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Study areaThere is a long history of diversified cropping patterns due to the climatic and topographic complexity in China4. Cropping intensity increases from north to south, and multiple cropping dominates in regions south of 400N4. For example, multiple cropping systems of double rice and winter wheat plus maize are popular in the Middle-lower Yangtze river plain and the Huang-Huai-Hai plain, respectively (Fig. 1)22. Three staple crops, maize, paddy rice, and wheat, are widely distributed across the country (Figure S1). These three major crops contributed to more than half (57.08%) of the total sown area in China in 2020 (http://www.stats.gov.cn/english/).Fig. 1The distribution map of cropping patterns in 2021, 9 agricultural regions and validation sites in China. Notes: A to I represented nine agricultural regions in China. (A) Middle-lower Yangtze River Plain; (B) Huang-Huai-Hai plain; (C) Northeast China; (D) Inner Mongolia and along the Great Wall; (E) Loess plateau; (F) Southwest China; (G) Southern China; (H) Gansu-Xinjiang region; (I) Qinghai-Tibet region.Full size imageMODIS images and pre-processingWe used the 500 m 8-day composite Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance products (MOD09A1) from 2015 to 2021. Three spectral indices were calculated: the 2-band Enhanced Vegetation Index (EVI2)23, LSWI16, and Normalized Multi-band Drought Index (NMDI)24 (Fig. 2). The functions of EVI2, LSWI, and NMDI are provided in Eqs. 1–3 as follows.$${rm{EVI2}}=2.5times left({rho }_{NIR}-{rho }_{{rm{Red}}}right)/left({rho }_{NIR}+2.4times {rho }_{{rm{Red}}}+1right)$$
(1)
$${rm{LSWI}}=left({rho }_{NIR}-{rho }_{SWIR6}right)/left({rho }_{NIR}+{rho }_{SWIR6}right)$$
(2)
$$NMDI=frac{{rho }_{NIR}-left({rho }_{SWIR6}-{rho }_{SWIR7}right)}{{rho }_{NIR}+left({rho }_{SWIR6}-{rho }_{SWIR7}right)}$$
(3)
where, ρNIR, ρRed, ρSWIR6 and ρSWIR7 represented the surface reflectance values from the red (620–670 nm), Near-infrared (841–875 nm), short wave infrared band centered at 1640 nm (1628–1652 nm) and 2130 nm (2105–2155 nm), respectively.Fig. 2The workflow of the methodology: Data preprocessing, deriving cropping intensity, mapping three staple crops and obtaining annual maps of cropping patterns in China.Full size imageFor each spectral index (EVI2, LSWI, and NMDI), a daily continuous time series was developed based on the cloud-free observations using the Whittaker Smoother (WS)25. The WS smoother performed well in multiple cropping regions and therefore was applied here26.Validation data and other datasetsThe validation data in this study included the ground truth reference data and agricultural census data. The ground truth reference data were collected in major agricultural regions with GPS receivers and digital cameras during the study period (2015–2021) (Fig. 1, Table S1). For each sampling site, the geographic location and crop types were recorded. The reliability of ground survey data was improved through visual confirmation based on high-resolution images in Google Earth. Some reference sites with small field sizes were removed to considering the mixed-pixel problems of MODIS images. Finally, we obtained a total of 18,379 ground samples collected during 2015–2021 (Table S1). All the ground truth reference data were used to validate the cropping pattern data in its corresponding year. Agricultural census data were obtained from the National Statistical Bureau of China (NSBC) (http://www.stats.gov.cn/english/), which was collected through sampling statistics. The crop calendar data from agro-meteorological stations recorded the sowing, seedling, tillering, heading, and harvesting dates of winter wheat (210 sites) or spring wheat (90 sites). The calendar data were applied to establish the trend surfaces of key phenological stages of winter wheat and spring wheat, respectively. The crop calendar data were provided by the National Meteorological Information Center, China Meteorological Administration.The cropland distribution data were derived from the 30 m GlobeLand30 global land cover data in 202027. The total accuracy of GlobeLand30 in 2020 is 85.72%, and the Kappa coefficient is 0.82 (www.globallandcover.com). To correspond to MODIS images, the 30 m cropland pixels from GlobeLand30 data were spatially aggregated to a 500 m cropland fraction map. For simplification, we classified pixel purity of MODIS pixels into three groups: cropland percentages of >90%, 50–90%, and More