Temperature time series with high spatial and temporal resolutions are important for several applications. The new MODIS Land Surface Temperature (LST) collection 6 provides numerous improvements compared to collection 5. However, being remotely sensed data in the thermal range, LST shows gaps in cloud-covered areas. With a novel method [1] we fully reconstructed the daily global MODIS LST products MOD11C1 and MYD11C1 (spatial resolution: 3 arc-min, i.e. approximately 5.6 km at the equator). For this, we combined temporal and spatial interpolation, using emissivity and elevation as covariates for the spatial interpolation. Here we provide a time series of these reconstructed LST data aggregated as monthly average, minimum and maximum LST maps.

[1] Metz M., Andreo V., Neteler M. (2017): A new fully gap-free time series of Land Surface Temperature from MODIS LST data. Remote Sensing, 9(12):1333. DOI: http://dx.doi.org/10.3390/rs9 121333

The data available here for download are the reconstructed global MODIS LST products MOD11C1/MYD11C1 at a spatial resolution of 3 arc-min (approximately 5.6 km at the equator; see https://lpdaac.usgs.gov/dataset_discovery/modis/modis_products_table ), aggregated to monthly data. The data are provided in GeoTIFF format. The Coordinate Reference System (CRS) is identical to the MOD11C1/MYD11C1 product as provided by NASA. In WKT as reported by GDAL:

GEOGCS["Unknown datum based upon the Clarke 1866 ellipsoid",

DATUM["Not specified (based on Clarke 1866 spheroid)",

SPHEROID["Clarke 1866",6378206.4,294.9786982138982,

AUTHORITY["EPSG","7008"]]],

PRIMEM["Greenwich",0],

UNIT["degree",0.0174532925199433]]

Acknowledgments: We are grateful to the NASA Land Processes Distributed Active Archive Center (LP DAAC) for making the MODIS LST data available. The dataset is based on MODIS Collection V006.

File name abbreviations:

avg = average of daily averages

min = minimum of daily minima

max = maximum of daily maxima

Meaning of pixel values:

The pixel values are coded in degree Celsius * 100 (hence, to obtain °C divide the pixel values by 100.0).