DataSheet_1_Remote sensing for field pea yield estimation: A study of multi-scale data fusion approaches in phenomics.docx

IntroductionRemote sensing using unmanned aerial systems (UAS) are prevalent for phenomics and precision agricultural applications. The high-resolution data for these applications can provide useful spectral characteristics of crops associated with performance traits such as seed yield. With the recent availability of high-resolution satellite imagery, there has been growing interest in using this technology for plot-scale remote sensing applications, particularly those related to breeding programs. This study compared the features extracted from high-resolution satellite and UAS multispectral imagery (visible and near-infrared) to predict the seed yield from two diverse plot-scale field pea yield trials (advanced breeding and variety testing) using the random forest model.
MethodsThe multi-modal (spectral and textural features) and multi-scale (satellite and UAS) data fusion approaches were evaluated to improve seed yield prediction accuracy across trials and time points. These approaches included both image fusion, such as pan-sharpening of satellite imagery with UAS imagery using intensity-hue-saturation transformation and additive wavelet luminance proportional approaches, and feature fusion, which involved integrating extracted spectral features. In addition, we also compared the image fusion approach to high-definition satellite data with a resolution of 0.15 m/pixel. The effectiveness of each approach was evaluated with data at both individual and combined time points.
Results and discussionThe major findings can be summarized as follows: (1) the inclusion of the texture features did not improve the model performance, (2) the performance of the model using spectral features from satellite imagery at its original resolution can provide similar results as UAS imagery, with variation depending on the field pea yield trial under study and the growth stage, (3) the model performance improved after applying multi-scale, multiple time point feature fusion, (4) the features extracted from the pan-sharpened satellite imagery using intensity-hue-saturation transformation (image fusion) showed better model performance than those with original satellite imagery or high definition imagery, and (5) the green normalized difference vegetation index and transformed triangular vegetation index were identified as key features contributing to high model performance across trials and time points. These findings demonstrate the potential of high-resolution satellite imagery and data fusion approaches for plot-scale phenomics applications.

引言
基于无人机系统（UAS）的遥感技术在表型组学与精准农业应用中已十分普及。此类应用所需的高分辨率数据可获取与作物产量性状（如籽粒产量）相关的有效光谱特征。随着高分辨率卫星影像的可及性不断提升，学界对将该技术应用于样地尺度遥感任务的兴趣日益浓厚，尤其是与育种项目相关的应用场景。本研究采用随机森林模型，对比了从高分辨率卫星及无人机多光谱影像（可见光与近红外波段）中提取的特征，以预测两类不同样地尺度田间豌豆产量试验（高级育种与品种测试试验）的籽粒产量。

方法
为提升不同试验与时间节点下的籽粒产量预测精度，本研究评估了多模态（光谱与纹理特征）及多尺度（卫星与无人机）数据融合方案。此类方案涵盖图像融合与特征融合两类：图像融合方面，采用强度-色调-饱和度变换与加性小波亮度比例法实现卫星影像与无人机影像的全色锐化；特征融合方面，则整合提取得到的光谱特征。此外，本研究还将图像融合方案与分辨率为0.15米/像素的高清卫星数据进行了对比。各方案的有效性通过单时间点及组合时间点的数据进行评估。

结果与讨论
主要研究结论可总结如下：（1）引入纹理特征并未提升模型性能；（2）采用原始分辨率卫星影像光谱特征的模型，其表现可与无人机影像相当，具体效果因所研究的田间豌豆产量试验及生育期而异；（3）应用多尺度、多时间点特征融合后，模型性能得到提升；（4）通过强度-色调-饱和度变换（图像融合）对卫星影像进行全色锐化后提取的特征，其模型表现优于原始卫星影像或高清卫星数据对应的特征；（5）绿波段归一化差分植被指数与变换三角植被指数被确定为在不同试验与时间节点下均能提升模型性能的关键特征。上述研究结果证明了高分辨率卫星影像及数据融合方案在样地尺度表型组学应用中的潜力。