new LAI model. Furthermore, this LAI model displayed stable performance under different sub-categories of growth stages, varieties, and eco-sites. In conclusion, this study could provide effective technical support to precisely monitor the crop growth with UAVs in various crop yield levels, which should prove helpful in family farm for the modern agriculture.

Highlights

Leaf area index (LAI) is a key parameter that determines the photosynthesis, respiration, and transpiration of vegetation [1,2]
The data from Experiment 2 were taken as an example to demonstrate the variation of the canopy spectral reflectance (Figure 3) and the LAI (Table 4) with the five critical growth stages of wheat
The major findings show that MTVI2 (800, 700, 550) has the best performance in monitoring wheat LAI during the five critical growth stages, and the new developed LAI model demonstrated higher stability in the sub-strategies which could broaden the applied range, when compared to the existing LAI model

Summary

Introduction

Leaf area index (LAI) is a key parameter that determines the photosynthesis, respiration, and transpiration of vegetation [1,2]. The common method of monitoring crops LAI is to use some data (vegetation index, degree of coverage, and so on) acquired by remote sensing sensors on ground-based [5], satellite or airborne [6,7] platforms. A recent study [11] compared three types of data from the ground-based ASD Field Spec Pro spectrometer (Analytical Spectral Devices, Boulder, CO, USA), UAVs mounted ADC-Lite multi-spectral sensor, and GaoFen-1 in retrieving soybean LAI. It found that UAVs mostly has the same high accuracy with the ASD hyperspectral spectroradiometer

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Conclusion