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 R2 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 R2, 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.

The divergent response of vegetation phenology to urbanization: A case study of Beijing city, China.

Characterizing the relationship between vegetation phenology and urbanization indicators is essential to understand the impacts of human activities on urban ecosystems. In this study, we explored the response of vegetation phenology to urbanization in Beijing (China) during 2001-2018, using impervious surface area (ISA) and the information of urban-rural gradients (i.e., concentric rings from the urban core to surrounding rural areas) as the urbanization indicators. We found the change rates of vegetation phenology in urban areas are 1.3 and 1.1 days per year for start of season (SOS) and end of season (EOS), respectively, about three times faster than that in forest. Moreover, we found a divergent response of SOS with the increase of ISA, which differs from previous results with advanced SOS in the urban environment than surrounding rural areas. This might be attributed to the mixed land cover types and the thermal environment caused by the urban heat island in the urban environment. Similarly, a divergent pattern of phenological indicators along the urban-rural gradient shows a non-linear response of vegetation phenology to urbanization. These findings provide new insights into the complicated interactions between vegetation phenology and urban environments. High-resolution weather data are required to support process-based vegetation phenology models in the future, particularly under different global urbanization and climate change scenarios.