Aeolian desertification is poorly understood despite its importance for indicating environment change. Here the authors exploit Gaofen-1(GF-1) and Moderate Resolution Imaging Spectroradiometer (MODIS) data to develop a quick and efficient method for large scale aeolian desertification dynamic monitoring in northern China.

This method, which is based on Normalized Difference Desertification Index (NDDI) calculated by band1 & band2 of MODIS reflectance data (MODIS09A1). Then the authors analyze spatial-temporal change of aeolian desertification area and detect its possible influencing factors, such as precipitation, temperature, wind speed and population by Convergent Cross Mapping (CCM) model. It suggests that aeolian desertification area with population indicates feedback (bi-directional causality) between the two variables (P < 0.05), but forcing of aeolian desertification area by population is weak.

Meanwhile, the authors find aeolian desertification area is significantly affected by temperature, as expected. However, there is no obvious forcing for the aeolian desertification area and precipitation. Aeolian desertification area with wind speed indicates feedback (bi-directional causality) between the two variables with significant signal (P < 0.01).

The authors infer that aeolian desertification is greatly affected by natural factors compared with anthropogenic factors. For the desertification in China, the authors are greatly convinced that desertification prevention is better than control.