Keith D. Hutchison

Applications of MODIS satellite data and products for monitoring air quality in the state of Texas

The Center for Space Research (CSR), in conjunction with the Monitoring Operations Division (MOD) of the Texas Commission on Environmental Quality (TCEQ), is evaluating the use of remotely sensed satellite data to assist in monitoring and predicting air quality in Texas. The challenges of meeting air quality standards established by the US Environmental Protection Agency (US EPA) are impacted by the transport of pollution into Texas that originates from outside our borders and are cumulative with those generated by local sources. In an attempt to quantify the concentrations of all pollution sources, MOD has installed ground-based monitoring stations in rural regions along the Texas geographic boundaries including the Gulf coast, as well as urban regions that are the predominant sources of domestic pollution. However, analysis of time-lapse GOES satellite imagery at MOD, clearly demonstrates the shortcomings of using only ground-based observations for monitoring air quality across Texas. These shortcomings include the vastness of State borders, that can only be monitored with a large number of ground-based sensors, and gradients in pollution concentration that depend upon the location of the point source, the meteorology governing its transport to Texas, and its diffusion across the region. With the launch of NASAs MODerate resolution Imaging Spectroradiometer (MODIS), the transport of aerosol-borne pollutants can now be monitored over land and ocean surfaces. Thus, CSR and MOD personnel have applied MODIS data to several classes of pollution that routinely impact Texas air quality. Results demonstrate MODIS data and products can detect and track the migration of pollutants. This paper presents one case study in which continental haze from the northeast moved into the region and subsequently required health advisories to be issued for 150 counties in Texas. It is concluded that MODIS provides the basis for developing advanced data products that will, when used in conjunction with ground-based observations, create a cost-effective and accurate pollution monitoring system for the entire state of Texas.

Automated cloud detection and classification of data collected by the Visible Infrared Imager Radiometer Suite (VIIRS)

The Visible Infrared Imager Radiometer Suite (VIIRS) is a high‐resolution Earth imager of the United States National Polar‐orbiting Operational Environmental Satellite System (NPOESS). VIIRS has its heritage in three sensors currently collecting imagery of the Earth—the Advanced Very High Resolution Radiometer, the Moderate Resolution Imaging Spectroradiometer, and the Operational Linescan Sensor. The first launch of the VIIRS sensor is on NASAs NPOESS Preparatory Project (NPP). Data collected by VIIRS will provide products to a variety of users, supporting applications from real‐time to long‐term climate change timescales. VIIRS has been uniquely designed to satisfy this full range of requirements. Cloud masks derived from the automated analyses of VIIRS data are critical data products for the NPOESS program. In this paper, the VIIRS cloud mask (VCM) performance requirements are highlighted, along with the algorithm developed to satisfy these requirements. The expected performance of the VCM algorithm is established using global synthetic cloud simulations and manual cloud analyses of VIIRS proxy imagery. These results show the VCM analyses will satisfy the performance expectations of products created from it, including land and ocean surface products, cloud microphysical products, and automated cloud forecast products. Finally, minor deficiencies that remain in the VCM algorithm logic are identified along with a mitigation plan to resolve each prior to NPP launch or shortly thereafter.

The VIIRS Cloud Mask: Progress in the first year of S-NPP toward a common cloud detection scheme

The Visible Infrared Imager Radiometer Suite (VIIRS) Cloud Mask (VCM) determines, on a pixel-by-pixel basis, whether or not a given location contains cloud. The VCM serves as an intermediate product (IP) between the production of VIIRS sensor data records and 22 downstream Environmental Data Records that each depends upon the VCM output. As such, the validation of the VCM IP is critical to the success of the Suomi National Polar-orbiting Partnership (S-NPP) product suite. The methods used to validate the VCM and the current results are presented in this paper. Detailed analyses of golden granules along with tools providing deep insights into granule performance, and specific cloud detection tests reveal the details behind a given granules performance. Matchup results with CALIPSO, in turn, indicate the large-scale performance of the VCM and whether or not it is meeting its specifications. Comparisons with other cloud masks indicate comparable performance for the determination of clear pixels. As of September 2013 the VCM is either meeting or within 2% of all of its documented requirements.

