A dataset of winter wheat aboveground biomass in China during 2007–2015 based on data assimilation

As a key variable to characterize the process of crop growth, the aboveground biomass (AGB) plays an important role in crop management and production. Process-based models and remote sensing are two important scientific methods for crop AGB estimation. In this study, we combined observations from agricultural meteorological stations and county-level yield statistics to calibrate a process-based crop growth model for winter wheat. After that, we assimilated a reprocessed temporal-spatial filtered MODIS Leaf Area Index product into the model to derive the 1 km daily AGB dataset of the main winter wheat producing areas in China from 2007 to 2015. The validation using ground measurements also suggests the derived AGB dataset agrees well with the filed observations, i.e., the R(2) is above 0.9, and the root mean square error (RMSE) reaches 1,377 kg·ha(−1). Compared to county-level statistics during 2007–2015, the ranges of R(2), RMSE, and mean absolute percentage error (MAPE) are 0.73~0.89, 953~1,503 kg·ha(−1), and 8%~12%, respectively. We believe our dataset can be helpful for relevant studies on regional agricultural production management and yield estimation.