Cutting-edge remote sensing technology has a significant role for managing the natural resources as well as the any other applications about the earth observation. Crop monitoring is the one of these applications since remote sensing provides us accurate, up-to-date and cost-effective information about the crop types at the different temporal and spatial resolution. In this study, the potential use of three different vegetation indices of RapidEye imagery on crop type classification as well as the effect of each indices on classification accuracy were investigated. The Normalized Difference Vegetation Index (NDVI), the Green Normalized Difference Vegetation Index (GNDVI), and the Normalized Difference Red Edge Index (NDRE) are the three vegetation indices used in this study since all of these incorporated the near-infrared (NIR) band. RapidEye imagery is highly demanded and preferred for agricultural and forestry applications since it has red-edge and NIR bands. The study area is located in Aegean region of Turkey. Radial Basis Function (RBF) kernel was used here for the Support Vector Machines (SVMs) classification. Original bands of RapidEye imagery were excluded and classification was performed with only three vegetation indices. The contribution of each indices on image classification accuracy was also tested with single band classification. Highest classification accuracy of 87, 46 % was obtained using three vegetation indices. This obtained classification accuracy is higher than the classification accuracy of any dual-combination of these vegetation indices. Results demonstrate that NDRE has the highest contribution on classification accuracy compared to the other vegetation indices and the RapidEye imagery can get satisfactory results of classification accuracy without original bands.

Due to recent climate changes and variability, drought has become a serious threat to Turkey, and its monitoring is of the highest priority. Remote sensing has certain weaknesses for monitoring droughts. Monitoring moisture levels using merely optical images may be inadequate in some cases due to the existence of natural plant cover and agricultural crop texture. The monitoring of moisture levels by meteorological satellites in recent years provides information only on smaller scales. Large‐scale studies necessitate the use of active systems. In this study, the contribution of SAR images to moisture level observations was investigated in a selected study area, enabling different levels of soil moisture to be studied simultaneously. In this respect, a Radarsat‐1 Fine Beam mode image of May 2006 pertaining to the low lands of Menemen Town, Izmir, was fused with a Satellite pour l'Observation de la Terre (SPOT)‐2 satellite image of the same date, and the variation of grey values in each band was studied. Finally, the moisture level of the soil was observed despite the plant cover using the Fuse algorithm from the PCI software. Thus, the variable soil moisture levels determined using the gravimetric method were found to be highly correlated with the grey values obtained from image fusion.

Considering the effects of rapid population growth, urbanisation and climate change in recent years, the protection of freshwater resources, the prevention of water pollution and the proper sharing of freshwater resources among different sectors have become important issues. Water footprint (WF) is a sign of freshwater use and is not only an indicator that can be used in the climate crisis, but also to protect water against nitrate pollution. In this study, the Küçük Menderes Basin was chosen as the study area due to different crop varieties. Agricultural crop patterns were classified using Rapideye and Sentinel-2 satellite images of the study area obtained in 2017. Thus, the cultivated areas were obtained for cotton and maize (grain and silage) in the basin. In particular, agricultural crop patterns were considered in which agricultural production was intensive and blue water was used predominantly. As a result, the first-crop corn production, which has a high blue WF of 3840 m³/ton in the basin, has the highest greywater footprint due to the use of intensive chemical fertilisers. This was followed by cotton with 2331 m³/ton, and the second-crop silage corn production had the lowest greywater footprint. Agriculture’s water footprint assessment provides a solid foundation for planning climate change adaptive crop production, managing nitrate-sensitive areas and anticipating future regional changes.