This paper describes an instrument designed to distinguish frozen from thawed land surfaces from an Earth satellite by bouncing signals back to Earth from deployable mesh antennas.

The Cyclone Global Navigation Satellite System (CYGNSS) is a new NASA earth science mission scheduled to be launched in 2016 that focuses on tropical cyclones (TCs) and tropical convection. The mission’s two primary objectives are the measurement of ocean surface wind speed with sufficient temporal resolution to resolve short-time-scale processes such as the rapid intensification phase of TC development and the ability of the surface measurements to penetrate through the extremely high precipitation rates typically encountered in the TC inner core. The mission’s goal is to support significant improvements in our ability to forecast TC track, intensity, and storm surge through better observations and, ultimately, better understanding of inner-core processes. CYGNSS meets its temporal sampling objective by deploying a constellation of eight satellites. Its ability to see through heavy precipitation is enabled by its operation as a bistatic radar using low-frequency GPS signals. The mission will deploy an eight-spacecraft constellation in a low-inclination (35°) circular orbit to maximize coverage and sampling in the tropics. Each CYGNSS spacecraft carries a four-channel radar receiver that measures GPS navigation signals scattered by the ocean surface. The mission will measure inner-core surface winds with high temporal resolution and spatial coverage, under all precipitating conditions, and over the full dynamic range of TC wind speeds.

The authors investigate reaction time, subjective assessments of memory processing, and confidence as predictors of memory for the details of a crime. The authors also examine the mediation of a previously identified difference between recognition tasks and recall tasks in the correlation between confidence and accuracy. College undergraduates (n = 111) answered either recognition or recall questions. Reaction time and subjective assessments of cognitive effort were both negatively related to confidence and accuracy. Subjective assessments, however, were superior predictors of confidence, whereas reaction time was a unique predictor of accuracy. The reaction time-confidence and reaction time-accuracy correlations were stronger under recall conditions than under recognition conditions. Multiple regression results suggested a possible explanation for the superior insight of recall participants into memory accuracy.

Most radar systems employ a feed-forward processing chain in which they first perform some low-level processing of received sensor data to obtain target detections and then pass the processed data on to some higher-level processor such as a tracker, which extracts information to achieve a system objective. System performance can be improved using adaptation between the information extracted from the sensor/processor and the design and transmission of subsequent illuminating waveforms. As such, cognitive radar systems offer much promise. In this paper, we develop a general cognitive radar framework for a radar system engaged in target tracking. The model includes the higher-level tracking processor and specifies the feedback mechanism and optimization criterion used to obtain the next set of sensor data. Both target detection (track initiation/termination) and tracking (state estimation) are addressed. By separating the general principles from the specific application and implementation details, our formulation provides a flexible framework applicable to the general tracking problem. We demonstrate how the general framework may be specialized for a particular problem using a distributed sensor model in which system resources (observation time on each sensor) are allocated to optimize tracking performance. The cognitive radar system is shown to offer significant performance gains over a standard feed-forward system.

A soil moisture retrieval algorithm is proposed that takes advantage of the simultaneous radar and radiometer measurements by the forthcoming NASA Soil Moisture Active Passive (SMAP) mission. The algorithm is designed to downscale SMAP L-band brightness temperature measurements at low resolution (∼ 40 km) to 9-km brightness temperature by using SMAP's L-band synthetic aperture radar (SAR) backscatter measurements at high resolution (1-3 km) in order to estimate soil moisture at 9-km resolution. The SMAP L-band SAR and radiometer instruments are designed to provide coincident observations at constant incidence angle, but at different spatial resolutions, across a wide swath. The algorithm described here takes advantage of the correlation between temporal fluctuations of brightness temperature and backscatter observed when viewing targets simultaneously at the same angle. Surface characteristics that affect the brightness temperature and backscatter measurements influence the signals at different time scales. This feature is applied in an approach in which fine-scale spatial heterogeneity detected by SAR observations is applied on coarser-scale radiometer measurements to produce an intermediate-resolution disaggregated brightness temperature field. These brightness temperatures are then used with established radiometer-based algorithms to retrieve soil moisture at the intermediate resolution.The capability of the overall algorithm is demonstrated using data acquired by the airborne passive and active L-band system from field campaigns and also by simulated global dataset. Results indicate that the algorithm has the potential to retrieve soil moisture at 9-km resolution, with the accuracy required for SMAP, over regions having vegetation up to 5-kg/m 2 vegetation water content. The results show a reduction in root mean square error of > 0.02 cm 3 /cm 3 volumetric soil moisture (40% improvement in the statistics) from the minimum performance defined as the soil moisture retrieved using radiometer measurements re-sampled to the intermediate scale.