Background The expanding world population is expected to double the worldwide demand for food by 2050. Eighty-eight percent of countries currently face a serious burden of malnutrition, especially in Africa and south and southeast Asia. About 95% of the food energy needs of humans are fulfilled by just 30 species, of which wheat, maize, and rice provide the majority of calories. Therefore, to diversify and stabilize the global food supply, enhance agricultural productivity, and tackle malnutrition, greater use of neglected or underutilized local plants (so-called orphan crops, but also including a few plants of special significance to agriculture, agroforestry, and nutrition) could be a partial solution. Results Here, we present draft genome information for five agriculturally, biologically, medicinally, and economically important underutilized plants native to Africa: Vigna subterranea , Lablab purpureus , Faidherbia albida , Sclerocarya birrea , and Moringa oleifera . Assembled genomes range in size from 217 to 654 Mb. In V. subterranea , L. purpureus , F. albida , S. birrea , and M. oleifera , we have predicted 31,707, 20,946, 28,979, 18,937, and 18,451 protein-coding genes, respectively. By further analyzing the expansion and contraction of selected gene families, we have characterized root nodule symbiosis genes, transcription factors, and starch biosynthesis-related genes in these genomes. Conclusions These genome data will be useful to identify and characterize agronomically important genes and understand their modes of action, enabling genomics-based, evolutionary studies, and breeding strategies to design faster, more focused, and predictable crop improvement programs.

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...

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