MCD15A3H. Acquired June 10, 2011. Tile H11V04. North Central US.
The MCD15A3H version 6 MODIS Level 4, Combined Fraction of Photosynthetically Active Radiation (FPAR), and Leaf Area Index (LAI) product is a 4-day composite data set with 500 meter pixel size. The algorithm chooses the “best” pixel available from all the acquisitions of both MODIS sensors located on NASA’s Terra and Aqua satellites from within the 4-day period.
LAI is defined as the one-sided green leaf area per unit ground area in broadleaf canopies and as one-half the total needle surface area per unit ground area in coniferous canopies. FPAR is defined as the fraction of incident photosynthetically active radiation (400-700nm) absorbed by the green elements of a vegetation canopy.
Improvements/Changes from Previous Versions
Citation
PI Name: Ranga Myneni
DOI: 10.5067/MODIS/MCD15A3H.006
Characteristic | Description |
---|---|
Temporal Granularity | 4-day |
Temporal Extent | July 2002 - Present |
Spatial Extent | Global |
File Size | ~3.71 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 | 6 |
Rows/Columns | 2400 cols x 2400 rows |
Pixel Size | 500 m |
SDS Layer Name | Description | Units | Data Type | Fill Value | Valid Range | Scaling Factor | Additional Offset |
---|---|---|---|---|---|---|---|
Fpar_500m | Fraction of photosynthetically active radiation | Dimensionless | 8-bit unsigned integer | 249-255 | 0 to 100 | 0.01 | N/A |
Lai_500m | Leaf area index | Dimensionless | 8-bit unsigned integer | 249-255 | 0 to 100 | 0.1 | N/A |
FparLai_QC | Quality for Lai and Fpar | Class-flag | 8-bit unsigned integer | 255 | 0 to 254 | N/A | N/A |
FparExtra_QC | Extra detail Quality for Lai and Fpar | Class-flag | 8-bit unsigned integer | 255 | 0 to 254 | N/A | -65.0 |
FparStdDev_500m | Standard deviation of Fpar | Dimensionless | 8-bit unsigned integer | 248-255 | 0 to 100 | 0.01 | N/A |
LaiStdDev_500m | Standard deviation for Lai | Dimensionless | 8-bit unsigned integer | 248-255 | 0 to 100 | 0.1 | N/A |
The QC layer is bit encoded. We recommend the unpack_sds_bits executable from the LDOPE tools to unpack these layers.
The QC bitmap for the QC layer is given in the User Guide on pages 8 and 9.
For complete information about the MCD15A3H known issues please refer to the MODIS Land Quality Assessment website.
Title | Author | Links |
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MODIS Land Products Quality Assurance (QA) Tutorial: Part 1 | LP DAAC | Tutorial |
EarthExplorer Introduction from the Land Remote Sensing Data Access Workshop | LP DAAC and EROS | Presentation |
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LP DAAC Introduction to MODIS Data | LP DAAC | Tutorial, Narrative |
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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 |
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Choosing a Data Access Tool: LP DAAC Data Pool and DAAC2Disk | LP DAAC | Video Tip |
Monthly MODIS Land Surface Temperature | LP DAAC | Video |
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Advanced Webinar: Creating and Using Normalized Difference Vegetation Index (NDVI) from Satellite Imagery | NASA ARSET | Webinar |
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An Introduction to MODIS Version 6 Data | LP DAAC | Video Tip |
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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 |