Field Spectroscopy for Assisting Water Quality Monitoring and Assessment in Water Treatment Reservoirs Using Atmospheric Corrected Satellite Remotely Sensed Imagery

Abstract

Abstract:The overall objective of this study was to use field spectro-radiometers for finding possible spectral regions in which chlorophyll-a (Chl-a) and particulate organic carbon (POC) could be identified so as to assist the assessment and monitoring of water quality using satellite remote sensing technology. This paper presents the methodology adopted in this study which is based on the application of linear regression analysis between the mean reflectance values (measured with the GER1500 field spectro-radiometer) across the spectrum and the concentrations of chlorophyll-a (µg/L) and POC (µg/L) acquired simultaneously on the same day and time in the Lower Thames Valley in West London (U.K.) from old campaigns. Each regression model (512 in total) corresponded to a measured wavelength of the GER1500 field spectro-radiometer. The achieved correlations presented as r 2 against wavelength, indicate the regions with high correlation values for both water quality variables. Based on the results from this study and by matching the spectral bands of the field spectro-radiometer with those of the Landsat TM satellite sensor (or any other sensor), it has been found that suitable spectral regions for monitoring water quality in water treatment reservoirs are the following: for chlorophyll-a, the spectral region of 0.45-0.52 μm (TM band 1), and for POC, the region 0.52-0.60 μm (TM bands 1 and 2). Then 12 atmospheric corrected Landsat TM/ETM+ band 1 images acquired from 2001 to 2010 were used for validation purposes to retrieve the Chl-a concentrations.

Improved Accuracy of Chlorophyll-a Concentration Estimates from MODIS Imagery Using a Two-Band Ratio Algorithm and Geostatistics: As Applied to the Monitoring of Eutrophication Processes over Tien Yen Bay (Northern Vietnam)

“…The dark-object subtraction (DOS), a histogram minimum method proposed by Chavez [29], was compared with the Quick Atmospheric Correction (QUAC) that is a VNIR-SWIR atmospheric correction method [30] in this study. The DOS was demonstrated as the most effective method for monitoring water quality using visible bands [31,32], while QUAC showed accurate performance for infrared spectral bands [30]. Ha and Koike [33] also identified that DOS was a more suitable atmospheric correction method for the MODIS image data of Tien Yen Bay than the near-infrared atmospheric correction and Fast Line-of-sight Atmospheric Analysis of Hyperspectral cubes (FLAASH).…”

Performance Analysis of MODIS 500-m Spatial Resolution Products for Estimating Chlorophyll-a Concentrations in Oligo- to Meso-Trophic Waters Case Study: Itumbiara Reservoir, Brazil

“…Remote sensing techniques have been extensively used for monitoring chl-a in aquatic systems [4,[8][9][10]. The advantages of using remote sensing techniques to monitor chl-a are: (1) the geographical coverage of satellite images, which provide information about the entire aquatic system; (2) remote sensing, which allows us to obtain information from inaccessible places; and (3) historical satellite images, which allow the inference of water quality information from the past records [11].…”

“…Several methods for estimating chl-a concentration in different turbid waters (Case 2) with remote sensing have been investigated [4][5][6][7][8][9][10][11][12]. Many studies have suggested that remote spectroscopic measurements from the Moderate Resolution Imaging Spectroradiometer (MODIS) should be used to estimate chl-a concentrations in Case 2 waters through empirical and semi-empirical algorithms [4,15,16].…”

Spatiotemporal Variation in Particulate Organic Carbon Based on Long-Term MODIS Observations in Taihu Lake, China

“…The POC/Chl-a is in the range of fresh phytoplankton ranges, between 33 and 100 [66]; this is much higher than that of sub-Antarctic water (50)(51)(52)(53)(54)(55)(56)(57)(58)(59)(60) [67], Great Wall Cove and Ardley Cove (74.62 ± 27.97) [65] and the East China Sea (64) [68]. However, it is much lower than that of dystrophic water (250-2500) [69], the Pacific Ocean (178 ± 30) [70], the Atlantic Ocean (253) and other ocean subsystems ( Table 2 in Fabiano et al [71]; Arrigo et al [72]; Table1 in Hadjimitsis et al [73]). The effect of atmospheric correction on the POC algorithm also should be considered in addition to the dominant water constituents of POC.…”