摘要
Wind erosion represents a formidable environmental challenge and has serious negative impacts on soil health and agricultural productivity, particularly in arid and semi-arid areas. The complex dynamics of wind erosion make its large-scale monitoring and quantification a daunting task. To facilitate the monitoring and quantification of wind erosion, various scientific approaches and methods have been employed. These include sophisticated wind erosion equations and models, wind tunnel experiments, and the application of radionuclides. Additionally, researchers have assessed soil physicochemical properties, used anemometers for wind speed measurement, and deployed dust collectors for particle capture. Remote sensing technologies, wind erosion monitoring stations, and evaluations of wind barriers have also been utilized. Recently, the adoption of machine learning methods has gained popularity. Despite their value, each of these techniques has limitations in capturing the full spectrum of the wind erosion process. This paper examines these limitations and assesses the effectiveness of each method in the context of wind erosion studies. It also outlines directions for future research and suggests pathways that could enhance the understanding and management of wind erosion.
Wind erosion represents a formidable environmental challenge and has serious negative impacts on soil health and agricultural productivity, particularly in arid and semi-arid areas. The complex dynamics of wind erosion make its large-scale monitoring and quantification a daunting task. To facilitate the monitoring and quantification of wind erosion, various scientific approaches and methods have been employed. These include sophisticated wind erosion equations and models, wind tunnel experiments, and the application of radionuclides. Additionally, researchers have assessed soil physicochemical properties, used anemometers for wind speed measurement, and deployed dust collectors for particle capture. Remote sensing technologies, wind erosion monitoring stations, and evaluations of wind barriers have also been utilized. Recently, the adoption of machine learning methods has gained popularity. Despite their value, each of these techniques has limitations in capturing the full spectrum of the wind erosion process. This paper examines these limitations and assesses the effectiveness of each method in the context of wind erosion studies. It also outlines directions for future research and suggests pathways that could enhance the understanding and management of wind erosion.
