Soil salinity is a major threat to the continuity of sustainable agriculture and food provision and the soil structure deterioration. In this context, determining, reducing and managing soil salinity is very important for creating sustainable modern agriculture. Determining soil salinity is generally carried out in the laboratory environment and devices used in land plots. Remote sensing is one of the important methods used for precise estimation and mapping of salinity. With remote sensing technology, soil salinity maps for large areas can be obtained with low cost and low effort. This study aims to compare remote sensing soil electrical conductivity from PlanetScope and ground measured data in wheat and beet fields in the farming areas of Alpu, Turkey. For that reason, electrical conductivity was measured at several points in wheat and beet fields using in-situ measurements and compared with various soil salinity indices from PlanetScope imagery acquired on the same day. Linear regression analysis was carried out to correlate the electrical conductivity data with their corresponding soil salinity spectral index values. The results show a high correlation (R² = 0.84) between soil salinity in wheat fields and some of the used indices. This study strengthens the idea that soil salinity maps can be obtained fast and accurately for large areas using remote sensing technology.

Coastal areas constitute the most important part of the world when considered in terms of their socio-economic and natural values. Measuring and monitoring the coastal areas accurately is an important issue for coastal management. Compared to ground-based studies, remote sensing applications enriched with machine learning algorithms such as Random Forest (RF) and Support Vector Machine (SVM) provide significant benefits in terms of cost, time, and size of the study area. Within the scope of this study, Sentinel-2 images for five coastal areas located in Turkey with different morphological and hydrodynamic properties were classified as land and water-bodies using SVM and RF algorithms. Water-body segmentation results of the SVM and RF classification for the different band combinations of Sentinel-2 images have been compared. The reasons affecting the results of the accuracy analysis were examined in accordance with the geography of each area. Experimental results show that the utilized machine learning methods provide satisfactory results for combinations involving the NIR band in all study areas.

With the increase in population in cities, economic, social and environmental problems continue to increase, and it is thought that the microclimatic conditions created by these problems will cause more environmental problems. One of the factors affecting the urban climate in urban areas is urban geometry. The climate of a region changes due to the shape of the land surface, human activities, atmospheric movements, and latent and sensible heat fluxes. Sky View Factor (SVF), Building View Factor (BVF) and Tree View Factor (TVF) are actively used in the determination of urban geometry. However, studies on how these factors affect the thermal state of the urban environment are insufficient. Determining the land surface changes and thermal condition characteristics (LST, NDVI, SHF and LHF) depending on urban growth and examining how these properties affect thermal conditions is very important in the construction of sustainable urban planning. In the study, SVF, BVF, TVF, LST, NDVI, SHF and LHF values ​​of 55 points determined for three different areas with different urban geometries were calculated. How these values ​​affect each other and their situation on urban outdoor thermal comfort is evaluated. In the study, statistical analysis was performed to evaluate the relationship between surface temperature, surface heat fluxes, different view factors, and vegetation. As a result of ANOVA analysis, it was determined as very significant (p > 0.01) in all regions. Both SHF and LHF values differ with SVF. The SHF value has a direct relationship with the SVF value. LHF is inversely proportional to the SVF value. The situation is reversed for SHF variation concerning vegetation. LHF and NDVI are directly related. SHF and NDVI are inversely proportional. SVF and NDVI values ​​also vary according to the LST value. It has been observed that LST has a direct relationship with SVF and an inverse relationship with NDVI.

Salinity is one of the most critical problems for agricultural lands. Soil salinity should be monitored with fast, economical and accurate data and methods. In this study, soil salinity was estimated using remote sensing data and machine learning algorithms, where fıve different methods were used, and the results were compared. As a study area, Alpu, Turkey has been selected. Within the scope of the study, on-site measurements were made in cultivation areas where there are different agricultural products such as beets, wheat, tomatoes, and corn in the district. The results show that machine learning algorithms and Planetscope images successfully determine soil salinity. Future studies will evaluate the methods by taking samples from different product classes and wet/arid lands.