Quantitative Soil Wind Erosion Potential Mapping for Central Asia Using the Google Earth Engine Platform

• Published in: Remote sensing (Basel, Switzerland) Vol. 12; no. 20; p. 3430
• Main Authors: Wang, Wei, Samat, Alim, Ge, Yongxiao, Ma, Long, Tuheti, Abula, Zou, Shan, Abuduwaili, Jilili
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-10-2020
• Subjects: Aerosols; Agricultural production; central Asia; Climate change; Cloud computing; Deserts; Dust storms; Environmental impact; Food security; GEE; Geographic information systems; Land cover; Land degradation; Land management; Mapping; Modelling; Optical analysis; Optical thickness; Outdoor air quality; Prediction models; Productivity; Regions; Remote sensing; RWEQ; Snowmelt; Soil erosion; Soils; Spatial data; Spatial discrimination; Spatial resolution; spatial-temporal variation; Spring (season); Sustainability management; Trends; Vegetation; Wind erosion; wind erosion modeling; Wind measurement; Wind speed; Central Asia; United States > US; Texas
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs12203430

A lack of long-term soil wind erosion data impedes sustainable land management in developing regions, especially in Central Asia (CA). Compared with large-scale field measurements, wind erosion modeling based on geospatial data is an efficient and effective method for quantitative soil wind erosion mapping. However, conventional local-based wind erosion modeling is time-consuming and labor-intensive, especially when processing large amounts of geospatial data. To address this issue, we developed a Google Earth Engine-based Revised Wind Erosion Equation (RWEQ) model, named GEE-RWEQ, to delineate the Soil Wind Erosion Potential (SWEP). Based on the GEE-RWEQ model, terabytes of Remote Sensing (RS) data, climate assimilation data, and some other geospatial data were applied to produce monthly SWEP with a high spatial resolution (500 m) across CA between 2000 and 2019. The results show that the mean SWEP is in good agreement with the ground observation-based dust storm index (DSI), satellite-based Aerosol Optical Depth (AOD), and Absorbing Aerosol Index (AAI), confirming that GEE-RWEQ is a robust wind erosion prediction model. Wind speed factors primarily determined the wind erosion in CA (r = 0.7, p < 0.001), and the SWEP has significantly increased since 2011 because of the reversal of global terrestrial stilling in recent years. The Aral Sea Dry Lakebed (ASDLB), formed by shrinkage of the Aral Sea, is the most severe wind erosion area in CA (47.29 kg/m2/y). Temporally, the wind erosion dominated by wind speed has the largest spatial extent of wind erosion in Spring (MAM). Meanwhile, affected by the spatial difference of the snowmelt period in CA, the wind erosion hazard center moved from the southwest (Karakum Desert) to the middle of CA (Kyzylkum Desert and Muyunkum Desert) during spring. According to the impacts of land cover change on the spatial dynamic of wind erosion, the SWEP of bareland was the highest, while that of forestland was the lowest.