In connection to drought, the southwestern United States is experiencing an increase in blowing dust, an increase in incidence of valley fever, an increase in visibility-related traffic accidents, reduction in solar panel efficiency, and earlier mountain snowmelt. The response of the land surface to the wind is greatly dependent on the proportion of land cover, soil and sediment characteristics, weather conditions, and spatial distribution of vegetation, all factors affecting aeolian processes. Combined use of optical and radar satellite imagery products can provide invaluable benefits in characterizing surface properties of desert playas (ephemeral lakes)- the preferred landform for wind erosion. As a home for high temporal coverage of Landsat optical images and a cloud computing platform, Google Earth Engine (GEE) provides a multi-petabyte catalog of satellite imagery, powerful algorithms, and application programming interface capabilities. Processing and analyzing moderate to high spatial resolution images from Landsat and Sentinel-2 in GEE are crucial resources for extracting contributions of different endmembers to pixel mixtures. Radar images, with the capability to penetrate through clouds, darkness, and rain, have a power to detect the extent and levels of changes in land cover and soil moisture. In this study, we identified the fractional abundance of soil, vegetation, and water endmembers of each pixel of images using the spectral unmixing algorithm in GEE in Lordsburg Playa (New Mexico, USA), which is prone to aeolian erosion. Here, we used Landsat-8 and Landsat-5 at 30 meters spatial resolution and Sentinel-2 Multispectral instrument at 10-20 meters resolution. By employing Interferometric Synthetic Aperture Radar (InSAR) techniques, we explored the changes in the water level of the playa and the possible hot spots of erosion-inducing sediment loading to the playa. In this data recipe, we analyzed a pair of Sentinel-1 images that bracket a monsoon day with high rainfall event and a pair of images representing dry day and monsoon day. The results from this approach clearly show locations prone to the change in phase and displacement due to water and thus sediment loading susceptible to wind.