Comparison of the Continuity of Vegetation Indices Derived from Landsat 8 OLI and Landsat 7 ETM+ Data among Different Vegetation Types

Landsat 8, the most recently launched satellite of the series, promises to maintain the continuity of Landsat 7. However, in addition to subtle differences in sensor characteristics and vegetation index (VI) generation algorithms, VIs respond differently to the seasonality of the various types of vegetation cover. The purpose of this study was to elucidate the effects of these variations on VIs between Operational Land Imager (OLI) and Enhanced Thematic Mapper Plus (ETM+). Ground spectral data for vegetation were used to simulate the Landsat at-senor broadband reflectance, with consideration of sensor band-pass differences. Three band-geometric VIs (Normalized Difference Vegetation Index (NDVI), Soil-Adjusted Vegetation Index (SAVI), Enhanced Vegetation Index (EVI)) and two band-transformation VIs (Vegetation Index based on the Universal Pattern Decomposition method (VIUPD), Tasseled Cap Transformation Greenness (TCG)) were tested to evaluate the performance of various VI generation algorithms in relation to multi-sensor continuity. Six vegetation types were included to evaluate the continuity in different vegetation types. Four pairs of data during four seasons were selected to evaluate continuity with respect to seasonal variation. The simulated data showed that OLI largely inherits the band-pass characteristics of ETM+. Overall, the continuity of band-transformation derived VIs was higher than band-geometry derived VIs. VI continuity was higher in the three forest types and the shrubs in the relatively rapid growth periods of summer and autumn, but lower for the other two non-forest types (grassland and crops) during the same periods.

[1]  Frank Veroustraete,et al.  Extending the SPOT-VEGETATION NDVI Time Series (1998–2006) Back in Time With NOAA-AVHRR Data (1985–1998) for Southern Africa , 2008, IEEE Transactions on Geoscience and Remote Sensing.

[2]  Edward J. Knight,et al.  Landsat-8 Operational Land Imager Design, Characterization and Performance , 2014, Remote. Sens..

[3]  Limin Yang,et al.  Derivation of a tasselled cap transformation based on Landsat 7 at-satellite reflectance , 2002 .

[4]  Yao Li,et al.  Calculating vegetation index based on the universal pattern decomposition method (VIUPD) using Landsat 8 , 2014, 2014 IEEE Geoscience and Remote Sensing Symposium.

[5]  S. Garrigues,et al.  Multiscale geostatistical analysis of AVHRR, SPOT-VGT, and MODIS global NDVI products , 2008 .

[6]  D. Roy,et al.  Multi-temporal MODIS-Landsat data fusion for relative radiometric normalization, gap filling, and prediction of Landsat data , 2008 .

[7]  Zhiming Feng,et al.  Cross-Comparison of Vegetation Indices Derived from Landsat-7 Enhanced Thematic Mapper Plus (ETM+) and Landsat-8 Operational Land Imager (OLI) Sensors , 2013, Remote. Sens..

[8]  Alexander P. Trishchenko,et al.  Effects of spectral response function on surface reflectance and NDVI measured with moderate resolution satellite sensors: Extension to AVHRR NOAA-17, 18 and METOP-A , 2009 .

[9]  Darrel L. Williams,et al.  Landsat: Yesterday, Today, and Tomorrow , 2006 .

[10]  Rob J Hyndman,et al.  Detecting trend and seasonal changes in satellite image time series , 2010 .

[11]  Neil Flood,et al.  Continuity of Reflectance Data between Landsat-7 ETM+ and Landsat-8 OLI, for Both Top-of-Atmosphere and Surface Reflectance: A Study in the Australian Landscape , 2014, Remote. Sens..

[12]  Michele Meroni,et al.  Evaluation of Agreement Between Space Remote Sensing SPOT-VEGETATION fAPAR Time Series , 2013, IEEE Transactions on Geoscience and Remote Sensing.

[13]  Alfredo R. Huete,et al.  Evaluation of sensor calibration uncertainties on vegetation indices for MODIS , 2000, IEEE Trans. Geosci. Remote. Sens..

[14]  Qingxi Tong,et al.  Derivation of a tasselled cap transformation based on Landsat 8 at-satellite reflectance , 2014 .

