Comparing Inter-Sensor NDVI for the Analysis of Horticulture Crops in South-Eastern Australia

Mohammad Abuzar; Kathryn Sheffield; Des Whitfield; Mark O’Connell; Andy McAllister

doi:doi:10.11648/j.ajrs.20140201.11

| Peer-Reviewed

Comparing Inter-Sensor NDVI for the Analysis of Horticulture Crops in South-Eastern Australia

Mohammad Abuzar, Kathryn Sheffield, Des Whitfield, Mark O’Connell, Andy McAllister

Published in American Journal of Remote Sensing (Volume 2, Issue 1)

Received: 24 February 2014 Published: 20 March 2014

Views: Downloads:

Download PDF

Share This Article

Twitter
Linked In
Facebook

Abstract

Synergistic application of NDVI from diverse sensors has been an interest of researchers in the field of natural resource management for over two decades. Attempts have been made to deal with the sensor-specific differences in NDVI which stem from a number of factors. In this study, an NDVI comparison has been made between Landsat-7 ETM+ and five other sensors of relatively fine resolution (ASTER, SPOT-5 XS, RapidEye, QuickBird-2 and WorldView-2) over an area of horticultural crops in south-eastern Australia during 2011-12. Translation equations have been developed using linear regression for specific sensors and specific horticultural crops (almond, table grape, wine grape, olive and vegetable). Cross-senor comparisons of NDVI showed strong positive relationships (p <0.001, R2>0.9) but in three cases (ASTER, SPOT-5 and RapidEye) the differences in NDVI values were significant (p<0.001) as well. Though in the other two cases (QuickBird-2 and WorldView-2) the differences were not significant, they were not negligible. Therefore the role of translation equations is considered important for cross-sensor NDVI compatibility. The results of this study will be used: (i) to convert NDVI from the selected sensors to a Landsat- equivalent NDVI for the analysis of irrigated horticultural crops, (ii) to optimise the temporal frequency of NDVI observations for long-term vegetation analyses, and (iii) to transfer Landsat ETM+-based measurements, particularly evapotranspiration (ET) estimates, to alternative sensors that lack thermal band capability which is critical for ET measurements. ET measurements will be used to estimate crop water requirement to help irrigation water management of horticultural crops.

Published in	American Journal of Remote Sensing (Volume 2, Issue 1)
DOI	10.11648/j.ajrs.20140201.11
Page(s)	1-9
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2014. Published by Science Publishing Group

Keywords

NDVI Comparison, Horticultural Crops, ASTER, SPOT-5, RapidEye, QuickBird-2, WorldView-2

