MODIS land cover uncertainty in regional climate simulations

MODIS land cover datasets are used extensively across the climate modeling community, but inherent uncertainties and associated propagating impacts are rarely discussed. This paper modeled uncertainties embedded within the annual MODIS Land Cover Type (MCD12Q1) products and propagated these uncertainties through the Regional Atmospheric Modeling System (RAMS). First, land cover uncertainties were modeled using pixel-based trajectory analyses from a time series of MCD12Q1 for Urumqi, China. Second, alternative land cover maps were produced based on these categorical uncertainties and passed into RAMS. Finally, simulations from RAMS were analyzed temporally and spatially to reveal impacts. Our study found that MCD12Q1 struggles to discriminate between grasslands and croplands or grasslands and barren in this study area. Such categorical uncertainties have significant impacts on regional climate model outputs. All climate variables examined demonstrated impact across the various regions, with latent heat flux affected most with a magnitude of 4.32 W/m2 in domain average. Impacted areas were spatially connected to locations of greater land cover uncertainty. Both biophysical characteristics and soil moisture settings in regard to land cover types contribute to the variations among simulations. These results indicate that formal land cover uncertainty analysis should be included in MCD12Q1-fed climate modeling as a routine procedure.

[1]  J. Ross Quinlan,et al.  C4.5: Programs for Machine Learning , 1992 .

[2]  Roger A. Pielke,et al.  Human Impacts on Weather and Climate , 1992 .

[3]  J. Townshend,et al.  Global discrimination of land cover types from metrics derived from AVHRR pathfinder data , 1995 .

[4]  Hans von Storch,et al.  Taking Serial Correlation into Account in Tests of the Mean. , 1995 .

[5]  Gerard B. M. Heuvelink,et al.  Error Propagation in Environmental Modelling with GIS , 1998 .

[6]  R. Pielke,et al.  Evidence that local land use practices influence regional climate, vegetation, and stream flow patterns in adjacent natural areas , 1998 .

[7]  Roger A. Pielke,et al.  Coupled Atmosphere–Biophysics–Hydrology Models for Environmental Modeling , 2000 .

[8]  J. Townshend,et al.  Global land cover classi(cid:142) cation at 1 km spatial resolution using a classi(cid:142) cation tree approach , 2004 .

[9]  H. Storch,et al.  Statistical Analysis in Climate Research , 2000 .

[10]  Limin Yang,et al.  Development of a global land cover characteristics database and IGBP DISCover from 1 km AVHRR data , 2000 .

[11]  Mark A. Friedl,et al.  Estimating pixel-scale land cover classification confidence using nonparametric machine learning methods , 2001, IEEE Trans. Geosci. Remote. Sens..

[12]  Zhao-Liang Li,et al.  Validation of the land-surface temperature products retrieved from Terra Moderate Resolution Imaging Spectroradiometer data , 2002 .

[13]  N. C. Strugnell,et al.  First operational BRDF, albedo nadir reflectance products from MODIS , 2002 .

[14]  A. Huete,et al.  Overview of the radiometric and biophysical performance of the MODIS vegetation indices , 2002 .

[15]  Alan H. Strahler,et al.  Global land cover mapping from MODIS: algorithms and early results , 2002 .

[16]  Steven W. Running,et al.  Comparisons of land cover and LAI estimates derived from ETM+ and MODIS for four sites in North America: a quality assessment of 2000/2001 provisional MODIS products , 2003 .

[17]  W. Cotton,et al.  RAMS 2001: Current status and future directions , 2003 .

[18]  W. Lucht,et al.  Terrestrial vegetation and water balance-hydrological evaluation of a dynamic global vegetation model , 2004 .

[19]  A. Belward,et al.  GLC2000: a new approach to global land cover mapping from Earth observation data , 2005 .

[20]  Chandra Giri,et al.  A comparative analysis of the Global Land Cover 2000 and MODIS land cover data sets , 2005 .

[21]  P. Palmer,et al.  Estimates of global terrestrial isoprene emissions using MEGAN (Model of Emissions of Gases and Aerosols from Nature) , 2006 .

[22]  Slobodan Nickovic,et al.  The impact of using different land cover data on wind-blown desert dust modeling results in the southwestern United States , 2007 .

[23]  R. Pielke,et al.  An overview of regional land-use and land-cover impacts on rainfall , 2007 .

[24]  E. Lambin,et al.  The emergence of land change science for global environmental change and sustainability , 2007, Proceedings of the National Academy of Sciences.

[25]  T. Vesala,et al.  Reduction of ecosystem productivity and respiration during the European summer 2003 climate anomaly: a joint flux tower, remote sensing and modelling analysis , 2007 .

[26]  Jianjun Ge,et al.  Impacts of land use/cover classification accuracy on regional climate simulations , 2007 .

[27]  Ashton Shortridge,et al.  Complex systems models and the management of error and uncertainty , 2008 .

[28]  A. Ducharne,et al.  Comprehensive data set of global land cover change for land surface model applications , 2008 .

[29]  Bicheron Patrice,et al.  The Most Detailed Portrait of Earth , 2008 .

[30]  M. Friedl,et al.  A new map of global urban extent from MODIS satellite data , 2009 .

[31]  Mark H. DeVisser,et al.  Optimum land cover products for use in a Glossina-morsitans habitat model of Kenya , 2015 .

[32]  Jianjun Ge,et al.  Biophysical Evaluation of Land-Cover Products for Land–Climate Modeling , 2009 .

[33]  Peng Gong,et al.  An assessment of MODIS Collection 5 global land cover product for biological conservation studies , 2010, 2010 18th International Conference on Geoinformatics.

[34]  Xin Li,et al.  Evaluation of four remote sensing based land cover products over China , 2010 .

[35]  A. Robock,et al.  Impacts of land cover data quality on regional climate simulations , 2010 .

[36]  Damien Sulla-Menashe,et al.  MODIS Collection 5 global land cover: Algorithm refinements and characterization of new datasets , 2010 .

[37]  Steffen Fritz,et al.  Highlighting continued uncertainty in global land cover maps for the user community , 2011 .

[38]  Gensuo Jia,et al.  Assessing disagreement and tolerance of misclassification of satellite-derived land cover products used in WRF model applications , 2013, Advances in Atmospheric Sciences.

[39]  George Z. Gertner,et al.  Spatial uncertainty analysis when mapping natural resources using remotely sensed data , 2013 .

[40]  Russell G. Congalton,et al.  Global Land Cover Mapping: A Review and Uncertainty Analysis , 2014, Remote. Sens..

[41]  An experimental design to test for the propagation of land cover uncertainty in climate modeling , 2014 .

[42]  Xiao Wang,et al.  Evaluation of the 2010 MODIS Collection 5.1 Land Cover Type Product over China , 2015, Remote. Sens..