[15]  J. G. Lyon,et al.  Hyperspectral Remote Sensing of Vegetation , 2011 .

[16]  Molly E. Brown,et al.  Evaluation of the consistency of long-term NDVI time series derived from AVHRR,SPOT-vegetation, SeaWiFS, MODIS, and Landsat ETM+ sensors , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[17]  Lawrence Ong,et al.  Landsat-8 Operational Land Imager Radiometric Calibration and Stability , 2014, Remote. Sens..

[18]  John L. Dwyer,et al.  Multi-platform comparisons of MODIS and AVHRR normalized difference vegetation index data , 2005 .

[19]  R. Kauth,et al.  The tasselled cap - A graphic description of the spectral-temporal development of agricultural crops as seen by Landsat , 1976 .

[20]  Lifu Zhang,et al.  A new vegetation index based on the universal pattern decomposition method , 2007 .

[21]  John L. Dwyer,et al.  Comparison of MODIS and AVHRR 16‐day normalized difference vegetation index composite data , 2004 .

[22]  A. Huete A soil-adjusted vegetation index (SAVI) , 1988 .

[23]  R. Jackson,et al.  Interpreting vegetation indices , 1991 .

[24]  Hui Qing Liu,et al.  A feedback based modification of the NDVI to minimize canopy background and atmospheric noise , 1995, IEEE Transactions on Geoscience and Remote Sensing.

[25]  Lifu Zhang,et al.  Assessment of the universal pattern decomposition method using MODIS and ETM+ data , 2007 .

[26]  B. Wardlow,et al.  Analysis of time-series MODIS 250 m vegetation index data for crop classification in the U.S. Central Great Plains , 2007 .

[27]  Larry Leigh,et al.  The Ground-Based Absolute Radiometric Calibration of Landsat 8 OLI , 2015, Remote. Sens..

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

[29]  Alfredo Huete,et al.  An empirical investigation of cross-sensor relationships of NDVI and red/near-infrared reflectance using EO-1 Hyperion data , 2006 .

[30]  C. Justice,et al.  High-Resolution Global Maps of 21st-Century Forest Cover Change , 2013, Science.

[31]  Julia A. Barsi,et al.  The next Landsat satellite: The Landsat Data Continuity Mission , 2012 .

[32]  Bo Liu,et al.  Comparison of the sensor dependence of vegetation indices based on Hyperion and CHRIS hyperspectral data , 2013 .

[33]  J. Hill,et al.  Comparative analysis of landsat-5 TM and SPOT HRV-1 data for use in multiple sensor approaches , 1990 .

[34]  M. Friedl,et al.  Detecting interannual variation in deciduous broadleaf forest phenology using Landsat TM/ETM+ data , 2013 .

[35]  C. Tucker Red and photographic infrared linear combinations for monitoring vegetation , 1979 .

[36]  Darrel L. Williams,et al.  Landsat sensor performance: history and current status , 2004, IEEE Transactions on Geoscience and Remote Sensing.

[37]  Samuel N. Goward,et al.  Landsat 7's long-term acquisition plan — an innovative approach to building a global imagery archive , 2001 .

[38]  Dirk Pflugmacher,et al.  Monitoring coniferous forest biomass change using a Landsat trajectory-based approach , 2013 .

[39]  Robert H. Fraser,et al.  Detecting long-term changes to vegetation in northern Canada using the Landsat satellite image archive , 2011 .

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

[41]  P. M. Teilleta,et al.  Radiometric cross-calibration of the Landsat-7 ETM+ and Landsat-5 TM sensors based on tandem data sets , 2001 .

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

[43]  J. Hill,et al.  Coupling spectral unmixing and trend analysis for monitoring of long-term vegetation dynamics in Mediterranean rangelands , 2003 .

[44]  Lifu Zhang,et al.  Sensor‐independent analysis method for hyperspectral data based on the pattern decomposition method , 2006 .

[45]  Lawrence Ong,et al.  Landsat-8 Operational Land Imager (OLI) Radiometric Performance On-Orbit , 2015, Remote. Sens..

[46]  Y. Ryu,et al.  Characteristics of Landsat 8 OLI-derived NDVI by comparison with multiple satellite sensors and in-situ observations , 2015 .