Examples of side-by-side comparisons of the standard and near-real time. The first shows a Land Surface Reflectance granule over the Midwest. There appears to be no difference between the products. However, under close examination the near-real time view shows slightly more haze West of the Great Lakes. In contrast, the second side-by-side comparison is for Cloud Top Temperature for the same granule and there are very obvious differences in the region West of the Great Lakes. This is as a result of the sensitivity of this product to the GDAS ancillary data.
AIRS false-color 3-km visible image (left), and the SO2 brightness temperature difference at 10-km resolution (right) BT_diff_SO2 values under-6K have likely volcanic SO2. Information from multiple sensors indicates that this eruption is mostly ash with little SO2 being expelled into the atmosphere.
This AMSR-E Brightness Temperature image with a GOES background shows Hurricane Alex entering Mexico, just as it is degraded to a tropical storm. Courtesy of US Naval Research Laboratory
OMI image showing the ash cloud from the Icelandic volcano as flights are cancelled all across Europe, impacting tens of thousands of customers. Shown with MODIS “Blue Marble” Background
MODIS Terra image taken on a relatively cloud free day just off the coast of New Orleans capturing the oil slick as it is being transported to the south via a loop current. The thicker oil slick appears brighter than surrounding water.
Do we have a more recent example from LANCE The maps they generate are available to governments and relief organizations. For flood detection, the Flood Observatory has defined at least 710 sections of rivers worldwide. Each section is 20 to 30 kilometers in length and 20 to 30 kilometers in width. MODIS monitors the sections, called reaches, to determine the status of each. When water levels rise to flood status, the river can be targeted by higher-resolution sensors.
The Canadian Ice Service uses MODIS images to chart out the daily ice conditions in the lakes, bays, and ocean around Canada.
Dust events over Iraq can be quite nerve-wracking. Using MODIS data, Dr. Miller from our group developed and published a technique that applied a multi-spectral approach to distinguish dust from both clouds and the very complex desert terrain. In this example, the true color image reveals a complicated terrain pattern over the region within NW Persian Gulf. The dust product revealed a mesoscale frontal pattern of dust. Adding model winds depicts the circulation pattern within the dust plume along with a hint of the projected movement within the plume. In this example, dust over the Red Sea is clearly visible, but the extent of the plume isn’t clearly evident until the dust product is observed. In this example over the desert and mountainous terrain of Afghanistan, the dust pattern is visible, however, the dust product exposes the thickness of the plume, the sources of dust production, and hidden pockets of dust as shown here. This final example depicts the envelope of dust. Throughout the Middle East campaign, the NRL dust product has been an invaluable resource for military planners. Although there are a number of dust products available, the true test of dust detection comes in this type of terrain. Miller, S. D., 2003: A consolidated technique for enhancing desert dust storms with MODIS. Geophys. Res. Lett., 30, No. 20, 2071. Courtesy: NRL Satellite FOCUS web team, Naval Research Laboratory, Marine Meteorology Division, Monterey, CA 93943
Land Atmosphere Near real-time Capability for EOSDIS (LANCE) Karen Michael, Kevin Murphy, Dawn Lowe, Jeanne Behnke - ESDIS Project, GSFC Martha Maiden, NASA HQ Chris Justice (UMd), Michael Goodman (NASA HQ) UWG Co-Chairs
The Land, Atmosphere Near-real-time Capability for EOS (LANCE)
Building on existing EOSDIS elements LANCE provides data from MODIS, OMI, AIRS, MLS, and AMSR-E instruments in near real-time (< 3 hours from observation)
Utilizes algorithms used for Standard Science Products, but relaxes requirements for slower ancillary data inputs
MODIS Products LANCE-MODIS-Terra Oil Spill in Gulf of Mexico May 24, 2010 16:50 UTC
Currently implementing geographic, parameter and band subsetting in addition to pan sharpening, reprojection, mosaicing, sub-sampling, flood mapping and Geo Tiff format conversion.
Future developments include integration of the MODIS Rapid Response, Web Coverage and Mapping along with the Fire Information for Resource Management System
Processing Lead: Edward Masuoka Instrument Product Categories Average Latency MODIS Radiances, Cloud/Aerosols, Water Vapor, Fire, Snow Cover, Sea Ice, Land Surface Reflectance, Land Surface Temperature 90 – 145 minutes Latency excludes L2G and L3
Sea - Ice Monitoring Source: http://www.ec.gc.ca/glaces-ice/default.asp?lang=En&n=D32C361E-1 Canadian Ice Service
Dust Detection Over Land The Application: Depicting dust storms over the bright deserts through enhanced imagery. DoD Context: Mission planning, aircraft routing/launch/recovery, weapons selection. Source: Jeff Hawkins Naval Research Laboratory Monterey The Approach: Use multi-spectral MODIS data to identify dust via color, thermal, spectral (11/12) contrast, and 1.38 cirrus filtering. ( ) Vis/NIR Index Temperature Cirrus Flag Split Window dust
Drought Monitoring Source: NASA MODIS NDVI Source: NASA MODIS NDVI Mean Vegetation Index Drought Impacted Vegetation Index (2007-08) USDA FAS / UMD / GSFC Dec 3-18, 2007 Dec 19-Jan1, 2008 Jan 1-Jan 16, 2008 Jan 17 – Feb 1, 2008 Feb 2 – Feb 17, 2008 Feb 18 – March 4, 2008 March 5- March 20, 2008 March 21- Apr 5, 2008 April 6- Apr 21, 2008 Vegetation Index Time Series for Cropped Areas in Iraq Vegetation Index Date Current Season (2007-2008) Mean (2000-2007) average average average
GEO Agricultural Monitoring Community of Practice
Several global/regional scale systems in place – many use MODIS data
Data latency is critical for all of these programs