Abstract
The spatial–temporal resolution of remote data covers coastal water variability, but this approach offers a lower accuracy than in situ observations. Two of the major error sources occur due to the parameterization of bio-optical models and spectral capability of the remote data. These errors were evaluated by exploiting data acquired in the coastal waters of Manfredonia Gulf. Chlorophyll-a concentrations, absorption of the colored dissolved organic material at 440 nm (aCDOM440nm), and tripton concentrations measured in situ varied between 0.09–1.76 mgm−3, 0.00–0.41 m−1, and 1.97–8.90 gm−3. In accordance with the position and time of in situ surveys, 36 local models, four daily models, and one total bio-optical model were parameterized and validated using in situ data before applying to Compact High-Resolution Imaging Spectrometer (CHRIS) mode 1, CHRIS mode 2, Landsat Thematic Mapper (TM), Multispectral Infrared and Visible Imaging Spectrometer (MIVIS), Moderate Resolution Imaging Spectroradiometer (MODIS), and Precursore Iperspettrale della Missione Applicativa (PRISMA) simulated data. Concentrations retrieved from PRISMA data using local models highlighted the smallest errors. Because tripton abundance is great and tripton absorptions were better resolved than those of chlorophyll-a and colored dissolved organic material (CDOM), tripton concentrations were adequately retrieved from all data using total models, while only local models adequately retrieved chlorophyll-a concentrations and aCDOM440nm from CHRIS mode 1, CHRIS mode 2, MIVIS, and MODIS data. Therefore, the application of local models shows smaller errors than those of daily and total models; however, the capability to resolve the absorption of water constituents and analyze their concentration range can dictate the model choice. Consequently, the integration of more models allows us to overcome the limitations of the data and sensors.
Highlights
The monitoring of waters with a passive remote sensing technique is based on the characterization of the optical properties of water constituents [1]
The mean and standard deviation values of root mean square (RMS)% obtained by validating the total model are smaller than 5.3%–2.0% and these values are slightly greater than the ones that were calculated by validating the daily models, whereas, these values are quite greater than the ones that were calculated by validating the local models
With reference to each measurement day, the mean and standard deviation values of RMS% varied from day to day, but the values obtained by validating the local models were always smaller than the ones that were obtained by validating the daily models and these values are always smaller than the ones that were obtained by validating the total model
Summary
The monitoring of waters with a passive remote sensing technique is based on the characterization of the optical properties of water constituents [1]. The empirical models exploit statistical relationships between AOPs and water constituent concentrations measured in situ, whereas the analytical algorithms utilize radiative transfer theory [7,8]. With reference to analytical models, the in situ measurements that are usefully exploited to develop the models are total absorption and backscattering spectra and absorption and backscattering spectra of each water constituent (i.e., IPOs); in situ data that are commonly used to validate the models include remote sensing reflectances measured above and below the water surface (Rrs and rrs, respectively; two AOPs) and in situ data that are commonly used to validate their results are the abundance of water constituents [3,4,5,6]. As coastal and inland waters are characterized by high spatial and temporal variability—including that of water constituents—the optimal calibration and validation procedure of the bio-optical models requires that the same water column is simultaneous acquired by in situ and remote surveys [3,5,6,7,9,11]
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