Hi,
Yes, this is somewhat common especially in daily data, practically every tile has some of these outliers. They should not be used, they are erroneous data.
Please see the statement below which gives more specifics on why this is the case:
The "anomalies" that you've identified, e.g. negative reflectance values,
are known issues in MODIS and Landsat surface reflectance products. The
atmospheric correction and surface reflectance retrieval algorithms are not
ideal for water bodies due to the inherently low level of water leaving
radiances and consequently very low SNR. Quantitative remote sensing
retrievals of water column constituents requires different algorithms.
Similarly reflectance values greater than 1.0 can be encountered over
bright targets such as snow and playas. These are computational
artifacts. The surface reflectance QA bands and product user guides should
be consulted.
Please let us know if you have any questions.
Thanks,
Brett
Yes, this is somewhat common especially in daily data, practically every tile has some of these outliers. They should not be used, they are erroneous data.
Please see the statement below which gives more specifics on why this is the case:
The "anomalies" that you've identified, e.g. negative reflectance values,
are known issues in MODIS and Landsat surface reflectance products. The
atmospheric correction and surface reflectance retrieval algorithms are not
ideal for water bodies due to the inherently low level of water leaving
radiances and consequently very low SNR. Quantitative remote sensing
retrievals of water column constituents requires different algorithms.
Similarly reflectance values greater than 1.0 can be encountered over
bright targets such as snow and playas. These are computational
artifacts. The surface reflectance QA bands and product user guides should
be consulted.
Please let us know if you have any questions.
Thanks,
Brett
Statistics: Posted by LP DAAC - lien — Mon Mar 04, 2024 9:40 am America/New_York