High Spatial resolution Soil Moisture Mapping using L-band radiometer and SMALL Unmanned aerial systems

Soil moisture is of fundamental importance to many hydrological, biological and geochemical processes, plays an important role in the development and evolution of convective weather and precipitation, and impacts water resource management, agriculture, and flood runoff prediction. The launch of NASA's Soil Moisture Active/Passive (SMAP) mission in 2015 have provided spaceborne global measurements of soil moisture and surface freeze/thaw state at fixed crossing times and with spatial resolutions as low as 9 km for some products. However, the observation of soil moisture on much smaller spatial scales and at arbitrary diurnal times for satellite data validation, precision agriculture, and evaporation and transpiration studies of boundary layer heat transport is yet needed. Such capabilities have recently been demonstrated using a unique Lobe Differencing Correlation Radiometer (LDCR) on a small unmanned aerial system (sUAS) platform. Compared with other methods of validation based on either in-situ measurements [1] or existing airborne sensors suitable for manned aircraft deployment [2], the integrated design of the LDCR on a lightweight sUAS can provide sub-watershed (~km scale) coverage at very high spatial resolution (~15 m) suitable for scaling studies, and at comparatively low operator cost. Field experiments using LDCR Revision A and Tempest sUAS had been performed in 2015 and 2016 to demonstrate their performance. The LDCR soil moisture maps are generated using linear minimum mean square error (LMMSE) estimation method from antenna temperature measurements, considering the LDCR antenna radiation pattern, soil-vegetation radiative transfer (RT) model, soil dielectric mixing model, surface roughness correction, and vegetation correction. Different soil moisture covariance functions are used based on different assumptions of soil moisture spatial covariance referring to vegetation cover types and land managements. The LDCR retrieved soil moisture are favorably compared with ground truth data. The LDCR Revision C is under development using both analog and digital correlator, and RFI mitigation algorithm will be studied using digital correlator data.