References

[1]	Miura, T., A. Huete, and H. Yoshioka, An empirical investigation of cross-sensor relationships of NDVI and red/near-infrared reflectance using EO-1 Hyperion data. Remote Sensing of Environment, 2006. 100(2): p. 223-236.
[2]	Miura, T., J.P. Turner, and A.R. Huete, Spectral compatibility of the NDVI across VIIRS, MODIS, and AVHRR: An analysis of atmospheric effects using EO-1 Hyperion. IEEE Transactions on Geoscience and Remote Sensing, , 2013. 51(3): p. 1349-1359.
[3]	Rouse, J.W., Haas, R. H,, Schell, J. A., Deering, D. W., Monitoring vegetation systems in the Great Plains with ERTS, Third ERTS Symposium1973, NASA SP-351 I: Washington, DC. p. 309-317.
[4]	Hatfield, J.L. and J.H. Prueger, Value of using different vegetative indices to quantify agricultural crop characteristics at different growth stages under varying management practices. Remote Sensing, 2010. 2(2): p. 562-578.
[5]	Rossini, M., et al., High resolution field spectroscopy measurements for estimating gross ecosystem production in a rice field. Agricultural and Forest Meteorology, 2010. 150(9): p. 1283-1296.
[6]	Marti, J., et al., Can wheat yield be assessed by early measurements of Normalized Difference Vegetation Index? Annals of Applied Biology, 2007. 150(2): p. 253-257.
[7]	Gu, Y., B.K. Wylie, and N.B. Bliss, Mapping grassland productivity with 250-m eMODIS NDVI and SSURGO database over the Greater Platte River Basin, USA. Ecological Indicators, 2013. 24(0): p. 31-36.
[8]	Mkhabela, M.S., et al., Crop yield forecasting on the Canadian Prairies using MODIS NDVI data. Agricultural and Forest Meteorology, 2011. 151(3): p. 385-393.
[9]	Jiang, Z., et al., Analysis of NDVI and scaled difference vegetation index retrievals of vegetation fraction. Remote Sensing of Environment, 2006. 101(3): p. 366-378.
[10]	Xu, H. and T. Zhang. Comparison of Landsat-7 ETM+ and ASTER NDVI measurements. In:Tong et al. (Ed.), Remote Sensing of the Environment: The 17th China Conference on Remote Sensing. 2011. Proc. of SPIE vol. 8203 82030K-1.
[11]	Ambinakudige, S., J. Choi, and S. Khanal, A comparative analysis of CBERS-2 CCD and Landsat-TM satellite images in vegetation mapping. RevistaBrasileira de Cartografia 2010. 63(1): p. 115-122.
[12]	Brown, M., Pinzón, JE, Didan, K, Morisette, JT, Tucker, CJ, Evaluation of the Consistency of Long-Term NDVI Time Series Derived From AVHRR, SPOT-Vegetation, SeaWiFS, MODIS, and Landsat ETM+ Sensors. IEEE Transactions on Geoscience and Remote Sensing, 2006. 41(7 Part 1): p. 1787 - 1793.
[13]	Steven, M.D., et al., Intercalibration of vegetation indices from different sensor systems. Remote Sensing of Environment, 2003. 88(4): p. 412-422.
[14]	Thenkabail, P.S., Inter-sensor relationships between IKONOS and Landsat-7 ETM+ NDVI data in three ecoregions of Africa. International Journal of Remote Sensing, 2004. 25(2): p. 389-408.
[15]	van Leeuwen, W.J.D., et al., Multi-sensor NDVI data continuity: Uncertainties and implications for vegetation monitoring applications. Remote Sensing of Environment, 2006. 100(1): p. 67-81.
[16]	Yin, H., et al., How normalized difference vegetation index (NDVI) trends from Advanced Very High Resolution Radiometer (AVHRR) and SystemeProbatoired'Observation de la Terre VEGETATION (SPOT VGT) time series differ in agricultural areas: An inner Mongolian case study. Remote Sensing, 2012. 4(11): p. 3364-3389.
[17]	Chavez, P.S., Image-based atmospheric corrections - Revisted and improved. Photogrammetric Engineering and Remote Sensing, 1996. 62(9): p. 1025-1036.
[18]	Martinez-Beltran, C., et al., Multisensor comparison of NDVI for a semi-arid environment in Spain. International Journal of Remote Sensing, 2009. 30(5): p. 1355-1384.
[19]	Gao, F., et al., Bidirectional NDVI and atmospherically resistant BRDF inversion for vegetation canopy. IEEE Transactions on Geoscience and Remote Sensing, 2002. 40(6): p. 1269-1278.
[20]	Miura, T., et al., Inter-Comparison of ASTER and MODIS surface reflectance and vegetation index products for synergistic applications to natural resource monitoring. Sensors, 2008. 8(4): p. 2480-2499.
[21]	Trishchenko, A.P., 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. Remote Sensing of Environment, 2009. 113(2): p. 335-341.
