Estimation of Crop Growth Parameters Using UAV-Based Hyperspectral Remote Sensing Data.

Huilin Tao,Liangji Xu,Mengke Miao,Huiling Long,Haikuan Feng,Jibo Yue,Zhenhai Li,Guijun Yang,Xiaodong Yang,Lingling Fan

doi:10.3390/s20051296

Abstract

Above-ground biomass (AGB) and the leaf area index (LAI) are important indicators for the assessment of crop growth, and are therefore important for agricultural management. Although improvements have been made in the monitoring of crop growth parameters using ground- and satellite-based sensors, the application of these technologies is limited by imaging difficulties, complex data processing, and low spatial resolution. Therefore, this study evaluated the use of hyperspectral indices, red-edge parameters, and their combination to estimate and map the distributions of AGB and LAI for various growth stages of winter wheat. A hyperspectral sensor mounted on an unmanned aerial vehicle was used to obtain vegetation indices and red-edge parameters, and stepwise regression (SWR) and partial least squares regression (PLSR) methods were used to accurately estimate the AGB and LAI based on these vegetation indices, red-edge parameters, and their combination. The results show that: (i) most of the studied vegetation indices and red-edge parameters are significantly highly correlated with AGB and LAI; (ii) overall, the correlations between vegetation indices and AGB and LAI, respectively, are stronger than those between red-edge parameters and AGB and LAI, respectively; (iii) Compared with the estimations using only vegetation indices or red-edge parameters, the estimation of AGB and LAI using a combination of vegetation indices and red-edge parameters is more accurate; and (iv) The estimations of AGB and LAI obtained using the PLSR method are superior to those obtained using the SWR method. Therefore, combining vegetation indices with red-edge parameters and using the PLSR method can improve the estimation of AGB and LAI.

Highlights

Crop growth can reflect soil conditions, nutritional status [1]
As shown in the table, for most of the Vegetation indices (VIs) and red-edge parameters, the correlations with Above-ground biomass (AGB) and the leaf area index (LAI) are significant at the p < 0.01 level
VIs and the red-edge parameters are significantly different between the four growth stages; overall, the correlations between both AGB and the LAI and the VIs and red-edge parameters are stronger in the flowering stage than in the other three growth stages

Summary

Introduction

Crop growth can reflect soil conditions, nutritional status [1]. Above-ground biomass (AGB)and the leaf area index (LAI) are two of the main crop growth parameters, and play important roles in monitoring crop growth in agricultural management [2,3]. The timely and accurate estimation of crop growth parameters can provide a strong basis for the formulation of timely agricultural policies and food trade, and can allow the estimation of the loss of crop yield caused by meteorological. Remote sensing technology has attracted attention as a way to better estimate crop growth parameters due to its ability to provide timely, dynamic, macro-scale observations. This method involves estimating crop growth parameters using spectral data of the crop canopy [7,8], and has been widely applied [9,10,11,12]. For ground-based remote sensing, non-imaging ground-feature spectrometers are usually used. Satellites can acquire image data over large areas, providing data with very high spatial resolution; they have long operating cycles, are affected by changes in weather and cloud cover, and high maintenance and operation costs

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Discussion

Conclusion