Precise and timely information on crop phenology, harvesting areas is paramount to agricultural monitoring, food security, and policy development. Currently, however, higher-resolution products over a larger scale are very few due to the scarcity of ground data. We firstly retrieved a 1 km grid wheat phenological dataset, ChinaCropPhen1km, from 2000 to 2015 based on GLASS LAI by an optimal filter-based phenology detection (OFP) approach including the inflection- and threshold-based methods. We then transferred random forest classifiers trained in limited regions with diversified growing conditions and land covers to the rest areas where ground data are scarce, with more than 130,000 Sentinel-2 images processed using the Google Earth Engine (GEE) platform. We established the 10 m crop maps for four major crops (i.e., maize, rapeseed, winter, and spring Triticeae crops) across 10 European Union (EU) countries from 2018 to 2019. Based on the above accurate information, we finally developed a general framework, Global Wheat Production Mapping System (GWPMS), using data-driven models across eight major wheat producing countries worldwide. We found GWPMS by LSTM could not only generate robust wheat maps with R2 consistently greater than 0.8, but also successfully captured a substantial fraction of yield variations with an average of 76%. Using the accurate wheat maps improved R2 by 6.7% compared to a popularly used dataset. GWPMS is able to map spatial distribution of harvesting areas in a scalable way and further estimate gridded-yield robustly, and it can be applied globally using publicly available data. GWPMS and the resultant datasets will greatly accelerate our understanding and studies on global food security.