Due to the growth of miniature unmanned aerial vehicles (UAVs) and small spacecraft (SmallSats) in recent years, there has been a push for the development of miniaturized spectral imagers to be incorporated with them. An efficient, compact hyperspectral imager integrated with these vehicles provides a cost-effective platform for environmental sensing applications that include the monitoring of agriculture, vegetation, geology, and pollutants. We present here the development and integration of a hyperspectral imaging system called the SNAP-IMS, originally used for biomedical detection, with an Octocopter UAV. The entire collected hyperspectral data cube is 350x400x55 (x,y,λ) spatial/spectral samples. The final system enclosure (288 mm x 150 mm x 160 mm) weighs 3.6 kg (7.9 lbs), offering minimal size and weight. The payload’s power consumption is marginal as there are no mechanical scanning components; the existing power requirements are dedicated exclusively to CCD frame acquisition. Experimental testing included several flights on board the Octocopter UAV, acquiring hyperspectral data cubes at 1/100 second. Snapshot mode and short integration times mitigate motion artifacts. The low size, weight, and power consumption can offer longer and higher flights at smaller drone sizes. These improvements augment the potential for additional instrument incorporation (i.e. LiDAR, Multi-spectral IR) in the future. Imaging results and system description are presented and discussed.
The Atmospheric Infrared Sounder (AIRS), together with the Advanced Microwave Sounding Unit (AMSU), represents
one of the most advanced space-based atmospheric sounding systems. Aside from monitoring changes in Earth's
climate, one of the objectives of AIRS is to provide sounding information with sufficient accuracy such that the
assimilation of the new observations, especially in data sparse regions, will lead to an improvement in weather forecasts.
The combined AIRS/AMSU system provides radiance measurements used as input to a sophisticated retrieval scheme
which has been shown to produce temperature profiles with an accuracy of 1 K over 1 km layers and humidity profiles
with accuracy of 10-15% in 2 km layers in both clear and partly cloudy conditions. The retrieval algorithm also provides
estimates of the accuracy of the retrieved values at each pressure level, allowing the user to select profiles based on the
required error tolerances of the application. The purpose of this paper is to describe a procedure to optimally assimilate
high-resolution AIRS profile data in a regional analysis/forecast model. The paper focuses on a U.S. East-Coast cyclone
from November 2005. Temperature and moisture profiles-containing information about the quality of each
temperature layer-from the prototype version 5.0 Earth Observing System (EOS) science team retrieval algorithm are
used in this study. The quality indicators are used to select the highest quality temperature and moisture data for each
profile location and pressure level. AIRS data are assimilated into the Weather Research and Forecasting (WRF)
numerical weather prediction model using the Advanced Regional Prediction System (ARPS) Data Analysis System
(ADAS), to produce near-real-time regional weather forecasts over the continental U.S. The preliminary assessment of
the impact of the AIRS profiles will focus on intelligent use of the quality indicators, analysis impact, and forecast
verification against rawinsondes and precipitation data.
The hyperspectral resolution measurements from the NASA Atmospheric Infrared Sounder (AIRS) are advancing climate research by mapping atmospheric temperature, moisture, and trace gases on a global basis with unprecedented accuracy. Using a sophisticated retrieval scheme, the AIRS is capable of diagnosing the atmospheric temperature in the troposphere with accuracies of less than 1 K over 1 km-thick layers and 10-20% relative humidity over 2 km-thick layers, under both clear and cloudy conditions. A unique aspect of the retrieval procedure is the specification of a vertically varying error estimate for the temperature and moisture profile for each retrieval. The error specification allows for the more selective use of the profiles in subsequent processing. In this paper, we describe a procedure to assimilate AIRS data into the Weather Research and Forecasting (WRF) model to improve short-term weather forecasts. The ARPS Data Analysis System (ADAS) developed by the University of Oklahoma is configured to optimally blend AIRS data with model background fields based on the AIRS error profiles. The WRF short-term forecasts with selected AIRS data show improvement over the control forecast. The use of the AIRS error profiles maximizes the impact of high quality AIRS data from portions of the profile in the assimilation/forecast process without degradation from lower quality data in the other portions of the profile.