The Lobe Differencing Correlation Radiometer (LDCR) on a small uncrewed aerial system (sUAS) is developed to map high spatial resolution soil moisture, and its performance had been validated during tens of successful fight sorties. A new calibration method is developed that gain and phase imbalances between the two receiver paths are determined and compensated, and a new calibration source is being built to provide a stable reference temperature for in-flight calibration. The LDCR was integrated into two sUAS platforms, one is fixed wing and the other one is multirotor. Flight altitude from a few meters to hundreds of meters can be achieved using these two sUAS platforms to fulfill various spatial resolution requirements. The soil moisture mapping algorithm had been further developed and the soil salinity and slope angle will be included and compensated in the retrieval algorithm.

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 τ-ω 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.

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) can provide 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 Rev A and Rev B, 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, and October 2017. 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 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.

This work considers a new application area for autonomous soaring through the examination of the energetic environment contained in supercell thunderstorms in an effort to extend sampling mission duration. It identifies the gust front associated with the rear-flank downdraft of the supercell thunderstorm as an interesting candidate for such a mission, and examines the potential for soaring in the region. This knowledge is combined with sampling techniques employed in previous field experiments to produce an energy efficient path at an altitude and location identified as an interesting area for in situ sampling. The path is tested using a flight through simulated storm data, and is shown to provide a net positive gain in energy to the vehicle. This encouraging result indicates the possibility of extending the sampling mission from its current limited length to a significant portion of the storm evolution, a capability that could provide invaluable information about the causes of tornadogenesis.

This paper presents an aerial robot system designed to perform in situ sampling of supercell thunderstorms, including those that produce tornadoes. A description of the system architecture and concept of operations is given, along with a brief discussion of the evolution of critical system components. Results are given from two field campaigns, an airmass boundary intercept in Northern Colorado during the spring of 2009, and two supercell transects during the Spring 2010 campaign of the VORTEX2 project. The 2010 results represent the first aloft sampling of the rear-flank downdraft in a tornadic supercelluar storm.

This paper presents a system for in situ atmospheric sensing using an aerial robot system in the presence of a strong wind field. The geostatistical concept of the variogram is used to characterize regions of the environment with high variability, which is assumed to correlate with scientific interest. After regions of interest are identified, ordered upwind methods are used to generate feasible trajectories in the face of strong background wind. The feasible trajectories are combined with the variogram characterization to select feasible paths that travel through the regions of highest interest. The system is tested in simulation using data from a simulated severe storm.

This paper presents a hierarchical control architecture that enables cooperative surveillance by a heterogeneous aerial robot network comprised of mothership unmanned aircraft and daughtership micro air vehicles. Combining the endurance, range, and processing capabilities of the motherships with the stealth, flexibility, and maneuverability of swarms of daughterships enables robust control of aerial robot networks conducting collaborative operations. The hierarchical control structure decomposes the system into components that take advantage of the abilities of the different types of vehicles. The motherships act as distributed databases, fusion centers, negotiation agents, and task supervisors while daughtership control is achieved using cooperative vector field tracking. This paper describes the overall architecture and then focuses on the assignment and tracking algorithms used once sub- teams of daughtership vehicles have been deployed. A summary of the communication, command, and control structure of a heterogeneous unmanned aircraft system is also given in this paper along with hardware-in-the-loop and software simulation results verifying several components of the distributed control architecture.

This paper presents a new task assignment algorithm that integrates area search and target tracking. A new tracking metric is derived that encodes the ability of each robot to reach an unsensed target before the uncertainty in its position passes a given upper bound. Target activation and tracking assignment using this method enable broader participation by individual robots in a cooperative surveillance architecture. Area search is added to the cooperative surveillance architecture through the use of a coordinated coverage map that represents the probability of new targets being detected in regions of the environment. A key feature of the coverage map is that it is updated by all robots, not just robots performing area search. Thus, tracking robots aid the search process. Simulation results validate the new integrated assignment process.