MCD43D40. Acquired August 1, 2005. Tile H11V05. Global.
The MCD43D40 Version 6 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) QA SnowStatus data set is a daily 16-day product. This product incorporates the Climate Modeling Grid (CMG) structure in which each file geographically covers the entire earth rather than the 10 degree x 10 degree latitude and longitude tiling system utilized by the standard MODIS land products. Unlike the standard CMG pixel resolution of 5600 meters the MCD43D products are 1000 meters, so consequently, because of the large file size each product contains just one layer. The Julian date in the granule ID of each specific file represents the 9th day of the 16 day retrieval period, and consequently the observations are weighted to estimate the values for that day.
The MCD43D40 contains the snow status quality layer for the corresponding BRDF/Albedo products retrieval period. Each pixel will have a value of either 0, 1, or 255. Zero represents “Snow –free Albedo Retrieved” and 1 is “Snow Albedo Retrieved”, while 255 is fill value. The MODIS BRDF/ALBEDO products have achieved stage 3 validation.
Improvements/Changes from Previous Versions
Citation
PI Name: Crystal Schaaf
DOI: 10.5067/MODIS/MCD43D40.006
Characteristic | Description |
---|---|
Temporal Granularity | Daily |
Temporal Extent | February 2000 -- Present |
Spatial Extent | Global |
File Size | ~16.95 MB |
Coordinate System | Lat/Long Grid |
Datum | N/A |
File Format | hdf-eos |
Geographic Dimensions | ~Entire Globe |
Characteristic | Description |
---|---|
Number of Science Dataset (SDS) Layers | 1 |
Columns/Rows | 43200 columns x 21600 rows |
Pixel Size | ~1000 |
SDS Name | Description | Units | Data Type | Fill Value | Valid Range | Scale Factor |
---|---|---|---|---|---|---|
Snow_BRDF_Albedo | Snow-free or snow BRDF/Albedo retrieved | Bit field | 8-bit unsigned integer | 255 | 0 to 254 | N/A |
The QC layer is bit encoded. We recommend the unpack_sds_bits executable from the LDOPE tools to unpack this layer: https://lpdaac.usgs.gov/tools/ldope_tools
For complete information about the MCD43D40 known issues refer to the MODIS Land Quality Assessment website: http://landweb.nascom.nasa.gov/cgi-bin/QA_WWW/getSummary.cgi?esdt=MCD43&type=C6
Title | Author | Links |
---|---|---|
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 |
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 |