Guangxi Forestry Research Institute, Key Laboratory of Central South Fast-Growing Timber Cultivation of Forestry Ministry of China, Nanning, China.
Key Laboratory of Cultivation and Protection for Non-Wood Forest Trees of National Ministry of Education, Central South University of Forestry and Technology, Changsha, China.
PLoS One. 2021 Jun 28;16(6):e0253385. doi: 10.1371/journal.pone.0253385. eCollection 2021.
Effective soil spectral band selection and modeling methods can improve modeling accuracy. To establish a hyperspectral prediction model of soil organic matter (SOM) content, this study investigated a forested Eucalyptus plantation in Huangmian Forest Farm, Guangxi, China. The Ranger and Lasso algorithms were used to screen spectral bands. Subsequently, models were established using four algorithms: partial least squares regression, random forest (RF), a support vector machine, and an artificial neural network (ANN). The optimal model was then selected. The results showed that the modeling accuracy was higher when band selection was based on the Ranger algorithm than when it was based on the Lasso algorithm. ANN modeling had the best goodness of fit, and the model established by RF had the most stable modeling results. Based on the above results, a new method is proposed in this study for band selection in the early phase of soil hyperspectral modeling. The Ranger algorithm can be applied to screen the spectral bands, and ANN or RF can then be selected to construct the prediction model based on different datasets, which is applicable to establish the prediction model of SOM content in red soil plantations. This study provides a reference for the remote sensing of soil fertility in forests of different soil types and a theoretical basis for developing portable equipment for the hyperspectral measurement of SOM content in forest habitats.
有效的土壤光谱波段选择和建模方法可以提高建模精度。为了建立土壤有机质（SOM）含量的高光谱预测模型，本研究调查了中国广西黄冕林场的一片桉树人工林。本研究使用 Ranger 和 Lasso 算法对光谱波段进行筛选。然后，使用偏最小二乘回归、随机森林（RF）、支持向量机和人工神经网络（ANN）四种算法建立模型。最后，选择最优模型。结果表明，基于 Ranger 算法的波段选择比基于 Lasso 算法的波段选择具有更高的建模精度。ANN 模型的拟合度最好，而 RF 模型的建模结果最稳定。基于上述结果，本研究提出了一种新的土壤高光谱建模早期波段选择方法。可以应用 Ranger 算法筛选光谱波段，然后根据不同数据集选择 ANN 或 RF 构建预测模型，适用于建立赤红壤人工林 SOM 含量的预测模型。本研究为不同土壤类型森林土壤肥力的遥感提供了参考，为开发用于森林生境 SOM 含量高光谱测量的便携式设备提供了理论依据。
