ISEE/JPSS Product Inventory



Collection:

Alphabetic list of 74 products:
  1. African Wild Fire targets
    ID: CSIR
    Southern Africa Wild Fire targets are fires detected by the MODIS sensor on the Terra and Aqua satellites. It is produced by CSIR (The Council for Scientific and Industrial Research) and updated every 60 minutes to include any new information.
  2. AQUA Orbit
    ID: POESNAV-AQUA
  3. CSPP VIIRS Flood Detection
    ID: cspp-flood
    Daily direct broadcast-produced flood products created by latest alpha version of the CSPP VIIRS Flood Detection software.
  4. CSPP VIIRS Flood Detection (no cloud)
    ID: cspp-flood-nocloud
    An alternate view of the CSPP VIIRS Flood Detection product with cloud & cloud shadow pixels set to transparent.
  5. CSPP VIIRS Flood Detection - Global (no clouds)
    ID: cspp-viirs-flood-globally-nocloud
    Global flood products created from Suomi-NPP SDRs by the latest alpha version of the CSPP VIIRS Flood Detection software. This product has cloudy & cloud shadow pixels removed so that, in cases where granules overlap, only cloud free data points are displayed.
  6. DNB ClearView
    ID: DNB-ClearView
    DNB-ClearView
  7. DNB ClearView Monthly - Test
    ID: dnb-monthly-nightlights
  8. Fire Radiative Power VIIRS 375m Alaska
    ID: FIRMS-VIIRS-Alaska-ActiveFires
    VIIRS 375m I-band high spatial resolution imagery provides a greater response over fires of relatively small areas and provides improved mapping of large fire perimeters. The 375m data also has improved nighttime performance. Consequently, these data are well suited for use in support of fire management (e.g., near real-time alert systems), as well as other science applications requiring improved fire mapping fidelity.
  9. Fire Radiative Power VIIRS 375m Australia
    ID: FIRMS-VIIRS-Australia-ActiveFires
    VIIRS 375m I-band high spatial resolution imagery provides a greater response over fires of relatively small areas and provides improved mapping of large fire perimeters. The 375m data also has improved nighttime performance. Consequently, these data are well suited for use in support of fire management (e.g., near real-time alert systems), as well as other science applications requiring improved fire mapping fidelity.
  10. Fire Radiative Power VIIRS 375m CAmerica
    ID: FIRMS-VIIRS-Central-America-ActiveFires
    VIIRS 375m I-band high spatial resolution imagery provides a greater response over fires of relatively small areas and provides improved mapping of large fire perimeters. The 375m data also has improved nighttime performance. Consequently, these data are well suited for use in support of fire management (e.g., near real-time alert systems), as well as other science applications requiring improved fire mapping fidelity.
  11. Fire Radiative Power VIIRS 375m Canada
    ID: FIRMS-VIIRS-Canada-ActiveFires
    VIIRS 375m I-band high spatial resolution imagery provides a greater response over fires of relatively small areas and provides improved mapping of large fire perimeters. The 375m data also has improved nighttime performance. Consequently, these data are well suited for use in support of fire management (e.g., near real-time alert systems), as well as other science applications requiring improved fire mapping fidelity.
  12. Fire Radiative Power VIIRS 375m ConUS
    ID: FIRMS-VIIRS-ConUS-Hawaii-ActiveFires
    VIIRS 375m I-band high spatial resolution imagery provides a greater response over fires of relatively small areas and provides improved mapping of large fire perimeters. The 375m data also has improved nighttime performance. Consequently, these data are well suited for use in support of fire management (e.g., near real-time alert systems), as well as other science applications requiring improved fire mapping fidelity.
  13. Fire Radiative Power VIIRS 375m Europe
    ID: FIRMS-VIIRS-Europe-ActiveFires
    VIIRS 375m I-band high spatial resolution imagery provides a greater response over fires of relatively small areas and provides improved mapping of large fire perimeters. The 375m data also has improved nighttime performance. Consequently, these data are well suited for use in support of fire management (e.g., near real-time alert systems), as well as other science applications requiring improved fire mapping fidelity.
