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The primary objective of this study is to employ the remote sensing data and Soil & Water Assessment Tool model to estimate sediment volume and assess the water balance of the Badra Basin (2,615km2) in eastern Iraq. Remote sensing data was utilized as the main input with the Soil & Water Assessment Tool model. These data involved a land use-land cover map that was constructed by the classification of the Landsat-8 satellite imagery for the year 2020, STMR digital elevation model, soil map was acquired from the Food and Agriculture Organization and climatic data were sourced from the NASA-funded prediction of Worldwide Energy Resource The results discovered that about 40 % and 18% of the yearly rainfall are losing by evapotranspiration and filtration. The average amount of annual sediment transported was predicted at 120.47 tons /ha, 2018 recorded the highest value of transported sediment which is about 360 tons /ha. The volume of annual runoff was assessed at about 340.74 million m3. These results proved that the Soil & Water Assessment tool model has the ability to estimation the sediment and runoff volume. The climatic elements, especially rainfall, in addition to soil classes, topography, and land use-land cover had a significant impact on the amount of transported sediments and the volume of runoff.

Sandy areas are the main problem in regions of arid and semi-arid climate in the world that threaten urban life, buildings, agricultural, and even human health. Remote sensing is one of the technologies that can be used as an effective tool in dynamic features study of sandy areas and sand accumulations. In this study, two new indices were developed to separate the sandy areas from the non-sandy areas. The first one is called the Normalized Differential Sandy Areas Index (NDSAI) that has been based on the assumption that the sandy area has the lowest water content (moisture) than the other land cover classes. The second other is called the Sandy Areas Surface Temperature index (SASTI) which was built on the assumption that the surface temperature of sandy soil is the highest. The results of proposed indices have been compared with two indices that were previously proposed by other researchers, namely the Normalized Differential Sand Dune Index NDSI and the Eolain Mapping Index (EMI). The accuracy assessment of the sandy indices showed that the NDSAI provides very good performance with an overall accuracy of 89 %. The SASTI can isolate many sandy and non-sandy pixels with an overall accuracy about 86 %. The performance of the NDSI is low with an overall accuracy about 82 %. It fails to classify or isolate the vegetation area from the sandy area and might have better performance in desert environments. The performing of NDSAI that is calculated with the SWIR1 band of the Landsat satellite is better than the performing of NDSI that is calculated with the SWIR2 band of the same satellite. EMI performance is less robust than other methods as it is not useful for extracting sandy surfaces in area with different land covers. Change detection techniques were used by comparing the areas of the sandy lands for the periods from 1987 to 2017. The results showed an increase in sandy areas over four decades. The percentage of this increase was about 20 % to 30 % during 2002 and 2017 compared to 1987.