Smartphone–Camera–Based Water Reflectance Measurement and Typical Water Quality Parameter Inversion
Smartphone–Camera–Based Water Reflectance Measurement and Typical Water Quality Parameter Inversion
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基于智能手机—相机—基于水体反射率测量和典型水质参数反演
DOI：
10.3390/rs14061371
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发表时间：
2022
期刊：
影响因子：
5
通讯作者：
Wei Shen
中科院分区：
文献类型：
--
作者：
Min Gao;Junsheng Li;Shenglei Wang;Fangfang Zhang;Kai Yan;Ziyao Yin;Ya Xie;Wei Shen
来自智能设备的众包数据在水质监测中发挥着越来越重要的作用。然而，保证和评估众包数据质量是一个关键问题。本研究旨在从具有可变曝光参数的智能手机照片中提取更准确的水反射率数据，并测试这些数据在推导水质参数中的可用性。设计了一套低成本的参考卡片，放置在靠近水面的照片中心，并提出了一种将照片数字数（DN）转换为水体反射率的计算模型。利用照片中参考卡的固有反射率和DN，构建了非线性DN-反射率模型。然后，在同一张照片中的水面的反射率估计。在7个不同水体的112个采样点对这一方案进行评价期间，观察到遥感反射率的估计值与实测值之间存在微小差异;红、绿色和蓝色条带的平均无偏相对误差（AURE）分别为25.7%、29.5%和35.2%，而三个条带的RMSE分别为0.0032、0.0051、0.0031。导出的水反射率数据被用来检索的Secchi盘深度（Zsd）和浊度，与准确度分别为72.4%和60.2%。实验结果表明，基于智能手机摄像头的遥感反演方法能够有效地获取遥感反射率和水质参数，且精度可接受。
Crowdsourced data from smart devices play an increasingly important role in water quality monitoring. However, guaranteeing and evaluating crowdsourced data quality is a key issue. This study aims to extract more accurate water reflectance data from smartphone photographs with variable exposure parameters, and to test the usability of these data in deriving water quality parameters. A set of low–cost reference cards was designed to be placed in the center of the photograph near the water surface, and a calculation model was proposed to convert the photograph digital numbers (DNs) to water reflectance. A nonlinear DN–to–reflectance model was constructed using the inherent reflectance and DN of the reference card in the photograph. Then, the reflectance of the water surface in the same photograph was estimated. During the evaluation of this scheme in seven different waterbodies with 112 sampling sites, small differences were observed between the estimated and measured remote sensing reflectance; the average unbiased relative errors (AUREs) for the red, green, and blue bands were 25.7%, 29.5%, and 35.2%, respectively, while the RMSEs for the three bands were 0.0032, 0.0051, 0.0031, respectively. The derived water reflectance data were used to retrieve the Secchi–disk depth (Zsd) and turbidity, with accuracies of 72.4% and 60.2%, respectively. The results demonstrate that the proposed method based on the smartphone camera can be used to derive the remote sensing reflectance and water quality parameters effectively with acceptable accuracy.