INVESTIGATION OF POTENTIAL DUST SOURCES USING SENTINEL-1 AND NEURAL NETWORK: A CASE STUDY FROM BANDAR-E EMAM-OMIDIYE

Abstract

Aeolian erosion is a serious environmental threat that damages soils. Dust storms are one example of the consequences of aeolian erosion in dry and semi-arid areas across the world. In this regard, soil surface roughness is an important parameter for monitoring climate changes on the Earth and modelling aeolian erosion. Synthetic Aperture Radar (SAR) systems are valuable resources for estimating soil surface roughness. In arid soils, SAR backscatter is sensitive to the soil surface roughness at higher frequencies and higher incident angles. Based on these facts and lack of studies in the field of dust and erosion using remote sensing methods, an Artificial Neural Network (ANN) along with Sentinel-1 images in two polarizations (VV and VH) were initially applied to estimate surface roughness for the first time in Bandar-e Emam-Omidiye, Khuzestan, Iran. Subsequently, the results were used to investigate potential dust sources. The parameters used to train the ANN included the radar backscatter coefficient, incident angle, and in-situ roughness. The training accuracy of the proposed ANN was relatively high with an RMSE of 0.8821 and RMSE = 0.8804 for VH and VV polarizations respectively. These data were subsequently used to identify areas prone to dust. The results obtained from the investigation of 25 stations located in areas with five different land covers indicated accurately that locals on clay flats (RMSE = 1.08) are the most prone to aeolian erosion in the form of dust.