Analysis of Turbid Water Quality Using Airborne Spectrometer Data with a Numerical Weather Prediction Model-aided Atmospheric Correction

Photogrammetric Engineering and Remote Sensing (Impact Factor: 1.61). 03/2008; 74(3):363–374. DOI: 10.14358/PERS.74.3.363


The effects of an atmospheric correction method for water quality estimation have been studied and validated for Airborne Imaging Spectrometer for Applications (AISA) data. This novel approach uses atmospheric input parameters from a numerical weather prediction model: HIRLAM (High
Resolution Limited Area Model). The atmospheric correction method developed by de Haan and Kokke (1996) corrects the spectrometer data according to the coefficients calculated using Moderate Resolution Transmittance Code (MODTRAN) radiative transfer code simulations. The airborne campaigns
were carried out at lake and coastal Case 2 type water areas between 1996 and 1998. The water quality interpretation was made using the MERIS satellite instrument wavelengths. The correction improved most of the water quality (turbidity, total suspended solids, and Secchi disk depth) estimates
when data from several flight campaigns were used jointly. The atmospheric correction reduced the standard deviation of the measurements conducted on different days. The highest improvement was obtained in the estimation of turbidity.

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    ABSTRACT: Morse Reservoir, a major water supply for the Indianapolis metropolitan area, IN, USA, experiences nuisance cyanobacterial blooms due to agricultural and point source nutrient loadings. Hyperspectral remote sensing data from both in situ and airborne AISA measurements were applied to an adaptive model based on Genetic Algorithms-Partial Least Squares (GA-PLS) by relating the spectral signal with total nitrogen (TN) and phosphorus (TP) concentrations. Results indicate that GA-PLS relating in situ spectral reflectance to the nutrients yielded high coefficients of determination (TN: R 2 = 0.88; TP: R 2 = 0.91) between measured and estimated TN (RMSE = 0.07 mg/L; Range: 0.6–1.88 mg/L), and TP (RMSE = 0.017; Range: 0.023–0.314 mg/L). The GA-PLS model also yielded high performance with AISA imaging data, showing close correlation between measured and estimated values (TN: RMSE = 0.11 mg/L; TP: RMSE = 0.02 mg/L). An analysis of in situ data indicated that TN and TP were highly correlated with chlorophyll-a and suspended matter in the water column, setting a basis for remotely sensed estimates of TN and TP. Spatial correlation of TN, TP with chlorophyll-a and suspended matters further confirmed that remote quantification of nutrients for inland waters is based on the strong association of optically active constituents with nutrients. Based on these results, in situ and airborne hyperspectral remote sensors can provide both quantitative and qualitative information on the distribution and concentration of nutrients in Morse Reservoir. Our modeling approach combined with hyperspectral remote sensing is applicable to other productive waters, where algal blooms are triggered by nutrients.
    Water Resources Management 07/2014; 28(9):2563-2581. DOI:10.1007/s11269-014-0627-x · 2.60 Impact Factor