Spectral normalization between Landsat-8/OLI, Landsat- 7/ETM+ and CBERS-4/MUX bands through linear regression and spectral unmixing

Monitoring changes on Earth's surface is a difficult task commonly performed using multi-spectral remote sensing. The increasing availability of remote sensing platforms providing data makes multi-source approaches promising, since it can increase temporal revisit rate. However, Digital image processing techniques are needed to integrate the data, since sensors can be quite different in terms of acquisition characteristics. This work addresses the spectral normalizing of three medium spatial resolution sensors: Landsat8/OLI, Landsat-7/ETM+ and CBERS-4/MUX, through linear regression and linear mixture model approaches. The results showed slight better results when using the linear regression approach.

[1]  J. K. Crowley,et al.  Mineral mapping on the Chilean-Bolivian Altiplano using co-orbital ALI, ASTER and Hyperion imagery: Data dimensionality issues and solutions , 2005 .

[2]  Birgit Kleinschmit,et al.  Derivation of long-term spatiotemporal landslide activity—A multi-sensor time series approach , 2016 .

[3]  François Petitjean,et al.  Satellite Image Time Series Analysis Under Time Warping , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[4]  J. R. Jensen.  Remote Sensing of the Environment: An Earth Resource Perspective , 2000 .

[5]  Joanne C. White,et al.  Forest Monitoring Using Landsat Time Series Data: A Review , 2014 .

[6]  Clement Atzberger,et al.  Smoothing and gap-filling of high resolution multi-spectral time series: Example of Landsat data , 2016, Int. J. Appl. Earth Obs. Geoinformation.

[7]  M. Claverie,et al.  Preliminary analysis of the performance of the Landsat 8/OLI land surface reflectance product. , 2016, Remote sensing of environment.

[8]  Christine Pohl,et al.  Multisensor image fusion in remote sensing: concepts, methods and applications , 1998 .

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

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

[11]  Christopher E. Holden,et al.  An analysis of Landsat 7 and Landsat 8 underflight data and the implications for time series investigations , 2016 .

[12]  Stefan Dech,et al.  Remote Sensing Time Series: Revealing Land Surface Dynamics , 2015 .

[13]  Bernhard Geiger,et al.  Spectral Normalization and Fusion of Optical Sensors for the Retrieval of BRDF and Albedo: Application to VEGETATION, MODIS, and MERIS Data Sets , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[14]  Pol Coppin,et al.  Review ArticleDigital change detection methods in ecosystem monitoring: a review , 2004 .

[15]  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.

[16]  Ned Horning,et al.  Land Cover Change or Change Detection , 2016 .

[17]  Birgit Kleinschmit,et al.  Robust Automated Image Co-Registration of Optical Multi-Sensor Time Series Data: Database Generation for Multi-Temporal Landslide Detection , 2014, Remote. Sens..

[18]  Hankui K. Zhang,et al.  A general method to normalize Landsat reflectance data to nadir BRDF adjusted reflectance , 2016 .

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

[20]  D. C. Patil,et al.  Soft Classification Techniques for RS Data , 2012 .

[21]  Calli Jenkerson.  User guide: Earth resources observation and science (EROS) center science processing architecture (ESPA) on demand interface , 2013 .

[22]  B. Markham,et al.  Summary of Current Radiometric Calibration Coefficients for Landsat MSS, TM, ETM+, and EO-1 ALI Sensors , 2009 .

[23]  W. Verhoef,et al.  Multi-temporal, multi-sensor retrieval of terrestrial vegetation properties from spectral–directional radiometric data , 2015 .

[24]  Frédéric Baret,et al.  Intercalibration of vegetation indices from different sensor systems , 2003 .

[25]  J. Cihlar,et al.  Effects of spectral response function on surface reflectance and NDVI measured with moderate resolution satellite sensors , 2002 .

[26]  Martha C. Anderson,et al.  Free Access to Landsat Imagery , 2008, Science.

[27]  W. Cohen,et al.  Selection of Remotely Sensed Data , 2003 .

[28]  Michael A. Wulder,et al.  Opening the archive: How free data has enabled the science and monitoring promise of Landsat , 2012 .

[29]  Larry Leigh,et al.  First in-Flight Radiometric Calibration of MUX and WFI on-Board CBERS-4 , 2016, Remote. Sens..

[30]  Leila Maria Garcia Fonseca,et al.  Assessment of a multi-sensor approach for noise removal on Landsat-8 OLI time series using CBERS-4 MUX data to improve crop classification based on phenological features , 2017, GEOINFO.