Cloud Tolerant Observations of the Diurnal Land Surface Temperature and Their Utility for Soil Moisture and Evaporation Retrieval

Satellite passive microwave (PMW) observations are an essential source of all-weather information on the hydrological state of the land surface. Most notable is their role in providing long term records of precipitation, soil moisture, snow water equivalent and perhaps even vegetation water content.

For soil moisture, passive microwave retrievals typically require knowledge of the physical land surface temperature (LST). This is needed to isolate the surface emissivity and the information on soil moisture it contains. To preserve the cloud tolerant aspect of PMW retrievals the temperature input ideally has a similar all-sky capability. The use of simple linear models to estimate the required temperature from higher-frequency PMW channels (e.g. Owe et al. (2001)), or the use of land surface model fields (e.g. SMAP) are approaches that can be associated with large diurnal errors (Holmes et al., 2009, 2012). These diurnal errors vary in intensity over the season and propagate to the soil moisture retrieval and complicate the combined analysis of ascending and descending paths of the same imager, let alone multiple PMW soil moisture observations within the same day.

To maximize the retrieval of surface information from PMW retrievals it is therefore needed to obtain an unbiased estimate of the diurnal LST under all sky conditions. Recent work at USDA and NASA has demonstrated that it is indeed possible to leverage the current constellation of PMW satellites to obtain such estimates (Holmes et al., 2016, 2015). The PMW-based diurnal LST product can not only help improve the emissivity separation for soil moisture and precipitation retrievals, it opens up a new application in thermal-based evapotranspiration (ET) retrievals (Holmes et al., 2018). This paper will present recent results of the implementation of PMW LST in an operational ET retrieval for drought monitoring, a new frontier in the PMW toolbox.