WebThe QA categories are defined using a bit index where 0 represents Fill, 1 for Dilated Cloud, 2 for Cirrus, 3 for Cloud, and 4 for Cloud Shadow. These can be found in the Landsat Collection 2 product documentation (table 6-2, page 13). The QA bits can be interpreted using the Transpose Bits raster function to create a QA mask. WebJun 21, 2024 · Landsat 8 data also has an indicator for cirrus clouds. Dilated clouds are initially included in the cloud category by marking a few pixels at the edge of the high confidence clouds as clouds (USGS, 2024a; USGS, 2024b; USGS, 2024c). Our objective was to discard all pixels contaminated by cloud, cloud shadow, and snow/ice, and only …
Transferring deep learning models for cloud detection between Landsat …
WebA general interpretation of atmospheric opacity is that values (after scaling by 0.001 is applied) less than 0.1 are clear, 0.1-0.3 are average, and values greater than 0.3 indicate haze or other cloud situations. SR values from pixels with high atmospheric opacity will be less reliable, especially under high solar zenith angle conditions. WebLandsat 8-9 Collection 2 Level 1 30m MS and Thermal: 16 (Combined) (14 OLI) x ... 4 Confidence bands. Fill, Dilated Cloud, Cirrus, Cloud, Cloud Shadow, Snow, Clear, Water, Data Saturation (8), Terrain occlusion bands. Metadata (*_MTL.xml) file Angle Coefficient (*_ANG.txt) file. Landsat 8-9 Collection 2 Level 1 30m Thermal: drifter12 yahoo.com
Cloud and pixel quality masking for Landsat - Digital Earth Africa
WebLandsat 4-7 Collection 2 (C2) Level 2 Science Product (L2SP) Guide (LSDS-1618 Version 3.0 October 2024) Confirmed products. Google Earth Engine: … WebLandsat data products are continually being matured into the highest quality possible. Level-1 data products are used to create higher-level science products such as U.S. ARD, surface reflectance, surface temperature, surface water, burned area and snow covered area. These products allow scientists to provide improved and more useful results for … WebFeb 17, 2024 · I am trying to fill the gaps created from cloud masking the Landsat 7 imagery I have. I have filtered by the study area (geometry), the cloud mask and the time period (01/07/2000 to 31/10/2000). ... Dilated Cloud // Bit 2 - Unused // Bit 3 - Cloud // Bit 4 - Cloud Shadow var qaMask = image.select('QA_PIXEL').bitwiseAnd(parseInt('11111', … eoff mj