  14. Fire Radiative Power VIIRS 375m Global >16
    ID: FIRMS-VIIRS-Global-ActiveFires-Filtered
    VIIRS 375m I-band high spatial resolution imagery provides a greater response over fires of relatively small areas and provides improved mapping of large fire perimeters. The 375m data also has improved nighttime performance. Consequently, these data are well suited for use in support of fire management (e.g., near real-time alert systems), as well as other science applications requiring improved fire mapping fidelity.
  15. Fire Radiative Power VIIRS 375m Global IMG
    ID: FIRMS-VIIRS-Global-ActiveFires-Raster
    VIIRS 375m I-band high spatial resolution imagery provides a greater response over fires of relatively small areas and provides improved mapping of large fire perimeters. The 375m data also has improved nighttime performance. Consequently, these data are well suited for use in support of fire management (e.g., near real-time alert systems), as well as otherscience applications requiring improved fire mapping fidelity.
  16. Fire Radiative Power VIIRS 375m NAfrica
    ID: FIRMS-VIIRS-Northern-Africa-ActiveFires
    VIIRS 375m I-band high spatial resolution imagery provides a greater response over fires of relatively small areas and provides improved mapping of large fire perimeters. The 375m data also has improved nighttime performance. Consequently, these data are well suited for use in support of fire management (e.g., near real-time alert systems), as well as other science applications requiring improved fire mapping fidelity.
  17. Fire Radiative Power VIIRS 375m Russia
    ID: FIRMS-VIIRS-Russia-ActiveFires
    VIIRS 375m I-band high spatial resolution imagery provides a greater response over fires of relatively small areas and provides improved mapping of large fire perimeters. The 375m data also has improved nighttime performance. Consequently, these data are well suited for use in support of fire management (e.g., near real-time alert systems), as well as other science applications requiring improved fire mapping fidelity.
  18. Fire Radiative Power VIIRS 375m SAfrica
    ID: FIRMS-VIIRS-Southern-Africa-ActiveFires
    VIIRS 375m I-band high spatial resolution imagery provides a greater response over fires of relatively small areas and provides improved mapping of large fire perimeters. The 375m data also has improved nighttime performance. Consequently, these data are well suited for use in support of fire management (e.g., near real-time alert systems), as well as other science applications requiring improved fire mapping fidelity.
  19. Fire Radiative Power VIIRS 375m SAmerica
    ID: FIRMS-VIIRS-South-America-ActiveFires
    VIIRS 375m I-band high spatial resolution imagery provides a greater response over fires of relatively small areas and provides improved mapping of large fire perimeters. The 375m data also has improved nighttime performance. Consequently, these data are well suited for use in support of fire management (e.g., near real-time alert systems), as well as other science applications requiring improved fire mapping fidelity.
  20. Fire Radiative Power VIIRS 375m SAsia
    ID: FIRMS-VIIRS-SAsia-ActiveFires
    VIIRS 375m I-band high spatial resolution imagery provides a greater response over fires of relatively small areas and provides improved mapping of large fire perimeters. The 375m data also has improved nighttime performance. Consequently, these data are well suited for use in support of fire management (e.g., near real-time alert systems), as well as other science applications requiring improved fire mapping fidelity.
  21. Fire Radiative Power VIIRS 375m SEAsia
    ID: FIRMS-VIIRS-SEAsia-ActiveFires
    VIIRS 375m I-band high spatial resolution imagery provides a greater response over fires of relatively small areas and provides improved mapping of large fire perimeters. The 375m data also has improved nighttime performance. Consequently, these data are well suited for use in support of fire management (e.g., near real-time alert systems), as well as otherscience applications requiring improved fire mapping fidelity.
