Reference spectra to classify Amazon water types

Abstract

Reference spectra extracted from spectral libraries can distinguish different water types in images when associated with limnological information. In this study, we compiled available databases into a single spectral library, using field water reflectance spectra and limnological data collected by different researchers and campaigns in the Amazonian region. By using an iterative clustering procedure based on the combination of reflectance and optically active components (OACs), reference spectra representative of the major Amazonian water types were defined from this library. Differences between the resultant limnological classes were also evaluated by paired t-tests at significance level 0.05. Finally, reference spectra were tested for Spectral Angle Mapper (SAM) classification of waters in Hyperion/Earth Observing-One (EO-1) and Medium Resolution Imaging Spectrometer (MERIS)/Environment Satellite (Envisat) images acquired simultaneously as the field campaigns. Results showed highly variable concentrations of OACs due to the complexity of the Amazonian aquatic environments. Ten classes were defined to represent this complexity, broadly grouped into four limnological characteristics: clear waters with low concentrations of OACs (class 1); black waters rich in dissolved organic carbon (DOC) (class 2); waters with large concentrations of inorganic suspended solids (ISSs) (classes 3-7); and waters dominated by chlorophyll-a (chl-a) (classes 8-10). Using the ten reference spectra, SAM classification of the field water curves produced an overall accuracy of 86% with the highest values observed for classes 3, 4, 6 and 7 and the lowest accuracy for classes 1 and 2. The results of paired t-tests confirmed the class differences based on the concentrations of OACs. SAM classification of the Hyperion and MERIS images using ground truth information resulted in overall classification accuracies of 48% and 67%, respectively, with the highest errors associated with specific portions of the scenes that were not adequately represented in the spectral library.

SNR (Signal-To-Noise Ratio) Impact on Water Constituent Retrieval from Simulated Images of Optically Complex Amazon Lakes

“…At the end of the dataset sensor simulation, the quantized noiseless water leaving radiance for each band L b w (B n ) and L * b w (B n ) were converted to R rs (B n ) according to Equation (11), in order to be used as the input for bio-optical algorithms.…”

Implications of scatter corrections for absorption measurements on optical closure of Amazon floodplain lakes using the Spectral Absorption and Attenuation Meter (AC-S-WETLabs)

“…The spatial-temporal monitoring of changes in water composition for Amazon floodplains at regional scale is challenging and has not been effectively achieved by the current remote sensing technologies. A few studies have demonstrated empirical relations between surface reflectance and OACs of Amazonian water bodies (e.g., Barbosa et al, 2010;Ferreira, Barbosa, Novo, & de, 2012;Lobo, Novo, Barbosa, & Galvão, 2012;Novo, Filho, & Melack, 2004;Rudorff, Galvão, & Novo, 2009), but the seasonal variation in range of OAC concentrations is such that empirical relations are limited to remote sensing applications at similar hydrological phases. Development of universal algorithms requires further detailed investigations of the underwater light field in Amazon floodplain waters, which are almost inexistent in the literature.…”

Classification of Several Optically Complex Waters in China Using in Situ Remote Sensing Reflectance

“…The three centroid sets corresponding to Classes 1-3 included 8, 28, and 26 samples, respectively. The method presented here and the study by Moore et al [1,24] are different in two aspects: (1) PCA transform is used before clustering in this work; (2) In the work of Moore et al, only Euclidean distance was used in FCM, whereas in our work, four different similarity distances-SAD, ED, OPD, and TD-were used and new centroid sets were extracted.…”

“…The following three-step clustering framework was adopted. The second step with Principal Component Analysis (PCA) transform and the third step based on more than one similarity measure constituted improvements on the classification method proposed by Moore et al [1,24].…”

“…Some studies classified waters using only water quality parameters (e.g., SD, turbidity, chlorophyll-a concentration (chl-a), yellow substance, and sea surface temperature) [19] or inherent optical properties (IOPs; e.g., absorption and scattering coefficients) [15,[20][21][22]. Some studies used a combination of water quality parameters and optical properties [21,23,24]. Classification results tend to improve as more parameters of different types (i.e., water quality parameters, IOPs, and apparent optical parameters (AOPs)) are included as inputs.…”