@article{Friedl2010168,
title = "\{MODIS\} Collection 5 global land cover: Algorithm refinements and characterization of new datasets ",
journal = "Remote Sensing of Environment ",
volume = "114",
number = "1",
pages = "168 - 182",
year = "2010",
note = "",
issn = "0034-4257",
doi = "http://dx.doi.org/10.1016/j.rse.2009.08.016",
url = "http://www.sciencedirect.com/science/article/pii/S0034425709002673",
author = "Mark A. Friedl and Damien Sulla-Menashe and Bin Tan and Annemarie Schneider and Navin Ramankutty and Adam Sibley and Xiaoman Huang",
keywords = "Global land cover",
keywords = "MODIS",
keywords = "Classification ",
abstract = "Information related to land cover is immensely important to global change science. In the past decade, data sources and methodologies for creating global land cover maps from remote sensing have evolved rapidly. Here we describe the datasets and algorithms used to create the Collection 5 \{MODIS\} Global Land Cover Type product, which is substantially changed relative to Collection 4. In addition to using updated input data, the algorithm and ancillary datasets used to produce the product have been refined. Most importantly, the Collection 5 product is generated at 500-m spatial resolution, providing a four-fold increase in spatial resolution relative to the previous version. In addition, many components of the classification algorithm have been changed. The training site database has been revised, land surface temperature is now included as an input feature, and ancillary datasets used in post-processing of ensemble decision tree results have been updated. Further, methods used to correct classifier results for bias imposed by training data properties have been refined, techniques used to fuse ancillary data based on spatially varying prior probabilities have been revised, and a variety of methods have been developed to address limitations of the algorithm for the urban, wetland, and deciduous needleleaf classes. Finally, techniques used to stabilize classification results across years have been developed and implemented to reduce year-to-year variation in land cover labels not associated with land cover change. Results from a cross-validation analysis indicate that the overall accuracy of the product is about 75% correctly classified, but that the range in class-specific accuracies is large. Comparison of Collection 5 maps with Collection 4 results show substantial differences arising from increased spatial resolution and changes in the input data and classification algorithm. "
}