Wilson, Adam M; Parmentier, Benoit; Walter, Jetz (2013): Global 2009 1km MODIS (MOD35/MOD09) cloud frequency and MOD35 processing path [dataset]. PANGAEA, https://doi.org/10.1594/PANGAEA.820938,

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Supplement to: Wilson, AM et al. (2013): Systematic landcover bias in collection 5 MODIS cloud mask and derived products - a global overview. Remote Sensing of Environment, conditionally accept, https://doi.org/10.1016/j.rse.2013.10.025

Identifying cloud interference in satellite-derived data is a critical step toward developing useful remotely sensed products. Most MODIS land products use a combination of the MODIS (MOD35) cloud mask and the 'internal' cloud mask of the surface reflectance product (MOD09) to mask clouds, but there has been little discussion of how these masks differ globally. We calculated global mean cloud frequency for both products, for 2009, and found that inflated proportions of observations were flagged as cloudy in the Collection 5 MOD35 product. These erroneously categorized areas were spatially and environmentally non-random and usually occurred over high-albedo land-cover types (such as grassland and savanna) in several regions around the world. Additionally, we found that spatial variability in the processing path applied in the Collection 5 MOD35 algorithm affects the likelihood of a cloudy observation by up to 20% in some areas. These factors result in abrupt transitions in recorded cloud frequency across landcover and processing-path boundaries impeding their use for fine-scale spatially contiguous modeling applications. We show that together, these artifacts have resulted in significantly decreased and spatially biased data availability for Collection 5 MOD35-derived composite MODIS land products such as land surface temperature (MOD11) and net primary productivity (MOD17). Finally, we compare our results to mean cloud frequency in the new Collection 6 MOD35 product, and find that landcover artifacts have been reduced but not eliminated. Collection 6 thus increases data availability for some regions and land cover types in MOD35-derived products but practitioners need to consider how the remaining artifacts might affect their analysis.

MOD09: The cloud flags from the MODIS Land Team 'PGE11' internal cloud mask algorithm were extracted from bit 10 of the "state_1km" Scientific Data Set (SDS) of the daily MODIS surface reflectance product (MOD09GA) using the Google Earth Engine (GEE, http://earthengine.google.org/). The MOD09 daily cloud mask time series were then summarized to mean cloud frequency (CF) by calculating the proportion of cloudy days during 2009.

MOD35: The cloud flags from the MOD35 algorithm were extracted from the "state_1km" Scientific Data Set (SDS) of the daily MODIS surface reflectance product (MOD09GA) using the Google Earth Engine (GEE, http://earthengine.google.org/). The MOD35 mask is contained in bits 0-1 of this SDS, which encodes four categories (with associated 'confidence' that the pixel is actually clear): confidently clear (confidence > 0.99), probably clear (0.99 >=confidence > 0.95), probably cloudy (0.95 >= confidence > 0.66), and confidently cloudy (confidence <=0.66). Following the advice of the MODIS science team, we binned "confidently clear" and "probably clear" together as "clear" and the other two classes as "cloudy." The MOD35 daily cloud mask time series were then summarized to mean cloud frequency (CF) by calculating the proportion of cloudy days during 2009.

MOD35 processing path: Global map of the processing path (water, coast, desert, land) used in the Collection 5 MODIS Cloud Mask (MOD35). Data were extracted from bits 6-7 of a global set of Collection 5 MOD35_L2 swaths downloaded from the Level 1 Atmosphere Archive and Distribution System (ftp://ladsweb.nascom.nasa.gov/allData/) and gridded using NASA's HEG tool (Data Management Office, 2011). GRASS GIS (GRASS Development Team, 2012) was then used to extract the modal processing path value for each 1-km pixel to generate the global mosaic.