Since the flow variations of Aksu River are strongly influenced by climate change and human activities which threat the local ecosystem and sustainable development, it is necessary to quantify the impact degree of the driving factors. Therefore, this study aims to quantify the impacts of climate change and human activities on the variability of runoff in the Aksu River Basin. The Mann-Kendall trend test and accumulative anomaly method were used to detect the break points of the flow difference value (FDV) between the upstream and downstream flume stations. The improved slope change ratio of cumulative quantity (SCRCQ) method and the Soil and Water Assessment Tool (SWAT) model were applied to decouple the contribution of each driving factor to the FDV variations. Furthermore, a Pearson Correlation Analysis was performed to show the relationships among the driving factors and the FDV. The time series prior to the year (1988) of break point was considered as the baseline period. Based on the annual precipitation and the potential evapotranspiration (PET), the relative impacts of precipitation, PET and human activities on FDV variations as determined by the SCRCQ method were 77.35%, −0.98% and 23.63%, respectively. In addition, the SWAT model indicated that climate factors and human activities were responsible for 92.28% and 7.72% of the variability, respectively. Thus, climate change and human activities showed a similar scale of impact on FDV changes.

As the cornerstone of China’s food security, Northeastern China contributes nearly 20% of national rice production. However, we are still lacking of high-resolution rice maps with detailed and long time-series in this region, impeding crop management decisions for food security. Here we generated an annual 30 m resolution rice distribution dataset for Northeastern China since the 21st century (NECAR) using the Google Earth Engine platform and random forest classification. The workflow involved (1) hierarchical screening principle to select ground samples, (2) the linear interpolation and Whittaker smoothing Landsat5/7/8 time series data and (3) enhanced spectral-feature sets. The resultant annual maps have high overall accuracy (OA) ranging from 0.93 to 0.99, and the satellite estimates corresponded well with statistics for most cities (R² ≥ 0.7, p < 0.01), with higher accuracy than that of similar crops mapping datasets. This is the first attempt in Northeastern China to reconstruct paddy rice patterns at a 30-m resolution over a detailed and extended time series, enabling in-depth analysis of potential environmental and economic impacts.