Cloud-type discrimination via multispectral textural analysis

In recent years, with the development of satellite and computer technology, Earth observation and atmospheric research have become highly dependent on digital imagery. One of the primary interests in digital image processing is the development of robust methods to perform feature detection, extraction, and classification. Until recently, classification methods for cloud discrimination were mainly based on the spectral information of the imagery. However, because of the spectral similarities of certain features (such as ice clouds and snow) and the effects of atmospheric attenuation, multispectral rule-based classifications do not necessarily produce accurate feature discrimination. Spectral homogeneity of two different features within a scene can lead to misclassification. Furthermore, the opposite problem can occur when one feature exhibits different spectral signatures locally but is homogeneous in its cyclic spatial variation. The exploration of spatial information is often advantageous in these discrimination problems. A texture-based method for feature identification has been investigated. This method uses a set of localized spatial filters known as 2-D Gabor functions. Gabor filters can be described as a sinusoidal plane wave within a 2-D Gaussian envelope. The frequency and orientation of the sine plane and the width of the Gaussian envelope are determined by the Gabor parameters. These tunable channels yield joint optimal information both in the spatial and the frequency domains. The new method has been applied to the thermal channels of the NOAA Advanced Very High Resolution Radiometer data for cloud-type discrimination. Results show that additional texture information improves discrimination between cloud types (especially thin cirrus).

Application of 1.38 μm imagery for thin cirrus detection in daytime imagery collected over land surfaces

Abstract While considerable effort has been expended on research into the analysis of optically thin cirrus clouds, the global detection and accurate identification of these clouds remains inadequate, especially in daytime meteorological satellite imagery collected over land surfaces. Recently, 1·38 μm imagery was recommended for the improved detection of these thin cirrus clouds. Since this channel is centred on a strong water vapour absorption band and water vapour is concentrated in the lower atmosphere, incident solar energy in the 1·38 μm spectral band is strongly attenuated once prior to reaching the Earths surface and a second time after being reflected back toward space under normal atmospheric conditions. Thus, it has been postulated that any energy measured by an airborne (or space-borne) radiometer operating in this spectral band would originate from scattering off of mid-level water and high-level ice clouds, making even thin cirrus readily detectable. While initial results have been encourag...

A Geometry-Based Approach to Identifying Cloud Shadows in the VIIRS Cloud Mask Algorithm for NPOESS

Abstract A geometry-based approach is presented to identify cloud shadows using an automated cloud classification algorithm developed for the National Polar-orbiting Operational Environmental Satellite System (NPOESS) program. These new procedures exploit both the cloud confidence and cloud phase intermediate products generated by the Visible/Infrared Imager/Radiometer Suite (VIIRS) cloud mask (VCM) algorithm. The procedures have been tested and found to accurately detect cloud shadows in global datasets collected by NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) sensor and are applied over both land and ocean background conditions. These new procedures represent a marked departure from those used in the heritage MODIS cloud mask algorithm, which utilizes spectral signatures in an attempt to identify cloud shadows. However, they more closely follow those developed to identify cloud shadows in the MODIS Surface Reflectance (MOD09) data product. Significant differences were necessary in the im...

Distinguishing Aerosols from Clouds in Global, Multispectral Satellite Data with Automated Cloud Classification Algorithms

Abstract A new approach is presented to distinguish between clouds and heavy aerosols with automated cloud classification algorithms developed for the National Polar-orbiting Operational Environmental Satellite System (NPOESS) program. These new procedures exploit differences in both spectral and textural signatures between clouds and aerosols to isolate pixels originally classified as cloudy by the Visible/Infrared Imager/Radiometer Suite (VIIRS) cloud mask algorithm that in reality contains heavy aerosols. The procedures have been tested and found to accurately distinguish clouds from dust, smoke, volcanic ash, and industrial pollution over both land and ocean backgrounds in global datasets collected by NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. This new methodology relies strongly upon data collected in the 0.412-μm bandpass, where smoke has a maximum reflectance in the VIIRS bands while dust simultaneously has a minimum reflectance. The procedures benefit from the VIIRS desig...

