Standardisation of near-infrared spectrometric instruments: A review

Bouveresse, E.; Massart, D.L.

doi:10.1016/0924-2031(95)00055-0

Cited by 168 publications

(93 citation statements)

References 24 publications

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“…Although a consistent number of spectral-transfer procedures were tested between two datasets both composed of laboratory spectra [38][39][40][41][42][43], the transfer between spectra acquired in laboratory conditions to those acquired by remote sensors is a still rare [44,45]. Moreover, in order to find a reliable transfer function, it is mandatory to collect and scan soil samples.…”

Section: Introductionmentioning

confidence: 99%

Soil Organic Carbon Estimation in Croplands by Hyperspectral Remote APEX Data Using the LUCAS Topsoil Database

et al. 2018

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Abstract:The most commonly used approach to estimate soil variables from remote-sensing data entails time-consuming and expensive data collection including chemical and physical laboratory analysis. Large spectral libraries could be exploited to decrease the effort of soil variable estimation and obtain more widely applicable models. We investigated the feasibility of a new approach, referred to as bottom-up, to provide soil organic carbon (SOC) maps of bare cropland fields over a large area without recourse to chemical analyses, employing both the pan-European topsoil database from the Land Use/Cover Area frame statistical Survey (LUCAS) and Airborne Prism Experiment (APEX) hyperspectral airborne data. This approach was tested in two areas having different soil characteristics: the loam belt in Belgium, and the Gutland-Oesling region in Luxembourg. Partial least square regression (PLSR) models were used in each study area to estimate SOC content, using both bottom-up and traditional approaches. The PLSR model's accuracy was tested on an independent validation dataset. Both approaches provide SOC maps having a satisfactory level of accuracy (RMSE = 1.5-4.9 g·kg −1 ; ratio of performance to deviation (RPD) = 1.4-1.7) and the inter-comparison did not show differences in terms of RMSE and RPD either in the loam belt or in Luxembourg. Thus, the bottom-up approach based on APEX data provided high-resolution SOC maps over two large areas showing the within-and between-field SOC variability.

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Section: Introductionmentioning

confidence: 99%

Soil Organic Carbon Estimation in Croplands by Hyperspectral Remote APEX Data Using the LUCAS Topsoil Database

et al. 2018

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“…This means that these legacy soil databases need to be standardized themselves before using them for such approach, as some attempts has been already done in that objective (Baume et al, 2011, Ciampalini, 2013 The standardisation applied in our study is composed of a boxcox transformation for normalization of the predicted data, and a scaling and centering of the normalized data. This standardisation process is comparable to one of the most widely used transfer methods for correcting predicted values which is the univariate slope and bias correction (SBC) (Bouveresse et al, 1996). Transfer methods have been developed to enable a calibration model to be effectively transferred between two "systems" (e.g., two spectroscopic instruments), thus eliminating the need for a full recalibration (Fearn 2001;Feudale et al, 2002).…”

Section: Discussionmentioning

confidence: 97%

Using Legacy Soil Data for Standardizing Predictions of Topsoil Clay Content Obtained From Vnir/Swir Hyperspectral Airborne Images

Gomez

Gholizadeh

Borůvka

et al. 2015

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Mapping of topsoil properties using Visible, Near-Infrared and Short Wave Infrared (VNIR/SWIR) hyperspectral imagery requires large sets of ground measurements for calibrating the models that estimate soil properties. To avoid collecting such expensive data, we proposed a procedure including two steps that involves only legacy soil data that were collected over and/or around the study site: 1) estimation of a soil property using a spectral index of the literature and 2) standardisation of the estimated soil property using legacy soil data. This approach was tested for mapping clay contents in a Mediterranean region in which VNIR/SWIR AISA-DUAL hyperspectral airborne data were acquired. The spectral index was the one proposed by Levin et al (2007) using the spectral bands at 2209, 2133 and 2225 nm. Two legacy soil databases were tested as inputs of the procedure: the Focused-Legacy database composed of 67 soil samples collected in 2000 over the study area, and the No-Focused-Legacy database composed of 64 soil samples collected between 1973 and 1979 around but outside of the study area. The results were compared with those obtained from 120 soil samples collected over the study area during the hyperspectral airborne data acquisition, which were considered as a reference.Our results showed that: 1) the spectral index with no further standardisation offered predictions with high accuracy in term of coefficient of correlation r (0.71), but also high bias (-414 g/kg) and SEP (439 g/kg), 2) the standardisation using both legacy soil databases allowed an increase of accuracy (r = 0.76) and a reduction of bias and SEP and 3) a better standardisation was obtained by using the Focused-Legacy database rather than the No-Focused-Legacy database. Finally, the clay predicted map obtained with standardisation using the Focused-Legacy database showed pedologically-significant soil spatial structures with clear short-scale variations of topsoil clay contents in specific areas.This study, associated with the coming availability of a next generation of hyperspectral VNIR/SWIR satellite data for the entire globe, paves the way for inexpensive methods for delivering high resolution soil properties maps.

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“…Nevertheless we believe that the SIC method has several advantages. It: (1) determines the unambiguous number of influential objects for the data and model under consideration; and (2) it takes into account not only X values, but also y values.…”

Section: Discussionmentioning

confidence: 99%

“…where y is the n-dimensional response vector, a is the p-dimensional vector of unknown parameters, X is the (n Â p) predictor matrix, e e is an unknown error vector; ordinarily rank of matrix X is less than p. The SIC approach is based on a single assumption that all errors, e e, involved in calibration problem (1) are limited (measurement errors in X and y, modeling errors, etc.) [7].…”

Section: Sic Basics Principlesmentioning

confidence: 99%

Subset selection strategy

Rodionova

Pomerantsev

2008

Journal of Chemometrics

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A new technique for representative subset selection is presented. The advocated method selects unambiguously the most important objects among the calibration set and uses this subset for the model development without significant deterioration in the predictive ability. The method is called boundary subset selection and it is an inherent part of the Simple Interval Calculation (SIC) approach. SIC is a method for linear modeling, which is based on the assumption of error boundedness. The primary SIC consequence is an object status classification (OSClas) that reveals the most influential objects and also designates the most stable and reliable ones. The OSClas is used as the main tool for representative subset selection. The presented results are compared with widely used Kennard-Stone algorithm and D-optimal design procedure employing three real-world examples.

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Standardisation of near-infrared spectrometric instruments: A review

Cited by 168 publications

References 24 publications

Soil Organic Carbon Estimation in Croplands by Hyperspectral Remote APEX Data Using the LUCAS Topsoil Database

Soil Organic Carbon Estimation in Croplands by Hyperspectral Remote APEX Data Using the LUCAS Topsoil Database

Using Legacy Soil Data for Standardizing Predictions of Topsoil Clay Content Obtained From Vnir/Swir Hyperspectral Airborne Images

Subset selection strategy

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