NortheastChinaSoybeanYield20m: an annual soybean yield dataset at 20 m in Northeast China from 2019 to 2023

Accurate monitoring of crop yield is important for ensuring food security. Current yield estimation methods, such as machine learning models or the assimilation of remotely sensed biophysical variables into crop growth models, depend heavily on ground observations and involve significant computational costs. To solve these problems, a hybrid framework coupling the World Food Studies Simulation Model (WOFOST) and the Gated Recurrent Unit model (GRU) was proposed for soybean yield estimation in Northeast China from 2019 to 2023.

This dataset provides 20 m annual soybena yield in Northeast China from 2019 to 2023.

*** The data file is in “.tif" format

*** Temporal Resolution: annually

*** Temporal coverage: 2019-2023

*** Pixel size: 20 m

*** Projection information: EPSG: 4326

准确监测作物产量对保障粮食安全至关重要。当前主流的产量估算方法，如机器学习模型，或通过将遥感生物物理变量同化至作物生长模型的方法，均高度依赖地面观测数据，且计算成本高昂。为解决上述问题，本研究提出了一种耦合世界粮食研究模拟模型（World Food Studies Simulation Model, WOFOST）与门控循环单元模型（Gated Recurrent Unit, GRU）的混合框架，用于2019—2023年中国东北地区的大豆产量估算。

本数据集包含2019—2023年中国东北地区20米分辨率的年度大豆产量数据。

*** 数据文件格式为".tif"格式
*** 时间分辨率：年度
*** 时间覆盖范围：2019—2023年
*** 像素尺寸：20米
*** 投影信息：EPSG: 4326