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Enhancing Spatio-Temporal Fusion of MODIS and Landsat Data by Incorporating 250 m MODIS Data

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Enhancing Spatio-Temporal Fusion of MODIS and Landsat Data by Incorporating 250 m MODIS Data. / Wang, Qunming; Zhang, Yihang; Onojeghuo, Alex Okiemute; Zhu, Xiaolin; Atkinson, Peter Michael.

In: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol. 10, No. 9, 09.2017, p. 4116-4123.

Research output: Contribution to journalJournal articlepeer-review

Harvard

Wang, Q, Zhang, Y, Onojeghuo, AO, Zhu, X & Atkinson, PM 2017, 'Enhancing Spatio-Temporal Fusion of MODIS and Landsat Data by Incorporating 250 m MODIS Data', IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 10, no. 9, pp. 4116-4123. https://doi.org/10.1109/JSTARS.2017.2701643

APA

Vancouver

Wang Q, Zhang Y, Onojeghuo AO, Zhu X, Atkinson PM. Enhancing Spatio-Temporal Fusion of MODIS and Landsat Data by Incorporating 250 m MODIS Data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2017 Sep;10(9):4116-4123. https://doi.org/10.1109/JSTARS.2017.2701643

Author

Wang, Qunming ; Zhang, Yihang ; Onojeghuo, Alex Okiemute ; Zhu, Xiaolin ; Atkinson, Peter Michael. / Enhancing Spatio-Temporal Fusion of MODIS and Landsat Data by Incorporating 250 m MODIS Data. In: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2017 ; Vol. 10, No. 9. pp. 4116-4123.

Bibtex

@article{588f6322fc494efb8a4965ff4ee945c5,
title = "Enhancing Spatio-Temporal Fusion of MODIS and Landsat Data by Incorporating 250 m MODIS Data",
abstract = "Spatio-temporal fusion of MODIS and Landsat data aims to produce new data that have simultaneously the Landsat spatial resolution and MODIS temporal resolution. It is an ill-posed problem involving large uncertainty, especially for reproduction of abrupt changes and heterogeneous landscapes. In this paper, we proposed to incorporate the freely available 250 m MODIS images into spatio-temporal fusion to increase prediction accuracy. The 250 m MODIS bands 1 and 2 are fused with 500 m MODIS bands 3-7 using the advanced area-to-point regression kriging approach. Based on a standard spatio-temporal fusion approach, the interim 250 m fused MODIS data are then downscaled to 30 m with the aid of the available 30 m Landsat data on temporally close days. The 250 m data can provide more information for the abrupt changes and heterogeneous landscapes than the original 500 m MODIS data, thus increasing the accuracy of spatio-temporal fusion predictions. The effectiveness of the proposed scheme was demonstrated using two datasets.",
author = "Qunming Wang and Yihang Zhang and Onojeghuo, {Alex Okiemute} and Xiaolin Zhu and Atkinson, {Peter Michael}",
note = "{\textcopyright}2017 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.",
year = "2017",
month = sep,
doi = "10.1109/JSTARS.2017.2701643",
language = "English",
volume = "10",
pages = "4116--4123",
journal = "IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing",
issn = "1939-1404",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
number = "9",

}

RIS

TY - JOUR

T1 - Enhancing Spatio-Temporal Fusion of MODIS and Landsat Data by Incorporating 250 m MODIS Data

AU - Wang, Qunming

AU - Zhang, Yihang

AU - Onojeghuo, Alex Okiemute

AU - Zhu, Xiaolin

AU - Atkinson, Peter Michael

N1 - ©2017 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

PY - 2017/9

Y1 - 2017/9

N2 - Spatio-temporal fusion of MODIS and Landsat data aims to produce new data that have simultaneously the Landsat spatial resolution and MODIS temporal resolution. It is an ill-posed problem involving large uncertainty, especially for reproduction of abrupt changes and heterogeneous landscapes. In this paper, we proposed to incorporate the freely available 250 m MODIS images into spatio-temporal fusion to increase prediction accuracy. The 250 m MODIS bands 1 and 2 are fused with 500 m MODIS bands 3-7 using the advanced area-to-point regression kriging approach. Based on a standard spatio-temporal fusion approach, the interim 250 m fused MODIS data are then downscaled to 30 m with the aid of the available 30 m Landsat data on temporally close days. The 250 m data can provide more information for the abrupt changes and heterogeneous landscapes than the original 500 m MODIS data, thus increasing the accuracy of spatio-temporal fusion predictions. The effectiveness of the proposed scheme was demonstrated using two datasets.

AB - Spatio-temporal fusion of MODIS and Landsat data aims to produce new data that have simultaneously the Landsat spatial resolution and MODIS temporal resolution. It is an ill-posed problem involving large uncertainty, especially for reproduction of abrupt changes and heterogeneous landscapes. In this paper, we proposed to incorporate the freely available 250 m MODIS images into spatio-temporal fusion to increase prediction accuracy. The 250 m MODIS bands 1 and 2 are fused with 500 m MODIS bands 3-7 using the advanced area-to-point regression kriging approach. Based on a standard spatio-temporal fusion approach, the interim 250 m fused MODIS data are then downscaled to 30 m with the aid of the available 30 m Landsat data on temporally close days. The 250 m data can provide more information for the abrupt changes and heterogeneous landscapes than the original 500 m MODIS data, thus increasing the accuracy of spatio-temporal fusion predictions. The effectiveness of the proposed scheme was demonstrated using two datasets.

U2 - 10.1109/JSTARS.2017.2701643

DO - 10.1109/JSTARS.2017.2701643

M3 - Journal article

VL - 10

SP - 4116

EP - 4123

JO - IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

JF - IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

SN - 1939-1404

IS - 9

ER -