  22. Fire Radiative Power VIIRS I-band DB
    ID: AFIMG-Points
    VIIRS 375m I-band high spatial resolution imagery provides a greater response over fires of relatively small areas and provides improved mapping of large fire perimeters. The 375m data also has improved nighttime performance. Consequently, these data are well suited for use in support of fire management (e.g., near real-time alert systems), as well as other science applications requiring improved fire mapping fidelity. These data represent mean fire radiative power from SNPP and NOAA-20 Direct Broadcast imagery processed with CSPP software.
  23. HRRR Alaska Near Surface Smoke
    ID: HRRR-AK-smoke-surface
    NOAA Earth System Research Laboratory High Resolution Rapid Refresh (HRRR) Surface Smoke forecast model, uses VIIRS inputs.
  24. HRRR Alaska Vertically Integrated Smoke
    ID: HRRR-AK-smoke-column
    NOAA Earth System Research Laboratory High Resolution Rapid Refresh (HRRR) Vertically Integrated Smoke forecast model, uses VIIRS inputs.
  25. HRRR ConUS Near Surface Smoke
    ID: HRRR-smoke-surface-2
    NOAA Earth System Research Laboratory High Resolution Rapid Refresh (HRRR) Surface Smoke forecast model, uses VIIRS inputs.
  26. HRRR ConUS Vertically Integrated Smoke
    ID: HRRR-smoke-column
    NOAA Earth System Research Laboratory High Resolution Rapid Refresh (HRRR) Vertically Integrated Smoke forecast model, uses VIIRS inputs.
  27. Landsat-7 Orbit
    ID: POESNAV-LSAT7
  28. Landsat-8 Orbit
    ID: POESNAV-LSAT8
  29. MADIS Surface DewPoint
    ID: MADIS-dewt
    The MADIS Surface Dewpoint uses a 2-dimensional boxcar spatial convolution to smooth hourly average surface observations from the NCEP Meteorological Assimilation Data Ingest System (MADIS) to a grid resolution of 0.7 degree latitude/longitude. The source data is obtained in near-real time from https://madis.ncep.noaa.gov/.
  30. MIMIC Total Precip Water Hi-Res color
    ID: MIMICTPWHRE
    MIMIC-TPW2 Hi-Res is an experimental global product of total precipitable water (TPW), using morphological compositing of the MIRS retrieval from several available operational microwave-frequency sensors. MIMIC stands for "Morphed Integrated Microwave Imagery at CIMSS." The specific technique used here was initially described in a 2010 paper by Wimmers and Velden. This Hi-Res Version is interpolated and smoothed from the MIMIC-TPW2 product to 2 km resolution.
  31. MIMIC Total Precip Water Hi-Res gray
    ID: MIMICTPWHR
    MIMIC-TPW2 Hi-Res is an experimental global product of total precipitable water (TPW), using morphological compositing of the MIRS retrieval from several available operational microwave-frequency sensors. MIMIC stands for "Morphed Integrated Microwave Imagery at CIMSS." The specific technique used here was initially described in a 2010 paper by Wimmers and Velden. This Hi-Res Version is interpolated and smoothed from the MIMIC-TPW2 product to 2 km resolution.
  32. MIMIC Total Precip Water v2 color
    ID: MIMICTPW2E
    MIMIC-TPW2 is an experimental global product of total precipitable water (TPW), using morphological compositing of the MIRS retrieval from several available operational microwave-frequency sensors. MIMIC stands for "Morphed Integrated Microwave Imagery at CIMSS." The specific technique used here was initially described in a 2010 paper by Wimmers and Velden. This Version 2 is developed from an older method (still running in real-time) that uses simpler, but more limited TPW retrievals and advection calculations.
  33. MIMIC Total Precip Water v2 gray
    ID: MIMICTPW2
    MIMIC-TPW2 is an experimental global product of total precipitable water (TPW), using morphological compositing of the MIRS retrieval from several available operational microwave-frequency sensors. MIMIC stands for "Morphed Integrated Microwave Imagery at CIMSS." The specific technique used here was initially described in a 2010 paper by Wimmers and Velden. This Version 2 is developed from an older method (still running in real-time) that uses simpler, but more limited TPW retrievals and advection calculations.
