State Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China; University of Chinese Academy of Sciences, Beijing 100049, China.
State Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China; University of Chinese Academy of Sciences, Beijing 100049, China.
Water Res. 2021 Mar 15;192:116844. doi: 10.1016/j.watres.2021.116844. Epub 2021 Jan 15.
Water clarity (expressed as Secchi disk depth (SDD)) reflects light transmission capacity of a water body and influences growth of aquatic plants, aquatic organisms, and primary productivity. Here, we calibrated and validated a general model based on Landsat series data for deriving SDD of various inland waters across China. The quality of remotely sensed reflectance products from different Landsat series images was assessed using in situ reflectance measurements. The results indicated that the products in the visible bands are the most robust and stable to estimate SDD for inland waters. Subsequently, a simple power function model based on red band was built using 887 pairs of in situ SDD measurements and concurrent Landsat images. The model was validated with an independent dataset of 246 SDD measurements, and the results showed that the mean relative error and normalized root mean square error were 34.2% and 55.4%, respectively. Finally, the model was applied to Landsat images acquired between 2016 and 2018 to investigate the SDD spatial distribution of all lakes with water area ≥ 10 km (total 641 lakes) in China. The estimation results demonstrated that the Eastern Plain Lake Zone and Northeast Plain Lake zone have relatively low SDD, with multiyear average SDD of 0.56±0.17 m and 0.47±0.29 m, respectively. The Yunnan-Guizhou Plateau Lake Zone and Tibetan Plateau Lake Zone have relatively high SDD, with multiyear average SDD of 1.48 ± 0.86 m and 1.30 ± 0.83 m, respectively. The results indicated that the proposed model exhibits strong ability to accurately construct SDD coverage for various lakes.
水的清澈度（以塞奇圆盘深度（SDD）表示）反映了水体的透光能力，并影响水生植物、水生生物和初级生产力的生长。在这里，我们基于 Landsat 系列数据，为中国各种内陆水体校准和验证了一个通用模型，用于推导 SDD。使用现场反射率测量评估了来自不同 Landsat 系列图像的遥感反射率产品的质量。结果表明，可见光波段的产品在估计内陆水域的 SDD 时最稳健和稳定。随后，使用 887 对现场 SDD 测量值和同期 Landsat 图像，基于红波段建立了一个简单的幂函数模型。该模型通过 246 个 SDD 测量的独立数据集进行了验证，结果表明，平均相对误差和归一化均方根误差分别为 34.2%和 55.4%。最后，该模型应用于 2016 年至 2018 年间获取的 Landsat 图像，以调查中国所有面积≥10km 的湖泊（共 641 个湖泊）的 SDD 空间分布。估计结果表明，东部平原湖区和东北平原湖区的 SDD 相对较低，多年平均 SDD 分别为 0.56±0.17m 和 0.47±0.29m。云南-贵州高原湖区和青藏高原湖区的 SDD 相对较高，多年平均 SDD 分别为 1.48±0.86m 和 1.30±0.83m。结果表明，所提出的模型具有准确构建各种湖泊 SDD 覆盖的强大能力。
