Lithological mapping with multispectral data – setup and application of a spectral database for rocks in the Balakot area, Northern Pakistan

Fuchs, Michael; Awan, Adnan A.; Akhtar, Sardar S.; Ahmad, Ijaz; Sadiq, Simon; Razzak, Asif; Haider, Naeem

doi:10.1007/s11629-016-4101-5

Cited by 3 publications

(3 citation statements)

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“…Robust ground truth data are required to derive crop traits from optical earth observation (EO) sensors [28]. The systematic collection of spectroscopic measurements accompanied by reference measurements of in situ traits to construct spectral libraries (SpecLibs) has therefore played an important role for many sub-disciplines of remote sensing [29][30][31][32][33][34][35][36][37]. SpecLibs provide an excellent foundation for both multi-and hyperspectral model development because they allow the simulation of arbitrary optical sensor configurations through spectral resampling [6,17,19].…”

Section: Introductionmentioning

confidence: 99%

“…SpecLibs provide an excellent foundation for both multi-and hyperspectral model development because they allow the simulation of arbitrary optical sensor configurations through spectral resampling [6,17,19]. In previous studies, SpecLibs were primarily used for classification purposes of, e.g., shrubland species [35], wetland species [32], rocks [36], urban materials [34], and crops [29,33,37]. Only [17,19] explored SpecLibs for crop nitrogen, grain nitrogen, and yield retrieval.…”

Section: Introductionmentioning

confidence: 99%

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MISPEL: A Multi-Crop Spectral Library for Statistical Crop Trait Retrieval and Agricultural Monitoring

2023

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Spatiotemporally accurate estimates of crop traits are essential for both scientific modeling and practical decision making in sustainable agricultural management. Besides efficient and concise methods to derive these traits, site- and crop-specific reference data are needed to develop and validate retrieval methods. To address this shortcoming, this study first includes the establishment of ’MISPEL’, a comprehensive spectral library (SpecLib) containing hyperspectral measurements and reference data for six key traits of ten widely grown crops. Secondly, crop-specific statistical leaf area index (LAI) models for winter wheat are developed based on a hyperspectral (MISPELFR) and a simulated Sentinel-2 (MISPELS2) SpecLib applying four nonparametric methods. Finally, an independent Sentinel-2 model evaluation at the DEMMIN test site in Germany is conducted, including a comparison with the commonly used SNAP-LAI product. To date, MISPEL comprises a set of 1411 spectra of ten crops and more than 6800 associated reference measurements. Cross-validations of winter wheat LAI models revealed that Elastic-net generalized linear model (GLMNET) and Gaussian process (GP) regressions outperformed partial least squares (PLS) and random forest (RF) regressions, showing RSQ values up to 0.86 and a minimal NRMSE of 0.21 using MISPELFR. GLMNET and GP models based on MISPELS2 further outperformed SNAP-based LAI estimates derived for the external validation site. Thus, it is concluded that the presented SpecLib ’MISPEL’ and applied methodology have a very high potential for deriving diverse crop traits of multiple crops in view of most recent and future multi-, super-, and hyperspectral satellite missions.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

MISPEL: A Multi-Crop Spectral Library for Statistical Crop Trait Retrieval and Agricultural Monitoring

2023

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“…Such spectral thus is comparable to the spectral library generated in the laboratory. This specification has resulted into the unique application of Hyperion images in various fields such as lithological mapping (Fuchs et al 2017;Ge et al 2018;P. Li and Song 2016), agriculture (Marshall and Thenkabail 2015), land classification and vegetation properties estimation (Eckert and Kneubühler 2004;Krishna et al 2018;Ntouros et al 2009; Upadhyay and Kumar 2018), geological mapping (Kruse et al 2003;Kruse et al 2002) and mineral exploration (Kiran Raj et al 2015;Kodikara et al 2012;Li et al 2017;van Ruitenbeek et al 2019).…”

Section: Introductionmentioning

confidence: 99%

Spectral enhancement of Landsat OLI images by using Hyperion data: a comparison between multilayer perceptron and radial basis function networks

2020

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The deactivation of Earth Observing-1 satellite has resulted in the termination of capturing Hyperion data as a unique source of hyperspectral satellite imagery. These images also were collected through an on-demand service and thereby are not available for the entire Earth's surface. The Operational Land Imager (OLI) sensor, on the other hand, provides a free source of multi-spectral images with global coverage. Recognized these facts, the aim of this paper is to enhance the spectral resolution of OLI images by using existing Hyperion imageries to generate a high spectral resolution image for a desired date and site. This was conducted through the artificial neural network (ANN). To find the suitable ANN, we compared the performance of multilayer perceptron (MLP) and radial basis function (RBF) networks for spectral enhancement.The research obtained two Hyperion and OLI images covering West Region of Tehran, Iran, on 4 January 2016. From 242 original Hyperion spectral bands, we selected 31 bands to reproduce from OLI spectral bands. These were determined through visual inspection, principal component analysis and Pearson's correlation test. The MLP and RBF networks were generated based on the OLI bands 1-7 and per 31 Hyperion bands as input and output layers respectively. The comparison between the spectral bands of spectra-enhanced image and original Hyperion data indicated a good agreement (0.884 > R 2 > 0.692). This study also found MLP network delivered higher accuracy against RBF network for spectral enhancement. The spectra-enhanced image can be used in studies with the need of images with continuous spectral bands.

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Construction of a plant spectral library based on an optimised feature selection method

Zhang

Wang

Yuan

et al. 2020

Biosystems Engineering

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Lithological mapping with multispectral data – setup and application of a spectral database for rocks in the Balakot area, Northern Pakistan

Cited by 3 publications

References 11 publications

MISPEL: A Multi-Crop Spectral Library for Statistical Crop Trait Retrieval and Agricultural Monitoring

MISPEL: A Multi-Crop Spectral Library for Statistical Crop Trait Retrieval and Agricultural Monitoring

Spectral enhancement of Landsat OLI images by using Hyperion data: a comparison between multilayer perceptron and radial basis function networks

Construction of a plant spectral library based on an optimised feature selection method

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