Improving NASA SMAP Soil Moisture Using Upper Missouri River Basin Data
In response to severe drought and flood events in the Upper Missouri River Basin (UMRB) between 2011 and 2019, the Infrastructure Investment and Jobs Act provided funding to improve water monitoring in the region. The UMRB Data Value Study, led by NOAA’s National Integrated Drought Information System (NIDIS), is the assessment element of this multi-component, multi-agency project.
This project, Improving NASA SMAP Soil Moisture Using Upper Missouri River Basin Data, was led by NASA’s Goddard Space Flight Center as a component of the UMRB Data Value Study. This project explored how data from new, higher-density soil moisture stations in the UMRB could enhance soil moisture products derived from NASA’s Soil Moisture Active Passive (SMAP) satellite. The researchers also described potential uses for these more accurate gridded soil moisture products to enhance land surface models, which can lead to improved drought and flood monitoring.
Project Approach and Key Findings
Satellite products are most effective when they can be calibrated and validated with multiple on-the-ground (in situ) stations located within the satellite’s scale of resolution. However, soil moisture monitoring stations are often sparse across large areas. Beginning in 2022, the U.S. Army Corps of Engineers is leading a build-out of hundreds of new mesonet stations across the UMRB. Even mid-way through installation, the density of these new stations offered a unique opportunity to assess and enhance NASA SMAP’s soil moisture estimations and improve land surface models that simulate soil moisture and the water cycle across the region.
This project found that adjusting NASA’s SMAP satellite products using the UMRB station data improved the accuracy of SMAP soil moisture estimates, which are widely used in land surface models used for drought tracking and flood prediction. Models using these adjusted inputs more accurately reflected real-world conditions than those using unadjusted data.
Researchers conducted two model tests to understand how using station-adjusted SMAP data affected drought and flood representation in land surface models. In one test, simulations of the 2019 South Dakota floods showed improved accuracy when using the station-adjusted SMAP soil moisture products. Results were mixed when researchers used station-adjusted soil moisture data together with the Normalized Difference Vegetation Index (NDVI)—a vegetation metric derived from another NASA satellite—to track drought through the relationship between soil moisture and vegetation health. In many parts of the UMRB, the land surface model’s ability to represent drought improved when using the station-adjusted SMAP data. However, model performance decreased in other areas of the basin — likely because of limited soil moisture records and limited station density in those regions.
In another test, the research team explored whether available in situ soil moisture data could be used to estimate missing measurements—either at additional depths or for gaps in time—using a multiple linear regression method. This technique predicts one variable (for example, soil moisture at 5 cm depth) based on its relationship with other variables (for example, soil moisture measurements at deeper layers). This method can help fill gaps in data records and generate soil moisture estimates for the shallow depths needed for comparison with some satellite products.
For more information, please contact Elise Osenga (elise.osenga@noaa.gov).
Research Snapshot
John Bolton, NASA Goddard Space Flight Center
Manh-Hung Le, Science Applications International Corp, NASA Goddard Space Flight Center; Kristen Whitney, Earth System Science Interdisciplinary Center (ESSIC), NASA Goddard Space Flight Center
Key Findings from This Research
- With its growing station density, scientists can use the expanded UMRB mesonet network for satellite calibration and validation research.
- Incorporating on-the-ground soil moisture station data into land surface models can reduce uncertainty for products related to drought and flood tracking and forecasts events.
- In this research, actual station data, when available, improved model accuracy more than data approximated using linear regression. However, approximated data approaches improved land surface model performance where observations were limited and station data were not available.
- Where multiple stations existed within a SMAP grid cell, the relationship between soil moisture and vegetation health was stronger when using the station-adjusted SMAP products than the unadjusted SMAP products. Tracking and understanding these links between soil moisture and plant health can better support informed decision-making and communication around fire risk, forage production, and ecological drought.
- The team also created a map showing the relative wetness or dryness of each SMAP grid cell containing one or more UMRB stations. The map illustrates how soil moisture at each site compares to nearby areas, providing useful context for interpreting regional soil moisture conditions across existing monitoring networks. Similar maps could guide planning for future soil moisture monitoring in the UMRB and beyond.
