An Improved STARFM with Help of an Unmixing-Based Method to Generate High Spatial and Temporal Resolution Remote Sensing Data in Complex Heterogeneous Regions

Remote sensing technology plays an important role in monitoring rapid changes of the Earth's surface. However, sensors that can simultaneously provide satellite images with both high temporal and spatial resolution haven’t been designed yet. This paper proposes an improved spatial and temporal adaptive reflectance fusion model (STARFM) with the help of an Unmixing-based method (USTARFM) to generate the high spatial and temporal data needed for the study of heterogeneous areas. The results showed that the USTARFM had higher accuracy than STARFM methods in two aspects of analysis: individual bands and of heterogeneity analysis. Taking the predicted NIR band as an example, the correlation coefficients (r) for the USTARFM, STARFM and unmixing methods were 0.96, 0.95, 0.90, respectively (p-value < 0.001); Root Mean Square Error (RMSE) values were 0.0245, 0.0300, 0.0401, respectively; and ERGAS values were 0.5416, 0.6507, 0.8737, respectively. The USTARM showed consistently higher performance than STARM when the degree of heterogeneity ranged from 2 to 10, highlighting that the use of this method provides the capacity to solve the data fusion problems faced when using STARFM. Additionally, the USTARFM method could help researchers achieve better performance than STARFM at a smaller window size from its heterogeneous land surface quantitative representation.

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

[2]  T. Kemper,et al.  A new tool for variable multiple endmember spectral mixture analysis (VMESMA) , 2005 .

[3]  Damien Arvor,et al.  Classification of MODIS EVI time series for crop mapping in the state of Mato Grosso, Brazil , 2011 .

[4]  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.

[5]  Antonio J. Plaza,et al.  Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-Based Approaches , 2012, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

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

[7]  Devendra Singh,et al.  Generation and evaluation of gross primary productivity using Landsat data through blending with MODIS data , 2011, Int. J. Appl. Earth Obs. Geoinformation.

[8]  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 .

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

[10]  Mingquan Wu,et al.  Generating daily high spatial land surface temperatures by combining ASTER and MODIS land surface temperature products for environmental process monitoring. , 2015, Environmental science. Processes & impacts.

[11]  F. Javier García-Haro,et al.  A comparison of STARFM and an unmixing-based algorithm for Landsat and MODIS data fusion , 2015 .

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

[13]  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 .

[14]  Mingquan Wu,et al.  Use of MODIS and Landsat time series data to generate high-resolution temporal synthetic Landsat data using a spatial and temporal reflectance fusion model , 2012 .

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

[16]  Mario Lillo-Saavedra,et al.  Fusion of multispectral and panchromatic satellite sensor imagery based on tailored filtering in the Fourier domain , 2005 .

[17]  Luis Guanter,et al.  Multitemporal Unmixing of Medium-Spatial-Resolution Satellite Images: A Case Study Using MERIS Images for Land-Cover Mapping , 2011, IEEE Transactions on Geoscience and Remote Sensing.

[18]  Ross S. Lunetta,et al.  North American Landscape Characterization dataset development and data fusion issues , 1998 .

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

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

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

[22]  J. Moreno,et al.  Seasonal variations of leaf area index of agricultural fields retrieved from Landsat data , 2008 .

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

[24]  Y. Knyazikhin,et al.  Effect of foliage spatial heterogeneity in the MODIS LAI and FPAR algorithm over broadleaf forests , 2003 .

[25]  Ruiliang Pu,et al.  Downscaling Thermal Infrared Radiance for Subpixel Land Surface Temperature Retrieval , 2008, Sensors.

[26]  W. Cohen,et al.  North American forest disturbance mapped from a decadal Landsat record , 2008 .

[27]  Mingquan Wu,et al.  Generating Daily Synthetic Landsat Imagery by Combining Landsat and MODIS Data , 2015, Sensors.

[28]  Alfonso Fernández-Manso,et al.  Spectral unmixing , 2012 .

[29]  Xianhong Xie,et al.  Land cover classification of finer resolution remote sensing data integrating temporal features from time series coarser resolution data , 2014 .

[30]  Tim R. McVicar,et al.  Assessing the accuracy of blending Landsat–MODIS surface reflectances in two landscapes with contrasting spatial and temporal dynamics: A framework for algorithm selection , 2013 .

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

[32]  Jing M. Chen,et al.  Predicting gross primary production from the enhanced vegetation index and photosynthetically active radiation: Evaluation and calibration , 2011 .

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

[34]  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 .

[35]  D. Roy,et al.  The availability of cloud-free Landsat ETM+ data over the conterminous United States and globally , 2008 .

[36]  M. Wulder,et al.  Mapping wildfire and clearcut harvest disturbances in boreal forests with Landsat time series data , 2011 .

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

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

[39]  Wei Zhang,et al.  An Enhanced Spatial and Temporal Data Fusion Model for Fusing Landsat and MODIS Surface Reflectance to Generate High Temporal Landsat-Like Data , 2013, Remote. Sens..

[40]  Deyu Meng,et al.  Spatial and Temporal Image Fusion via Regularized Spatial Unmixing , 2015, IEEE Geoscience and Remote Sensing Letters.