부산대학교 도서관

Soil moisture (SM) plays a significant role in many natural and anthropogenic systems. Thus, accurate assessment of changes in SM globally is of great value, including long-term historical assessment. The European Space Agency established the Climate Change Initiative (CCI) program to produce long time-series surface SM datasets starting from 1978 to the present. However, the SM Active Passive (SMAP) mission, launched in 2015, has shown more satisfactory performance in both spatial accuracy and in capturing the pattern of temporal changes. In this article, a random forest (RF) model was proposed to extend the SMAP dataset historically (named Hist_SMAP), using the corresponding CCI SM time-series. We assumed that the temporal changes in the SMAP SM dataset are similar generally to those in the available CCI dataset. Accordingly, the RF model was constructed using the temporal (extracted from the CCI SM data), coupled with terrain and location characteristics (LCs), and migrated to predict the Hist_SMAP dataset. The available in-situ and the real SMAP data were used as references for validation. Compared with the CCI dataset, the predicted Hist_SMAP dataset is closer to the in-situ SM data and the real SMAP data. Moreover, the historical Hist_SMAP dataset is more accurate than the widely used Global Land Evaporation Amsterdam Model (GLEAM) dataset. Thus, the Hist_SMAP dataset was shown to be a reliable substitute for the historical CCI dataset. The new long time-series Hist_SMAP dataset is provided with free access and will be of great value for research and practical application in a range of fields.