[22]	Guyot, G. and X.-F. Gu, Effect of radiometric corrections on NDVI-determined from SPOT-HRV and Landsat-TM data. Remote Sensing of Environment, 1994. 49(3): p. 169-180.
[23]	Gallo, K.P. and J.C. Eidenshink, Differences in visible and near-IR responses, and derived vegetation indices, for the NOAA-9 and NOAA-10 AVHRRs. Photogrammetric Engineering and Remote Sensing 1988. 54(4): p. 485-490.
[24]	Gallo, K.P. and C.S.T. Daughtry, Differences in vegetation indices for simulated Landsat-5 MSS and TM, NOAA-9 AVHRR, and SPOT-1 sensor systems. Remote Sensing of Environment, 1987. 23(3): p. 439-452.
[25]	Teillet, P.M., K. Staenz, and D.J. William, Effects of spectral, spatial, and radiometric characteristics on remote sensing vegetation indices of forested regions. Remote Sensing of Environment, 1997. 61(1): p. 139-149.
[26]	Anderson, J.H., et al., Intercalibration and Evaluation of ResourceSat-1 and Landsat-5 NDVI. Canadian Journal of Remote Sensing, 2011. 37(2): p. 213-219.
[27]	Goward, S.N., et al., Empirical comparison of Landsat 7 and IKONOS multispectral measurements for selected Earth Observation System (EOS) validation sites. Remote Sensing of Environment, 2003. 88: p. 80-99.
[28]	Soudani, K., et al., Comparative analysis of IKONOS, SPOT, and ETM+ data for leaf area index estimation in temperate coniferous and deciduous forest stands. Remote Sensing of Environment, 2006. 102: p. 161-175.
[29]	Tarnavsky, E., S. Garrigues, and M.E. Brown, Multiscalegeostatistical analysis of AVHRR, SPOT-VGT, and MODIS global NDVI products. Remote Sensing of Environment, 2008. 112(2): p. 535-549.
[30]	Townshend, J.R.G., et al., The impact of misregistration on change detection. IEEE Transactions on Geoscience and Remote Sensing, 1992. 30(5): p. 1054-1060.
[31]	Verbyla, D.L. and S.H. Boles, Bias in land cover change estimates due to misregistration. International Journal of Remote Sensing, 2000. 21(18): p. 3553-3560.
[32]	Hill, J. and D. Aifadopoulou, Comparative analysis of landsat-5 TM and SPOT HRV-1 data for use in multiple sensor approaches. Remote Sensing of Environment, 1990. 34(1): p. 55-70.
[33]	Maselli, F., Monitoring forest conditions in a protected Mediterranean coastal area by the analysis of multiyear NDVI data. Remote Sensing of Environment, 2004. 89(4): p. 423-433.
[34]	Allen, R., et al., Satellite-based ET estimation in agriculture using SEBAL and METRIC. Hydrological Processes, 2011. 25(26): p. 4011-4027.
[35]	O’Connell, M.G., et al., Satellite Remote Sensing of water use and vegetation cover to derive crop coefficients for crops grown in Sunraysia Irrigation Region of Victoria, Australia. Acta Horticulturae, 2011. 889: p. 543-549.
[36]	Abuzar, M., et al., Satellite-based measurements for benchmarking regional irrigation performance in Goulburn-Murray Catchment. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2012. XXXIX-B8: p. 221-223.
[37]	Whitfield, D.M., et al., SEBAL-METRIC estimates of crop water requirement in horticultural crops grown in SE Australia. Acta Horticulturae, 2011. 922: p. 141-148.
[38]	Tasumi, M., et al., Satellite-Based Energy Balance to Assess Within-Population Variance of Crop Coefficient Curves. Journal of Irrigation and Drainage Engineering, 2005. 131(1): p. 94-109.
[39]	Tasumi, M. and R.G. Allen, Satellite-based ET mapping to assess variation in ET with timing of crop development. Agricultural Water Management, 2007. 88(1-3): p. 54-62.
[40]	Chander, G., B.L. Markham, and D.L. Helder, Summary of current radiometric calibration coefficients for Landsat MSS, TM, ETM+, and EO-1 ALI sensors. Remote Sensing of Environment, 2009. 113(5): p. 893-903.
[41]	Abrams, M., S. Hook, and B. Ramachandran, ASTER User Handbook, Version 2, 2002, JPL, NASA.
[42]	RapidEye, RapidEye Standard Image Product Specifications2009, Germany.
[43]	Krause, K., Radiometric Use of QuickBird Imagery - Technical Note2005: DigitalGlobe, Longmont, Colorado, USA.
[44]	Updike, T. and C. Comp, Radiometric Use of WorldView-2 Imagery2010: DigitalGlobe Inc.
[45]	Thome, K.J., S.F. Biggar, and P.N. Slater, Effects of assumed solar spectral irradiance on intercomparisons of Earth-observing sensors. Proceedings SPIE 2001. 4540: p. 260-269.
[46]	Chander, G., et al., Foreword to the Special Issue on Intercalibration of Satellite Instruments. IEEE Transactions on Geoscience and Remote Sensing, 2013. 51(3): p. 1052-1055.

