Mapping and Analyzing Winter Wheat Yields in the Huang-Huai-Hai Plain: A Climate-Independent Perspective

Accurate diagnostics of crop yields are essential for climate-resilient agricultural planning; however, conventional datasets often conflate environmental covariates during model training. Here, we present HHHWheatYield1km, a 1 km resolution winter wheat yield dataset for China’s Huang-Huai-Hai Plain spanning 2000–2019. By integrating climate-independent multi-source remote sensing metrics with a Random Forest model, calibrated against municipal statistical yearbooks, the dataset exhibits strong agreement with county-level records (R = 0.90, RMSE = 542.47 kg/ha, MRE = 9.09%), ensuring independence from climatic influences for robust driver analysis. Using Geodetector, we reveal pronounced spatial heterogeneity in climate–yield interactions, highlighting distinct regional disparities: precipitation variability exerts the strongest constraints on yields in Henan and Anhui, whereas Shandong and Jiangsu exhibit weaker climatic dependencies. In Beijing–Tianjin–Hebei, March temperature emerges as a critical determinant of yield variability. These findings underscore the need for tailored adaptation strategies, such as enhancing water-use efficiency in inland provinces and optimizing agronomic practices in coastal regions. With its dual ability to resolve pixel-scale yield dynamics and disentangle climatic drivers, HHHWheatYield1km represents a resource for precision agriculture and evidence-based policymaking in the face of a changing climate.