Maize, rice, and soybean are major crops in Northeast China, a major grain-producing region. Precisely quantifying the annual yield of these crops are crucial for food production planning and agricultural management. Traditional methods mostly rely on downscaling statistical data which fail to capture actual yield variations at the field scale, and they depend on numerous parameters and require high costs for field measurements.

In a study published in Scientific Data, Prof. SHI Wenjiao's team from the Institute of Geographic Sciences and Natural Resources Research of the Chinese Academy of Sciences generated 10 m resolution yield maps for maize, rice, and soybean in Northeast China from 2016 to 2021. This high-accuracy dataset fills a gap in multi-crop, long-term, high-resolution yield information.

Researchers developed a new estimation approach of crop yields without the need for field-measured yield data for model training. Based on the Vegetation Photosynthesis Model, remotely sensed images generated by the Sentinel-2 satellite, meteorological data, and statistical information, they integrated dynamic observation indices and comprehensive conversion coefficients, and produced the first 10 m resolution yield datasets for maize, rice, and soybean across Northeast China for 2016-2021.

This approach greatly enhanced both efficiency and accuracy. Researchers determined the accuracy of the dataset through field observations and official statistics. The results showed that the mean relative errors for maize, rice, and soybean were 12%, 12%, and 14%, respectively, indicating high reliability and consistency. Comparing this approach with Global Gridded Crop yield datasets, researchers showed that the overall accuracy for the three crops improved by 32%.

This crop yield dataset provides scientific data and methodological support for the research on spatio-temporal analysis and simulation of agricultural systems, as well as for agricultural production management and food security policy formulation.