A global image of the Aqua MODIS land surface temperature (LST) data product from MYD21C2 January 17 - 24, 2021.View full-size image
A new suite of Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature and Emissivity (LST&E) products are available in Collection 6.1. The MYD21 Land Surface Temperature (LST) algorithm differs from the algorithm of the MYD11 LST products, in that the MYD21 algorithm is based on the ASTER Temperature/Emissivity Separation (TES) technique, whereas the MYD11 uses the split-window technique. The MYD21 TES algorithm uses a physics-based algorithm to dynamically retrieve both the LST and spectral emissivity simultaneously from the MODIS thermal infrared bands 29, 31, and 32. The TES algorithm is combined with an improved Water Vapor Scaling (WVS) atmospheric correction scheme to stabilize the retrieval during very warm and humid conditions.
The MYD21C2 dataset is an 8-day composite LST product that uses an algorithm based on a simple averaging method. The algorithm calculates the average from all the cloud free MYD21A1D and MYD21A1N daily acquisitions from the 8-day period. Unlike the MOD21A1 data sets where the daytime and nighttime acquisitions are separate products, the MYD21A2 contains both daytime and nighttime acquisitions as separate Science Dataset (SDS) layers within a single Hierarchical Data Format (HDF) file. The LST, Quality Control (QC), view zenith angle, and viewing time have separate day and night SDS layers, while the values for the MODIS emissivity bands 29, 31, and 32 are the average of both the nighttime and daytime acquisitions. Additional details regarding the method used to create this Level 3 (L3) product are available in the Algorithm Theoretical Basis Document (ATBD).
Validation at stage 1 has been achieved for the MODIS Land Surface Temperature and Emissivity data products. Further details regarding MODIS land product validation for the MYD21 data products are available from the MODIS Land Team Validation site.
|File Size||~110 MB|
|Temporal Extent||2002-07-04 to Present|
|Coordinate System||Geographic Latitude and Longitude|
|Number of Science Dataset (SDS) Layers||27|
|Columns/Rows||7200 x 3600|
|Pixel Size||5600 m|
|SDS Name||Description||Units||Data Type||Fill Value||No Data Value||Valid Range||Scale Factor||Offset|
|Count_Day||Count of Daytime Input Values||N/A||16-bit unsigned integer||0||N/A||1 to 65535||N/A||N/A|
|Count_Night||Count of Nighttime Input Values||N/A||16-bit unsigned integer||0||N/A||1 to 65535||N/A||N/A|
|QC_Day||Quality Control for Daytime LST and Emissivity||N/A||8-bit unsigned integer||N/A||N/A||0 to 255||N/A||N/A|
|QC_Night||Quality Control for Nighttime LST and Emissivity||N/A||8-bit unsigned integer||N/A||N/A||0 to 255||N/A||N/A|
|LST_Day||Average Daytime Land Surface Temperature||Kelvin||16-bit unsigned integer||0||N/A||7500 to 65535||0.02||N/A|
|LST_Night||Average Nighttime Land Surface Temperature||Kelvin||16-bit unsigned integer||0||N/A||7500 to 65535||0.02||N/A|
|LST_Day_err||Root-mean-square-error Daytime Land Surface Temperature||Kelvin||8-bit unsigned integer||0||N/A||1 to 255||0.04||N/A|
|LST_Night_err||Root-mean-square-error Nighttime Land Surface Temperature||Kelvin||8-bit unsigned integer||0||N/A||1 to 255||0.04||N/A|
|Day_view_angle||Average Daytime View Zenith Angle||Degree||8-bit unsigned integer||255||N/A||0 to 130||N/A||-65|
|Night_view_angle||Average Nighttime View Zenith Angle||Degree||8-bit unsigned integer||255||N/A||0 to 130||N/A||-65|
|Day_view_time||Average Daytime View Time (UTC)||Hours||8-bit unsigned integer||255||N/A||0 to 120||0.2||N/A|
|Night_view_time||Average Nighttime View Time (UTC)||Hours||8-bit unsigned integer||255||N/A||0 to 120||0.2||N/A|
|Emis_29_Day||Average Daytime Band 29 Emissivity||N/A||8-bit unsigned integer||0||N/A||1 to 255||0.002||0.49|
|Emis_29_Night||Average Nighttime Band 29 Emissivity||N/A||8-bit unsigned integer||0||N/A||1 to 255||0.002||0.49|
|Emis_29_Day_err||Root-mean-square-error Daytime Band 29 Emissivity||N/A||16-bit unsigned integer||0||N/A||1 to 65535||0.0001||N/A|
|Emis_29_Night_err||Root-mean-square-error Nighttime Band 29 Emissivity||N/A||16-bit unsigned integer||0||N/A||1 to 65535||0.0001||N/A|
