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A persistent challenge in the measurement of soil moisture from satellites stems from the inherently low spatial resolution using active or passive system. The lobe differencing correlation radiometer (LDCR) on a small unmanned aerial system (sUAS) is shown to provide a capability to measure soil moisture at high spatial resolution for a range of scientific and operational purposes. Flight tests of LDCR on a fixed wing sUAS were performed at the Canton, Oklahoma Soilscape site in September 2015, and Irrigation Research Foundation (IRF) in Yuma, Colorado, in June 2016. The LDCR design and performance are discussed, and the calibration using both preflight lab test data and in-flight data over a calm pond was performed to calibrate the radiometer. Radio frequency interference (RFI) from the sUAS platform was observed and mitigated. The LDCR sampling processes are detailed and an implementation of the $\tau -\omega $ vegetation correction model along with a semiempirical surface roughness correction model incorporating a full-domain soil moisture mapping algorithm is presented. The algorithm uses a weakly nonlinear observation operator suitable for irregular sUAS flight trajectories that maps volumetric soil moisture (VSM) on a user-defined product grid from the sUAS sampling grid. Using LDCR radiometric and thermal measurements, along with Landsat-based vegetation water content (VWC) and soil texture information, soil moisture was mapped at decameter spatial resolution. The retrieved VSM data are favorably compared with in situ VSM measurements and irrigation records. A method for determining LDCR VSM estimation errors is developed to quantify the mapping algorithm accuracy and assess the impact of error sources.

A lobe-differencing antenna designed as a $2\times 2$ rectangular L-band microstrip array used for the CU-BlackSwift Technologys (BST) lobe-differencing correlation radiometer is presented. The antenna array was integrated into the fuselage of the BST tempest small unmanned aerial system (sUAS). A prototype $2\times 1$ microstrip colinear (MiCo) antenna array operating at center frequency 1.4135 GHz was characterized to validate the accuracy of HFSS simulation results of input impedance. Furthermore, a $2\times 2$ MiCo antenna array with effective use of styrofoam blocks for frequency tuning and providing mechanical stability was built. The effect of separation of the upper and lower array pairs on mutual coupling and radiometer sensitivity and the tuning effect of the styrofoam blocks on resonant frequency were studied. An optimal vertical separation distance was identified that minimizes mutual coupling and provides maximum main-to-back lobe ratio. A method of antenna frequency tuning using dielectric overlay material is presented to correct the dielectric impact of the sUAS fuselage.

The Lobe Differencing Correlating Radiometer (LDCR) developed at Center for Environmental Technology (CET) at CU, Boulder is a lightweight payload for small unmanned aerial systems (sUAS) enabling remote soil moisture measurement for precision agriculture applications. The second revision of the payload (LDCR Rev B) includes a digital correlation detector for which the data processing flow, instrument calibration, and test data analysis is presented. Cross frequency peak detection, time and frequency domain kurtosis, and complex coherence phase are used to detect radio frequency interference (RFI) in the high spectral resolution radiometric data. A quiescent state performance analysis using matched loads and antennas revealing RFI generated from the radiometer high speed data acquisition system, and the sUAS communication system is discussed.

The Lobe Differencing Correlating Radiometer (LDCR) developed at Center for Environmental Technology (CET) at CU, Boulder is a lightweight payload for small unmanned aerial systems (sUAS) enabling remote soil moisture measurement for precision agriculture applications. The second revision of the payload (LDCR Rev B) includes a digital correlation detector for which the data processing flow, instrument calibration, and test data analysis is presented. Cross frequency peak detection, time and frequency domain kurtosis, and complex coherence phase were used to characterize radio frequency interference (RFI) in the high spectral resolution radiometric data. A quiescent state performance analysis using matched loads and antennas revealing RFI generated from the radiometer high speed data acquisition system, and the sUAS communication system is discussed.