Cite This Article

Plain Text BibTeX RIS

APA Style

Mohammad Abuzar, Kathryn Sheffield, Des Whitfield, Mark O’Connell, Andy McAllister. (2014). Comparing Inter-Sensor NDVI for the Analysis of Horticulture Crops in South-Eastern Australia. American Journal of Remote Sensing, 2(1), 1-9. https://doi.org/10.11648/j.ajrs.20140201.11

Copy | Download

ACS Style

Mohammad Abuzar; Kathryn Sheffield; Des Whitfield; Mark O’Connell; Andy McAllister. Comparing Inter-Sensor NDVI for the Analysis of Horticulture Crops in South-Eastern Australia. Am. J. Remote Sens. 2014, 2(1), 1-9. doi: 10.11648/j.ajrs.20140201.11

Copy | Download

AMA Style

Mohammad Abuzar, Kathryn Sheffield, Des Whitfield, Mark O’Connell, Andy McAllister. Comparing Inter-Sensor NDVI for the Analysis of Horticulture Crops in South-Eastern Australia. Am J Remote Sens. 2014;2(1):1-9. doi: 10.11648/j.ajrs.20140201.11

Copy | Download

@article{10.11648/j.ajrs.20140201.11,
  author = {Mohammad Abuzar and Kathryn Sheffield and Des Whitfield and Mark O’Connell and Andy McAllister},
  title = {Comparing Inter-Sensor NDVI for the Analysis of Horticulture Crops in South-Eastern Australia},
  journal = {American Journal of Remote Sensing},
  volume = {2},
  number = {1},
  pages = {1-9},
  doi = {10.11648/j.ajrs.20140201.11},
  url = {https://doi.org/10.11648/j.ajrs.20140201.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajrs.20140201.11},
  abstract = {Synergistic application of NDVI from diverse sensors has been an interest of researchers in the field of natural resource management for over two decades. Attempts have been made to deal with the sensor-specific differences in NDVI which stem from a number of factors. In this study, an NDVI comparison has been made between Landsat-7 ETM+ and five other sensors of relatively fine resolution (ASTER, SPOT-5 XS, RapidEye, QuickBird-2 and WorldView-2) over an area of horticultural crops in south-eastern Australia during 2011-12. Translation equations have been developed using linear regression for specific sensors and specific horticultural crops (almond, table grape, wine grape, olive and vegetable). Cross-senor comparisons of NDVI showed strong positive relationships (p 0.9) but in three cases (ASTER, SPOT-5 and RapidEye) the differences in NDVI values were significant (p<0.001) as well. Though in the other two cases (QuickBird-2 and WorldView-2) the differences were not significant, they were not negligible. Therefore the role of translation equations is considered important for cross-sensor NDVI compatibility. The results of this study will be used: (i) to convert NDVI from the selected sensors to a Landsat- equivalent NDVI for the analysis of irrigated horticultural crops, (ii) to optimise the temporal frequency of NDVI observations for long-term vegetation analyses, and (iii) to transfer Landsat ETM+-based measurements, particularly evapotranspiration (ET) estimates, to alternative sensors that lack thermal band capability which is critical for ET measurements. ET measurements will be used to estimate crop water requirement to help irrigation water management of horticultural crops.},
 year = {2014}
}

