Next Article in Journal
Development of Dense Time Series 30-m Image Products from the Chinese HJ-1A/B Constellation: A Case Study in Zoige Plateau, China
Previous Article in Journal
Climatic Controls on Spring Onset of the Tibetan Plateau Grasslands from 1982 to 2008
Open AccessArticle

Optimization of a Semi-Analytical Algorithm for Multi-Temporal Water Quality Monitoring in Inland Waters with Wide Natural Variability

Tropical Marine Science Institute, National University of Singapore, 18 Kent Ridge Road, Singapore 119227, Singapore
*
Author to whom correspondence should be addressed.
Academic Editors: Deepak R. Mishra, Magaly Koch and Prasad S. Thenkabail
Remote Sens. 2015, 7(12), 16623-16646; https://doi.org/10.3390/rs71215845
Received: 8 October 2015 / Revised: 18 November 2015 / Accepted: 1 December 2015 / Published: 8 December 2015
Current spectrometer design and the increasingly affordable price of field hyperspectral sensors are making feasible their use for water quality monitoring. In this study, we parameterized a semi-analytical algorithm to derive constituent concentrations from field spectroradiometer measurements in ten freshwater reservoirs over two years. In contrast to algorithms parameterized for single airborne or satellite sensor deployments, we optimized the algorithm for robust performance across all reservoirs and for multi-temporal application. Our algorithm produced chlorophyll-a concentration estimates with a root mean squared error (RMSE) of 7.7 mg∙m−3 over a range of 4–135 mg∙m−3. The model also produced estimates of total suspended solids (TSS) concentration with an RMSE of 4.0 g∙m−3 over a range of 0–25 g∙m−3. Choosing a non-linear objective function during inversion reduced variance of residuals in chlorophyll-a and TSS estimates by 20 and 18 percentage points, respectively. Application of our algorithm to two years of data and over ten study sites allowed us to specify sources of suboptimal parameterization and measure the non-stationarity of algorithm performance, analyses difficult for short or single deployments. Suboptimal parameterization, especially of backscatter properties between reservoirs, was the greatest source of error in our algorithm, accounting for 17%–20% of all error. In only one reservoir was time-dependent error apparent. In this reservoir, decreases in TSS over time resulted in less TSS estimate error due to imperfect model parameterization. For future applications, especially with ground-based sensors, model performance can easily be improved by using non-linear inversion procedures and replicating spectral measurements. View Full-Text
Keywords: semi-analytical bio-optical algorithm; hyperspectral; water quality monitoring; inland waters; Singapore semi-analytical bio-optical algorithm; hyperspectral; water quality monitoring; inland waters; Singapore
Show Figures

Graphical abstract

MDPI and ACS Style

Bramante, J.F.; Sin, T.M. Optimization of a Semi-Analytical Algorithm for Multi-Temporal Water Quality Monitoring in Inland Waters with Wide Natural Variability. Remote Sens. 2015, 7, 16623-16646. https://doi.org/10.3390/rs71215845

AMA Style

Bramante JF, Sin TM. Optimization of a Semi-Analytical Algorithm for Multi-Temporal Water Quality Monitoring in Inland Waters with Wide Natural Variability. Remote Sensing. 2015; 7(12):16623-16646. https://doi.org/10.3390/rs71215845

Chicago/Turabian Style

Bramante, James F.; Sin, Tsai M. 2015. "Optimization of a Semi-Analytical Algorithm for Multi-Temporal Water Quality Monitoring in Inland Waters with Wide Natural Variability" Remote Sens. 7, no. 12: 16623-16646. https://doi.org/10.3390/rs71215845

Find Other Styles

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop