The VNP13A2 vegetation indices product over Brazil, July 21 - August 5, 2018.
View full-size imageThe NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Vegetation Indices (VNP13A2) Version 1 data product provides vegetation indices by a process of selecting the best available pixel over a 16-day acquisition period at 1 kilometer (km) resolution. The VNP13 data products are designed after the Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua Vegetation Indices product suite to promote the continuity of the Earth Observation System (EOS) mission.
The VNP13 algorithm process produces three vegetation indices: The Normalized Difference Vegetation Index (NDVI), the Enhanced Vegetation Index (EVI), and the Enhanced Vegetation Index-2 (EVI2). NDVI is one of the longest continual remotely sensed time series observations, using both the red and near-infrared (NIR) bands. EVI is a slightly different vegetation index that is more sensitive to canopy cover, while NDVI is more sensitive to chlorophyll. EVI2 is a reformation of the standard 3-band EVI, using the red band and NIR band. This reformation addresses arising issues when comparing VIIRS EVI to other EVI models that do not include a blue band. EVI2 will eventually become the standard EVI.
Along with the three Vegetation Indices layers, this product also includes layers for NIR reflectance; three shortwave infrared (SWIR) reflectance; red, blue, and green reflectance; composite day of year; pixel reliability; relative azimuth, view, and sun angles, and a quality layer. Two low resolution browse images are also available for each VNP13A2 product: EVI and NDVI.
Validation at stage 1 has been achieved for the VIIRS Vegetation Index (VI) product suite. Visit VIIRS Land Product Quality Assessment Product Maturity for information on product maturity status.
| Characteristic | Description |
|---|---|
| Collection | Suomi NPP VIIRS |
| DOI | 10.5067/VIIRS/VNP13A2.001 |
| File Size | ~13.5 MB |
| Temporal Resolution | Multi-Day |
| Temporal Extent | 2012-01-17 to Present |
| Spatial Extent | Global |
| 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 | 16 |
| Columns/Rows | 1200 x 1200 |
| Pixel Size | 1000 m |
| SDS Name | Description | Units | Data Type | Fill Value | No Data Value | Valid Range | Scale Factor |
|---|---|---|---|---|---|---|---|
| 1 km 16 days EVI | 3 band Enhanced Vegetation Index | EVI | 16-bit signed integer | -15000, -13000 | N/A | -10000 to 10000 | 10000 |
| 1 km 16 days EVI2 | 2 band Enhanced Vegetation Index | EVI2 | 16-bit signed integer | -15000, -13000 | N/A | -10000 to 10000 | 10000 |
| 1 km 16 days NDVI | Normalized Difference Vegetation Index | NDVI | 16-bit signed integer | -15000, -13000 | N/A | -10000 to 10000 | 10000 |
| 1 km 16 days NIR reflectance | Near-infrared Radiation reflectance (846–885 nm) | N/A | 16-bit signed integer | -1000 | N/A | 0 to 10000 | 10000 |
| 1 km 16 days SWIR1 reflectance | Shortwave Infrared Radiation reflectance (1230–1250 nm) | N/A | 16-bit signed integer | -1000 | N/A | 0 to 10000 | 10000 |
| 1 km 16 days SWIR2 reflectance | Shortwave Infrared Radiation reflectance (1580–1640 nm) | N/A | 16-bit signed integer | -1000 | N/A | 0 to 10000 | 10000 |
| 1 km 16 days SWIR3 reflectance | Shortwave Infrared Radiation reflectance (2225–2275 nm) | N/A | 16-bit signed integer | -1000 | N/A | 0 to 10000 | 10000 |
| 1 km 16 days VI Quality | Bit field that assigns each pixel a Quality Assessment (QA) attribute | Bit Field | 16-bit unsigned integer | 65535 | N/A | 0 to 65534 | N/A |
| 1 km 16 days blue reflectance | Blue band reflectance (478–498 nm) | N/A | 16-bit signed integer | -1000 | N/A | 0 to 10000 | 10000 |
| 1 km 16 days composite day of the year | Day of year for the pixel | Julian day | 16-bit signed integer | -1 | N/A | 1 to 366 | N/A |
| 1 km 16 days green reflectance | Green band reflectance (545–565 nm) | N/A | 16-bit signed integer | -1000 | N/A | 0 to 10000 | 10000 |
| 1 km 16 days pixel reliability | Pixel usefulness using a simple rank class | Rank | 8-bit signed integer | -4, -1 | N/A | 0 to 11 | N/A |
| 1 km 16 days red reflectance | Red band reflectance (600–680 nm) | N/A | 16-bit signed integer | -1000 | N/A | 0 to 10000 | 10000 |
| 1 km 16 days relative azimuth angle | Relative azimuth angle for each pixel | Degree | 16-bit signed integer | -20000 | N/A | -18000 to 18000 | 100 |
| 1 km 16 days sun zenith angle | Sun zenith angle for each pixel | Degree | 16-bit signed integer | -20000 | N/A | 0 to 18000 | 100 |
| 1 km 16 days view zenith angle | View zenith angle for each pixel | Degree | 16-bit signed integer | -20000 | N/A | 0 to 18000 | 100 |
All scale factors are to be divided.
| Fill Value | Description |
|---|---|
| -15000 | over ocean/water |
| -13000 | over land |
| Fill Value | Description |
|---|---|
| -4 | over ocean/water |
| -1 | over land |
| Rank | Description |
|---|---|
| 0 | Excellent |
| 1 | Good |
| 2 | Acceptable |
| 3 | Marginal |
| 4 | Pass |
| 5 | Questionable |
| 6 | Poor |
| 7 | Cloud Shadow |
| 8 | Snow/Ice |
| 9 | Cloud |
| 10 | Estimated |
| 11 | LTAVG |
Product Quality information can be found in the combined User Guide and Algorithm Theoretical Basis Document (ATBD). For the “500 m 16 days pixel reliability” SDS Layer, a rank quality table for each pixel can be found in Table 5. For the “500 m 16 days VI Quality” SDS Layer, for use with all VI’s, a bit field Quality Assurance table can be found in Table 13, with additional information available in Table 14. Accuracy Precision and Uncertainty information is available in Section 12.1.4.
The Quality Assessment (QA) layer is stored in an efficient bit-encoded manner. The unpack_sds_bits executable from the LDOPE Tools is available to the user community to help parse and interpret the quality layer.
Quality assurance information should be considered when determining the usability of data for a particular science application. The ArcGIS MODIS-VIIRS Python Toolbox contains tools capable of decoding quality data layers while producing thematic quality raster files for each quality attribute.
In addition to data access and transformation processes, AppEEARS also has the capability to unpack and interpret the quality layers.
The incorrect representation of the aerosol quantities (low, average, high) in the VIIRS Land Surface Reflectance product (VNP09) may have impacted VNP13 Vegetation Index data products particularly over arid bright surfaces.
Corrections will be implemented in Collection 2 reprocessing in 2022.
For complete information about known issues please refer to the MODIS/VIIRS Land Quality Assessment website.