A comparative analysis of three different MODIS NDVI datasets for Alaska and adjacent Canada

Moderate Resolution Imaging Spectroradiometer (MODIS) data offer great potential for monitoring vegetation dynamics in Alaska. However, certain MODIS image quality issues, such as geometric distortion, have been analyzed and documented in high latitudes and regions distant from the Greenwich Meridian. To improve MODIS data usability, the Canada Centre for Remote Sensing (CCRS) developed a seven-band reflectance dataset (10 day composite) at 250 m resolution for Canada and North America. More recently, the US Geological Survey Earth Resources Observation and Science Center produced an eMODIS dataset that includes a 7 day composite of normalized difference vegetation index (NDVI) and surface reflectance data at 250 m, 500 m, and 1 km resolutions for the conterminous United States and Alaska. Although these two datasets are based on the same MODIS level 1B data as those of the standard MODIS products, they are processed to improve their use in high-latitude regions. In this study, we conducted a comparative analysis of the standard MODIS, CCRS MODIS, and eMODIS Alaska 250 m NDVI products using the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) images as a reference source. CCRS MODIS and eMODIS Alaska NDVI images have significantly improved geometric features over those of the standard MODIS product. Pixel-by-pixel comparisons of the MODIS datasets indicated that all retained the original MODIS radiometric characteristics, but considerable mismatches at the pixel level were found due to geometric distortions caused by resampling. All three MODIS datasets agreed well as images were degraded to 5 or 10 km resolution.

[1]  G. Dedieu,et al.  SMAC: a simplified method for the atmospheric correction of satellite measurements in the solar spectrum , 1994 .

[2]  Ranga B. Myneni,et al.  The impact of gridding artifacts on the local spatial properties of MODIS data : Implications for validation, compositing, and band-to-band registration across resolutions , 2006 .

[3]  C. Justice,et al.  Atmospheric correction of MODIS data in the visible to middle infrared: first results , 2002 .

[4]  C. Justice,et al.  Atmospheric correction of visible to middle-infrared EOS-MODIS data over land surfaces: Background, operational algorithm and validation , 1997 .

[5]  David L. Verbyla,et al.  Assessment of the MODIS Leaf Area Index Product (MOD15) in Alaska , 2005 .

[6]  Yi Luo,et al.  A method for downscaling MODIS land channels to 250-m spatial resolution using adaptive regression and normalization , 2006, SPIE Remote Sensing.

[7]  W. Ricker.  Linear Regressions in Fishery Research , 1973 .

[8]  L. Ji,et al.  An Agreement Coefficient for Image Comparison , 2006 .

[9]  Yi Luo,et al.  Developing clear-sky, cloud and cloud shadow mask for producing clear-sky composites at 250-meter spatial resolution for the seven MODIS land bands over Canada and North America , 2008 .

[10]  Ralph Dubayah,et al.  Monitoring start of season in Alaska with GLOBE, AVHRR, and MODIS data , 2008 .

[11]  Konstantin V. Khlopenkov,et al.  Novel method for reprojection of MODIS level 1B images based on concurrent gradient search , 2006, SPIE Remote Sensing.

[12]  Edward J. Masuoka,et al.  Introduction to MODIS and an Overview of Associated Activities , 2006 .

[13]  E. Laws,et al.  Appropriate use of regression analysis in marine biology , 1981 .

[14]  G. J. Babu,et al.  Linear regression in astronomy. II , 1990 .

[15]  Robert E. Wolfe,et al.  MODIS Land Products and Data Processing , 2006 .

[16]  Kurtis J. Thome,et al.  Atmospheric correction of ASTER , 1998, IEEE Trans. Geosci. Remote. Sens..

[17]  John L. Dwyer,et al.  Agreement evaluation of AVHRR and MODIS 16‐day composite NDVI data sets , 2008 .

[18]  R. Brace.  Fitting straight lines to experimental data. , 1977, The American journal of physiology.

[19]  Y. Soh,et al.  Properties of the Geometric Mean Functional Relationship , 1988 .

[20]  J. Heiskanen,et al.  Biomass estimation over a large area based on standwise forest inventory data and ASTER and MODIS satellite data: A possibility to verify carbon inventories , 2007 .

[21]  F. James Rohlf,et al.  Biometry: The Principles and Practice of Statistics in Biological Research , 1969 .

[22]  W. E. Ricker,et al.  Computation and uses of central trend lines , 1984 .

[23]  Bradley C. Reed,et al.  Integration of MODIS-derived metrics to assess interannual variability in snowpack, lake ice, and NDVI in southwest Alaska. , 2009 .

[24]  Hirokazu Yamamoto,et al.  Inter-Comparison of ASTER and MODIS Surface Reflectance and Vegetation Index Products for Synergistic Applications to Natural Resource Monitoring , 2008, Sensors.

[25]  Konstantin V. Khlopenkov,et al.  Implementation and Evaluation of Concurrent Gradient Search Method for Reprojection of MODIS Level 1B Imagery , 2008, IEEE Transactions on Geoscience and Remote Sensing.

[26]  David P. Roy,et al.  MODIS land data storage, gridding, and compositing methodology: Level 2 grid , 1998, IEEE Trans. Geosci. Remote. Sens..

[27]  Masami Kaneko,et al.  Comparison of image data acquired with AVHRR, MODIS, ETM+ and ASTER over Hokkaido, Japan , 2003 .