MYD13A1. Acquired April 23, 2009. Tile H11V05. Southeast US.
The MYD13A1 Version 6 product provides Vegetation Index (VI) values at a per pixel basis at 500 meter (m) spatial resolution. There are two primary vegetation layers. The first is the Normalized Difference Vegetation Index (NDVI), which is referred to as the continuity index to the existing National Oceanic and Atmospheric Administration-Advanced Very High Resolution Radiometer (NOAA-AVHRR) derived NDVI. The second vegetation layer is the Enhanced Vegetation Index (EVI), which has improved sensitivity over high biomass regions. The algorithm for this product chooses the best available pixel value from all the acquisitions from the 16 day period. The criteria used is low clouds, low view angle, and the highest NDVI/EVI value.
Provided along with the vegetation layers and two quality assurance (QA) layers are reflectance bands 1 (red), 2 (near-infrared), 3 (blue), and 7 (mid-infrared), as well as four observation layers.
Validation at stage 3 has been achieved for all MYD13 vegetation products. Further details regarding product validation for the MYD13A1 data product is available from the MODIS land team validation site.
Improvements/Changes from Previous Versions
Citation
PI Name: Kamel Didan
DOI: 10.5067/MODIS/MYD13A1.006
Characteristic | Description |
---|---|
Temporal Granularity | Multi-Day |
Temporal Extent | 2002-07-04 to Present |
Spatial Extent | Global |
File Size | ~28 MB |
Coordinate System | Sinusoidal |
Datum | N/A |
File Format | HDF-EOS |
Geographic Dimensions | 1200 km x 1200 km |
Characteristic | Description |
---|---|
Number of Science Dataset (SDS) Layers | 12 |
Columns/Rows | 2400 x 2400 |
Pixel Size | 500 m |
SDS Name | Description | Units | Data Type | Fill Value | No Data Value | Valid Range | Scale Factor | Offset |
---|---|---|---|---|---|---|---|---|
500m 16 days NDVI |
500m 16 days NDVI |
NDVI | 16-bit signed integer |
-3000 |
N/A |
-2000 to 10000 |
0.0001 | N/A |
500m 16 days EVI |
500m 16 days EVI |
EVI | 16-bit signed integer |
-3000 |
N/A |
-2000 to 10000 |
0.0001 | N/A |
500m 16 days VI Quality |
VI quality indicators |
Bit Field | 16-bit unsigned integer |
65535 |
N/A |
0 to 65534 |
N/A | N/A |
500m 16 days red reflectance |
Surface Reflectance Band 1 |
N/A | 16-bit signed integer |
-1000 |
N/A |
0 to 10000 |
0.0001 | N/A |
500m 16 days NIR reflectance |
Surface Reflectance Band 2 |
N/A | 16-bit signed integer |
-1000 |
N/A |
0 to 10000 |
0.0001 | N/A |
500m 16 days blue reflectance |
Surface Reflectance Band 3 |
N/A | 16-bit signed integer |
-1000 |
N/A |
0 to 10000 |
0.0001 | N/A |
500m 16 days MIR reflectance |
Surface Reflectance Band 7 |
N/A | 16-bit signed integer |
-1000 |
N/A |
0 to 10000 |
0.0001 | N/A |
500m 16 days view zenith angle |
View zenith angle of VI Pixel |
Degree | 16-bit signed integer |
-10000 |
N/A |
0 to 18000 |
0.01 | N/A |
500m 16 days sun zenith angle |
Sun zenith angle of VI pixel |
Degree | 16-bit signed integer |
-10000 |
N/A |
0 to 18000 |
0.01 | N/A |
500m 16 days relative azimuth angle |
Relative azimuth angle of VI pixel |
Degree | 16-bit signed integer |
-4000 |
N/A |
-18000 to 18000 |
0.01 | N/A |
500m 16 days composite day of the year |
Day of year VI pixel |
Julian day | 16-bit signed integer |
-1 |
N/A |
1 to 366 |
N/A | N/A |
500m 16 days pixel reliability |
Quality reliability of VI pixel |
Rank | 8-bit signed integer |
-1 |
N/A |
0 to 3 |
N/A | N/A |
The "500m 16 days VI Quality" 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.
In addition to data access and transformation processes, AppEEARS also has the capability to unpack and interpret the QC layers.
The QA bit flags for the "500m 16 days pixel reliability" layer are provided in Table 4 on page 15 and the "500m 16 days VI Quality" layer in Table 5 on page 16 of the User Guide.
In areas where no valid VI is retrieved, VI Quality bits 11-13 are correctly assigned to indicate the land/water type. The other bits are set to 1 which can create a false notion of the parameter description; however, since no valid VI is retrieved these values can be ignored.
The following issues have been detected:
For instances where the VI Quality (bits 0-1) is flagged as good and the VI Usefulness (bits 2-5) indicates the same pixels have the lowest usefulness score, users are advised to disregard the usefulness score.
Corrections will be implemented in Collection 6.1 reprocessing in 2019.
For complete information about the MYD13A1 known issues please refer to the MODIS Land Quality Assessment website.
