Remote Estimation of Biomass in Winter Oilseed Rape (Brassica napus L.) Using Canopy Hyperspectral Data at Different Growth Stages

Yi Ma,Dong Wang,Shenghui Fang,Yi Peng,Yan Gong

doi:10.3390/app9030545

Abstract

The dry aboveground biomass (AGB) is an important parameter in assessing crop growth and predicting yield. This study aims to ascertain the optimal methods for the spectroscopic estimation of winter oilseed rape (WOR) biomass. The different fertilizer-N gradients WOR were planted to collect biomass data and canopy hyperspectral data in two years of field experiments. Correlation analyses and partial least squares regression (PLSR) were performed between canopy hyperspectral data and AGB, and the linear and non-linear regression models simulated the quantitative relation between the vegetation indices (VIs) and AGB at four different growth stages (seeding, bolting, flowering, and pod stage). The results indicated that VIs that were derived from canopy hyperspectral data could estimate AGB accurately: (1) At the seeding and bolting stage, the CIred edge showed excellent performance with the higher accuracy (R2 ranged from 0.60–0.95) as compared to the other six VIs (Green chlorophyll index (CIgreen), normalized difference vegetation index (NDVI), Green normalized difference vegetation index (GNDVI), ratio vegetation index (RVI), DVI, and soil adjusted vegetation index (SAVI)); (2) Correlation analyses and PLSR can effectively extract the feature wavelengths (800 nm and 1200 nm) for biomass estimation. The modified vegetation indices NDVI (800, 1200) significantly improved AGB estimation accuracy (R2 > 0.80, RMSE < 1530 kg/hm2, RPD > 2.3) without saturation phenomenon at the total for four stages, and retained good robustness and reduced the influence of flower and pod for estimating AGB; (3) it was vital to pay more attention to the near-infrared (NIR) bands that could represent WOR growth phenology, and selecting suitable VIs and modeling algorithms could also have a relatively large effect on the success of AGB estimation. The overall results indicated that WOR AGB could be reliably estimated by canopy hyperspectral data, although the plant architecture and coverage of WOR were significantly different during its entire growing period.

Highlights

Winter oilseed rape (WOR) is one of the major commercial crops, being grown mainly in temperate regions [1,2,3]
Correlation analyses were performed between canopy hyperspectral data and aboveground biomass (AGB), and the linear and non-linear regression statistical models simulated the quantitative relation between vegetation indices (VIs) and AGB at different growth stages
The wide range of AGB statistical characteristics showed that the amount of nitrogen fertilizer significantly affected AGB, and the relatively discretized statistical characteristics may influence the accuracy of AGB estimation at different growth stages

Summary

Introduction

Winter oilseed rape (WOR) is one of the major commercial crops, being grown mainly in temperate regions [1,2,3]. It is cultivated mostly for its oil-rich seeds that are widely used for food, biofuel, and medicine [4,5]. The dry aboveground biomass (AGB) is an important parameter in indicating crop growth status, farmers need crop biomass information at different growth stages for guiding their applying fertilizer, and the early estimation of AGB can be utilized for yield prediction [6]. AGB estimation was based on ground destructive sampling, which was both time-consuming and unsuitable for large areas [7,8,9].

Objectives

Methods

Results

Discussion

Conclusion