Estimation of biomass in wheat using random forest regression algorithm and remote sensing data
Estimation of biomass in wheat using random forest regression algorithm and remote sensing data
复制标题
利用随机森林回归算法和遥感数据估算小麦生物量
DOI：
10.1016/j.cj.2016.01.008
复制
发表时间：
2016-06-01
期刊：
影响因子：
6.6
通讯作者：
Guo, Wenshan
中科院分区：
文献类型：
--
作者：
Wang, Li'ai;Zhou, Xudong;Guo, Wenshan
小麦生物量可以使用适当的光谱植被指数估计。但是，在农田作物管理中，估算的准确性还有待进一步提高。以往的研究主要集中在发展植被指数，但有限的研究存在的建模算法。新兴的随机森林（RF）机器学习算法被认为是回归建模最精确的预测方法之一。本研究的目的是（1）调查RF回归算法用于远程估计小麦生物量的适用性，（2）测试RF回归模型的性能，以及（3）比较RF算法与支持向量回归（SVR）和人工神经网络（ANN）机器学习算法用于小麦生物量估计的性能。利用江苏省小麦试验点的HJ-CCD图像，对小麦拔节期、孕穗期和开花期进行了研究。根据这些图像计算了15个植被指数。在HJ-CCD数据采集过程中，对小麦地上干生物量进行了原位测量。结果表明，RF模型在各阶段对小麦生物量的估计精度均高于SVR和ANN模型，其鲁棒性与SVR相当，但优于ANN。RF算法为中国南方大范围小麦生物量的估计提供了一种有益的探索和预测工具。(C)2016年中国农业科学院作物科学研究所、中国作物学会制作和主办：Elsevier B. V.
Wheat biomass can be estimated using appropriate spectral vegetation indices. However, the accuracy of estimation should be further improved for on-farm crop management. Previous studies focused on developing vegetation indices, however limited research exists on modeling algorithms. The emerging Random Forest (RF) machine-learning algorithm is regarded as one of the most precise prediction methods for regression modeling. The objectives of this study were to (1) investigate the applicability of the RF regression algorithm for remotely estimating wheat biomass, (2) test the performance of the RF regression model, and (3) compare the performance of the RF algorithm with support vector regression (SVR) and artificial neural network (ANN) machine-learning algorithms for wheat biomass estimation. Single HJ-CCD images of wheat from test sites in Jiangsu province were obtained during the jointing, booting, and anthesis stages of growth. Fifteen vegetation indices were calculated based on these images. In-situ wheat above-ground dry biomass was measured during the HJ-CCD data acquisition. The results showed that the RF model produced more accurate estimates of wheat biomass than the SVR and ANN models at each stage, and its robustness is as good as SVR but better than ANN. The RF algorithm provides a useful exploratory and predictive tool for estimating wheat biomass on a large scale in Southern China. (C) 2016 Crop Science Society of China and Institute of Crop Science, CAAS. Production and hosting by Elsevier B.V.