How is the C Function of Mask algorithm used with Landsat Level-1 data?

The C Function of Mask (CFMask) algorithm populates cloud, cloud confidence, cloud shadow, and snow/ice pixels in the processing of Landsat Collections Level-1 data products, with the results represented as bit-mapped values within the Landsat Collection 1 Level-1 Quality Assessment (QA) Band.  CFMask derives from the Function of Mask (FMask), an algorithm written in MATLAB at Boston University and translated into the C programming language at USGS EROS to facilitate its implementation in a production environment.

CFMask is a multi-pass algorithm that uses decision trees to prospectively label pixels in the scene; it then validates or discards those labels according to scene-wide statistics. It also creates a cloud shadow mask by iteratively estimating cloud heights and projecting them onto the ground.

While the CFMask algorithm is designed by default to be run with thermal data, it is possible to employ it without a thermal input. This may be necessary at times, such as when thermal data from Landsat 8 Thermal Infrared Sensor (TIRS) is not immediately available.  

Currently, non-thermal CFMask is run by removing all thermal threshold tests from the algorithm. Users should be aware that this change does alter the results of the cloud, cloud confidence, cloud shadow, and snow/ice detection routines.  

Learn more: CFMask Algorithm

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What are the Landsat Collection 1 Level-1 data product file sizes?

The file sizes of Landsat Collection 1 Level-1 data products are listed in the table below. Sensor Compressed file Uncompressed file Landsat 8 OLI/TIRS 919 MB 1.61 GB Landsat 7 ETM+ 235 MB 785 MB Landsat 4-5 TM 150 MB 500 MB Landsat 1-5 MSS 20 MB 75 MB

How do I rescale Landsat Level-1 digital numbers to reflectance, radiance, and brightness temperature?

Landsat Level-1 data can be rescaled to the top of atmosphere (TOA) reflectance and/or radiance using radiometric rescaling coefficients provided in the metadata file (MTL.txt) that is delivered with the Landsat Level-1 product. The MTL file also contains the thermal constants needed to convert thermal band data to TOA brightness temperature (BT...

I have a list of historical Landsat Level-1 Scene IDs; how can I determine the corresponding Landsat Collections Level-1 Product Identifier?

The historical Landsat Level-1 Scene ID is in the EarthExplorer metadata. After entering a search using the historical Scene ID, the Landsat Level-1 Collection 1 Product Identifier will be returned in the search results. The results can be exported from EarthExplorer in a variety of formats for metadata parsing. We also encourage use of the Bulk...

What is the naming convention for Landsat Collections Level-1 scenes?

The Landsat Collection 1 Level-1 product identifier includes the Collection processing levels, processing date, collection number, and collection iter category: LXSS_LLLL_PPPRRR_YYYYMMDD_yyyymmdd_CC_TX Where: L = Landsat X = Sensor (“C”=OLI/TIRS combined, “O”=OLI-only, “T”=TIRS-only, “E”=ETM+, “T”=“TM, “M”=MSS) SS = Satellite (”07”=Landsat 7, “08...

Does Landsat Level-1 data processing include atmospheric correction?

Landsat Collection 1 Level-1 data are not corrected for atmospheric conditions, however Landsat Science Products , which include Level-2 Surface Reflectance and Provisional Surface Temperature, and Level-3 Burned Area, Dynamic Surface Water Extent, and Fractional Snow Covered Area are atmospherically corrected. Learn more: Landsat Collections...

What are the processing levels for Landsat Level-1 data?

The table below shows the Landsat Collections Level-1 processing levels: Landsat Level-1 Processing Levels Processing Level Description Terrain Precision Correction (L1TP) Radiometrically calibrated and orthorectified using ground control points (GCPs) and digital elevation model (DEM) data to correct for relief displacement.The highest quality...
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Landsat 8 OLI/TIRS - London, England
June 29, 2018

Landsat 8 OLI/TIRS - London, England

Landsat 8 OLI/TIRS - London, England - Path 201 Row 024 - June 29, 2018

April 18, 2018

Landsat in Action - The Accuracy of Landsat with Jeff Masek

Landsat 9 Project Scientist Jeff Masek discusses the ways Landsat data is used and how important it is to have high quality data.
 

February 27, 2018

Landsat's Calibration and Validation

Engineers and scientists from both Landsat and Sentinel missions are working together to calibrate observation data and validate its quality to improve the science using these resources.
 

December 31, 2017

Landsat Collections

Landsat Collections: Providing a Stable Environmental Record for Time Series Analysis

This video introduces the new U.S. Geological Survey (USGS) Landsat Collection 1 inventory structure. Collection 1 required the reprocessing of all archived Landsat data to achieve radiometric and geometric consistency of Level-1 products through time and across all Landsat sensors

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September 19, 2017

Landsat Collections — What are Tiers?

This is the third video in a series describing the new U.S. Geological Survey (USGS) Landsat Collection 1 inventory structure. Collection 1 required the reprocessing of all archived Landsat data to achieve radiometric and geometric consistency of Level-1 products through time and across all Landsat sensors. This video explains how Landsat Collection Level-1 data products

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June 30, 2017

Landsat Collections - RMSE

Landsat Collections: Providing a Stable Environment Record for Time Series Analysis

This is the second of a three-part video series describing the new U.S. Geological Survey (USGS) Landsat Collection 1 inventory structure. Collection 1 required the reprocessing of all archived Landsat data to achieve radiometric and geometric consistency of Level-1 products through

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Landsat 8 Collection 1 Quality Assessment Example
March 7, 2017

Landsat 8 Collection 1 Quality Assessment Example

Landsat Level-1 Quality Assessment (QA) bands are a helpful resource for evaluating the overall usefulness of a Landsat pixel. Each pixel in the QA band contains a decimal value that represents bit-packed combinations of surface, atmospheric, and sensor conditions that can affect the individual pixel quality. 

This is an example of a Quality Assessment (QA) Band

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