JWSS - Journal of Water and Soil Science

By utilizing land surface temperature (LST), valuable insights can be gained regarding the impact of land use on energy balance processes. Therefore, this study aimed to investigate the trend of LST changes due to land use changes in the Gorab rural district. Four land use types, including water bodies, bare land, Agricultural area, and forest, were determined from 2013 to 2024 for the maximum likelihood classification (MLC) and support vector machine (SVM) models. The surveys showed that the area of water in the dry period decreased from 0.9 km2 in 2013 to 0.4 km2 in 2024, a decrease of 0.5 km2. In contrast, the area of forest areas increased from 136.1 km2 in the dry period of 2013 to 147.2 km2 in 2024. The Kappa coefficient values for the SVM and MLC models during the wet season of 2021 were 53.94 and 68.7, respectively. Based on this, it was found that the MLC model has higher accuracy. To match spectral indices with LST values, NDVI, NDSI, and NDWI were calculated. Land use changes during the 2013-2024 period affected land surface temperatures, causing fluctuations from 11.5°C to 21.18°C in the wet season and from 13.81°C to 31.45°C in the dry season. The highest LST values were associated with barren land, while water bodies and vegetation cover had the lowest LST values. Among the spectral indices, the highest positive correlation was observed with NDWI, with a value of 0.64 in 2024. The highest negative correlation, -0.66, was observed with NDVI in the same year. Over the 11 years, the area of forest cover increased by 8.15%, while agricultural land decreased by 33.5%. The most significant change occurred in agricultural lands, which declined in area from 35.5 km² to 23.6 km².

Soil, as one of the vital natural resources, plays a fundamental role in ecosystem sustainability and global food security; however, degradation caused by unsustainable management, intensive agriculture, and pollution threatens its capacity. The use of organic amendments such as hydrochar is considered an innovative approach to improve soil physicochemical properties and enhance the Soil Quality Index (SQI). This study aimed to investigate the effects of different levels of hydrochar on soil properties and evaluate SQI. The treatments included control and three hydrochar levels (H10, H20, and H50). Soil properties such as pH, porosity, bulk density, electrical conductivity, organic carbon, total nitrogen, and available phosphorus were measured and normalized, and parameter weighting was conducted using entropy and principal component analysis (PCA). Results showed that nitrogen and organic carbon had the greatest importance in soil quality. The H50 treatment recorded the highest SQI (0.815), significantly greater than other treatments, while H20 (0.546) and H10 (0.336) also showed positive effects compared to the control (0.159). Hydrochar application improved organic carbon, nitrogen, and phosphorus and reduced bulk density. Although an increase in electrical conductivity was observed in H50. Overall, hydrochar application had a positive and gradual effect on SQI, with H20 recommended as an optimal level to improve fertility and reduce long-term salinity risks.

Soil contamination by metals is a major problem in terrestrial ecosystems, particularly in and around industrial areas, and can pose a threat to human health in various ways. Therefore, in the present study, the bioaccumulation of heavy metals in the earthworm (Eisenia fetida) in soil around the Abik Cement Factory in Alborz Province was evaluated. Sixty soil samples were collected at depths of 0-20 cm at distances of 500, 1000, and 2000, in the directions of north, south, east, and west. Earthworm samples were also randomly collected from these distances and directions. The wet digestion method was employed to determine heavy metal concentrations in soil and earthworms, and Zn, Cu, Pb, Cr, and Ni were measured using an atomic absorption spectrometer. One- way ANOVA and Kruskal- Wallis statistical methods were used to examine the effects of distance and direction. The bioaccumulation factor (BAF) was used to evaluate the ability of earthworms as a biological indicator. According to the results, the average concentrations of heavy metals in the soil around the lands of the Abik Cement Factory for Cr, Pb, Cu, Zn, and Ni were 22. 20, 10. 19, 6. 34, 7. 63, and 135. 75 (mg/kg), respectively. The average concentrations of heavy metals in earthworms in this region for Cr, Pb, Cu, Zn, and Ni were 51.57. 57, 9. 66, 5. 18, 5. 45, and 106. 17 (mg/kg), respectively. The results indicated that the distance effect was significant only for Ni (p< 0. 05), and the direction effect was significant for all heavy metals except Zn (p> 0. 05). A decreasing trend in the concentrations of Cu, Zn, and Pb was observed with increasing distance from the cement factory, indicating the impact of the Abik Cement Factory on heavy metal pollution in the agricultural soil of the surrounding lands. The highest bioaccumulation factor (BAF) was calculated for zinc and was greater than 1, indicating bioaccumulation of Zn in earthworms. It can be concluded that earthworms can be used as a suitable biological indicator species for monitoring soil contamination with heavy metals, especially for Zn.

Accurate monitoring of water surface dynamics in semi-arid regions poses challenges due to uncertainties regarding the optimal spectral index and sensor selection for effective water resource management. This study assessed the comparative performance of nine spectral water indices across the Landsat-8 and Sentinel-2 platforms to identify the best index-sensor combinations for monitoring semi-arid reservoirs. Utilizing the Google Earth Engine cloud computing platform, 181 satellite images were processed for Golestan Dam in northeastern Iran, comprising 107 Landsat-8 scenes from 2013 to 2023 and 74 Sentinel-2 scenes from 2018 to 2024. After applying atmospheric corrections using the LEDAPS and Sen2Cor algorithms, nine spectral indices (NDWI, MNDWI, ANDWI, AWEI, WI2015, WI1, WI2, LSWI, and NDTI) were calculated and evaluated against the WI2 reference through RMSE, R², and Nash-Sutcliffe efficiency metrics. MNDWI showed superior performance for Sentinel-2 (RMSE=21.42 ha, R²=0.998, NS=0.996), while ANDWI was optimal for Landsat-8 (RMSE=54.54 ha, R²=0.977, NS=0.976). Time-series analysis revealed a 35% reduction in mean annual reservoir area, decreasing from 7.28 km² in 2014 to 4.72 km² in 2021. Consistent seasonal patterns were observed, with spring maxima (9.33 km² in March) and autumn minima (3.35 km² in September) evident across both sensors. A high inter-sensor correlation (r = 0.933) supports the potential for multi-sensor integration in comprehensive monitoring efforts. The LSWI and NDTI indices displayed systematic overestimation due to interference from soil moisture and vegetation, making them unsuitable for quantifying water area. These findings highlight the sensor-dependent nature of optimal index selection, recommending MNDWI-Sentinel-2 pairing for short-term monitoring and ANDWI-Landsat-8 for long-term trend analysis in the management of semi-arid reservoirs.