Water quality assessment using remote sensing and its terrestrial components is carried out in short time for larger areas. Another issue that is as important regarding water availability is access to quality water. It is important to investigate the availability of the analysis of remotely sensed data instead of environmental and chemical analysis that determines water quality and usability. To examine the detection of water qualities without taking water samples in situ, spectral library data was used in the Hafik Region. In this context we used spectral measurement data of water samples previously taken from İmranlı, where the Kızılırmak River originates, and used for spectral classification of water quality. Matched filtering was used for integrating spectral data and CHRIS Proba image as the spectral classification method. To conduct an accuracy analysis, chemical oxygen demand measurement was carried out at 10 points determined as 1st and 2nd water quality in the study area on the river and lakes according to the Ministry of the Environment and Urbanization. The overall accuracy of the classification was calculated as 70%. The results of this study have shown the importance of spectral classification of satellite imagery in evaluating water quality and monitoring water resources.

This study examines the combined use of ground penetrating radar (GPR) and Sentinel-1 synthetic aperture radar (SAR) data for estimating soil moisture in a 25-decare field in Sivas, Türkiye. Soil moisture, vital for sustainable agriculture and ecosystem management, was assessed using in situ measurements, SAR backscatter analysis, and GPR-derived dielectric constants. A novel empirical model adapted from the classical soil moisture index (SSM) was developed for Sentinel-1, while GPR data were processed using the reflected wave method for estimating moisture at 0–10 cm depth. GPR demonstrated a stronger correlation within situ measurements (R² = 74%) than Sentinel-1 (R² = 32%), reflecting its ability to detect localized moisture variations. Sentinel-1 provided broader trends, revealing its utility for large-scale analysis. Combining these techniques overcame individual limitations, offering detailed spatial insights and actionable data for precision agriculture and water management. This integrated approach highlights the complementary strengths of GPR and SAR, enabling accurate soil moisture mapping in heterogeneous conditions. The findings emphasize the value of multi-technique methods for addressing challenges in sustainable resource management, improving irrigation strategies, and mitigating climate impacts.

Forest fires pose significant environmental and economic risks, particularly in fire-prone regions like the Mediterranean coast of Türkiye. This study presents a comprehensive Forest Fire Danger Assessment System (FoFiDAS), by integrating Geographic Information Systems (GIS), a literature-based model, the Analytical Hierarchy Process (AHP), and machine learning (ML) to improve forest fire danger classification. Both models integrate 13 key parameters identified through the literature. A comparison of these models revealed 53% overlap in fire danger classifications. While the AHP model, based on expert-weighted assessment, provided a more structured and localized classification, the literature-based model relied on broader scientific data but lacked adaptability. Pearson correlation analysis demonstrated a strong correlation between fire danger classifications and historical fire occurrences, with correlation scores of 0.927 (AHP) and 0.939 (literature-based). Further ROC analysis confirmed the predictive performance of both models, yielding AUC values of 0.91 and 0.9121 for the literature-based and AHP models, respectively. Five ML algorithms were used to validate classification performances, with Artificial Neural Network (ANN) achieving the highest accuracy (86.5%). The accuracy of the ANN algorithm exceeded 0.93 for each danger class, and the F1-Score was above 0.85. FoFiDAS offers a reliable tool for fire danger assessment, supporting early intervention and decision making.

Remote sensing technology and its terrestrial components are more useful than classical geological investigation in mineral exploration and mapping the hydrothermal alteration areas and help to investigate larger areas in short time. Intrusive and volcanic rocks, namely Kösedag syenite and Karatas volcanics in Kösedag (Zara) area shows argillic alteration zones. Two different test area were chosen and sampled for mineralogical studies. XRD-CF investigations showed that kaolinite and illite are the dominant clay minerals in test areas of A and B respectively. The spectroradiometer measurements were carried out 5 times on different surfaces of clayey samples with self-illuminated contact-probe lens within the wavelength range of 350-2500 nm. The spectroradiometer measurements used as endmember were resampled to ASTER short wave infrared (SWIR) bandwidths. Band ratio, principal component and decorrelation stretching analysis were performed to visualize the distribution of clay minerals. In spectral classification method, matched filtering (MF) was used for integrating the satellite image and spectroradiometer measurement data. It was concluded that co-interpretations of the band ratio, principal component, decorrelation stretching analysis, MF results and geological map are very useful in determining, classifying and mapping of the argillic alteration zones related to hydrothermal processes on ASTER image and they seem to be very useful to identify the target areas for mineral exploration.