Chen Yinwen, Qiu Yuanlin, Zhang Zhitao, Zhang Junrui, Chen Ce, Han Jia, Liu Dan
Department of Foreign Languages, Northwest A&F University, Yangling, Shaanxi, China.
College of Water Resources and Architectural Engineering, Northwest A&F University, Yangling, Shaanxi, China.
PeerJ. 2020 Dec 21;8:e10585. doi: 10.7717/peerj.10585. eCollection 2020.
The accurate and timely monitoring of the soil salt content (SSC) at different depths is the prerequisite for the solution to salinization in the arid and semiarid areas. Sentinel-2 has demonstrated significant superiority in SSC inversion for its higher temporal, spatial and spectral resolution, but previous research on SSC inversion with Sentinel-2 mainly focused on the unvegetated surface soil. Based on Sentinel-2 data, this study aimed to build four machine learning models at five depths (0∼20 cm, 20∼40 cm, 40∼60 cm, 0∼40 cm, and 0∼60 cm) in the vegetated area, and evaluate the sensitivity of Sentinel-2 to SSC at different depths and the inversion capability of the models. Firstly, 117 soil samples were collected from Jiefangzha Irrigation Area (JIA) in Hetao Irrigation District (HID), Inner Mongolia, China during August, 2019. Then a set of independent variables (IVs, including 12 bands and 32 spectral indices) were obtained based on the Sentinel-2 data (released by the European Space Agency), and the full subset selection was used to select the optimal combination of IVs at five depths. Finally, four machine learning algorithms, back propagation neural network (BPNN), support vector machine (SVM), extreme learning machine (ELM) and random forest (RF), were used to build inversion models at each depth. The model performance was assessed using adjusted coefficient of determination ( ), root mean square error (RMSE) and mean absolute error (MAE). The results indicated that 20∼40 cm was the optimal depth for SSC inversion. All the models at this depth demonstrated a good fitting ( ≈ 0.6) and a good control of the inversion errors (RMSE < 0.16%, MAE < 0.12%). At the depths of 40∼60 cm and 0∼20 cm the inversion performance showed a slight and a great decrease respectively. The sensitivity of Sentinel-2 to SSC at different depths was as follows: 20∼40 cm > 40∼60 cm > 0∼40 cm > 0∼60 cm > 0∼20 cm. All four machine learning models demonstrated good inversion performance ( > 0.46). RF was the best model with high fitting and inversion accuracy. Its at five depths were between 0.5 to 0.68. The SSC inversion capabilities of all the four models were as follows: RF model > ELM model > SVM model > BPNN model. This study can provide a reference for soil salinization monitoring in large vegetated area.
准确及时地监测不同深度的土壤盐分含量（SSC）是解决干旱和半干旱地区土壤盐碱化问题的前提。哨兵2号卫星在SSC反演方面表现出显著优势，因其具有更高的时间、空间和光谱分辨率，但此前利用哨兵2号进行SSC反演的研究主要集中在无植被覆盖的表层土壤。基于哨兵2号数据，本研究旨在构建植被覆盖区域五个深度（0至20厘米、20至40厘米、40至60厘米、0至40厘米和0至60厘米）的四个机器学习模型，并评估哨兵2号对不同深度SSC的敏感性以及模型的反演能力。首先，2019年8月在中国内蒙古河套灌区解放闸灌区内采集了117个土壤样本。然后，基于欧洲航天局发布的哨兵2号数据获得了一组自变量（IVs，包括12个波段和32个光谱指数），并采用全子集选择法来选择五个深度的IVs最佳组合。最后，使用四种机器学习算法，即反向传播神经网络（BPNN）、支持向量机（SVM）、极限学习机（ELM）和随机森林（RF），在每个深度构建反演模型。利用调整决定系数（ ）、均方根误差（RMSE）和平均绝对误差（MAE）来评估模型性能。结果表明，20至40厘米是SSC反演的最佳深度。该深度下的所有模型均表现出良好的拟合度（ ≈ 0.6）和对反演误差的良好控制（RMSE < 0.16%，MAE < 0.12%）。在40至60厘米和0至20厘米深度处，反演性能分别略有下降和大幅下降。哨兵2号对不同深度SSC的敏感性如下：20至40厘米 > 40至60厘米 > 0至40厘米 > 0至60厘米 > 0至20厘米。所有四个机器学习模型均表现出良好的反演性能（ > 0.46）。随机森林是拟合度和反演精度最高的最佳模型。其在五个深度处的 在0.5至0.68之间。四个模型的SSC反演能力如下：随机森林模型 > 极限学习机模型 > 支持向量机模型 > 反向传播神经网络模型。本研究可为大面积植被覆盖区域的土壤盐碱化监测提供参考。