Cloud base heights retrieved during night-time conditions with MODIS data

The capability to retrieve cloud base heights was developed under the US National Polar‐orbiting Operational Environmental Satellite System (NPOESS) programme as one of 27 data products to be created from data collected by the Visible Infrared Imager Radiometer Suite (VIIRS). First launch of the VIIRS sensor, which is the high‐resolution Earth imager of the NPOESS programme, comes on National Aeronautics & Space Administrations (NASA) NPOESS Preparatory Project (NPP). In preparation for this launch, extensive testing of the VIIRS cloud algorithms was completed to verify that product performance will satisfy system requirements before the cloud algorithms were hosted in the NPOESS ground processing centre. The approach taken to retrieve cloud base height converts cloud optical properties into a geometric thickness which is then subtracted from the cloud top height. Performance of the cloud base height algorithms has been verified recently using MODIS data, together with temporarily and spatial coincident observations of cloud thickness values made with the millimetre cloud radar operated at the US Department of Energy Atmospheric Radiation Measurement (ARM) Program Southern Great Plains site in Oklahoma. Of particular significance is the clear demonstration that both cloud optical properties and cloud base heights are retrieved accurately during night‐time conditions with the VIIRS algorithms, since neither of these products is currently produced by the NASA EOS programme. Based upon these analyses, the VIIRS cloud algorithms are expected to satisfy NPOESS requirements, making VIIRS the first operational satellite sensor capable of retrieving three‐dimensional cloud fields.

The use of global synthetic data for pre-launch tuning of the VIIRS cloud mask algorithm

A methodology is presented to perform pre-launch tuning of thresholds used in the Visible Infrared Imager/Radiometer Suite (VIIRS) cloud mask (VCM) algorithm. The approach relies upon several data sources, including global synthetic data (GSD), Moderate Resolution Imaging Spectroradiometer (MODIS) and VIIRS relative spectral responses (RSRs) and MODIS top-of-atmosphere (TOA) radiance, reflectance and brightness temperature data. The GSD are used first to derive cloud cover distributions, that is, 0%, 50% and 100%, at VIIRS moderate resolution (800 m) for each VCM cloud detection test, based on inputs to the radiative transfer models. These cloud distributions are then used to update the values of the low, mid and high cloud-free confidence thresholds in the VCM algorithm. The approach is demonstrated by using MODIS RSRs with the GSD to set these thresholds and then analysing granules of MODIS data with the updated VCM. Performance is quantified through comparisons with manually generated cloud masks created from the MODIS imagery. The performance of the tuned VCM with MODIS data improved substantially for all major background conditions. The probability of correct typing (PCT) improved nearly 4% over the ocean to 97.5% and nearly 20% over snow-covered surfaces to 95.1%. The PCT values over land improved from 87.1% to 93.4% and over desert from 87.2% to 93.9%. The process was then repeated using VIIRS RSRs and the updated thresholds were forwarded to the National Polar-Orbiting Operational Satellite System (NPOESS) Preparatory Project (NPP) ground segment for incorporation into the operational system. It is concluded that GSD are invaluable for the pre-launch tuning of the VCM algorithm, which is now expected to exceed system requirements soon after the launch of the NPP satellite.

Cloud top phase determination from the fusion of signatures in daytime AVHRR imagery and HIRS data

An improved methodology for the retrieval of water vapour profiles from DMSP SSM/T-2 microwave sounder data has been demonstrated using cloud-top temperatures derived from NOAA AVHRR imagery as a constraint. However, the automated analysis of cloud-top temperature in AVHRR imagery is complicated by the presence of optically-thin cirrus clouds, since a component of the upwelling radiation from below passes unatttenuated to space. Therefore, cloud-top phase must first be determined to ensure the accurate specification of cloud-top temperature. In this paper, a new approach is presented for the specification of cloud-top phase in an operational environment. The methodology combines results from bi-spectral cloud tests for ice and water clouds in daytime AVHRR imagery with cloud-top pressure analyses based upon the CO 2 slicing of HIRS data. The accuracy of the automated cloud-top phase analyses is measured quantitatively against manual analyses of the AVHRR imagery. It is concluded that the fusion of cloud signatures in AVHRR imagery and HIRS data improves the specification of cloud-top phase in the higher resolution imagery and reduces the ambiguity inherent in analyses based solely upon bi-spectral techniques.