  34. MIRS 90Ghz Brightness Temperature
    ID: MIRS-BT90
    MIRS 90Ghz Brightness Temperature
  35. MIRS Rain Rate
    ID: MIRS-RainRate
    MIRS Rain Rate
  36. NOAA-15 Orbit
    ID: POESNAV-N15
  37. NOAA-18 Orbit
    ID: POESNAV-N18
  38. NOAA-20 Orbit
    ID: POESNAV-N20
  39. NUCAPS-MADIS-SBCAPE
    ID: NUCAPS-MADIS-SBCAPE
    The MADIS-NUCAPS Surface-Based CAPE merges hourly average surface observations from the NCEP Meteorological Assimilation Data Ingest System (MADIS) with NOAA NUCAPS soundings from the most recent overpass of operational meteorological satellites (SNPP, METOP, or NOAA-20). The SB-CAPE is computed using the SHARPYpy software derived from software used by the NWS Storm Prediction Center (SPC). The satellite data are obtained using the SSEC direct broadcast antennae, processed using CSPP software in near-real time, and displayed in near-real time using SSEC"s RealEarth.
  40. NUCAPS-MADIS Mean Layer CAPE
    ID: NUCAPS-MADIS-MLCAPE
    NUCAPS-MADIS-MLCAPE
  41. NUCAPS-MADIS Mean Layer CIN
    ID: NUCAPS-MADIS-MLCIN
    NUCAPS-MADIS-MLCIN
  42. NUCAPS-MADIS Mean Layer LI
    ID: NUCAPS-MADIS-MLLI
    NUCAPS-MADIS-MLLI
  43. NUCAPS-MADIS Surface CAPE
    ID: MADIS-NUCAPS-Surface-CAPE
    The MADIS-NUCAPS Surface-Based CAPE merges hourly average surface observations from the NCEP Meteorological Assimilation Data Ingest System (MADIS) with NOAA NUCAPS soundings from the most recent overpass of operational meteorological satellites (SNPP, METOP, or NOAA-20). The SB-CAPE is computed using the SHARPYpy software derived from software used by the NWS Storm Prediction Center (SPC). The satellite data are obtained using the SSEC direct broadcast antennae, processed using CSPP software in near-real time, and displayed in near-real time using SSEC"s RealEarth.
  44. NUCAPS-MADIS Surface CIN
    ID: NUCAPS-MADIS-SBCIN
    NUCAPS-MADIS-SBCIN
  45. NUCAPS-MADIS Surface LI
    ID: NUCAPS-MADIS-SBLI
    NUCAPS-MADIS-SBLI
  46. NUCAPS CAA Temp 180mb
    ID: NUCAPS-CAA-temp-180mb
  47. NUCAPS CAA Temp 200mb
    ID: NUCAPS-CAA-temp-200mb
  48. NUCAPS CAA Temp 235mb
    ID: NUCAPS-CAA-temp-235mb
  49. NUCAPS CAA Temp 260mb
    ID: NUCAPS-CAA-temp-260mb
  50. NUCAPS CAA Temp 286mb
    ID: NUCAPS-CAA-temp-286mb
  51. RAP North America Near Surface Smoke
    ID: RAP-smoke-surface
    RAP-smoke-surface
  52. RAP North America Vertically Integrated Smoke
    ID: RAP-smoke-column
    RAP-smoke-column
  53. Sea Ice Concentration
    ID: NPP-SIC-ENH
    The Sea Ice Concentration product is based on NOAA Enterprise Algorithm. The original spatial resolution is 750 m as the data input are VIIRS M band at 750 m resolution. It is regridded to the original resolution to 1 km EASE2-Grid. For the reference, you can refer to Liu, Y., Key, J., & Mahoney, R. (2016). Sea and freshwater ice concentration from VIIRS on Suomi NPP and the future JPSS satellites. Remote Sensing, 8(6), 523.
