An evaluation of the effect of the spectral response function of satellite sensors on the precision of the universal pattern decomposition method

Zhang, Lifu; Бо, Лю; Zhang, Bing; Tong, Qingxi

doi:10.1080/01431160903246675

Cited by 5 publications

(4 citation statements)

References 13 publications

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“…VIUPD was proved to be more sensitive in reflecting vegetation activity than the normalized difference vegetation index (NDVI) and the enhanced vegetation index (EVI), using ground data (Zhang et al 2007b) and hyperspectral data (Airborne Visible Infrared Imaging Spectrometer (AVIRIS)) (Zhang, Yan, and Yang 2006b). In addition, UPDM and VIUPD have been applied to multispectral data from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Enhanced Thematic Mapper Plus (ETM+) to evaluate their dependence on the sensor type (Zhang et al 2007a(Zhang et al , 2010, with results indicating that VIUPD is more sensor independent than NDVI and EVI.…”

Section: Introductionmentioning

confidence: 99%

Comparison of the sensor dependence of vegetation indices based on Hyperion and CHRIS hyperspectral data

Chen

Zhang

Xia

et al. 2012

International Journal of Remote Sensing

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In previous studies of the universal pattern decomposition method (UPDM), spectral shifts, which are very common in hyperspectral imaging spectrometers, were not taken into account when calculating standard spectral pattern vectors. This study evaluated the effect of spectral shifts on the sensor dependence of the vegetation index based on the UPDM (VIUPD) and 11 other vegetation indices (VIs). Spectral shifts were calculated using Gao's spectrum-matching method. The influences of smoothing techniques (moving average and Savitzky-Golay filters) on the consistency of these VIs were also evaluated and compared. Data from the typical narrowband imaging spectrometers, Hyperion and the Compact High Resolution Imaging Spectrometer (CHRIS), were chosen for the study. For all VIs, both smoothing and spectral calibration changed the consistency between Hyperion and CHRIS. Spectral calibration had a positive effect on the majority of VIs, whereas smoothing improved the performance of some VIs but decreased the consistency of others. When compared with spectral calibration and Savitzky-Golay smoothing, moving average generated greater variations within the results. Among the smoothing techniques employed, moving average smoothing exhibited a larger distortion of VI sensor dependency than that of Savitzky-Golay smoothing of the same order. VIUPD based on narrowband hyperspectral data was sensitive to spectral operations (spectral calibration and smoothing). For VIUPD, spectral calibration increased its sensor independence, whereas smoothing had a negative effect. After spectral calibration, VIUPD was more sensor independent than any other VI examined in this study.

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Section: Introductionmentioning

confidence: 99%

Comparison of the sensor dependence of vegetation indices based on Hyperion and CHRIS hyperspectral data

Chen

Zhang

Xia

et al. 2012

International Journal of Remote Sensing

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“…It is shown that UNVI derived from MODIS and OLI exhibits a sensor-consistent sensitivity and shows a relatively strong sensitivity to canopy water content (CWC). The sensor independency of the predecessor of UNVI, i.e., VIUPD (VI based on UPDM), has been demonstrated in a previous study [33]. Recent studies have demonstrated the advantage of VIUPD with respect to NDVI in agricultural drought monitoring, which may benefit from its sensitivity to CWC [34,35].…”

Section: Resultsmentioning

confidence: 83%

A Global Sensitivity Analysis of Commonly Used Satellite-Derived Vegetation Indices for Homogeneous Canopies Based on Model Simulation and Random Forest Learning

Wang

Dong

et al. 2019

Remote Sensing

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Remote sensing (RS) provides operational monitoring of terrestrial vegetation. For optical RS, vegetation information is generally derived from surface reflectance (ρ). More generally, vegetation indices (VIs) are built on the basis of ρ as proxies for vegetation traits. At canopy level, ρ can be affected by a variety of factors, including leaf constituents, canopy structure, background reflectivity, and sun-sensor geometry. Consequently, VIs are mixtures of different information. In this study, a global sensitivity analysis (GSA) is made for several commonly used satellite-derived VIs in order to better understand the application of these VIs at large scales. The sensitivities of VIs to different parameters are analyzed on the basis of PROSPECT-SAIL (PROSAIL) radiative transfer model simulations, which apply for homogeneous canopies, and random forest (RF) learning. Specifically, combined factors such as canopy chlorophyll content (CCC) and canopy water content (CWC) are introduced in the RF-based GSA. We find that for most VIs, the leaf area index is the most influential factor, while the broad-band sensor-derived enhanced VI (EVI) exhibits a strong sensitivity to CCC, and the universal normalized VI (UNVI) is sensitive to CWC. The potential and uncertainty for the application of all the considered VIs are analyzed according to the GSA results. The results can help to improve the use of VIs in different contexts, and the RF-based GSA method can be further applied in more sophisticated situations.

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“…where λ s (i) and λ e (i) represent the starting and ending wavelength of band i respectively, and P k (λ) is the stand spectral pattern of continuous wavelength λ. The VIUPD expressed as a linear sum of the pattern decomposition coefficients (Zhang et al, 2003;Zhang et al, 2010) was calculated as…”

Section: Selection and Calculation Of Spectral Indexmentioning

confidence: 99%

Evaluation of spectral scale effects in estimation of vegetation leaf area index using spectral indices methods

Jiang

Zhang

et al. 2016

Chin. Geogr. Sci.

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Spectral index methodology has been widely used in Leaf Area Index (LAI) retrieval at different spatial scales. There are differences in the spectral response of different remote sensors and thus spectral scale effect generated during the use of spectral indices to retrieve LAI. In this study, PROSPECT, leaf optical properties model and Scattering by Arbitrarily Inclined Layers (SAIL) model, were used to simulate canopy spectral reflectance with a bandwidth of 5 nm and a Gaussian spectral response function was employed to simulate the spectral data at six bandwidths ranging from 10 to 35 nm. Additionally, for bandwidths from 5 to 35 nm, the correlation between the spectral index and LAI, and the sensitivities of the spectral index to changes in LAI and bandwidth were analyzed. Finally, the reflectance data at six bandwidths ranging from 40 to 65 nm were used to verify the spectral scale effect generated during the use of the spectral index to retrieve LAI. Results indicate that Vegetation Index of the Universal Pattern Decomposition (VIUPD) had the highest accuracy during LAI retrieval. Followed by Normalized Difference Vegetation Index (NDVI), Modified Simple Ratio Indices (MSRI) and Triangle Vegetation Index (TVI), although the coefficient of determination R 2 was higher than 0.96, the retrieved LAI values were less than the actual value and thus lacked validity. Other spectral indices were significantly affected by the spectral scale effect with poor retrieval results. In this study, VIUPD, which exhibited a relatively good correlation and sensitivity to LAI, was less affected by the spectral scale effect and had a relatively good retrieval capability. This conclusion supports a purported feature independent of the sensor of this model and also confirms the great potential of VIUPD for retrieval of physicochemical parameters of vegetation using multi-source remote sensing data.

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An evaluation of the effect of the spectral response function of satellite sensors on the precision of the universal pattern decomposition method

Cited by 5 publications

References 13 publications

Comparison of the sensor dependence of vegetation indices based on Hyperion and CHRIS hyperspectral data

Comparison of the sensor dependence of vegetation indices based on Hyperion and CHRIS hyperspectral data

A Global Sensitivity Analysis of Commonly Used Satellite-Derived Vegetation Indices for Homogeneous Canopies Based on Model Simulation and Random Forest Learning

Evaluation of spectral scale effects in estimation of vegetation leaf area index using spectral indices methods

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