An integrated approach of deep learning convolutional neural network and google earth engine for salt storm monitoring and mapping

Abstract

This study aims to develop an integrated approach of deep learning convolutional neural network (DL-CNN) and Google Earth Engine (GEE) platform for salt storm modeling and monitoring. First, we selected several ST's predisposing factors, including Land Surface Temperature (LST), soil salinity, AOD, NDWI and NDVI to train models. We then collected 957 Ground Control Points (GCPs) from the study area, which were randomly divided into training (70%) and validation (30%) datasets. Finally, ReLu, Cross-Entropy, and Adam employed as activation function, loss function and optimizer, respectively. Our findings demonstrate the efficiency of an integrated DL-CNN and GEE for monitoring salt storms (Overall Accuracy (OA) = 0.93.02, 0.92.99, 0.93.88, and 0.92.01 for years 2002, 2010, 2015 and 2021, respectively). The results also show an increase in the frequency of salt storm in the study area from 2002 to 2021. Such approach is a promising step toward understanding, controlling, and managing salt storms and recommend salt storm spatial monitoring in other favored areas with similar environmental conditions. In addition, the results of this study provide critical insights into the environmental impacts of the Lake Urmia drought and its intensive environmental impacts on the human health and wellbeing of the residents.