ABSTRACT:The land surface influences local, regional and global climate across many time scales. Accurate representation of land surfaces is an important factor for climate modelling studies because land surfaces control the partitioning of available energy and water. Here we introduce new, up-to-date and accurate land cover data for the Marmara Region, Turkey derived from Landsat Enhanced Thematic Mapper (ETM+) images into the Weather Research and Forecasting (WRF) model. We used several image processing techniques to create accurate land cover data from Landsat sensor images obtained between 2001 and 2005. By comparing the new land cover data with the default WRF land cover data, we found that there are two types of error in WRF land cover data that caused misrepresentation of the study region. WRF uses Global Land Cover Characteristics (GLCC) data created from images acquired during 1992 and 1993 and it does not reflect current land cover. And the GLCC includes misclassifications. As a result of these errors, GLCC data do not represent urban areas in the cities of Istanbul, Izmit and Bursa and there are spectral mixing problems between classes, e.g. croplands, urban areas and forests. We used WRF land cover and our new land cover data to conduct numerical simulations. Using meteorological station data within the study area, we found that simulation with the new land cover dataset produces more accurate temperature simulations for the region, thus demonstrating the importance of accurate land cover data.

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Abstract:This paper focuses mainly on the investigation of water reserve changes in Salt Lake, Turkey, using remote-sensing data. The study is performed in two stages: (1) correlation analysis for real-time ground and satellite data and (2) assessment of water reserve changes using multi-temporal Landsat imagery. First, correlation analysis is conducted to investigate the relationship between digital data from Landsat-5 TM and spectral (in situ) measurements collected using a field spectroradiometer on the same day and time. A radiometric correction procedure, including conversions from digital numbers to radiance and from radiance to at-satellite reflectance, is executed to make satellite data comparable to in situ measurements. This procedure show that simultaneous ground and satellite remote-sensing data are highly correlated (0Ð84 > R 2 > 97) and the near-infrared region (for this study TM4-Landsat-5 TM, band 4) is the best spectral range to distinguish salt and water on the satellite data for the multi-temporal analysis of the water reserve in Salt Lake. It also shows that the use of shortwave infrared band(s) will result in confusion for the determination of the water reserve in this water-covered study area. In a second and last phase, the water reserve change in the lake is examined using multi-temporal Landsat imagery collected in 1990, 2001 and 2005. The remotely sensed, sampled and treated data show that the water reserve in the lake has decreased markedly between 1990 and 2005 due to drought and uncontrolled water usage. It is suggested that the use of water supplies around Salt Lake should be controlled and that the lake should regularly be monitored by up-to-date remote-sensing data (at least annually) for better management of water resources.