Comment on essd-2022-348

Abstract

<strong class="journal-contentHeaderColor">Abstract.</strong> Motivated by the lack of long-term global soil moisture products with both high spatial and temporal resolutions, a global 1-km daily spatiotemporally continuous soil moisture product (GLASS SM) was generated from 2000 to 2020 using an ensemble learning model (eXtreme Gradient Boosting&mdash;XGBoost). The model was developed by integrating multiple datasets, including albedo, land surface temperature, and leaf area index products from the Global Land Surface Satellite (GLASS) product suite, as well as the European reanalysis (ERA5-Land) soil moisture product, in situ soil moisture dataset from the International Soil Moisture Network (ISMN), and auxiliary datasets (Multi-Error-Removed Improved-Terrain DEM and SoilGrids). Given the relatively large scale differences between point-scale in situ measurements and other datasets, the triple collocation (TC) method was adopted to select the representative soil moisture stations and their measurements for creating the training samples. To fully evaluate the model performance, three validation strategies were explored: random, site-independent, and year-independent. Results showed that for the random test samples, the XGBoost model trained with representative stations selected by the TC method achieved the highest accuracy, with an overall correlation coefficient (R) of 0.941 and root mean square error (RMSE) of 0.038 m³ m^-3; whereas for both the site- and year-independent test samples, although the overall model performance was comparatively lower, training the model with representative stations could still considerably improve its overall accuracy. Meanwhile, compared to the model developed without station filtering, the validation accuracies of the model trained with representative stations improved significantly on most station, with the median R and unbiased RMSE (ubRMSE) of the model for each station increasing from 0.64 to 0.74, and decreasing from 0.055 to 0.052 m³ m^-3, respectively. Further validation of the GLASS SM product across four independent soil moisture networks revealed its ability to capture the temporal dynamics of measured soil moisture (R = 0.69&ndash;0.89; ubRMSE = 0.033&ndash;0.048 m³ m^-3). Lastly, the inter-comparison between the GLASS SM product and two global microwave soil moisture datasets&mdash;the 1-km Soil Moisture Active Passive/Sentinel-1 L2 Radiometer/Radar soil moisture product and the European Space Agency Climate Change Initiative combined soil moisture product at 0.25&deg;&mdash;indicated that the derived product maintained a more complete spatial coverage, and exhibited high spatiotemporal consistency with those two soil moisture products. The annual average GLASS SM dataset from 2000 to 2020 can be freely downloaded from <a href="https://doi.org/10.5281/zenodo.7172664" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.7172664</a> (Zhang et al., 2022a), and the complete product at daily scale is available at <a href="http://glass.umd.edu/soil_moisture/" target="_blank" rel="noopener">http://glass.umd.edu/soil_moisture/</a>.