论文标题
使用机器学习来自原位测量的全球土壤水分-SOMO.ML
Global soil moisture from in-situ measurements using machine learning -- SoMo.ml
论文作者
论文摘要
虽然土壤水分信息对于多种水文和气候应用至关重要，但空间连续的土壤水分数据只能从卫星观测或模型模拟中获得。在这里，我们提出了使用机器学习，somo.ml。通过原位测量产生的全球长期数据集的全球土壤水分数据集。我们基于从全球1,000多个站点收集的原位数据，训练长期记忆（LSTM）模型，以推断空间中的每日土壤水分动态。在2000-2019期间，Somo.ml提供了0.25°的空间和每日时间分辨率的多层土壤水分数据（0-10厘米，10-30厘米和30-50厘米）。通过交叉验证和与现有土壤水分数据集相互比较来评估所得数据集的性能。 SOMO.ML在时间动力学方面表现特别出色，使其对于需要时间变化的土壤水分（例如异常检测和记忆分析）特别有用。 SOMO.ML鉴于其独立和新颖的推导，以支持大规模的水文，气象和生态分析，对现有的建模和卫星数据集进行了补充。
While soil moisture information is essential for a wide range of hydrologic and climate applications, spatially-continuous soil moisture data is only available from satellite observations or model simulations. Here we present a global, long-term dataset of soil moisture generated from in-situ measurements using machine learning, SoMo.ml. We train a Long Short-Term Memory (LSTM) model to extrapolate daily soil moisture dynamics in space and in time, based on in-situ data collected from more than 1,000 stations across the globe. SoMo.ml provides multi-layer soil moisture data (0-10 cm, 10-30 cm, and 30-50 cm) at 0.25° spatial and daily temporal resolution over the period 2000-2019. The performance of the resulting dataset is evaluated through cross validation and inter-comparison with existing soil moisture datasets. SoMo.ml performs especially well in terms of temporal dynamics, making it particularly useful for applications requiring time-varying soil moisture, such as anomaly detection and memory analyses. SoMo.ml complements the existing suite of modelled and satellite-based datasets given its independent and novel derivation, to support large-scale hydrological, meteorological, and ecological analyses.
