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[Display omitted] • Cropping cycles were depicted based on dynamic patterns of vegetation and brownness indices. • The CI mapping algorithm was efficient in reducing the omission errors of double rice. • The first 10-m CI map over conterminous China was generated using Sentinel-1/2 data. • Around 28% of cropped fields are cultivated by multiple cropping in China in 2020. Multiple cropping is a widespread agricultural intensification for increasing food production. National-scale Cropping Intensity (CI) mapping is important for achieving sustainable development goals. However, previous studies have largely relied on vegetation indices (VI) waves for detecting valid cropping cycles, which is challenged by the complexity of agricultural systems. i.e. winter crops with multiple VI waves and double rice with less distinctive waves. This study proposed a robust framework for Mapping cropping Intensity through better-characterizing crop Life cycles based on combined considerations of vegetative and productive Stages (MILS). The number of cropping cycles was estimated by the frequency of valid coupled patterns of vegetation and brownness indices from Sentinel-2 (S2) MultiSpectral Instrument (MSI) time series and further improved through fusing Sentinel-1 (S1) SAR and S2 data to reduce the omission errors of double rice. The MILS algorithm was implemented using the Google Earth Engine platform and applied to China, which is dominated by smallholder farms with diverse cropping patterns. This study produced the first 10 m updated CI map over conterminous China (CIChina10m) in 2020 by fusing S1 and S2 time series. The CIChina10m had an overall accuracy of 93.93% when validated with 14,468 widely spread reference sites. The cropping intensity was 1.2769 on the national scale, which illustrated higher values in the Middle and lower reaches of the Yangtze River plain (CI = 1.5879) and South China (CI = 1.5503). There were 1086,620, 412,620 and 1,441 km2 areas cultivated by single, double and triple cropping in China, which accounted for 72.4%, 27.5% and 0.1% of cropland, respectively. The proposed MILS algorithm showed good performances in detecting complex agricultural systems, which can be further applied to generate continental or global 10-m CI products with good quality. Codes of the MILS algorithm are publicly available at https://code.earthengine.google.com/a7f24f76291bf901ee25a130025a7ce6 , and the first 10m national CI data products in China with good accuracy (ChinaCI10m) are also publicly accessed at https://doi.org/10.6084/m9.figshare.23939505. [ABSTRACT FROM AUTHOR]