The University of Southampton
University of Southampton Institutional Repository

Monitoring, fusing landsat and MODIS data for vegetation

Monitoring, fusing landsat and MODIS data for vegetation
Monitoring, fusing landsat and MODIS data for vegetation
Crop condition and natural vegetation monitoring require high resolution remote sensing imagery in both time and space - a requirement that cannot currently be satisfied by any single Earth observing sensor in isolation. The suite of available remote sensing instruments varies widely in terms of sensor characteristics, spatial resolution and acquisition frequency. For example, the Moderate-resolution Imaging Spectroradiometer (MODIS) provides daily global observations at 250m to 1km spatial resolution. While imagery from coarse resolution sensors such as MODIS are typically superior to finer resolution data in terms of their revisit frequency, they lack spatial detail to capture surface features for many applications. The Landsat satellite series provides medium spatial resolution (30m) imagery which is well suited to capturing surface details, but a long revisit cycle (16-day) has limited its use in describing daily surface changes. Data fusion approaches provide an alternative way to utilize observations from multiple sensors so that the fused results can provide higher value than can an individual sensor alone. In this paper, we review the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) and two extended data fusion models (STAARCH and ESTARFM) that have been used to fuse MODIS and Landsat data. The fused MODISLandsat results inherit the spatial details of Landsat (30 m) and the temporal revisit frequency of MODIS (daily). The theoretical basis of the fusion approach is described and recent applications are presented. While these approaches can produce imagery with high spatiotemporal resolution, they still rely on the availability of actual satellite images and the quality of ingested remote sensing products. As a result, data fusion is useful for bridging gaps between medium resolution image acquisitions, but cannot replace actual satellite missions.
MODIS, reflectivity, remote sensing, satellites, spatial resolution, vegetation mapping
47-60
Gao, Feng
aca681b2-72af-492c-bfec-6683b491705f
Hilker, Thomas
c7fb75b8-320d-49df-84ba-96c9ee523d40
Zhu, Xiaolin
f1878a92-aa4b-4858-9799-15018090bb69
Anderson, Martha
c300f3fa-67d2-49c2-87b3-2a095c6d2945
Masek, Jeffrey
8ff44c70-3eef-4ecc-a56f-69dc476f60e4
Wang, Peijuan
6bcbcc43-ba91-491b-b710-4d517cff8370
Yang, Yun
339df16a-6974-422a-a997-021841827960
Gao, Feng
aca681b2-72af-492c-bfec-6683b491705f
Hilker, Thomas
c7fb75b8-320d-49df-84ba-96c9ee523d40
Zhu, Xiaolin
f1878a92-aa4b-4858-9799-15018090bb69
Anderson, Martha
c300f3fa-67d2-49c2-87b3-2a095c6d2945
Masek, Jeffrey
8ff44c70-3eef-4ecc-a56f-69dc476f60e4
Wang, Peijuan
6bcbcc43-ba91-491b-b710-4d517cff8370
Yang, Yun
339df16a-6974-422a-a997-021841827960

Gao, Feng, Hilker, Thomas, Zhu, Xiaolin, Anderson, Martha, Masek, Jeffrey, Wang, Peijuan and Yang, Yun (2015) Monitoring, fusing landsat and MODIS data for vegetation. IEEE Geoscience and Remote Sensing, 3 (3), 47-60. (doi:10.1109/MGRS.2015.2434351).

Record type: Article

Abstract

Crop condition and natural vegetation monitoring require high resolution remote sensing imagery in both time and space - a requirement that cannot currently be satisfied by any single Earth observing sensor in isolation. The suite of available remote sensing instruments varies widely in terms of sensor characteristics, spatial resolution and acquisition frequency. For example, the Moderate-resolution Imaging Spectroradiometer (MODIS) provides daily global observations at 250m to 1km spatial resolution. While imagery from coarse resolution sensors such as MODIS are typically superior to finer resolution data in terms of their revisit frequency, they lack spatial detail to capture surface features for many applications. The Landsat satellite series provides medium spatial resolution (30m) imagery which is well suited to capturing surface details, but a long revisit cycle (16-day) has limited its use in describing daily surface changes. Data fusion approaches provide an alternative way to utilize observations from multiple sensors so that the fused results can provide higher value than can an individual sensor alone. In this paper, we review the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) and two extended data fusion models (STAARCH and ESTARFM) that have been used to fuse MODIS and Landsat data. The fused MODISLandsat results inherit the spatial details of Landsat (30 m) and the temporal revisit frequency of MODIS (daily). The theoretical basis of the fusion approach is described and recent applications are presented. While these approaches can produce imagery with high spatiotemporal resolution, they still rely on the availability of actual satellite images and the quality of ingested remote sensing products. As a result, data fusion is useful for bridging gaps between medium resolution image acquisitions, but cannot replace actual satellite missions.

Full text not available from this repository.

More information

e-pub ahead of print date: 30 September 2015
Published date: 5 October 2015
Keywords: MODIS, reflectivity, remote sensing, satellites, spatial resolution, vegetation mapping
Organisations: Geography & Environment

Identifiers

Local EPrints ID: 384680
URI: https://eprints.soton.ac.uk/id/eprint/384680
PURE UUID: ccef0bd1-2572-4119-8ea5-252c4f0a3a01

Catalogue record

Date deposited: 13 Jan 2016 08:54
Last modified: 17 Jul 2017 20:03

Export record

Altmetrics

Contributors

Author: Feng Gao
Author: Thomas Hilker
Author: Xiaolin Zhu
Author: Martha Anderson
Author: Jeffrey Masek
Author: Peijuan Wang
Author: Yun Yang

University divisions

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of https://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×