Long-term annual mapping and spatial-temporal dynamic analysis of winter wheat in Shandong Province based on spatial-temporal data fusion (2000-2022)

Winter wheat, as one of the world's key staple crops, plays a crucial role in ensuring food security and shaping international food trade policies. However, there has been a relative scarcity of high-resolution, long time-series winter wheat maps over the past few decades. This study utilized Landsat and Sentinel-2 data to produce maps depicting winter wheat distribution in Google Earth Engine (GEE). We further analyzed the comprehensive spatial-temporal dynamics of winter wheat cultivation in Shandong Province, China. The gap filling and Savitzky-Golay filter method (GF-SG) was applied to address temporal discontinuities in the Landsat NDVI (Normalized Difference Vegetation Index) time series. Six features based on phenological characteristics were used to distinguish winter wheat from other land cover types. The resulting maps spanned from 2000 to 2022, featuring a 30-m resolution from 2000 to 2017 and an improved 10-m resolution from 2018 to 2022. The overall accuracy of these maps ranged from 80.5 to 93.3%, with Kappa coefficients ranging from 71.3 to 909% and F1 scores from 84.2 to 96.9%. Over the analyzed period, the area dedicated to winter wheat cultivation experienced a decline from 2000 to 2011. However, a notable shift occurred with an increase in winter wheat acreage observed from 2014 to 2017 and a subsequent rise from 2018 to 2022. This research highlights the viability of using satellite observation data for the long-term mapping and monitoring of winter wheat. The proposed methodology has long-term implications for extending this mapping and monitoring approach to other similar areas.