  54. SENTINEL 2A Orbit
    ID: POESNAV-SEN2A
    POESNAV-SEN2A
  55. SENTINEL 2B Orbit
    ID: POESNAV-SEN2B
    POESNAV-SEN2B
  56. SNPP Day/Night AM Composite - Adaptive
    ID: nppadpam
    NPP Day/Night AM Composite - Adaptive
  57. SNPP Day/Night Band (DNB) - Honolulu DB
    ID: nppdnbdyn-hnl
    NPP Day/Night Band (DNB) - Honolulu DB
  58. SNPP Day/Night Band (DNB) - Madison DB
    ID: nppdnbdyn-msn
    Suomi NPP Day/Night Band (DNB) imagery received and processed by the SSEC UW-Madison direct reception facility by Direct Broadcast from the satellite.
  59. SNPP Day/Night Band (DNB) - Puerto Rico DB
    ID: nppdnbdyn-upr
    NPP Day/Night Band (DNB) - Puerto Rico DB
  60. SNPP Day/Night Band - Dynamic
    ID: nppdnb
    NPP Day/Night Band - Dynamic
  61. SNPP False Color
    ID: nppfc
    NPP False Color
  62. SNPP NUCAPS CO-MR-496mb
    ID: CO-MR-496mb
    This a proof of concept example of NUCAPS from Suomi NPP CrIS/ATMS data, converted to a gridded NetCDF.
  63. SNPP Orbit
    ID: POESNAV-NPP
  64. SNPP Sea Surface Temperature
    ID: nppsst
    NPP Sea Surface Temperature
  65. SNPP Sea Surface Temperature (SST) - Madison DB
    ID: nppsst-msn
    NPP Sea Surface Temperature (SST) - Madison DB
  66. SNPP True Color (TC) - Honolulu DB
    ID: npptc-hnl
    NPP True Color (TC) - Honolulu DB
  67. SNPP True Color (TC) - Puerto Rico DB
    ID: npptc-upr
    NPP True Color (TC) - Puerto Rico DB
  68. SNPP VIIRS False Color - Madison DB
    ID: nppfc-msn
    Suomi-NPP VIIRS False Color imagery received and processed by the SSEC UW-Madison direct reception facility by Direct Broadcast from the satellite.
  69. SNPP VIIRS True Color - Madison DB
    ID: npptc-msn
    Suomi-NPP VIIRS True Color imagery received and processed by the SSEC UW-Madison direct reception facility by Direct Broadcast from the satellite.
  70. TERRA Orbit
    ID: POESNAV-TERRA
  71. VIIRS Fire RGB - GINA
    ID: DayLandCloudFire-RGB-GINA
    This RGB is created by assigning the VIIRS 3.74um channel to red, 0.87um channel to green, and the 0.64um channel to blue. It is used to assess fire perimeters and burn scars. These data are produced by the Geographic Information Network of Alaska (GINA).
  72. VIIRS Fire Temp RGB - GINA
    ID: FireTemperature-RGB-GINA
    This RGB is created by assigning the VIIRS 3.74um channel to red, 2.25um channel to green, and the 1.61um channel to blue. It is used to assess fire intensity and size, with fires ranging from red (lowest) to yellow to white (hottest or biggest). These data are produced by the Geographic Information Network of Alaska (GINA).
  73. VIIRS i04 - GINA
    ID: VIIRS-i04-GINA
    This is the VIIRS 3.74um single channel i-band with 376 m resolution. It is an IR channel that is very sensitive to fires and hot spots and is available day or night. A special colormap is used to enhance the warm-hot pixels. The sensors can become saturated by very intense fires and daytime radiance values can affected by reflected sunlight. These data are produced by the Geographic Information Network of Alaska (GINA).
  74. VIIRS Snowmelt - GINA
    ID: VIIRS-Snowmelt-GINA
    This RGB is created by assigning the VIIRS 1.61um channel to red, 1.24um channel to green, and the 0.64um channel to blue. The blue shades identify snow cover characteristics. Darker blue shows wetter or older snow and lighter blues show drier or newer snow. These data are produced by the Geographic Information Network of Alaska (GINA).