A Weighted Algorithm Based on Normalized Mutual Information for Estimating the Chlorophyll-a Concentration in Inland Waters Using Geostationary Ocean Color Imager (GOCI) Data

Abstract

Due to the spatiotemporal variations of complex optical characteristics, accurately estimating chlorophyll-a (Chl-a) concentrations in inland waters using remote sensing techniques remains challenging. In this study, a weighted algorithm was developed to estimate the Chl-a concentrations based on spectral classification and weighted matching using normalized mutual information (NMI). Based on the NMI algorithm, three water types (Class 1 to Class 3) were identified using the in situ normalized spectral reflectance data collected from Taihu Lake. Class-specific semi-analytic algorithms for the Chl-a concentrations were established based on the GOCI data. Next, weighted factors, which were used to determine the matching probabilities of different water types, were calculated OPEN ACCESS Remote Sens. 2015, 7 11732 between the GOCI data and each water type using the NMI algorithm. Finally, Chl-a concentrations were estimated using the weighted factors and the class-specific inversion algorithms for the GOCI data. Compared to the non-classification and hard-classification algorithms, the accuracies of the weighted algorithms were higher. The mean absolute error and root mean square error of the NMI weighted algorithm decreased to 22.63% and 9.41 mg/m 3 , respectively. The results also indicated that the proposed algorithm could reduce discontinuous or jumping effects associated with the hard-classification algorithm.

Regional Models for High-Resolution Retrieval of Chlorophyll a and TSM Concentrations in the Gorky Reservoir by Sentinel-2 Imagery

“…Chl a concentration was measured by SF-14 spectrophotometer (Russia), previously calibrated using pure chlorophyll (Sigma) as a standard. Despite the fact that the spectrophotometric method does not satisfy the NASA protocols [41], it is often used to retrieve the concentration of Chl a as the most accessible method providing reliable accuracy (for example [24,39,42,43]). Intercomparison of the spectrophotometric method with two others valid by NASA protocols, fluorimetric and high-performance liquid chromatography methods were performed, for example, in [44,45].…”

“…Such observation geometry goes beyond the NASA protocol [38], but not significantly. Therefore, it is often used in ship measurements (for example, [42]). These radiances were registered at the start and finish points of each track (Figure 6d).…”

Estimation of Water Quality Parameters with High-Frequency Sensors Data in a Large and Deep Reservoir

“…Figure 4 shows that MAPE mainly occurs when the water quality parameters show obvious fluctuations and when they reach their lowest point in a year. The sensitivity of sensors and the attributes of water quality parameters can shed light on why MAPE occurs when the water quality parameters fluctuate [37]. In seasons when these parameters easily fluctuate, sensors cannot easily capture the changes in these parameters in time.…”

High Temporal Resolution Monitoring of Suspended Matter Changes from GOCI Measurements in Lake Taihu

“…Because the water quality parameters are moving along with the lake current rapidly [26][27][28], GOCI data with extremely high time resolution is suitable for the work to monitor the lake. For instance, Huang used GOCI to monitor the distribution of suspended matter and algae in Lake Taihu [29,30], and Bao monitored chlorophyll concentration in Lake Taihu by GOCI [31]. The plumes can affect Lake Taihu water quality in a short period of time, therefore MODIS data used in the existing study is obviously unable to accurately obtain the variation tendency of the plume [8].…”