College of Environmental Science and Engineering / Sino-Canada Joint R&D Centre for Water and Environmental Safety, Nankai University, Tianjin, 300350, China.
College of Environmental Science and Engineering / Sino-Canada Joint R&D Centre for Water and Environmental Safety, Nankai University, Tianjin, 300350, China.
Environ Pollut. 2021 Nov 1;288:117734. doi: 10.1016/j.envpol.2021.117734. Epub 2021 Jul 6.
Dissolved oxygen (DO) is an effective indicator for water pollution. However, since DO is a non-optically active parameter and has little impact on the spectrum captured by satellite sensors, research on estimating DO by remote sensing at multiple spatiotemporal scales is limited. In this study, the support vector regression (SVR) models were developed and validated using the remote sensing reflectance derived from both Landsat and Moderate Resolution Imaging Spectroradiometer (MODIS) data and synchronous DO measurements (N = 188) and water temperature of Lake Huron and three other inland waterbodies (N = 282) covering latitude between 22-45 °N. Using the developed models, spatial distributions of the annual and monthly DO variability since 1984 and the annual monthly DO variability since 2000 in Lake Huron were reconstructed for the first time. The impacts of five climate factors on long-term DO trends were analyzed. Results showed that the developed SVR-based models had good robustness and generalization (average R = 0.91, root mean square percentage error = 2.65%, mean absolute percentage error = 4.21%), and performed better than random forest and multiple linear regression. The monthly DO estimates by Landsat and MODIS data were highly consistent (average R = 0.88). From 1984 to 2019, the oxygen loss in Lake Huron was 6.56%. Air temperature, incident shortwave radiation flux density, and precipitation were the main climate factors affecting annual DO of Lake Huron. This study demonstrated that using SVR-based models, Landsat and MODIS data could be used for long-term DO retrieval at multiple spatial and temporal scales. As data-driven models, combining spectrum and water temperature as well as extending the training set to cover more DO conditions could effectively improve model robustness and generalization.
溶解氧（DO）是水污染的有效指标。然而，由于 DO 是非光活性参数，对卫星传感器捕获的光谱影响不大，因此在多个时空尺度上通过遥感估算 DO 的研究受到限制。本研究利用 Landsat 和中分辨率成像光谱仪（MODIS）数据以及同步 DO 测量（N=188）和水温数据（N=282），开发并验证了支持向量回归（SVR）模型，这些数据来自于五大湖（休伦湖）和其他三个内陆水体，覆盖北纬 22-45°。利用所建立的模型，首次重建了自 1984 年以来休伦湖的年度和月度 DO 变化的空间分布，以及自 2000 年以来的年度和月度 DO 变化。分析了五个气候因素对长期 DO 趋势的影响。结果表明，所建立的基于 SVR 的模型具有良好的稳健性和泛化能力（平均 R=0.91，均方根百分比误差=2.65%，平均绝对百分比误差=4.21%），并且优于随机森林和多元线性回归。Landsat 和 MODIS 数据的月度 DO 估算值高度一致（平均 R=0.88）。从 1984 年到 2019 年，休伦湖的氧气损失了 6.56%。空气温度、入射短波辐射通量密度和降水量是影响休伦湖年 DO 的主要气候因素。本研究表明，利用基于 SVR 的模型和 Landsat 以及 MODIS 数据可以在多个时空尺度上进行长期 DO 反演。作为数据驱动的模型，结合光谱和水温，以及扩展训练集以覆盖更多 DO 条件，可以有效地提高模型的稳健性和泛化能力。