Title | Author | Links |
---|---|---|
MODIS Land Products Quality Assurance (QA) Tutorial: Part 1 | LP DAAC | Tutorial |
MODIS Land Products Quality Assurance (QA) Tutorial: Part 2 | LP DAAC | Tutorial |
EarthExplorer Introduction from the Land Remote Sensing Data Access Workshop | LP DAAC and EROS | Presentation |
EarthExplorer Use Cases from the Land Remote Sensing Data Access Workshop | LP DAAC and EROS | Tutorial |
LP DAAC Introduction to MODIS Data | LP DAAC | Tutorial, Narrative |
Obtaining Remotely Sensed Imagery: A Guide to Online Resources for Satellite and Other Data | DOIRSWG and RSAC | Tutorial |
Diving into the NASA Data Pools with DAAC2Disk | LP DAAC | Webinar, Presentation |
Overview of LP DAAC Products | LP DAAC | Presentation |
LP DAAC Data Access through OPeNDAP and Web Services | LP DAAC | Presentation, Webinar |
Discover NASA Land Processes Data with Web Services | LP DAAC | Presentation, Webinar |
Using AppEEARS Quality Service to Extract Information from MODIS Quality Layers | LP DAAC | Tutorial, Jupyter |
Choosing a Data Access Tool: AppEEARS | LP DAAC | Video Tip |
Understanding Land Surface Temperature Dynamics | LP DAAC | Video |
Choosing a Data Access Tool: LP DAAC Data Pool and DAAC2Disk | LP DAAC | Video Tip |
Monthly MODIS Land Surface Temperature | LP DAAC | Video |
Using NASA Remote Sensing for Disaster Management | NASA ARSET | Webinar |
Advanced Webinar: Creating and Using Normalized Difference Vegetation Index (NDVI) from Satellite Imagery | NASA ARSET | Webinar |
Decoding MODIS Version 6 Quality Science Datasets using MODIS Python Toolbox for ArcGIS | LP DAAC | Tutorial |
An Introduction to MODIS Version 6 Data | LP DAAC | Video Tip |
Changes in Canopy Cover with NASA MODIS Leaf Area Index Data | LP DAAC | Video Tip |
Working with Land Remote Sensing Data in a GIS Environment | LP DAAC | Presentation |
R You Ready to Python? An Introduction to Working with Land Remote Sensing Data in R and Python | LP DAAC | Webinar, Presentation |
MODIS Observes Snowpack in the Sierra Nevada Mountain Range | LP DAAC | Video |
Getting Started with MODIS Version 6 Vegetation Indices Data Part 1: All About Accessing Data | LP DAAC | Video Tip |
Getting Started with MODIS Version 6 Vegetation Indices Data Part 2: Using the Data | LP DAAC | Video Tip |
Getting Started with MODIS Version 6 Vegetation Indices Data Part 3: Interpreting Quality Information | LP DAAC | Video Tip |
Getting Started with MODIS Version 6 Land Surface Temperature Data Part 1: All About Accessing Data | LP DAAC | Video Tip |
Getting Started with MODIS Version 6 Land Surface Temperature Data Part 2: Using the Data | LP DAAC | Video Tip |
Getting Started with MODIS Version 6 Land Surface Temperature Data Part 3: Interpreting Quality Information | LP DAAC | Video Tip |
Getting Started with MODIS Version 6 Thermal Anomalies and Fire Data Part 1: All About Accessing the Data. | LP DAAC | Video Tip |
Getting Started with MODIS Version 6 Thermal Anomalies and Fire Data Part 2: Using the Data | LP DAAC | Video Tip |
Getting Started with MODIS Version 6 Thermal Anomalies and Fire Data Part 3: Interpreting Quality Information | LP DAAC | Video Tip |
Getting Started with MODIS V6 Surface Reflectance Data Part 1: All About Accessing the Data | LP DAAC | Video Tip |
Getting Started with MODIS V6 Surface Reflectance Data Part 2: Using the Data | LP DAAC | Video Tip |
Getting Started with MODIS V6 Surface Reflectance Data Part 3: Interpreting Quality Information | LP DAAC | Video Tip |
Using NASA's AppEEARS to Slice and Dice Big Earth Data | LP DAAC | Presentation, Webinar |
Choosing a Data Access Tool: AppEEARS Area Sampler | LP DAAC | Video Tip |
Observing Land from Space: Interacting with Geospatial Data from NASA's LP DAAC | LP DAAC | YouTube Recording, Presentation |
Working with AppEEARS NetCDF-4 Output Data in R | LP DAAC | Tutorial, R Notebook |
Working with AppEEARS NetCDF-4 Output Data in Python | LP DAAC | Tutorial, Jupyter Notebook |
Masking, Visualizing, and Plotting AppEEARS Output GeoTIFF Time Series in Python | LP DAAC | Tutorial, Jupyter Notebook |
How to Access the LP DAAC Data Pool with R | LP DAAC | Tutorial |
How to Access the LP DAAC Data Pool with Python | LP DAAC | Tutorial |
How to Access LP DAAC Data from the Command Line | LP DAAC | Tutorial |