Comparison of Contemporary In Situ, Model, and Satellite Remote Sensing Soil Moisture With a Focus on Drought Monitoring

Soil moisture is a key drought indicator; however, current in situ soil moisture infrastructure is inadequate for large-scale drought monitoring. One initiative of the ongoing National Soil Moisture Network program is the development of a near real-time drought monitoring product that integrates in situ, model, and satellite remote sensing data. Data integration from diverse sources requires large-scale validation prior to integration. This study develops a framework for assessing the fidelity of in situ, model, and satellite soil moisture data sets. Here we evaluate data from over 100 in situ monitoring stations that are part of nine monitoring networks; North American Land Data Assimilation System Phase 2 and Climate Prediction Center land surface models; and Soil Moisture Active-Passive, Soil Moisture and Ocean Salinity, and European Space Agency-Climate Change Initiative (ESA-CCI) satellite products. The results indicate the majority of in situ stations exhibit low error variance and are spatially representative; however, some networks and individual stations exhibit anomalously high error variance or are sited in a way that make them not spatially representative of a larger area. Overall, North American Land Data Assimilation System Phase 2 is the modeled product that consistently performed best, and Soil Moisture Active-Passive L3 is the remotely sensed product that consistently performed the best. They were able to both capture in situ soil moisture variability and provide an accurate depiction of drought conditions. The methods and verification framework applied in this study can be used to evaluate any soil moisture data set in any region of the world. Plain Language Summary Soil moisture is an important indicator of drought; however, there are very few monitoring stations that directly measure soil moisture across the contiguous United States. High-quality model and/or satellite remote sensing soil moisture estimates can help fill in the gaps of in-ground soil moisture measurements. However, integration of soil moisture data from diverse sources requires assessment of data quality. This study applies a series of methods for evaluating the integrity of in-ground, model, and satellite soil moisture data sets across the United States. The results indicate that the majority of in-ground sensors tested exhibit high data quality, although some exhibit high measurement error and/or low spatial representativeness. The results also indicate that the land surface models that are part of the North American Land Data Assimilation System Phase 2 and the Soil Moisture Active-Passive remotely sensed soil moisture products best match in-ground soil moisture measurement variability. Additionally, North American Land Data Assimilation System Phase 2 and Soil Moisture Active-Passive data sets accurately depict drought occurrence. The methods applied here can be used to evaluate the quality of soil moisture data sets in any region of the world.