Viewing geometry is one of the most important factors to consider when water bodies are observed from satellite sensors with large field of view. We examine the directional and angular effects on the reflectance of waters with different concentrations of total suspended solids (TSSs). In the laboratory, we measure the reflectance in five view zenith angles (VZAs) and eight view azimuth angles (VAAs) for optically shallow waters having four concentrations of TSSs. Seven empirical models to estimate TSSs based only on the reflectance of the red band (∼660 nm) are evaluated. In addition, we analyze Moderate Resolution Imaging Spectroradiometer (MODIS)/Terra reflectance measured in 13 consecutive satellite overpasses. The results show that the reflectance of the inland-like water is affected by data acquisition geometry. The best wavelength to estimate TSS is 625 nm for most VZAs and VAAs. The lowest correlations between reflectance and TSS are observed at extreme viewing with the anisotropy decreasing with increasing concentrations of TSSs. Directional and angular effects are also observed for MODIS (acquired and simulated data) with TSS underestimates observed close to the orthogonal plane for all VZAs, and TSS overestimates observed in the principal scattering plane in the forward scattering direction. More anisotropic waters are observed for VZA greater than ±30 deg. Results highlight the need for correcting MODIS data for bidirectional effects in inland water studies.
KEYWORDS: Sensors, Spectral resolution, Feature selection, Reflectivity, Signal to noise ratio, MODIS, Short wave infrared radiation, Image classification, Data acquisition, Near infrared
Next generation imaging spectrometers with higher signal-to-noise ratio and broader swath-width bring new perspectives for crop classification over large areas. Here, we used Hyperion/Earth Observing-One data collected over Brazilian soybean fields to evaluate the performance of four classification techniques (maximum likelihood - ML; spectral angle mapper - SAM; spectral information divergence - SID; support vector machine - SVM) to discriminate five soybean varieties. The spectral resolution influence on classifying them was analyzed by simulating the spectral bands of seven multispectral sensors using Hyperion data. Before classification, the Waikato environment for knowledge analysis was used for feature selection. Results showed the importance of the green, red-edge, near-infrared, and shortwave infrared to discriminate the soybean varieties. Because the soybean variety Monsoy 8411 was sensed by Hyperion in a later reproductive stage, it was more easily discriminated than the other varieties. The best classification techniques were ML and SVM with overall accuracy of 89.80% and 81.76%, respectively. The accuracy of spectral matching techniques was lower (70.84% for SAM and 72.20% for SID). When ML was applied to the simulated spectral resolution of the multispectral sensors, moderate resolution imaging spectroradiometer and enhanced thematic mapper plus presented the highest accuracy, whereas advanced very high resolution radiometer showed the lowest one.
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