Copy | Download

TY  - JOUR
T1  - Comparing Inter-Sensor NDVI for the Analysis of Horticulture Crops in South-Eastern Australia
AU  - Mohammad Abuzar
AU  - Kathryn Sheffield
AU  - Des Whitfield
AU  - Mark O’Connell
AU  - Andy McAllister
Y1  - 2014/03/20
PY  - 2014
N1  - https://doi.org/10.11648/j.ajrs.20140201.11
DO  - 10.11648/j.ajrs.20140201.11
T2  - American Journal of Remote Sensing
JF  - American Journal of Remote Sensing
JO  - American Journal of Remote Sensing
SP  - 1
EP  - 9
PB  - Science Publishing Group
SN  - 2328-580X
UR  - https://doi.org/10.11648/j.ajrs.20140201.11
AB  - Synergistic application of NDVI from diverse sensors has been an interest of researchers in the field of natural resource management for over two decades. Attempts have been made to deal with the sensor-specific differences in NDVI which stem from a number of factors. In this study, an NDVI comparison has been made between Landsat-7 ETM+ and five other sensors of relatively fine resolution (ASTER, SPOT-5 XS, RapidEye, QuickBird-2 and WorldView-2) over an area of horticultural crops in south-eastern Australia during 2011-12. Translation equations have been developed using linear regression for specific sensors and specific horticultural crops (almond, table grape, wine grape, olive and vegetable). Cross-senor comparisons of NDVI showed strong positive relationships (p 0.9) but in three cases (ASTER, SPOT-5 and RapidEye) the differences in NDVI values were significant (p<0.001) as well. Though in the other two cases (QuickBird-2 and WorldView-2) the differences were not significant, they were not negligible. Therefore the role of translation equations is considered important for cross-sensor NDVI compatibility. The results of this study will be used: (i) to convert NDVI from the selected sensors to a Landsat- equivalent NDVI for the analysis of irrigated horticultural crops, (ii) to optimise the temporal frequency of NDVI observations for long-term vegetation analyses, and (iii) to transfer Landsat ETM+-based measurements, particularly evapotranspiration (ET) estimates, to alternative sensors that lack thermal band capability which is critical for ET measurements. ET measurements will be used to estimate crop water requirement to help irrigation water management of horticultural crops.
VL  - 2
IS  - 1
ER  -

Copy | Download

Author Information

Mohammad Abuzar

Department of Environment and Primary Industries, 32 Lincoln Square North, Carlton, Victoria3053, Australia
Kathryn Sheffield

Department of Environment and Primary Industries, 32 Lincoln Square North, Carlton, Victoria3053, Australia
Des Whitfield

Department of Environment and Primary Industries, 255 Ferguson Road, Tatura, Victoria3616, Australia
Mark O’Connell

Department of Environment and Primary Industries, 255 Ferguson Road, Tatura, Victoria3616, Australia
Andy McAllister

Department of Environment and Primary Industries, 255 Ferguson Road, Tatura, Victoria3616, Australia

Download PDF

Sections

Plain Text BibTeX RIS

APA Style

Mohammad Abuzar, Kathryn Sheffield, Des Whitfield, Mark O’Connell, Andy McAllister. (2014). Comparing Inter-Sensor NDVI for the Analysis of Horticulture Crops in South-Eastern Australia. American Journal of Remote Sensing, 2(1), 1-9. https://doi.org/10.11648/j.ajrs.20140201.11

Copy | Download

ACS Style

Mohammad Abuzar; Kathryn Sheffield; Des Whitfield; Mark O’Connell; Andy McAllister. Comparing Inter-Sensor NDVI for the Analysis of Horticulture Crops in South-Eastern Australia. Am. J. Remote Sens. 2014, 2(1), 1-9. doi: 10.11648/j.ajrs.20140201.11

Copy | Download

AMA Style

Mohammad Abuzar, Kathryn Sheffield, Des Whitfield, Mark O’Connell, Andy McAllister. Comparing Inter-Sensor NDVI for the Analysis of Horticulture Crops in South-Eastern Australia. Am J Remote Sens. 2014;2(1):1-9. doi: 10.11648/j.ajrs.20140201.11

