Spatially extensive high-resolution soil moisture mapping is valuable in practical forestry and land management, but challenging. Here we present a novel technique involving use of LIDAR-derived terrain indices and machine learning (ML) algorithms capable of accurately modeling soil moisture at 2 m spatial resolution across the entire Swedish forest landscape. We used field data from about 20,000 sites across Sweden to train and evaluate multiple ML models. The predictor features (variables) included a suite of terrain indices generated from a national LIDAR digital elevation model and ancillary environmental features, including surficial geology, climate and land use, enabling adjustment of soil moisture class maps to regional or local conditions. Extreme gradient boosting (XGBoost) provided better performance for a 2-class model, manifested by Cohen’s Kappa and Matthews Correlation Coefficient (MCC) values of 0.69 and 0.68, respectively, than the other tested ML methods: Artificial Neural Network, Random Forest, Support Vector Machine, and Naïve Bayes classification. The depth to water index, topographic wetness index, and ‘wetland’ categorization derived from Swedish property maps were the most important predictors for all models. The presented technique enabled generation of a 3-class model with Cohen’s Kappa and MCC values of 0.58. In addition to the classified moisture maps, we investigated the technique’s potential for producing continuous soil moisture maps. We argue that the probability of a pixel being classified as wet from a 2-class model can be used as a 0–100% index (dry to wet) of soil moisture, and the resulting maps could provide more valuable information for practical forest management than classified maps.

空间广泛的高分辨率土壤水分测绘在实际林业和土地管理中很有价值，但具有挑战性。在这里，我们提出了一种新技术，涉及使用 LIDAR 衍生的地形指数和机器学习 (ML) 算法，能够在整个瑞典森林景观中以 2 m 的空间分辨率准确模拟土壤水分。我们使用来自瑞典约 20,000 个站点的现场数据来训练和评估多个 ML 模型。预测特征（变量）包括一套由国家激光雷达数字高程模型生成的地形指数和辅助环境特征，包括地表地质、气候和土地利用，能够根据区域或当地条件调整土壤湿度等级图。极限梯度提升 (XGBoost) 为 2-class 模型提供了更好的性能，表现在 Cohen 的 Kappa 和 Matthews 相关系数 (MCC) 值分别为 0.69 和 0.68，与其他测试的 ML 方法相比：人工神经网络、随机森林、支持向量机和朴素贝叶斯分类。源自瑞典财产地图的水深指数、地形湿度指数和“湿地”分类是所有模型最重要的预测因子。所提出的技术能够生成具有 0.58 的 Cohen's Kappa 和 MCC 值的 3 类模型。除了分类的水分图外，我们还研究了该技术制作连续土壤水分图的潜力。我们认为，一个像素从 2 类模型中被归类为湿的概率可以用作土壤湿度的 0-100%（干到湿）指数，