Xia Ke, Wu Taixia, Li Xintao, Wang Shudong, Shen Qiang
School of Earth Sciences and Engineering, Hohai University, Nanjing 211100, China.
School of Earth Sciences and Engineering, Hohai University, Nanjing 211100, China.
Water Res. 2024 May 15;255:121560. doi: 10.1016/j.watres.2024.121560. Epub 2024 Mar 31.
The Forel Ule water color index (FUI) based on satellite inversion can characterize the comprehensive characteristics of water quality on a large spatiotemporal scale. The high-frequency observations and rich historical data of the MODIS surface reflectance product (MODIS-500 m) provide important data support for monitoring the FUI of inland lakes. However, MODIS-500 m has only three bands in the visible light range, resulting in significant uncertainty in FUI inversion. To address this problem, this study developed an improved FUI inversion model using 500 synthetic spectra covering natural waters. The model, with a performance threshold set at 170° (FUI = 8), used a segmented algorithm across the entire color space. Validated with on-site measurement datasets (3500 samples), the model exhibited excellent performance, with mean relative error (MRE) and root mean square error (RMSE) of 1.71 % and 3.63°, respectively. Compared to existing models, it was more suitable for long-term FUI inversion in various types of lakes, particularly in eutrophic regions. Subsequently, the model was applied to MODIS-500 m observations from 2000 to 2022, revealing the spatiotemporal dynamics of FUI in 180 large lakes and reservoirs (hereinafter referred to as lakes) in China. The results indicated that the long-term mean FUI in the study area was 9, with 7 and 12 in the western and eastern regions, respectively, showing a distinct spatial distribution of "blue in the west and green in the east." The mean change rate of hue angle for all lakes was -0.085°/yr, showing an overall decreasing trend. Environmental factors' relative contributions to long-term water color changes in each lake region were quantified using the multiple general linear model (GLM). Although each lake region exhibited different driving forces, they were primarily influenced by vegetation, lake surface area, and anthropogenic factors. Additionally, the seasonal types of lake water color were analyzed, with the west and east showing opposite patterns, reflecting the significant influence of topographic features and seasonal changes in climate on water color. The research results provide techniques for accurate inversion of FUI using MODIS-500 m data, while deepening the understanding of long-term water color changes in inland lakes in China.
基于卫星反演的福尔勒乌勒水色指数（FUI）能够在大时空尺度上表征水质的综合特征。中分辨率成像光谱仪（MODIS）地表反射率产品（MODIS - 500米）的高频观测数据和丰富的历史数据，为内陆湖泊FUI监测提供了重要的数据支持。然而，MODIS - 500米在可见光范围内仅有三个波段，导致FUI反演存在显著的不确定性。为解决这一问题，本研究利用涵盖天然水体的500条合成光谱，开发了一种改进的FUI反演模型。该模型在整个颜色空间采用分段算法，性能阈值设定为170°（FUI = 8）。通过现场测量数据集（3500个样本）验证，该模型表现出优异的性能，平均相对误差（MRE）和均方根误差（RMSE）分别为1.71%和3.63°。与现有模型相比，它更适合于各类湖泊的长期FUI反演，特别是在富营养化区域。随后，该模型应用于2000年至2022年的MODIS - 500米观测数据，揭示了中国180个大型湖泊和水库（以下简称湖泊）FUI的时空动态。结果表明，研究区域的长期平均FUI为9，西部地区和东部地区分别为7和12，呈现出“西蓝东绿”的明显空间分布。所有湖泊的色调角平均变化率为 - 0.085°/年，呈整体下降趋势。利用多元一般线性模型（GLM）量化了环境因素对各湖泊区域长期水色变化的相对贡献。尽管每个湖泊区域表现出不同的驱动因素，但它们主要受植被、湖泊表面积和人为因素的影响。此外，分析了湖泊水色的季节类型，西部和东部呈现相反的模式，反映了地形特征和气候季节变化对水色的显著影响。研究结果为利用MODIS - 500米数据准确反演FUI提供了技术方法，同时加深了对中国内陆湖泊长期水色变化的理解。
