At least 50% of patients with tuberous sclerosis complex present with intractable epilepsy; for these patients, resective surgery is a treatment option. Here, we report a nationwide multicentre retrospective study and analyse the long-term seizure and neuropsychological outcomes of epilepsy surgery in patients with tuberous sclerosis complex. There were 364 patients who underwent epilepsy surgery in the study. Patients’ clinical data, postoperative seizure outcomes at 1-, 4-, and 10-year follow-ups, preoperative and postoperative intelligence quotients, and quality of life at 1-year follow-up were collected. The patients’ ages at surgery were 10.35 ± 7.70 years (range: 0.5–47). The percentage of postoperative seizure freedom was 71% (258/364) at 1-year, 60% (118/196) at 4-year, and 51% (36/71) at 10-year follow-up. Influence factors of postoperative seizure freedom were the total removal of epileptogenic tubers and the presence of outstanding tuber on MRI at 1- and 4-year follow-ups. Furthermore, monthly seizure (versus daily seizure) was also a positive influence factor for postoperative seizure freedom at 1-year follow-up. The presence of an outstanding tuber on MRI was the only factor influencing seizure freedom at 10-year follow-up. Postoperative quality of life and intelligence quotient improvements were found in 43% (112/262) and 28% (67/242) of patients, respectively. Influence factors of postoperative quality of life and intelligence quotient improvement were postoperative seizure freedom and preoperative low intelligence quotient. The percentage of seizure freedom in the tuberectomy group was significantly lower compared to the tuberectomy plus and lobectomy groups at 1- and 4-year follow-ups. In conclusion, this study, the largest nationwide multi-centre study on resective epilepsy surgery, resulted in improved seizure outcomes and quality of life and intelligence quotient improvements in patients with tuberous sclerosis complex. Seizure freedom was often achieved in patients with an outstanding tuber on MRI, total removal of epileptogenic tubers, and tuberectomy plus. Quality of life and intelligence quotient improvements were frequently observed in patients with postoperative seizure freedom and preoperative low intelligence quotient.

Abstract. Early-season crop identification is of great importance for monitoring crop growth and predicting yield for decision makers and private sectors. As one of the largest producers of winter wheat worldwide, China outputs more than 18 % of the global production of winter wheat. However, there are no distribution maps of winter wheat over a large spatial extent with high spatial resolution. In this study, we applied a phenology-based approach to distinguish winter wheat from other crops by comparing the similarity of the seasonal changes of satellite-based vegetation index over all croplands with a standard seasonal change derived from known winter wheat fields. Especially, this study examined the potential of early-season large-area mapping of winter wheat and developed accurate winter wheat maps with 30 m spatial resolution for 3 years (2016–2018) over 11 provinces, which produce more than 98 % of the winter wheat in China. A comprehensive assessment based on survey samples revealed producer's and user's accuracies higher than 89.30 % and 90.59 %, respectively. The estimated winter wheat area exhibited good correlations with the agricultural statistical area data at the municipal and county levels. In addition, the earliest identifiable time of the geographical location of winter wheat was achieved by the end of March, giving a lead time of approximately 3 months before harvest, and the optimal identifiable time of winter wheat was at the end of April with an overall accuracy of 89.88 %. These results are expected to aid in the timely monitoring of crop growth. The 30 m winter wheat maps in China are available via an open-data repository (DOI: https://doi.org/10.6084/m9.figshare.12003990, Dong et al., 2020a).

Timely and accurate mapping of winter crop planting areas in China is important for food security assessment at a national level. Time-series of vegetation indices, such as the normalized difference vegetation index (NDVI), are widely used for crop mapping, as they can characterize the growth cycle of crops. However, with the moderate spatial resolution optical imagery acquired by Landsat and Sentinel-2, it is difficult to obtain complete time-series curves for vegetation indices due to the influence of the revisit cycle of the satellite and weather conditions. Therefore, in this study, we propose a method for compositing the multi-temporal NDVI, in order to map winter crop planting areas with the Landsat-7 and -8 and Sentinel-2 optical images. The algorithm composites the multi-temporal NDVI into three key values, according to two time-windows-a period of low NDVI values and a period of high NDVI values-for the winter crops. First, we identify the two time-windows, according to the time-series of the NDVI obtained from daily Moderate Resolution Imaging Spectroradiometer observations. Second, the 30 m spatial resolution multi-temporal NDVI curve, derived from the Landsat-7 and -8 and Sentinel-2 optical images, is composited by selecting the maximal value in the high NDVI value period, and the minimal and median values in the low NDVI value period, using an algorithm of the Google Earth Engine. Third, a decision tree classification method is utilized to perform the winter crop classification at a pixel level. The results indicate that this method is effective for the large-scale mapping of winter crops. In the study area, the area of winter crops in 2018 was determined to be 207,641 km 2 , with an overall accuracy of 96.22% and a kappa coefficient of 0.93. The method proposed in this paper is expected to contribute to the rapid and accurate mapping of winter crops in large-scale applications and analyses.Resolution Imaging Spectroradiometer (MODIS) Land Cover Collections [12], and the 1 km Global Land Cover 2000 (GLC2000) [13]). However, more specific information on winter crops is still limited in existing land-cover products [2,14]. On the other hand, the mapping of winter crop planting areas using the traditional agricultural census approach requires a good deal of manpower, money, and time [15]. Further, the timeliness of the traditional approach is poor. For example, the Chinese government generally publishes this information at least six months after the crops have been harvested. Furthermore, the quality of the survey data tends to suffer from the influence of human errors [3].Remote sensing techniques provide a very effective means to map crops [2,11,16,17], due to their fast responses, periodic observations, wide field of view, and low cost [18][19][20]. However, there are still some challenges associated with large-scale (e.g., provincial-or national-scale) winter crop mapping by remote sensing. MODIS data was the major data source for large-scale crop mapping in previous studies, due to its high revisit...