|Emis_31_Day||Average Daytime Band 31 Emissivity||N/A||8-bit unsigned integer||0||N/A||1 to 255||0.002||0.49|
|Emis_31_Night||Average Nighttime Band 31 Emissivity||N/A||8-bit unsigned integer||0||N/A||1 to 255||0.002||0.49|
|Emis_31_Day_err||Root-mean-square-error Daytime Band 31 Emissivity||N/A||16-bit unsigned integer||0||N/A||1 to 65535||0.0001||N/A|
|Emis_31_Night_err||Root-mean-square-error Nighttime Band 31 Emissivity||N/A||16-bit unsigned integer||0||N/A||1 to 65535||0.0001||N/A|
|Emis_32_Day||Average Daytime Band 32 Emissivity||N/A||8-bit unsigned integer||0||N/A||1 to 255||0.002||0.49|
|Emis_32_Night||Average Nighttime Band 32 Emissivity||N/A||8-bit unsigned integer||0||N/A||1 to 255||0.002||0.49|
|Emis_32_Day_err||Root-mean-square-error Daytime Band 32 Emissivity||N/A||16-bit unsigned integer||0||N/A||1 to 65535||0.0001||N/A|
|Emis_32_Night_err||Root-mean-square-error Nighttime Band 32 Emissivity||N/A||16-bit unsigned integer||0||N/A||1 to 65535||0.0001||N/A|
|Percent_land_in_grid||Percent of Land Detections in Grid Cell||Percent||8-bit unsigned integer||255||N/A||1 to 100||N/A||N/A|
|Clear_sky_days||Bitmap of Clear Sky Days (1 = clear, LSB = 1st day)||N/A||8-bit unsigned integer||N/A||N/A||0 to 255||N/A||N/A|
|Clear_sky_nights||Bitmap of Clear Sky Nights (1 = clear, LSB = 1st day)||N/A||8-bit unsigned integer||N/A||N/A||0 to 255||N/A||N/A|
The QC layer is stored in an efficient bit-encoded manner. The unpack_sds_bits executable from the LDOPE Tools is available to the user community to help parse and interpret the quality layer.
Quality assurance information should be considered when determining the usability of data for a particular science application. The ArcGIS MODIS-VIIRS Python Toolbox contains tools capable of decoding quality data layers while producing thematic quality raster files for each quality attribute.
The final quality bit for the output product reflects the lowest quality values from all observations that went into the final average. The bit definition index for the QC layer is available in Table 13 of the User Guide.
Users of MODIS LST products may notice an increase in occurrences of extreme high temperature outliers in the unfiltered MxD21 Version 6 and 6.1 products compared to the heritage MxD11 LST products. This can occur especially over desert regions like the Sahara where undetected cloud and dust can negatively impact both the MxD21 and MxD11 retrieval algorithms.
In the MxD11 LST products, these contaminated pixels are flagged in the algorithm and set to fill values in the output products based on differences in the band 32 and band 31 radiances used in the generalized split window algorithm. In the MxD21 LST products, values for the contaminated pixels are retained in the output products (and may result in overestimated temperatures), and users need to apply Quality Control (QC) filtering and other error analyses for filtering out bad values. High temperature outlier thresholds are not employed in MxD21 since it would potentially remove naturally occurring hot surface targets such as fires and lava flows.
High atmospheric aerosol optical depth (AOD) caused by vast dust outbreaks in the Sahara and other deserts highlighted in the example documentation are the primary reason for high outlier surface temperature values (and corresponding low emissivity values) in the MxD21 LST products. Future versions of the MxD21 product will include a dust flag from the MODIS aerosol product and/or brightness temperature look up tables to filter out contaminated dust pixels. It should be noted that in the MxD11B day/night algorithm products, more advanced cloud filtering is employed in the multi-day products based on a temporal analysis of historical LST over cloudy areas. This may result in more stringent filtering of dust contaminated pixels in these products.
In order to mitigate the impact of dust in the MxD21 V6 and 6.1 products, the science team recommends using a combination of the existing QC bits, emissivity values, and estimated product errors, to confidently remove bad pixels from analysis. For more details, refer to this dust and cloud contamination example documentation.
For complete information about known issues please refer to the MODIS/VIIRS Land Quality Assessment website.