<p>The spatiotemporal dynamics of salinity in hypersaline lakes is strongly dependent on the rate of water flow feeding the lake, evaporation rate, and the phenomena of precipitation and dissolution. Although in-situ observations are most reliable in quantifying water quality variables, the spatiotemporal distribution of such data are typically limited or cannot be readily extrapolated for long-term projections. Alternatively, remotely-sensed imagery has facilitated less expensive and stronger ability to estimate water quality over a wide range of spatiotemporal resolutions. This study introduces a machine learning model that leverages in-situ measurements and high-resolution satellite imagery to estimate the salinity concentration in water bodies. To this end, 123 points were sampled in April and July of 2019 across the Lake Urmia surface covering the wide range of salinity fluctuations. Among the artificial neural networks, ANFIS, and linear regression tools examined to determine the relationship between salinity and surface reflectance, artificial neural networks yielded the best accuracy evidenced by R<sup>2</sup> = 0.94 and RMSE = 6.8%. The results show that the seasonal change of salinity is linearly correlated with the volume of water feeding the lake, witnessing that dilution imposes a stronger control on the salinity than bed salt dissolution. The impact of disturbance in the lake circulation due to the causeway is also evident from the sharp changes of salinity around the bridge piers near spring when the mixing of fresh and hypersaline water from the southern and northern parts, respectively, takes place. The results of this study prove the promising potential of machine learning tools fed multi-spectral satellite information to map other water quality metrics than salinity as well.</p>
<p>Wetlands are accounted as important providers of ecosystem services, which yield several functionalities such as the support of biodiversity, flood control, soil stabilization to reduce dust generation, natural treatment of surface waters, groundwater replenishment, climate regulation and economic benefits. Over the past decades, the impacts of anthropogenic manipulations amplified by climatic changes have threatened both the quantity and quality of wetlands, worldwide. A continuous monitoring of wetlands is thus necessary to protect them from further destruction, as well as to devise and assess the success of any rehabilitation plans. The conventional methods of water body monitoring chiefly include field surveying, which is time consuming, costly, and limited in extent. Alternatively, remotely sensed data have facilitated a much less expensive and more extensive monitoring of water bodies over a wide range of spatiotemporal resolutions. In this study, we implemented a learning-based classification framework fed by remote sensing data to evaluate the historical trends of the most important wetlands across Iran using the Google Earth Engine cloud computing platform. To this end, we used Landsat imagery between 2000 and 2020 to extract the water body of wetlands in dry seasons to consider the most critical condition. We also examined different spectral indices to identify the best combination giving the largest classification accuracy for each wetland, separately, based on their distinct conditions of water depth and vegetation cover. We then quantified the contribution of wetlands drying to the generation of dust storms via a frequency-intensity index given the annual number of dusty days and the Aerosol Optical Depth (AOD) provided by MODIS. According to the results, the majority of the studied wetlands show significant descending trends with the average loss of 31% in surface area. The aerosol analysis also witnesses the expansion of dust generation sources around most of the retreated wetlands, particularly in those years when the wetlands areas were smaller than the long-term average. The above observations point out a potential threat for the agricultural activities and highlight serious consequences for the health of nearby urban and rural residents.</p><p><strong>Keywords: </strong>Wetland, Dust Storm, Remote Sensing, Environmental Monitoring, Ecosystem Protection</p>
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