Copy | Download

@article{10.11648/j.ajrs.20140201.11,
  author = {Mohammad Abuzar and Kathryn Sheffield and Des Whitfield and Mark O’Connell and Andy McAllister},
  title = {Comparing Inter-Sensor NDVI for the Analysis of Horticulture Crops in South-Eastern Australia},
  journal = {American Journal of Remote Sensing},
  volume = {2},
  number = {1},
  pages = {1-9},
  doi = {10.11648/j.ajrs.20140201.11},
  url = {https://doi.org/10.11648/j.ajrs.20140201.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajrs.20140201.11},
  abstract = {Synergistic application of NDVI from diverse sensors has been an interest of researchers in the field of natural resource management for over two decades. Attempts have been made to deal with the sensor-specific differences in NDVI which stem from a number of factors. In this study, an NDVI comparison has been made between Landsat-7 ETM+ and five other sensors of relatively fine resolution (ASTER, SPOT-5 XS, RapidEye, QuickBird-2 and WorldView-2) over an area of horticultural crops in south-eastern Australia during 2011-12. Translation equations have been developed using linear regression for specific sensors and specific horticultural crops (almond, table grape, wine grape, olive and vegetable). Cross-senor comparisons of NDVI showed strong positive relationships (p 0.9) but in three cases (ASTER, SPOT-5 and RapidEye) the differences in NDVI values were significant (p<0.001) as well. Though in the other two cases (QuickBird-2 and WorldView-2) the differences were not significant, they were not negligible. Therefore the role of translation equations is considered important for cross-sensor NDVI compatibility. The results of this study will be used: (i) to convert NDVI from the selected sensors to a Landsat- equivalent NDVI for the analysis of irrigated horticultural crops, (ii) to optimise the temporal frequency of NDVI observations for long-term vegetation analyses, and (iii) to transfer Landsat ETM+-based measurements, particularly evapotranspiration (ET) estimates, to alternative sensors that lack thermal band capability which is critical for ET measurements. ET measurements will be used to estimate crop water requirement to help irrigation water management of horticultural crops.},
 year = {2014}
}

Copy | Download

TY  - JOUR
T1  - Comparing Inter-Sensor NDVI for the Analysis of Horticulture Crops in South-Eastern Australia
AU  - Mohammad Abuzar
AU  - Kathryn Sheffield
AU  - Des Whitfield
AU  - Mark O’Connell
AU  - Andy McAllister
Y1  - 2014/03/20
PY  - 2014
N1  - https://doi.org/10.11648/j.ajrs.20140201.11
DO  - 10.11648/j.ajrs.20140201.11
T2  - American Journal of Remote Sensing
JF  - American Journal of Remote Sensing
JO  - American Journal of Remote Sensing
SP  - 1
EP  - 9
PB  - Science Publishing Group
SN  - 2328-580X
UR  - https://doi.org/10.11648/j.ajrs.20140201.11
AB  - Synergistic application of NDVI from diverse sensors has been an interest of researchers in the field of natural resource management for over two decades. Attempts have been made to deal with the sensor-specific differences in NDVI which stem from a number of factors. In this study, an NDVI comparison has been made between Landsat-7 ETM+ and five other sensors of relatively fine resolution (ASTER, SPOT-5 XS, RapidEye, QuickBird-2 and WorldView-2) over an area of horticultural crops in south-eastern Australia during 2011-12. Translation equations have been developed using linear regression for specific sensors and specific horticultural crops (almond, table grape, wine grape, olive and vegetable). Cross-senor comparisons of NDVI showed strong positive relationships (p 0.9) but in three cases (ASTER, SPOT-5 and RapidEye) the differences in NDVI values were significant (p<0.001) as well. Though in the other two cases (QuickBird-2 and WorldView-2) the differences were not significant, they were not negligible. Therefore the role of translation equations is considered important for cross-sensor NDVI compatibility. The results of this study will be used: (i) to convert NDVI from the selected sensors to a Landsat- equivalent NDVI for the analysis of irrigated horticultural crops, (ii) to optimise the temporal frequency of NDVI observations for long-term vegetation analyses, and (iii) to transfer Landsat ETM+-based measurements, particularly evapotranspiration (ET) estimates, to alternative sensors that lack thermal band capability which is critical for ET measurements. ET measurements will be used to estimate crop water requirement to help irrigation water management of horticultural crops.
VL  - 2
IS  - 1
ER  -

Copy | Download