Gap-Free Global Annual Soil Moisture: 15km Grids for 1991-2016

We provide a set of 26 soil moisture predictions across 15km grids at the global scale. We modeled and predicted the ESA-CCI soil moisture values across 26 years of available data (1991-2016) using a ML based kernel method and multiple terrain parameters (e.g., slope, wetness index) as prediction factors. We used ground information from the International Soil Moisture Network (ISMN, n=13376) for evaluating soil moisture predictions. Our downscaled soil moisture predictions across 15km grids showed a statistical accuracy varying 0.69-0.87% and 0.04 m3/m3 of cross-validated explained variance and root mean squared error (RMSE). We found a negative bias (-0.01 to -0.08 m3/m3 ) underestimating the values of ISMN when comparing with the ESA-CCI and our predictions across the analyzed years and a relatively better performance between 1998 and 2016. We found no significant differences between the ESA-CCI and our predictions, but we found discrepancy between multiple evaluation metrics (e.g., bias vs efficiency) comparing the ESA-CCI with the ISMN. However, the temporal analysis as revealed by a robust trend detection strategy (e.g., Theil-Sen estimator), suggests a decline of soil moisture at the global scale that is consistent in both gridded estimates and field measurements of soil moisture varying from -0.7[-0.77, -0.62]% in the ESA-CCI product, -0.9[-1.01, -0.8]% in the downscaled predictions and -1.6 [-1.7, -1.5]% in the ISMN. These results highlight the large potential of digital terrain parameters for improving the accuracy and spatial detail of satellite soil moisture grids at the global scale. The soil moisture predictions provided here (folder: predicted-2001-2016) could be useful for quantifying long term soil moisture emergent patterns (i.e., trends) across areas with low availability of soil moisture information in the ESA-CCI. To ensure reproducible results of this study, we also provide the R code and (also in R native format *.rds) the topographic prediction factors for soil moisture across 15 km grids (file: topographic_predictors_15km_grids.rds). This site also includes the harmonized ISMN data with the ESA-CCI and the downscaled predictions based on terrain analysis in an annual basis (files: harmonizedISMNvsESACCI.rds and harmonizedISMNvsPREDICTED.rds) that we used for validating our prediction framework. The soil moisture predictions provided here could be useful for quantifying soil moisture spatial and temporal dynamics across areas with low availability of soil moisture information in the original ESA-CCI database.

本数据集提供了全球范围内，以15公里网格为单位，针对26种土壤湿度预测的集合。通过基于机器学习的核方法以及多种地形参数（例如，坡度、湿润指数）作为预测因素，我们模拟并预测了欧洲空间局-气候变化综合观测计划（ESA-CCI）在可用数据26年（1991-2016年）内的土壤湿度值。为了评估土壤湿度预测，我们使用了国际土壤湿度网络（ISMN，样本数n=13376）的地面信息。在15公里网格上的降尺度土壤湿度预测显示出0.69%-0.87%的统计精度，以及0.04 m3/m3的交叉验证解释方差和均方根误差（RMSE）。在分析年份中，与ESA-CCI及我们的预测值相比，我们发现ISMN的值低估了（-0.01至-0.08 m3/m3），但1998年至2016年间表现相对更优。在ESA-CCI与我们的预测值之间，我们没有发现显著差异，但在多个评估指标（例如，偏差与效率）之间，ESA-CCI与ISMN之间存在差异。然而，通过稳健的趋势检测策略（例如，Theil-Sen估计量）揭示的时间序列分析表明，在全球范围内，土壤湿度呈现一致的下降趋势，这种趋势在网格估计和土壤湿度现场测量中是一致的，从ESA-CCI产品中的-0.7[-0.77, -0.62]%，到降尺度预测中的-0.9[-1.01, -0.8]%，再到ISMN中的-1.6 [-1.7, -1.5]%。这些结果突显了数字地形参数在提高全球尺度卫星土壤湿度网格精度和空间细节方面所具有的巨大潜力。此处提供的土壤湿度预测（文件夹：predicted-2001-2016）可用于量化ESA-CCI中土壤湿度信息匮乏区域的长期土壤湿度涌现模式（即趋势）。为确保本研究的可重复性，我们还提供了R代码以及（以R原生格式*.rds）15公里网格上的地形预测因素（文件：topographic_predictors_15km_grids.rds）。本站点还包括了与ESA-CCI和基于地形分析的降尺度预测的调和ISMN数据（文件：harmonizedISMNvsESACCI.rds和harmonizedISMNvsPREDICTED.rds），这些数据用于验证我们的预测框架。此处提供的土壤湿度预测对于量化原始ESA-CCI数据库中土壤湿度信息匮乏区域的土壤湿度空间和时间动态可能是有用的。