针对土壤与气候数据差异，提出空间分布偏移感知的机器学习模型提升碳排放预测精度。
Spatial Distribution-Shift Aware Knowledge-Guided Machine Learning
- 引入位置相关参数，捕捉同一区域多站点土壤湿度的空间异质性。
- 在中西部各州实现更高本地化预测准确率，优于传统方法。
- 适合需要高精度区域碳排放建模的研究者与政策制定者。
基于来自不同区域的多样土壤特性与气候数据，本研究旨在构建能精准预测土地碳排放的模型。该问题至关重要，因为农业生态系统中碳循环的精确量化对于缓解气候变化和保障可持续粮食生产具有关键意义。准确预测土地碳排放面临挑战，主要源于土壤属性、水分及环境条件在决策尺度上的高度异质性难以校准。传统方法因采用与位置无关的参数，未能充分挖掘空间异质性，且需大量数据支持。为此，本文提出空间分布偏移感知的知识引导机器学习（SDSA-KGML），利用位置依赖参数，有效建模同一区域内多个站点间的显著土壤湿度空间差异。实验结果表明，SDSA-KGML 模型在中西部特定州份实现了更高的局部预测精度。
原文摘要 · Abstract (English)
Given inputs of diverse soil characteristics and climate data gathered from various regions, we aimed to build a model to predict accurate land emissions. The problem is important since accurate quantification of the carbon cycle in agroecosystems is crucial for mitigating climate change and ensuring sustainable food production. Predicting accurate land emissions is challenging since calibrating the heterogeneous nature of soil properties, moisture, and environmental conditions is hard at decision-relevant scales. Traditional approaches do not adequately estimate land emissions due to location-independent parameters failing to leverage the spatial heterogeneity and also require large datasets. To overcome these limitations, we proposed Spatial Distribution-Shift Aware Knowledge-Guided Machine Learning (SDSA-KGML), which leverages location-dependent parameters that account for significant spatial heterogeneity in soil moisture from multiple sites within the same region. Experimental results demonstrate that SDSA-KGML models achieve higher local accuracy for the specified states in the Midwest Region.
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