Generation of Spectral-Temporal Response Surfaces by Combining Multispectral Satellite and Hyperspectral UAV Imagery for Precision Agriculture Applications
Gevaert, Caroline ; Suomalainen, Juha ; Tang, Jing LU
and Kooistra, Lammert
(2015)
In IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
8(6).
p.3140-3146
- Abstract
- Precision agriculture requires detailed crop status information at high spatial and temporal resolutions. Remote sensing can provide such information, but single sensor observations are often incapable of meeting all data requirements. Spectral-temporal response surfaces (STRSs) provide continuous reflectance spectra at high temporal intervals. This is the first study to combine multispectral satellite imagery (from Formosat-2) with hyperspectral imagery acquired with an unmanned aerial vehicle (UAV) to construct STRS. This study presents a novel STRS methodology which uses Bayesian theory to impute missing spectral information in the multispectral imagery and introduces observation uncertainties into the interpolations. This new method is... (More)
- Precision agriculture requires detailed crop status information at high spatial and temporal resolutions. Remote sensing can provide such information, but single sensor observations are often incapable of meeting all data requirements. Spectral-temporal response surfaces (STRSs) provide continuous reflectance spectra at high temporal intervals. This is the first study to combine multispectral satellite imagery (from Formosat-2) with hyperspectral imagery acquired with an unmanned aerial vehicle (UAV) to construct STRS. This study presents a novel STRS methodology which uses Bayesian theory to impute missing spectral information in the multispectral imagery and introduces observation uncertainties into the interpolations. This new method is compared to two earlier published methods for constructing STRS: a direct interpolation of the original data and a direct interpolation along the temporal dimension after imputation along the spectral dimension. The STRS derived through all three methods are compared to field measured reflectance spectra, leaf area index (LAI), and canopy chlorophyll of potato plants. The results indicate that the proposed Bayesian approach has the highest correlation (r = 0.953) and lowest RMSE (0.032) to field spectral reflectance measurements. Although the optimized soil-adjusted vegetation index (OSAVI) obtained from all methods have similar correlations to field data, the modified chlorophyll absorption in reflectance index (MCARI) obtained from the Bayesian STRS outperform the other two methods. A correlation of 0.83 with LAI and 0.77 with canopy chlorophyll measurements are obtained, compared to correlations of 0.27 and 0.09, respectively, for the directly interpolated STRS. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/7975590
- author
- Gevaert, Caroline
; Suomalainen, Juha
; Tang, Jing
LU
and Kooistra, Lammert
- organization
- publishing date
- 2015
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Crop phenology, data fusion, hyperspectral imaging, image resolution, precision agriculture
- in
- IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
- volume
- 8
- issue
- 6
- pages
- 3140 - 3146
- publisher
- IEEE - Institute of Electrical and Electronics Engineers Inc.
- external identifiers
-
- wos:000359264000070
- scopus:85027951032
- ISSN
- 2151-1535
- DOI
- 10.1109/JSTARS.2015.2406339
- language
- English
- LU publication?
- yes
- id
- 1fef6003-38df-4f3e-bfc0-8d29eaa972cf (old id 7975590)
- date added to LUP
- 2016-04-01 10:07:39
- date last changed
- 2022-04-27 18:31:07
@article{1fef6003-38df-4f3e-bfc0-8d29eaa972cf,
abstract = {{Precision agriculture requires detailed crop status information at high spatial and temporal resolutions. Remote sensing can provide such information, but single sensor observations are often incapable of meeting all data requirements. Spectral-temporal response surfaces (STRSs) provide continuous reflectance spectra at high temporal intervals. This is the first study to combine multispectral satellite imagery (from Formosat-2) with hyperspectral imagery acquired with an unmanned aerial vehicle (UAV) to construct STRS. This study presents a novel STRS methodology which uses Bayesian theory to impute missing spectral information in the multispectral imagery and introduces observation uncertainties into the interpolations. This new method is compared to two earlier published methods for constructing STRS: a direct interpolation of the original data and a direct interpolation along the temporal dimension after imputation along the spectral dimension. The STRS derived through all three methods are compared to field measured reflectance spectra, leaf area index (LAI), and canopy chlorophyll of potato plants. The results indicate that the proposed Bayesian approach has the highest correlation (r = 0.953) and lowest RMSE (0.032) to field spectral reflectance measurements. Although the optimized soil-adjusted vegetation index (OSAVI) obtained from all methods have similar correlations to field data, the modified chlorophyll absorption in reflectance index (MCARI) obtained from the Bayesian STRS outperform the other two methods. A correlation of 0.83 with LAI and 0.77 with canopy chlorophyll measurements are obtained, compared to correlations of 0.27 and 0.09, respectively, for the directly interpolated STRS.}},
author = {{Gevaert, Caroline and Suomalainen, Juha and Tang, Jing and Kooistra, Lammert}},
issn = {{2151-1535}},
keywords = {{Crop phenology; data fusion; hyperspectral imaging; image resolution; precision agriculture}},
language = {{eng}},
number = {{6}},
pages = {{3140--3146}},
publisher = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
series = {{IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing}},
title = {{Generation of Spectral-Temporal Response Surfaces by Combining Multispectral Satellite and Hyperspectral UAV Imagery for Precision Agriculture Applications}},
url = {{http://dx.doi.org/10.1109/JSTARS.2015.2406339}},
doi = {{10.1109/JSTARS.2015.2406339}},
volume = {{8}},
year = {{2015}},
}