Spectral reflectance from water surfaces was measured in small (0.01-5 km 2 ), turbid, eutrophic fishponds and mesotrophic quarry lakes in the Třeboň basin (South Bohemia, Czech Republic). A spectral scanner for direct field measurements from water surfaces and a hyperspectral airborne scanner were both used. The quarry lakes and fishponds differed in their spectral signature, which reflected the extent of their eutrophication. Their chlorophyll-a (chl-a) concentrations ranged from 2 to 455 lg/l -1 . Various algorithms were tested to best fit the relationships between reflectance patterns and the water-quality parameters used-concentration of chl-a and the total amount of suspended solids. The reflectance ratios at 714 and 650 nm gave the best estimates for chla concentrations, and simple reflectance at near infrared wavelengths, especially at 806 nm, gave the best predictive values for total suspended solid evaluation (r 2 = 0.89). Field surface reflectance and airborne sensing measurements were well correlated; however, airborne reflectance data showed higher variability (r 2 = 0.93 and 0.86, respectively). The results support the validity of reflectance measurements, both field and airborne, as a rapid tool for evaluating water quality in many turbid and greatly disturbed, small water bodies.
The study established the possibility of rapid evaluation of land cover structure and situation using as an example the Temelín NPP (Nuclear Power Plant) emergency zone. The composition, surface representation and spatial distribution of crop species in the area of interest were assessed on the basis of satellite data analysis (Landsat 5 TM).The supervised classification method of Landsat data yielded 92% accuracy of classification into the land cover classes. A comparison of satellite data classification and field investigation (farmers' and LPIS data) showed that the combination of both methods appears to be ideal for the classification of land cover. Analysis of the assessment of Landsat satellite data showed it was possible to process data in a few days. However, obtaining data was problematic; in our case it was 44 days. The results of the classification as well as other outputs (biomass growth model, expense-to-revenue ratio of measures, route network, LPIS database parcel structure, etc.) serve as a basis for the modelling of potential agricultural production contamination. The subsequent model is available for decision making and the selection of a suitable countermeasure in the event of potential radiation contamination.
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