Soil moisture is of fundamental importance to many hydrological, biological and biogeochemical processes, plays an important role in the development and evolution of convective weather and precipitation, and impacts precision agriculture, water resource management, and flood runoff prediction. The launch of NASA's Soil Moisture Active/Passive (SMAP) mission in 2015 provide new passive global measurements of soil moisture and surface freeze/thaw state at fixed crossing times and spatial resolutions of 36 km. There exists a need for measurements of soil moisture on much smaller spatial scales and arbitrary diurnal times for SMAP validation, precision agriculture, flood runoff prediction, evaporation and transpiration studies of boundary layer heat transport, and tundra thaw studies. The Lobe Differencing Correlation Radiometer (LDCR) provides a means of mapping soil moisture on spatial scales as small as several meters (i.e., the height of the platform). Compared with various other proposed methods of validation based on either in situ measurements or existing airborne sensors suitable for manned aircraft deployment, the integrated design of the LDCR on a lightweight small unmanned aerial system (sUAS) is capable of providing sub-watershed (∼km scale) coverage at very high spatial resolution (∼15 m) suitable for scaling scale studies. The Tempest sUAS, flies at very low operator cost compared to manned aircraft. To demonstrate the LDCR several flights had been performed during field experiments at the Canton Oklahoma Soilscape site on September 8th and 9th, 2015 and Yuma Colorado Irrigation Research Foundation (IRF) site from June to August, 2016. The LDCR antenna temperature and soil moisture maps will be presented, and scientific intercomparisons between LDCR soil moisture data and in-situ measurements will be presented.

Soil moisture is of fundamental importance to many hydrological, biological and biogeochemical 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 promises to provide new passive global measurements of soil moisture and surface freeze/thaw state at fixed crossing times and spatial resolutions of ∼30 km. With the failure of the SMAP radar there exists a need for measurements of soil moisture on much smaller spatial scales and arbitrary diurnal times for SMAP validation, precision agriculture, evaporation and transpiration studies of boundary layer heat transport, and tundra thaw studies. The Lobe Differencing Correlation Radiometer (LDCR) provides a means of mapping soil moisture on spatial scales as small as several meters (i.e., the height of the platform). Compared with various other proposed methods of validation based on either in situ measurements or existing airborne sensors suitable for manned aircraft deployment, the integrated design of the LDCR on a lightweight small unmanned aerial system (sUAS) is capable of providing sub-watershed (∼km scale) coverage at very high spatial resolution (∼15 m) suitable for scaling scale studies. This sUAS, the Tempest, flies at very low operator cost compared to manned aircraft. To demonstrate the LDCR several flights had been performed during a field experiment at the Canton Oklahoma Soilscape site on September 8th and 9th, 2015. These tests were flown at 25–35 m altitude to obtain differing spatial resolutions. The LDCR brightness temperature and soil moisture mapping algorithm will be analyzed, and scientific intercomparisons of LDCR, SMAP and SMOS data along with situ measurements will be presented.

Polarimetric synthetic aperture radar (SAR) images describe objects via polarization synthesis and present much richer information than monopolarized SAR images. Furthermore, multidirectional flights of polarimetric SAR can see 3-D stereo objects in different angles. The height and the location of the 3-D objects can be retrieved from the polarimetric SAR images in multidirectional flights. This letter presents a tractable approach for the reconstruction of 3-D stereo buildings from two airborne PI-SAR images taken from opposite directions (at X-band with a spatial resolution of 1.5 m).

The height and location of the three dimensional (3D) objects can be retrieved from polarimetric SAR images in multidirectional flights, and the 3D stereo objects can then be reconstructed. Polarimetric SAR image describes the objects via polarization synthesis, and presents much richer information than mono-polarized SAR image. Furthermore, multi-aspect flights of polarimetric SAR can see the 3D stereo objects in different angles and become feasible to invert the height and location of the objects. This paper presents an attractable approach for reconstruction of the 3D stereo buildings from two converse flights of airborne PI-SAR images (at X band with spatial resolution 1.5 m).