Han Liang, Yang Guijun, Dai Huayang, Xu Bo, Yang Hao, Feng Haikuan, Li Zhenhai, Yang Xiaodong
Key Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture, Beijing Research Center for Information Technology in Agriculture, Beijing, 100097 China.
2College of Architecture and Geomatics Engineering, Shanxi Datong University, Datong, 037003 China.
Plant Methods. 2019 Feb 4;15:10. doi: 10.1186/s13007-019-0394-z. eCollection 2019.
Above-ground biomass (AGB) is a basic agronomic parameter for field investigation and is frequently used to indicate crop growth status, the effects of agricultural management practices, and the ability to sequester carbon above and below ground. The conventional way to obtain AGB is to use destructive sampling methods that require manual harvesting of crops, weighing, and recording, which makes large-area, long-term measurements challenging and time consuming. However, with the diversity of platforms and sensors and the improvements in spatial and spectral resolution, remote sensing is now regarded as the best technical means for monitoring and estimating AGB over large areas.
In this study, we used structural and spectral information provided by remote sensing from an unmanned aerial vehicle (UAV) in combination with machine learning to estimate maize biomass. Of the 14 predictor variables, six were selected to create a model by using a recursive feature elimination algorithm. Four machine-learning regression algorithms (multiple linear regression, support vector machine, artificial neural network, and random forest) were evaluated and compared to create a suitable model, following which we tested whether the two sampling methods influence the training model. To estimate the AGB of maize, we propose an improved method for extracting plant height from UAV images and a volumetric indicator (i.e., BIOVP). The results show that (1) the random forest model gave the most balanced results, with low error and a high ratio of the explained variance for both the training set and the test set. (2) BIOVP can retain the largest strength effect on the AGB estimate in four different machine learning models by using importance analysis of predictors. (3) Comparing the plant heights calculated by the three methods with manual ground-based measurements shows that the proposed method increased the ratio of the explained variance and reduced errors.
These results lead us to conclude that the combination of machine learning with UAV remote sensing is a promising alternative for estimating AGB. This work suggests that structural and spectral information can be considered simultaneously rather than separately when estimating biophysical crop parameters.
地上生物量（AGB）是田间调查的一项基本农艺参数，常用于指示作物生长状况、农业管理措施的效果以及地上和地下的碳固存能力。获取AGB的传统方法是采用破坏性采样方法，需要人工收割作物、称重并记录，这使得大面积、长期测量具有挑战性且耗时。然而，随着平台和传感器的多样性以及空间和光谱分辨率的提高，遥感现在被视为大面积监测和估算AGB的最佳技术手段。
在本研究中，我们利用无人机（UAV）遥感提供的结构和光谱信息，结合机器学习来估算玉米生物量。在14个预测变量中，通过使用递归特征消除算法选择了6个来创建模型。评估并比较了四种机器学习回归算法（多元线性回归、支持向量机、人工神经网络和随机森林）以创建合适的模型，随后我们测试了两种采样方法是否会影响训练模型。为了估算玉米的AGB，我们提出了一种从无人机图像中提取株高的改进方法和一个体积指标（即BIOVP）。结果表明：（1）随机森林模型给出了最平衡的结果，训练集和测试集的误差均较低，解释方差比例较高。（2）通过预测变量重要性分析，BIOVP在四种不同机器学习模型中对AGB估算的强度效应最大。（3）将三种方法计算的株高与地面人工测量值进行比较表明，所提出的方法提高了解释方差比例并减少了误差。
这些结果使我们得出结论，机器学习与无人机遥感相结合是估算AGB的一种有前景的替代方法。这项工作表明，在估算作物生物物理参数时，可以同时考虑结构和光谱信息，而不是分别考虑。
