Temperature, precipitation and Normalized Difference Vegetation Index (NDVI) from 1982 to 2009 in the Koshi River Basin of the Middle Himalayas

The aim of this paper is to find out if there is a significant difference in using NDVI dataset processed by harmonic analysis method to evaluate its dynamic and response to climate change, compared with the original data.

Animation with all MODIS fire product maps from 2000 to 2011, Yakutia, Siberia

The video FireMovie_2000-2011.avi shows an animation with all MODIS fire product maps of the area sequenced over time. Colors in the video describe MODIS classes as follows: MODIS classification and color scale: Class 0 - not processed - Dark blue (1 frame) Class 3 - water - Light Blue (rivers and some lakes) Class 4 - clouds - Green blue Class 5 - non fire land - Yellow green Class 8 - nominal confidence fire - Red Class 9 - high confidence fire - Dark red

Global 2009 1km MODIS (MOD35/MOD09) cloud frequency and MOD35 processing path

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.

Percent cropland coverage for sub-Saharan Africa

The dataset contains a cropland percent coverage map for Africa created through the combination of five existing land cover products: GLC-2000, MODIS Land Cover, GlobCover, MODIS Crop Likelihood and AfriCover. A synergy map was created in which the products are ranked by experts, which reflects the likelihood or probability that a given pixel is cropland. The cropland map was calibrated with national and sub-national crop statistics using a novel approach. Preliminary validation of the map was undertaken. The resulting cropland map has an accuracy of 83%, which is higher than the accuracy of any of the individual maps. The cropland percent coverage map for Africa is available for overlay on Google Earth or for download at http://agriculture.geo-wiki.org.