Study of crop growth parameters using Airborne Imaging Spectrometer data

Patel, N. K.; Patnaik, C.; Dutta, Subashisa; Shekh, A. M.; Dave, Akshat

doi:10.1080/014311601300229881

Cited by 22 publications

(6 citation statements)

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“…The IG model has been fitted to laboratory spectral reflectance data [28] and airborne imaging spectrometer data [29]. The red edge parameters extracted from those spectral data with the IG modeling have been used to estimate leaf chlorophyll concentration and vegetation stress [17], [27].…”

Section: ) Inverted-gaussian Modelingmentioning

confidence: 99%

Extraction of red edge optical parameters from hyperion data for estimation of forest leaf area index

Gong

Biging

et al. 2003

IEEE Trans. Geosci. Remote Sensing

140

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Abstract-A correlation analysis was conducted between forest leaf area index (LAI) and two red edge parameters: red edge position (REP) and red well position (RWP), extracted from reflectance image retrieved from Hyperion data. Field spectrometer data and LAI measurements were collected within two days after the Earth Observing One satellite passed over the study site in the Patagonia region of Argentina. The two red edge parameters were extracted with four approaches: four-point interpolation, polynomial fitting, Lagrangian technique, and inverted-Gaussian (IG) modeling. Experimental results indicate that the four-point approach is the most practical and suitable method for extracting the two red edge parameters from Hyperion data because only four bands and a simple interpolation computation are needed. The polynomial fitting approach is a direct method and has its practical value if hyperspectral data are available. However, it requires more computation time. The Lagrangian method is applicable only if the first derivative spectra are available; thus, it is not suitable to multispectral remote sensing. The IG approach needs further testing and refinement for Hyperion data.Index Terms-Hyperion, leaf area index, red edge position, red well position.

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Section: ) Inverted-gaussian Modelingmentioning

confidence: 99%

Extraction of red edge optical parameters from hyperion data for estimation of forest leaf area index

Gong

Biging

et al. 2003

IEEE Trans. Geosci. Remote Sensing

140

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“…This property enables discrimination of materials based on the radiance spectrum obtained by hyperspectral imagery. HSI has found many applications in various fields such as military [2]- [4], agriculture [5], [6], and mineralogy [7]. One of the important applications of HSI is target detection, which can be viewed as a two-class classification problem where pixels are labeled as target (target present) or background (target absent) based on their spectral characteristics.…”

Section: Introductionmentioning

confidence: 99%

Sparse Representation for Target Detection in Hyperspectral Imagery

Chen

Nasrabadi

Tran

2011

IEEE J. Sel. Top. Signal Process.

380

186

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Abstract-In this paper, we propose a new sparsity-based algorithm for automatic target detection in hyperspectral imagery (HSI). This algorithm is based on the concept that a pixel in HSI lies in a low-dimensional subspace and thus can be represented as a sparse linear combination of the training samples. The sparse representation (a sparse vector corresponding to the linear combination of a few selected training samples) of a test sample can be recovered by solving an 0 -norm minimization problem. With the recent development of the compressed sensing theory, such minimization problem can be recast as a standard linear programming problem or efficiently approximated by greedy pursuit algorithms. Once the sparse vector is obtained, the class of the test sample can be determined by the characteristics of the sparse vector on reconstruction. In addition to the constraints on sparsity and reconstruction accuracy, we also exploit the fact that in HSI the neighboring pixels have a similar spectral characteristic (smoothness). In our proposed algorithm, a smoothness constraint is also imposed by forcing the vector Laplacian at each reconstructed pixel to be minimum all the time within the minimization process. The proposed sparsity-based algorithm is applied to several hyperspectral imagery to detect targets of interest. Simulation results show that our algorithm outperforms the classical hyperspectral target detection algorithms, such as the popular spectral matched filters, matched subspace detectors, adaptive subspace detectors, as well as binary classifiers such as support vector machines.

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“…This parameter provides an additional measure of the position of the Red Edge reflectance of vegetation and is less affected by variations in soil/rock background compared to commonly used vegetation indices such as NDVI or SAVI. Studies have demonstrated the potential of this parameter for the retrieval of vegetation parameters using linear and non-linear regression models [35], [29]. As an example, Figure 7 depicts the excellent correlation between leaf chlorophyll a+b and the inflection point (R 2 = 0.98, RMS = ± 3.8 µg/cm 2 ) using a second order polygon.…”

Section: Spectral Sensitivity Analysis For the Red-edge Bandsmentioning

confidence: 99%

“…This area has been selected as the main focus of many investigations due to its unique spectral characteristics. Investigations based on laboratory and field spectra have shown that the position and shape of the Red Edge contain information about the biomass, chlorophyll content, physiological stress of vegetation and Leaf Area Index (LAI) [25]- [29]. This region is also strongly influenced by the spectral characteristics of transition elements in soils and rocks.…”

Section: Spectral Sensitivity Analysis For the Red-edge Bandsmentioning

confidence: 99%

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End-to-end sensor simulation for spectral band selection and optimization with application to the Sentinel-2 mission

et al. 2012

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An end-to-end sensor simulation is a proper tool for the prediction of the sensor's performance over a range of conditions which cannot be easily measured. In this study (ESA-Contract: 19962/06/NL/EL) such a tool has been developed that enables the assessment of the optimum spectral resolution configuration of a sensor based on key applications. It employs the spectral molecular absorption and scattering properties of materials that are used for the identification and determination of the abundances of surface and atmospheric constituents and their interdependence on spatial resolution and signal-to-noise ratio (SNR) as a basis for the detailed design and consolidation of spectral bands for the future Sentinel-2 sensor. The developed tools allow the computation of synthetic Sentinel-2 spectra that form the frame for the subsequent twofold analysis of bands in atmospheric absorption and window regions. One part of the study comprises the assessment of optimal spatial and spectral resolution configurations for those bands used for atmospheric correction, optimized with regard to the retrieval of aerosols, water vapor and the detection of cirrus clouds. The second part of the study presents the optimization of thematic bands, mainly driven by the spectral characteristics of vegetation constituents and minerals. The investigation is performed for different wavelength ranges because most remote sensing applications require the use of specific band combinations rather than single bands. The results from the important "Red Edge" and the "Short Wave Infrared" domain are presented. The recommended optimum spectral design predominantly confirms the sensor parameters given by ESA. The system is capable of retrieving atmospheric and geo-biophysical parameters with enhanced quality compared to existing multispectral sensors. Minor spectral changes of single bands are discussed in the context of typical remote sensing applications, supplemented by the recommendation of a few new bands for the next generation of optical Sentinel sensors.

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Study of crop growth parameters using Airborne Imaging Spectrometer data

Cited by 22 publications

References 0 publications

Extraction of red edge optical parameters from hyperion data for estimation of forest leaf area index

Extraction of red edge optical parameters from hyperion data for estimation of forest leaf area index

Sparse Representation for Target Detection in Hyperspectral Imagery

End-to-end sensor simulation for spectral band selection and optimization with application to the Sentinel-2 mission

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