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A review of potential image fusion methods for remote sensing-based irrigation management: part II

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Abstract

Satellite-based sensors provide data at either greater spectral and coarser spatial resolutions or lower spectral and finer spatial resolutions due to complementary spectral and spatial characteristics of optical sensor systems. In order to overcome this limitation, image fusion has been suggested to obtain higher spatial and spectral resolution images at the same time. Image fusion has been a valuable technique in digital image analysis and comparison because of the availability of multi-spatial and multispectral images from satellite and airborne sensors. It has been applied to merge coarser spatial resolution of multispectral images with a finer spatial resolution panchromatic image to enhance visual apprehension and to provide images that are more informative. Part I companion paper presented and discussed the image downscaling methods. In this paper (part II), the main objective is to review existing image fusion methods for their capability to downscale coarser spatial resolution images for irrigation management applications. A literature review indicated that image fusion methods have not been actively used in obtaining high-resolution land surface temperature (LST) and evapotranspiration (ET) images for irrigation management. However, there is a great potential for applying image fusion methods to retrieve finer LST and ET images from coarser thermal images by fusing them with finer non-thermal color or panchromatic images for irrigation scheduling and management purposes.

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Abbreviations

ARSIS:

Amélioration de la résolution spatiale par injection de structures meaning improvement of the spatial resolution by injection of structures

ASTER:

Advanced spaceborne thermal emission and reflection radiometer

BDF:

Bayesian data fusion

CS:

Component substitution

DFB:

Directional filter bank

DI:

Disturbance index

DN:

Digital number

DSCK:

Downscaling co-kriging

DWT:

Discrete wavelet transform

ENVI:

ENvironment for Visualizing Images

ERGAS:

Erreur relative globale adimensionelle de synthése meaning relative dimensionless global error in synthesis

ET:

Evapotranspiration

ETM+:

Enhanced thematic mapper plus

GLP:

Generalized Laplacian pyramid

GP:

Gaussian pyramid

GS:

Gram–Schmidt

H:

Hue

HPF:

High-pass filter

HWT:

Haar wavelet transform

I:

Intensity

IHS:

Intensity, hue, and saturation

LHS:

Lightness, hue, and saturation

LP:

Laplacian pyramid

LPF:

Low pass filter

LST:

Land surface temperature

MODIS:

Moderate resolution imaging spectroradiometer

MRA:

Multi-resolution analysis

MS:

Multispectral

NDVI:

Normalized difference vegetation index

PAN:

Panchromatic

PBIM:

Pixel block intensity modulation

PCA:

Principal component analysis

PFS:

Pyramidal in Fourier space

PL:

Pyramidal Laplacian

PSF:

Point spread function

RASE:

Relative average spectral error

RGB:

Red, green, and blue

RMSE:

Root mean square error

ROLPP:

Ratio of low pass pyramid

S:

Saturation

SAM:

Spectral angle mapper

SFIM:

Smoothing filter-based intensity modulation

SNR:

Signal-to-noise ratio

SPOT:

Systeme pour l’observation de la terre

STAARCH:

Spatial temporal adaptive algorithm for mapping reflectance change

STARFM:

Spatial and temporal adaptive reflectance fusion model

TM:

Thematic mapper

WT:

Wavelet transform

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Acknowledgments

Funding for this study was provided by USDA-ARS and NASA Terrestrial Hydrology Program (Proposal No. 08-THP07-0053). Authors are grateful to reviewers who provided valuable comments.

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Correspondence to Wonsook Ha.

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Communicated by I. Dodd.

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Ha, W., Gowda, P.H. & Howell, T.A. A review of potential image fusion methods for remote sensing-based irrigation management: part II. Irrig Sci 31, 851–869 (2013). https://doi.org/10.1007/s00271-012-0340-6

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