Remote Estimation of Vegetation Fraction and Flower Fraction in Oilseed Rape with Unmanned Aerial Vehicle Data

Shenghui Fang,Yan Gong,Wenchao Tang,Yi Peng,Can Dai,Kan Liu,Ruhui Chai

doi:10.3390/rs8050416

Abstract

This study developed an approach for remote estimation of Vegetation Fraction (VF) and Flower Fraction (FF) in oilseed rape, which is a crop species with conspicuous flowers during reproduction. Canopy reflectance in green, red, red edge and NIR bands was obtained by a camera system mounted on an unmanned aerial vehicle (UAV) when oilseed rape was in the vegetative growth and flowering stage. The relationship of several widely-used Vegetation Indices (VI) vs. VF was tested and found to be different in different phenology stages. At the same VF when oilseed rape was flowering, canopy reflectance increased in all bands, and the tested VI decreased. Therefore, two algorithms to estimate VF were calibrated respectively, one for samples during vegetative growth and the other for samples during flowering stage. The results showed that the Visible Atmospherically Resistant Index (VARIgreen) worked most accurately for estimating VF in flower-free samples with an Root Mean Square Error (RMSE) of 3.56%, while the Enhanced Vegetation Index (EVI2) was the best in flower-containing samples with an RMSE of 5.65%. Based on reflectance in green and NIR bands, a technique was developed to identify whether a sample contained flowers and then to choose automatically the appropriate algorithm for its VF estimation. During the flowering season, we also explored the potential of using canopy reflectance or VIs to estimate FF in oilseed rape. No significant correlation was observed between VI and FF when soil was visible in the sensor’s field of view. Reflectance at 550 nm worked well for FF estimation with coefficient of determination (R2) above 0.6. Our model was validated in oilseed rape planted under different nitrogen fertilization applications and in different phenology stages. The results showed that it was able to predict VF and FF accurately in oilseed rape with RMSE below 6%.

Highlights

The results showed that it was able to predict Vegetation Fraction (VF) and Flower Fraction (FF) accurately in oilseed rape with Root Mean Square Error (RMSE) below 6%
Since the intercepted radiation is closely related to foliage cover [5], VF is widely used in the modeling fraction of Absorbed Photosynthetically-Active Radiation that directly relates with canopy photosynthesis capacity and vegetation productivity [6]
We found that the data of the group with higher Vegetation Indices (VI) values were samples from vegetative growth when FF = 0 (Figure 3c); while samples of the other group were in the flowering stage when bright yellow flowers were obvious in the sensor’s field of view (Figure 3d)

Summary

Introduction

Vegetation Fraction (VF) is defined as the vertical projection of the crown or shoot area of vegetation to the ground surface expressed as the fraction or the percent of the reference area [1,2].In the growth of crops, VF is one of the principal variables that can indicate many of cropRemote Sens. 2016, 8, 416; doi:10.3390/rs8050416 www.mdpi.com/journal/remotesensingRemote Sens. 2016, 8, 416 biophysical characteristics, such as plant density, phenology, Leaf Area Index (LAI) and yield [3,4].Since the intercepted radiation is closely related to foliage cover [5], VF is widely used in the modeling fraction of Absorbed Photosynthetically-Active Radiation (fAPAR) that directly relates with canopy photosynthesis capacity and vegetation productivity [6]. Bare soil and vegetation lines were defined in a spectral space constructed by two bands, and VF was estimated based on the distances of the sample from the defined soil line and vegetation line. This approach was successfully applied for VF estimation in maize, soybean and wheat [2,10]. Vegetation Indices (VI), calculated based on mathematical combinations of spectral reflectance at different wavelengths, are widely used to assess vegetation growth situations (e.g., [13,14,15]).

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