We present an operational system for multi-sensor data fusion implemented at the Finnish Environment Institute. The system uses Ensemble Kalman filter and smoother algorithms, which are often used for probabilistic analysis of multi-sensor data. Uncertainty and spatial and temporal correlations present in the available observation data are accounted for to obtain accurate and realistic results. To test the data fusion system, daily chlorophyll-a concentration has been modelled across northern shoreline of Gulf of Finland over the period of August 1st – October 31st 2011. Chlorophyll-a data from routine monitoring stations, ferrybox measurements, and data derived from Medium Resolution Imaging Spectrometer (MERIS) instrument on board the ENVISAT satellite has been used as input. The data fusion system demonstrates the use of existing and well-known Ensemble Kalman filtering and smoothing methods for improving water quality monitoring programs and for ensuring compliance with ecological standards.

我们提出了在芬兰环境研究所实施的多传感器数据融合操作系统。该系统使用集成卡尔曼滤波器和平滑算法，这些算法通常用于多传感器数据的概率分析。考虑到可用观测数据中存在的不确定性以及空间和时间相关性，以获得准确和现实的结果。为了测试数据融合系统，我们对 2011 年 8 月 1 日至 10 月 31 日期间芬兰湾北部海岸线的每日叶绿素 a 浓度进行了建模。来自常规监测站的叶绿素 a 数据、轮渡箱测量数据和来自 Medium 的数据ENVISAT 卫星上的分辨率成像光谱仪 (MERIS) 仪器已用作输入。