Year: 2026
Type: data-paper
Abstract: Accurate mapping of crop yields is essential for informed agricultural decision-making and optimal allocation of resources. Current crop yield datasets are deficient in large-scale, high-resolution information regarding the long-term spatial and temporal distribution of crop yields. To address this challenge, we developed a method of vegetation photosynthesis model combined with transition coefficient, producing a detailed dataset with 10 m resolution, covering major regions of maize, rice, and soybean in Northeast China from 2016 to 2021. The method introduces a dynamic observation index ( \({{\rm{APAR}}}_{{\varepsilon }_{g}}\) ) and a composite yield-conversion coefficient ( a ), which presents an innovative method for estimating crop yields without field measurements. Validation results show that, for maize, rice, and soybean, the model achieves r values of 0.39, 0.51, and 0.52; MREs of 12.14%, 11.96%, and 14.06%; and rRMSEs of 16.97%, 16.12%, and 17.26%, respectively. The dataset offers valuable insights into crop yield distribution, supporting better agricultural decision-making and resource optimization.
Source: Scientific Data
Authors: Fei Teng, Minglei Wang, Wenjiao Shi, Li Pan, Jinghan Guo, Xiangming Xiao
Language: English
Subfield: Ecology
Field: Environmental Science
Domain: Physical Sciences
SDG: Zero hunger
Open Access status: gold
Estimated APC paid: $2,490
Awards: G2763078649, G4049631902, G7492125530
OpenAlex ID: W7127312397