Estimating soil moisture from satellite data

Soil moisture estimates can help farmers assess plant health, aid in predicting crop yields, let governments can help alleviate and protect against floodings, measure urban water use for city planners, and more.

Ground sensors are the most accurate tools to track water, but are expensive and difficult to rapidly relocate. Using satellite sources however provides an economic option to monitor across large and remote areas and can be invaluable in assessing change and understanding trends for such things as drought assessments or river overflow effectiveness.


How to assess soil moisture from space

The thermal-optical trapezoid model (TOTRAM) is a widely-used model for estimating soil moisture via satellite data. It combines data from thermal and optical sensors. The basis for the model is that ground surface temperature is correlated to soil water content.

Scientists use the optical data to look at vegetation and soil type, as well as other parameters, determining the relationship between ground temperature and soil moisture in a specific area, on a specific day. Then the thermal data is used to calculate moisture based on that relationship.


Removing temperature from the equation

Some satellites, such as Landsat-8, carry both thermal and optical sensors. But not all do. This can limit the data sources when trying to look at an area or date range. This is why Utah State University scientists Morteza Sadeghi and Scott Jones in collaboration with their colleagues Ebrahim Babaeian and Markus Tuller at the University of Arizona decided to create a new model that would only use optical measurements [Sadeghi et al. 2017. The optical trapezoid model: A novel approach to remote sensing of soil moisture applied to Sentinel-2 and Landsat-8 observations. Remote Sensing of Environment, 198, 52-68]. With this, they are able to use data from both Landsat-8 and Sentinel-2 satellites, even though Sentinel-2 does not capture thermal data.

The researchers also found the previous model, TOTRAM, to be somewhat cumbersome, as it required additional calibration for each environment, and for each observation date. For example, scientists had to account for near surface air temperature, relative humidity, wind speed, or other environmental factors that could change the relationship between ground temperature and soil moisture.

A ground reflectance model

To counter these limitations, Sadeghi and colleagues decided to use ground reflectance instead of temperature to assess the amount of water in the ground surface layer. The new optical-trapezoid model (OPTRAM) was developed based on a recently developed physical relationship between soil moisture and shortwave infrared transformed reflectance. The concept is that water, even in the ground, reflects certain wavelengths in a specific way and this would be evident in the data.

Sadeghi and colleagues used ground measurements of soil moisture in several US location to gauge accuracy and to validate and calibrate the model.  Ground testing soon revealed the new model (OPTRAM) to be just as accurate as the old model (TOTRAM).

The relationship between reflectance and moisture is also less affected by the environment than temperature-soil moisture. This meant the new model only required to be calibrated once per location, and was much less dependent on when the data was being captured.

With this new model, Sadeghi and colleagues can now track soil moisture using not only Landsat-8 but also Sentinel-2 satellites.  Indeed in principle any optical satellites covering these spectral bands can now be used.  This offers scientists another helpful resource to help track soil moisture. With less calibration required, the model is also much less costly in computing resources as well.


Expanding the model

Now that the initial research, funded by the U.S. National Science Foundation, is published, Sadeghi and colleagues are continuing to work on improving their new OPTRAM model to better assess calibration and to look for additional remote-sensing datasets that could be used to infer soil moisture, such as MODIS.