College of Chemistry and Environmental Engineering, Shenzhen University, Shenzhen, 518060, China; College of Physics and Optoelectronic Engineering, Shenzhen University, Shenzhen, 518060, China; Geography Department, Hanshan Normal University, Chaozhou, 521041, China.
Key Laboratory for Geo-Environmental Monitoring of Great Bay Area of the Ministry of Natural Resources & Guangdong Key Laboratory of Urban Informatics & Shenzhen Key Laboratory of Spatial Smart Sensing and Services, Shenzhen University, Shenzhen, 518060, China.
Environ Res. 2021 Mar;194:110636. doi: 10.1016/j.envres.2020.110636. Epub 2020 Dec 30.
The degradation of watersheds creates immense pressure on water quality, especially in arid and semiarid regions. Total suspended solids (TSS) provide essential information to water environmental quality assessments. However, the calibration of direct retrieval models requires complicated preparations and further increases uncertainties. Here, we hypothesized that a common remote sensing index (NDVI, normalized difference vegetation index) could be used to estimate TSS concentrations in water due to the effects of canopy cover. To address this hypothesis, we collected 65 water samples from the Ebinur Lake Watershed, northwest China, to investigate the potential relationships between TSS concentrations and Sentinel-2-based NDVI at various scales (100, 200, 300, 400, and 500 m). Subsequently, we established a classical measurement error (CME) model for the estimation of TSS concentrations. The results showed that TSS concentration is negatively related to the NDVI value at all buffer distances. The 300 m scale mean NDVI value showed the most effective explanation of the variations in TSS concentrations (R = 0.83, P-value < 0.001), which indicated that the TSS concentration can be assessed by NDVI. The CME model showed that NDVI values played an important role in the assessment of TSS concentrations in surface water. Furthermore, the results of both leave-one-out cross-validation and the accuracy measure suggested that this specific method is satisfactory. Compared with previous statistical and field monitoring results, the proposed method is promising for cost-effective monitoring of TSS concentrations in water, especially in data-poor watersheds. This specific method may provide the basis for the conservation and management of nonpoint source pollution in arid regions.
流域退化给水质带来巨大压力，尤其在干旱和半干旱地区。总悬浮固体（TSS）为水质环境评估提供了重要信息。但是，直接反演模型的校准需要复杂的准备工作，并且会进一步增加不确定性。在这里，我们假设由于冠层覆盖的影响，一种常见的遥感指数（NDVI，归一化差异植被指数）可以用于估算水中的 TSS 浓度。为了验证这一假设，我们从中国西北部的艾比湖流域采集了 65 个水样，以研究 TSS 浓度与基于 Sentinel-2 的 NDVI 在不同尺度（100、200、300、400 和 500 m）下的潜在关系。随后，我们建立了一个经典的测量误差（CME）模型来估算 TSS 浓度。结果表明，在所有缓冲区距离上，TSS 浓度与 NDVI 值呈负相关。300 m 尺度平均 NDVI 值对 TSS 浓度变化的解释效果最好（R = 0.83，P 值 < 0.001），表明可以通过 NDVI 来评估 TSS 浓度。CME 模型表明，NDVI 值在评估地表水 TSS 浓度方面起着重要作用。此外，留一法交叉验证和精度度量的结果表明，该方法令人满意。与之前的统计和现场监测结果相比，该方法有望成为在数据匮乏的流域中进行 TSS 浓度低成本监测的一种方法。该方法可能为干旱地区非点源污染的保护和管理提供基础。
