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Wetlands provide essential benefits, including flood control, water quality enhancement, shoreline erosion prevention, natural resource conservation, recreational opportunities, and esthetic value. However, climate change and human activities have recently posed significant threats to these ecosystems. To address this issue, we employed an integrated approach combining remote sensing and the cloud-free Google Earth Engine (GEE) to monitor the impacts of climate change and human activities on Parishan Wetland in Iran. In this study, various climatic and anthropogenic factors, including air temperature (AT), precipitation, built-up area, croplands, and groundwater storage, were analyzed over the period from 2001 to 2010 to explore their potential effects on wetland conditions. The Pearson correlation test was used to assess the relationships between these variables and wetland health. Also, non-parametric Mann–Kendall (MK) and Pettitt tests were employed to identify monotonic trends and shifts in the time series. The findings suggest a complex interplay of climatic and anthropogenic factors impacting the wetland’s ecosystem. Groundwater availability emerged as the most influential factor, with a very strong positive correlation of 0.92, highlighting the critical role of groundwater in sustaining wetland ecosystems. Air temperature values in recent years have shown a significant increasing trend, while precipitation exhibits a statistically significant decreasing trend. These factors, along with the slightly increasing built-up area, which negatively impacts the natural ecosystem, indicate an urgent need to restore the wetland.

Groundwater storage refers to the water stored in the pore spaces of underground aquifers, which has been increasingly affected by both climate change and anthropogenic activities in recent decades. Therefore, monitoring their changes and the factors that affect it is of great importance. Although the influence of natural factors on groundwater is well-recognized, the impact of human activities, despite being a major contributor to its change, has been less explored due to the challenges in measuring such effects. To address this gap, our study employed an integrated approach using remote sensing and the Google Earth Engine (GEE) cloud-free platform to analyze the effects of various anthropogenic factors such as built-up areas, cropland, and surface water on groundwater storage in the Lake Urmia Basin (LUB), Iran. Key anthropogenic variables and groundwater data were pre-processed and analyzed in GEE for the period from 2000 to 2022. The processes linking these variables to groundwater storage were considered. Built-up area expansion often increases groundwater extraction and reduces recharge due to impervious surfaces. Cropland growth raises irrigation demand, especially in semi-arid areas like the LUB, leading to higher groundwater use. In contrast, surface water bodies can supplement water supply or enhance recharge. The results were then exported to XLSTAT software2019, and statistical analysis was conducted using the Mann–Kendall (MK) non-parametric trend test on the variables to investigate their potential relationships with groundwater storage. In this study, groundwater storage refers to variations in groundwater storage anomalies, estimated using outputs from the Global Land Data Assimilation System (GLDAS) model. Specifically, these anomalies are derived as the residual component of the terrestrial water budget, after accounting for soil moisture, snow water equivalent, and canopy water storage. The results revealed a strong negative correlation between built-up areas and groundwater storage, with a correlation coefficient of −1.00. Similarly, a notable negative correlation was found between the cropland area and groundwater storage (correlation coefficient: −0.85). Conversely, surface water availability showed a strong positive correlation with groundwater storage, with a correlation coefficient of 0.87, highlighting the direct impact of surface water reduction on groundwater storage. Furthermore, our findings demonstrated a reduction of 168.21 mm (millimeters) in groundwater storage from 2003 to 2022. GLDAS represents storage components, including groundwater storage, in units of water depth (mm) over each grid cell, employing a unit-area, mass balance approach. Although storage is conceptually a volumetric quantity, expressing it as depth allows for spatial comparison and enables conversion to volume by multiplying by the corresponding surface area.

Land surface temperature (LST) trends, influenced by climate change, affect vegetation health and productivity, while vegetation, in turn, alters LST by regulating the surface energy balance. These interactions vary by region and vegetation type. In this study, we aimed to (1) examine long-term trends in vegetation conditions and LST over time, and (2) investigate the interactions between vegetation conditions and LST within distinct vegetation types across the Arasbaran Biosphere Reserve. Sentinel-2 spectral-temporal features and the Random Forest model were employed to classify different vegetation types. Time series data for the normalized difference vegetation index (NDVI), normalized difference water index (NDWI), and LST were generated using harmonized Landsat data from 1987 to 2023. Various spatial statistical analyses were applied to address the research questions. The results revealed significant spatial and temporal variations in NDVI, NDWI, and LST among vegetation types. The highest volatility in vegetation conditions occurred in dense and sparse forests, while grasslands exhibited the lowest levels of variability. This variability coincided with an overall increasing trend in NDVI, NDWI, and LST, which was most pronounced in dense forests. Furthermore, a strong negative correlation between NDVI, NDWI, and LST was observed, particularly in croplands. These findings collectively indicate a greening trend in the study area, with forests showing the most pronounced increases. The results also underscore the role of forests and dense vegetation in mitigating projected temperature increases. These insights can inform local land management strategies and decision-making.