VNP09A1. Acquired January 1, 2015. Tile H08V05. Western US.
The 8-day Visible Infrared Imaging Radiometer Suite (VIIRS) surface reflectance (VNP09A1) Version 1 composite product provides an estimate of land surface reflectance from the Suomi National Polar-Orbiting Partnership (S-NPP) VIIRS sensor for nine moderate-resolution bands (M1, M2, M3, M4, M5, M7, M8, M10, M11) at nominal 1 kilometer resolution (~926 meter). The 1 kiliometer dataset is derived through resampling the native 750 meter VIIRS resolution in the L2 input product. The data are corrected for atmospheric conditions such as the effects of molecular gases, including ozone and water vapor, and for the effects of atmospheric aerosols. Each pixel represents the best possible Level 2G observation during an 8-day period that is selected on the basis of high observation coverage, low sensor angle, the absence of clouds or cloud shadow, and aerosol loading. Included in the product along with the nine reflectance bands are day of year, reflectance band quality, relative azimuth, sensor and solar zenith angle layers. Validation at stage 1 is complete for VIIRS surface reflectance products.
Citation
PI Name: Eric Vermote (NASA GSFC)
DOI: 10.5067/VIIRS/VNP09A1.001
| Characteristic | Description |
|---|---|
| Temporal Granularity | 8-day |
| Temporal Extent | January 19, 2012 -- Present |
| Spatial Extent | Global |
| File Size | ~23 MB |
| Coordinate System | Sinusoidal |
| Datum | N/A |
| File Format | HDF-EOS5 |
| Geographic Dimensions | 1200 km x 1200 km |
| Characteristic | Description |
|---|---|
| Number of Science Dataset (SDS) Layers | 15 |
| Columns/Rows | 1200 x 1200 |
| Pixel Size | 1000 m |
| SDS Name | Description | Units | Data Type | Fill Value | Valid Range | Scale Factor |
|---|---|---|---|---|---|---|
| RelativeAzimuth |
Relative Azimuth Angle |
Degree | 16-bit signed integer |
0 |
-18000 to 18000 |
0.01 |
| SolarZenith |
Solar Zenith Angle |
Degree | 16-bit signed integer |
0 |
0 to 18000 |
0.01 |
| SensorZenith |
SensorZenith Angle |
Degree | 16-bit signed integer |
0 |
0 to 18000 |
0.01 |
| SurfReflect_Day_Of_Year |
Day of Year |
Julian Day | 16-bit unsigned integer |
65535 |
1 to 366 |
NA |
| SurfReflect_M1_1 |
1 km Surface Reflectance Band M1 |
Reflectance | 16 bit signed integer |
-28672 |
-100 to 16000 |
0.0001 |
| SurfReflect_M2_1 |
1 km Surface Reflectance Band M2 |
Reflectance | 16 bit signed integer |
-28672 |
-100 to 16000 |
0.0001 |
| SurfReflect_M3_1 |
1 km Surface Reflectance Band M3 |
Reflectance | 16 bit signed integer |
-28672 |
-100 to 16000 |
0.0001 |
| SurfReflect_M4_1 |
1 km Surface Reflectance Band M4 |
Reflectance | 16 bit signed integer |
-28672 |
-100 to 16000 |
0.0001 |
| SurfReflect_M5_1 |
1 km Surface Reflectance Band M5 |
Reflectance | 16 bit signed integer |
-28672 |
-100 to 16000 |
0.0001 |
| SurfReflect_M7_1 |
1 km Surface Reflectance Band M7 |
Reflectance | 16 bit signed integer |
-28672 |
-100 to 16000 |
0.0001 |
| SurfReflect_M8_1 |
1 km Surface Reflectance Band M8 |
Reflectance | 16 bit signed integer |
-28672 |
-100 to 16000 |
0.0001 |
| SurfReflect_M10_1 |
1 km Surface Reflectance Band M10 |
Reflectance | 16 bit signed integer |
-28672 |
-100 to 16000 |
0.0001 |
| SurfReflect_M11_1 |
1 km Surface Reflectance Band M11 |
Reflectance | 16 bit signed integer |
-28672 |
-100 to 16000 |
0.0001 |
| SurfReflect_State |
1 km State Flags |
Bit Field | 16-bit unsigned integer |
65535 |
NA |
NA |
| SurfReflect_QC |
Surface Reflectance Quality Control |
Bit Field | 32-bit unsigned integer |
1073741824 |
NA |
NA |
The bit field mappings for QC and State QA layers are provided under Section 3.3 Tables 18 and 19 of the User Guide.
For complete information about product quality, refer to the VIIRS Land Product Quality website.
Known issues for VIIRS Surface Reflectance data products can be found in Section 4.0 “Caveats and Known Problems” of the User Guide.
| Title | Author | Links |
|---|---|---|
| How to Reformat and Georeference VIIRS Surface Reflectance HDF-EOS5 Files | LP DAAC | Tutorial |
| R You Ready to Python? An Introduction to Working with Land Remote Sensing Data in R and Python | LP DAAC | Webinar, Presentation |