An Enhanced Spatial and Temporal Data Fusion Model for Fusing Landsat and MODIS Surface Reflectance to Generate High Temporal Landsat-Like Data

Remotely sensed data, with high spatial and temporal resolutions, can hardly be provided by only one sensor due to the tradeoff in sensor designs that balance spatial resolutions and temporal coverage. However, they are urgently needed for improving the ability of monitoring rapid landscape changes at fine scales (e.g., 30 m). One approach to acquire them is by fusing observations from sensors with different characteristics (e.g., Enhanced Thematic Mapper Plus (ETM+) and Moderate Resolution Imaging Spectroradiometer (MODIS)). The existing data fusion algorithms, such as the Spatial and Temporal Data Fusion Model (STDFM), have achieved some significant progress in this field. This paper puts forward an Enhanced Spatial and Temporal Data Fusion Model (ESTDFM) based on the STDFM algorithm, by introducing a patch-based ISODATA classification method, the sliding window technology, and the temporal-weight concept. Time-series ETM+ and MODIS surface reflectance are used as test data for comparing the two algorithms. Results show that the prediction ability of the ESTDFM algorithm has been significantly improved, and is even more satisfactory in the near-infrared band (the contrasting average absolute difference [AAD]: 0.0167 vs. 0.0265). The enhanced algorithm will support subsequent research on monitoring land surface dynamic changes at finer scales.

[1]  Lucien Wald,et al.  Quality of high resolution synthesised images: Is there a simple criterion ? , 2000 .

[2]  Michael E. Schaepman,et al.  Unmixing-Based Landsat TM and MERIS FR Data Fusion , 2008, IEEE Geoscience and Remote Sensing Letters.

[3]  Claudia Notarnicola,et al.  Remote Sensing Snow Cover Maps from Modis Images at 250 M Resolution, Part 1: Algorithm Description , 2022 .

[4]  M. E. Schaepman,et al.  Using MERIS fused images for land-cover mapping and vegetation status assessment in heterogeneous landscapes , 2011 .

[5]  M. Schaepman,et al.  Downscaling time series of MERIS full resolution data to monitor vegetation seasonal dynamics , 2009 .

[6]  Dieter Oertel,et al.  Unmixing-based multisensor multiresolution image fusion , 1999, IEEE Trans. Geosci. Remote. Sens..

[7]  J. C. Price.  How unique are spectral signatures , 1994 .

[8]  Hang Zhou,et al.  Deriving long term snow cover extent dataset from AVHRR and MODIS data: Central Asia case study , 2013 .

[9]  Arno Schäpe,et al.  Multiresolution Segmentation : an optimization approach for high quality multi-scale image segmentation , 2000 .

[10]  M. V. Velzen,et al.  Self-organizing maps , 2007 .

[11]  Zheng Niu,et al.  A model for spatial and temporal data fusion: A model for spatial and temporal data fusion , 2012 .

[12]  Robert E. Wolfe,et al.  A Landsat surface reflectance dataset for North America, 1990-2000 , 2006, IEEE Geoscience and Remote Sensing Letters.

[13]  J. Townshend,et al.  Beware of per-pixel characterization of land cover , 2000 .

[14]  Fabio Maselli,et al.  Definition of Spatially Variable Spectral Endmembers by Locally Calibrated Multivariate Regression Analyses , 2001 .

[15]  Hiroyuki Ohno,et al.  Spatio-temporal distribution of rice phenology and cropping systems in the Mekong Delta with special reference to the seasonal water flow of the Mekong and Bassac rivers , 2006 .

[16]  U. Benz,et al.  Multi-resolution, object-oriented fuzzy analysis of remote sensing data for GIS-ready information , 2004 .

[17]  Pol Coppin,et al.  Endmember variability in Spectral Mixture Analysis: A review , 2011 .

[18]  J. Settle,et al.  Linear mixing and the estimation of ground cover proportions , 1993 .

[19]  Xiaolin Zhu,et al.  An enhanced spatial and temporal adaptive reflectance fusion model for complex heterogeneous regions , 2010 .

[20]  Wang Jie,et al.  A model for spatial and temporal data fusion , 2012 .

[21]  J. Townshend,et al.  Impact of sensor's point spread function on land cover characterization: Assessment and deconvolution , 2002 .

[22]  Luciano Alparone,et al.  Image fusion—the ARSIS concept and some successful implementation schemes , 2003 .

[23]  Michele Meroni,et al.  Combining medium and coarse spatial resolution satellite data to improve the estimation of sub-pixel NDVI time series , 2008 .

[24]  William J. Emery,et al.  Unmixing multiple land-cover type reflectances from coarse spatial resolution satellite data , 1995 .

[25]  Mathew R. Schwaller,et al.  On the blending of the Landsat and MODIS surface reflectance: predicting daily Landsat surface reflectance , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[26]  Joan E. Luther,et al.  Development of large-area land cover and forest change indicators using multi-sensor Landsat imagery: Application to the Humber River Basin, Canada , 2011, Int. J. Appl. Earth Obs. Geoinformation.

[27]  M. Marfai,et al.  Coastal dynamic and shoreline mapping: multi-sources spatial data analysis in Semarang Indonesia , 2008, Environmental monitoring and assessment.

[28]  Joanne C. White,et al.  A new data fusion model for high spatial- and temporal-resolution mapping of forest disturbance based on Landsat and MODIS , 2009 .

[29]  W. G. Rees,et al.  Mapping land cover change in a reindeer herding area of the Russian Arctic using Landsat TM and ETM+ imagery and indigenous knowledge , 2003 .

[30]  Luis Alonso,et al.  Regularized Multiresolution Spatial Unmixing for ENVISAT/MERIS and Landsat/TM Image Fusion , 2011, IEEE Geoscience and Remote Sensing Letters.

[31]  Luis Alonso,et al.  Multitemporal fusion of Landsat/TM and ENVISAT/MERIS for crop monitoring , 2013, Int. J. Appl. Earth Obs. Geoinformation.

[32]  J. R. Santillan,et al.  Integrated Landsat Image Analysis and Hydrologic Modeling to Detect Impacts of 25-Year Land-Cover Change on Surface Runoff in a Philippine Watershed , 2011, Remote. Sens..

[33]  Joanne C. White,et al.  Generation of dense time series synthetic Landsat data through data blending with MODIS using a spatial and temporal adaptive reflectance fusion model. , 2009 .

[34]  D. Roy,et al.  Achieving sub-pixel geolocation accuracy in support of MODIS land science , 2002 .

[35]  Nandamudi Lankalapalli Vijaykumar,et al.  A Multi-Resolution Multi-Temporal Technique for Detecting and Mapping Deforestation in the Brazilian Amazon Rainforest , 2011, Remote. Sens..

[36]  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 .

[37]  Audrey Minghelli-Roman,et al.  Spatial resolution improvement by merging MERIS-ETM images for coastal water monitoring , 2006, IEEE Geoscience and Remote Sensing Letters.