Wang Nan, Wang Zhiguo, Huang Pingping, Zhai Yongguang, Yang Xiangli, Su Jianyu
College of Information Engineering, Inner Mongolia University of Technology, Hohhot 010080, China.
Inner Mongolia Key Laboratory of Radar Technology and Application, Hohhot 010051, China.
Sensors (Basel). 2024 Jun 27;24(13):4181. doi: 10.3390/s24134181.
As one of the important lakes in the "One Lake and Two Seas" of the Inner Mongolia Autonomous Region, the monitoring of water quality in Lake Daihai has attracted increasing attention, and the concentration of chlorophyll-a directly affects the water quality, making the monitoring of chlorophyll-a concentration in Lake Daihai particularly crucial. Traditional methods of monitoring chlorophyll-a concentration are not only inefficient but also require significant human and material resources. Remote sensing technology has the advantages of wide coverage and short update cycles. For lakes such as Daihai with a high salinity content, salinity is considered a key factor when inverting the concentration of chlorophyll-a. In this study, machine learning models, including model stacking from ensemble learning, a ridge regression model, and a random forest model, were constructed. After comparing the training accuracy of the three models on Zhuhai-1 satellite data, the random forest model, which had the highest accuracy, was selected as the final training model. By comparing the accuracy changes before and after adding salinity factors to the random forest model, a high-precision model for inverting chlorophyll-a concentration in hypersaline lakes was obtained. The research results show that, without considering the salinity factor, the root mean square error (RMSE) of the model was 0.056, and the coefficient of determination (R) was 0.64, indicating moderate model performance. After adding the salinity factor, the model accuracy significantly improved: the RMSE decreased to 0.047, and the R increased to 0.92. This study provides a solid basis for the application of remote sensing technology in hypersaline aquatic environments, confirming the importance of considering salinity when estimating chlorophyll-a concentration in hypersaline waters. This research helps us gain a deeper understanding of the water quality and ecosystem evolution in Daihai Lake.
作为内蒙古自治区“一湖两海”中的重要湖泊之一，岱海水质监测受到越来越多的关注，叶绿素a浓度直接影响水质，使得岱海叶绿素a浓度监测尤为关键。传统的叶绿素a浓度监测方法不仅效率低下，而且需要大量人力和物力。遥感技术具有覆盖范围广、更新周期短的优势。对于盐度较高的湖泊如岱海，在反演叶绿素a浓度时，盐度被视为关键因素。本研究构建了包括集成学习中的模型堆叠、岭回归模型和随机森林模型在内的机器学习模型。在比较这三种模型对珠海一号卫星数据的训练精度后，选择精度最高的随机森林模型作为最终训练模型。通过比较随机森林模型添加盐度因子前后的精度变化，得到了高盐湖泊叶绿素a浓度反演的高精度模型。研究结果表明，在不考虑盐度因子时，模型的均方根误差（RMSE）为0.056，决定系数（R）为0.64，表明模型性能中等。添加盐度因子后，模型精度显著提高：RMSE降至0.047，R升至0.92。本研究为遥感技术在高盐水生环境中的应用提供了坚实基础，证实了在估算高盐水体叶绿素a浓度时考虑盐度的重要性。本研究有助于我们更深入地了解岱海湖的水质和生态系统演变。
