The main purpose of this paper is to investigate multitemporal land surface temperature (LST) changes by using satellite remote sensing data. The study included a real-time field work performed during the overpass of Landsat-5 satellite on 21/08/2011 over Salt Lake, Turkey. Normalized vegetation index (NDVI), vegetation condition index (VCI), and temperature vegetation index (TVX) were used for evaluating drought impact over the region between 1984 and 2011. In the image processing step, geometric and radiometric correction procedures were conducted to make satellite remote sensing data comparable with in situ measurements carried out using thermal infrared thermometer supported by hand-held GPS. The results showed that real-time ground and satellite remote sensing data were in good agreement with correlation coefficient (R 2) values of 0.90. The remotely sensed and treated satellite images and resulting thematic indices maps showed that dramatic land surface temperature changes occurred (about 2°C) in the Salt Lake Basin area during the 28-year period (1984–2011). Analysis of air temperature data also showed increases at a rate of 1.5–2°C during the same period. Intensification of irrigated agriculture particularly in the southern basin was also detected. The use of water supplies, especially groundwater, should be controlled considering particularly summer drought impacts on the basin.

The main goal of this study was to investigate spatial patterns in water quality in Lake Beysehir, which is the largest freshwater reservoir in Turkey, by using Landsat-5TM (Thematic Mapper) data and ground surveys. Suspended sediment (SS), turbidity, Secchi disk depth (SDD), and chlorophyll-a (chl-a) data were collected from 40 sampling stations in August, 2006. Spatial patterns in these parameters were estimated using bivariate and multiple regression (MR) techniques based on Landsat-5TM multispectral data and water quality sampling data. Single TM bands, band ratios, and combinations of TM bands were estimated and correlated with the measured water quality parameters. The best regression models showed that the measured and estimated values of water quality parameters were in good agreement (0.60 < R 2 < 0.71). TM3 provided a significant relationship (R 2 =0.67, p<0.0001) with SS concentration. MR between chl-a and various combinations of TM bands showed that TM1, TM2, and TM4 are strongly correlated with measured chl-a concentrations (R 2 =0.60, p<0.0001). MR of turbidity showed that TM1, TM2, and TM3 explain 60% (p< 0.0001) of the variance in turbidity. MR of SDD showed a strong relationship with measured SDD, with R 2 =0.71 (p<0.0001) for the ratio TM1/TM3 and TM1 band combinations. The spatial distribution maps present apparent spatial variations of selected parameters for the study area covering the largest freshwater lake and drinking water reservoir in Turkey. Interpretation of thematic water quality maps indicated similar spatial distributions for SS, turbidity, and SDD. A large area in the middle portion of the lake showed very low chl-a concentrations as it is far from point and nonpoint sources of incoming nutrients. The trophic state index values were calculated from chl-a and SDD measurements. Lake Beysehir was classified as a mesotrophic or eutrophic lake according to chl-a or SDD parameters, respectively.

This paper presents an application of high resolution satellite remote sensing data for mapping water quality in the Goldon Horn, Istanbul. It is an applied research emphasizing the present water quality conditions in this region for water quality parameters; secchi disc depth (SDD), chlorophyl-a (chl-a) and total suspended sediment (TSS) concentration. The study also examines the retrievals of these parameters through high resolution IKONOS multispectral data supported by in situ measurements. Image processing procedure involving radiometric correction is carried out for conversion from digital numbers (DNs) to spectral radiance to correlate water quality parameters and satellite data by using multiple regression technique. The retrieved and verified results show that the measured and estimated values of water quality parameters in good agreement (R 2 >0.97). The spatial distribution maps are developed by using multiple regression algorithm belonging to water quality parameters. These maps present apparent spatial variations of selected parameters and inform the decision makers of water quality variations in a large water region in the Istanbul metropolitan area.

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