Data_share.zip

Soil moisture plays a critical role in land-atmosphere interactions. Prediction of its dynamics is still a grand challenge. While in-situ measurements using sensors offer highly temporally resolved and accurate information compare to satellite observations, existing sensor networks are sparse and scarce. Here we propose a deep learning model for bridging the gap between infrequent satellite observations and sparse in-situ sensor network to improve near-term soil moisture predictions. The Long Short-Term Memory (LSTM)-based deep learning model was used to forecast soil moisture dynamics using soil parameters and climatic variables (e.g. air temperature, relative humidity, pressure, wind speed, turbulent fluxes, solar and terrestrial waves) collected from a dense network of sensors in a field located in Germany in an area of about 20 hectares. The dynamic time-lagged cross-correlation between soil moisture and other co-located soil and climatic features was calculated and a set of optimal predictors for training the LSTM model was selected. To efficiently learn the long-term dependency of soil moisture on its historical trends and to improve the prediction capability of the model, we optimized the LSTM structure, hyperparameters, and the size of the sliding window based on the goodness of fit ($R^2$ score) of the model. We also examined the feasibility of employing the model developed using the temporal data from one location for the prediction of soil moisture at other locations across the landscape. The results illustrate the robustness and efficiency of the proposed model for the spatio-temporal prediction of soil moisture.

土壤水分在陆气相互作用中扮演着至关重要的角色。对其动态变化的预测仍是一项重大挑战。尽管采用传感器的原位（in-situ）测量相较于卫星观测，能够提供高时间分辨率且精准的信息，但现有的传感器网络仍较为稀疏匮乏。为此，我们提出一种深度学习模型，以填补低频卫星观测与稀疏原位传感器网络之间的信息缺口，从而提升短期土壤水分预测精度。本研究采用基于长短期记忆网络（Long Short-Term Memory, LSTM）的深度学习模型，借助土壤参数与气候变量（如气温、相对湿度、气压、风速、湍流通量、太阳辐射与地面辐射波）进行土壤水分动态预测；这些数据采自德国一处约20公顷农田内的高密度传感器网络。研究计算了土壤水分与其他同位置土壤、气候特征间的动态时滞互相关系数，并筛选出用于训练LSTM模型的最优特征集。为高效学习土壤水分对其历史变化趋势的长期依赖关系并提升模型预测性能，我们基于模型的拟合优度（决定系数$R^2$得分）优化了LSTM的网络结构、超参数以及滑动窗口大小。此外，我们还验证了利用单一场站的时序数据训练得到的模型，能否推广至研究区其他站点的土壤水分预测任务。实验结果表明，所提模型在土壤水分时空预测任务中具备良好的鲁棒性与高效性。

搜集汇总

背景与挑战

背景概述

该数据集包含用于土壤湿度预测的深度学习模型数据，来自德国20公顷区域的传感器网络，结合土壤参数和气候变量，采用优化的LSTM模型进行时空预测。