Land use and land cover (LULC) are fundamental units of human activities. Therefore, it is of significance to accurately and in a timely manner obtain the LULC maps where dramatic LULC changes are undergoing. Since 2017 April, a new state-level area, Xiong’an New Area, was established in China. In order to better characterize the LULC changes in Xiong’an New Area, this study makes full use of the multi-temporal 10-m Sentinel-2 images, the cloud-computing Google Earth Engine (GEE) platform, and the powerful classification capability of random forest (RF) models to generate the continuous LULC maps from 2017 to 2020. To do so, a novel multiple RF-based classification framework is adopted by outputting the classification probability based on each monthly composite and aggregating the multiple probability maps to generate the final classification map. Based on the obtained LULC maps, this study analyzes the spatio-temporal changes of LULC types in the last four years and the different change patterns in three counties. Experimental results indicate that the derived LULC maps achieve high accuracy for each year, with the overall accuracy and Kappa values no less than 0.95. It is also found that the changed areas account for nearly 36%, and the dry farmland, impervious surface, and other land-cover types have changed dramatically and present varying change patterns in three counties, which might be caused by the latest planning of Xiong’an New Area. The obtained 10-m four-year LULC maps in this study are supposed to provide some valuable information on the monitoring and understanding of what kinds of LULC changes have taken place in Xiong’an New Area.

Classification of staple crops is of significance to acquire the updating information on crop distribution, crop yield and related market storage. In the existing studies, the open accessible satellite images are mostly used, especially for MODIS and Landsat images. However, the coarser spatial resolution cannot accurately depict the crop spatial distributions. Additionally, the classification accuracy is often carried out through some randomly selected pixels or polygons, which might not fully evaluate the classification results. In view of this, this study utilizes the Gaofen-1 and Gaofen-6 satellite images with a spatial resolution of 16 m, to extract crop distributions, which is rarely explored in the literature. Several Experiments are conducted in Yanshou county, where a full-scale field investigation is carried out to collect the validation sample data. Through this rigorous assessment, the crop classification maps are thoroughly evaluated in terms of accuracy and running cost. The results indicate that Gaofen-1 and Gaofen-6 images are qualified to accurately classify the staple crops and the splitting tree number variable for Random Forest model can be set to some mid-value such as 100 on consideration of both accuracy and computational efficiency.

Land Use and Land Cover (LULC) are the basic units of human activities and also serve as the significant factors to assess the climate change studies and environmental protection. Therefore, it is of significance to accurately and timely obtain the LULC maps on the earth’s surface, in particular for those regions where dramatic LULC changes are undergoing. Since 2017 April, a new state-level new area, Xiong’an New Area, was established in China, which would inevitably lead to some LULC changes in this region. In order to better understand what kinds of LULC change in this region with more details and further evaluate the potential impacts that the LULC changes will bring, this study makes full use of the 10-m multi-temporal Sentinel-2 images on the cloud-computing Google Earth Engine (GEE) and the powerful classification capability of Random Forest (RF) models, to generate the continuous LULC maps in Xiong’an New Area from 2017 to 2020. The derived LULC map for each year in Xiong’an New Area achieves high accuracy, with OA and Kappa values less than 0.9. Based on the obtained LULC maps, this study analyzed the spatial and temporal changes of LULC types in the last four years. It revealed that the estimated dry farmland has decreased from 2017 to 2018 and then kept almost stable, and the majority of it has converted to non-cropland, especially to the tree seedlings and landscape dual-use regions. The estimated impervious areas that were mainly composed of buildings and transport infrastructures first increased due to the newly constructed Xiong’an Station with some high-speed roads and then decreased from 2019 to 2020, mainly due to the relocation of some small villages. Accordingly, some building groups and aggregations were going to show in this region, and are expected to further developed in the future. Other LULC types such as lakes and forests have not changed dramatically, indicating that the environmental protection in Xiong’an New Area was, to date, satisfactory. The obtained 10-m and 4-year LULC maps in this study can provide some valuable information on the monitoring and understanding of what kinds of changes were and will be undergoing in Xiong’an New Area, and can also be used for evaluating the potential impacts and challenges such as environmental protection. Additionally, the utilization of GEE and the multi-temporal 10-m Sentinel-2 images can help to achieve LULC mapping in this region in an accurate and timely manner, which can be used in future studies.