Application of principal component analysis–multivariate adaptive regression splines for the simultaneous spectrofluorimetric determination of dialkyltins in micellar media

Ghasemi, Jahan B.; Zolfonoun, Ehsan

doi:10.1016/j.saa.2013.06.054

Cited by 24 publications

(19 citation statements)

References 34 publications

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“…The results are similar to those obtained by Nguyen et al [71] in a study carried out in South Korea in which it was confirmed that the 6S algorithm fitted by the kNN technique (RMSE = 22.5 Mg¨ha´1) produced better results for estimating AGB than the other algorithms tested (DOS, FLAASH and ToA), especially for Landsat ETM bands in the infrared region. Furthermore, various studies have concluded that the MARS technique is a flexible method that yields robust predictions [72,73].…”

Section: Discussionmentioning

confidence: 99%

Evaluation of Radiometric and Atmospheric Correction Algorithms for Aboveground Forest Biomass Estimation Using Landsat 5 TM Data

et al. 2016

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Solar radiation is affected by absorption and emission phenomena during its downward trajectory from the Sun to the Earth's surface and during the upward trajectory detected by satellite sensors. This leads to distortion of the ground radiometric properties (reflectance) recorded by satellite images, used in this study to estimate aboveground forest biomass (AGB). Atmospherically-corrected remote sensing data can be used to estimate AGB on a global scale and with moderate effort. The objective of this study was to evaluate four atmospheric correction algorithms (for surface reflectance), ATCOR2 (Atmospheric Correction for Flat Terrain), COST (Cosine of the Sun Zenith Angle), FLAASH (Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes) and 6S (Second Simulation of Satellite Signal in the Solar), and one radiometric correction algorithm (for reflectance at the sensor) ToA (Apparent Reflectance at the Top of Atmosphere) to estimate AGB in temperate forest in the northeast of the state of Durango, Mexico. The AGB was estimated from Landsat 5 TM imagery and ancillary information from a digital elevation model (DEM) using the non-parametric multivariate adaptive regression splines (MARS) technique. Field reference data for the model training were collected by systematic sampling of 99 permanent forest growth and soil research sites (SPIFyS) established during the winter of 2011. The following predictor variables were identified in the MARS model: Band 7, Band 5, slope (β), Wetness Index (WI), NDVI and MSAVI2. After cross-validation, 6S was found to be the optimal model for estimating AGB (R 2 = 0.71 and RMSE = 33.5 Mg¨ha´1; 37.61% of the average stand biomass). We conclude that atmospheric and radiometric correction of satellite images can be used along with non-parametric techniques to estimate AGB with acceptable accuracy.

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Section: Discussionmentioning

confidence: 99%

Evaluation of Radiometric and Atmospheric Correction Algorithms for Aboveground Forest Biomass Estimation Using Landsat 5 TM Data

et al. 2016

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“…These results support the findings by Shepherd and Walsh [72], Bilgili et al [54] and Nawar et al [59], which showed that MARS predictive models provided more robust predictions of soil properties than PLSR models. This is because MARS is a non-linear and flexible modeling method, capable of fitting complex and non-linear relationships and specifying the interaction effects, as well as the linear combinations of variables [53,54,90]. Several studies showed that the prediction of soil properties implies some non-linear relationship between the measured soil values and soil reflectance spectra [26,50,52].…”

Section: Modeling Soil Properties Using Mars and Plsr Methodsmentioning

confidence: 99%

“…Several studies showed that the prediction of soil properties implies some non-linear relationship between the measured soil values and soil reflectance spectra [26,50,52]. MARS makes it possible to reduce the effects of the multistep process and any other unknown nonlinearity and, therefore, produces superior and more effective models compared to the PLSR method [90]. It should be noted, however, that although the PLSR method assumes a linear relationship between the ECe and soil reflectance spectra, a small deviation from linearity is acceptable and can be suppressed by including additional modeling factors [91].…”

Section: Modeling Soil Properties Using Mars and Plsr Methodsmentioning

confidence: 99%

Digital Mapping of Soil Properties Using Multivariate Statistical Analysis and ASTER Data in an Arid Region

2015

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Modeling and mapping of soil properties has been identified as key for effective land degradation management and mitigation. The ability to model and map soil properties at sufficient accuracy for a large agriculture area is demonstrated using Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) imagery. Soil samples were collected in the El-Tina Plain, Sinai, Egypt, concurrently with the acquisition of ASTER imagery, and measured for soil electrical conductivity (ECe), clay content and soil organic matter (OM). An ASTER image covering the study area was preprocessed, and two predictive models, multivariate adaptive regression splines (MARS) and the partial least squares regression (PLSR), were constructed based on the ASTER spectra. For all three soil properties, the results of MARS models were better than those of the respective PLSR models, with cross-validation estimated R

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“…These results support the findings of Bilgili et al [32] and Nawar et al [33], which demonstrated that the MARS predictive models provided more robust predictions of soil salinity than the PLSR models. This difference is probably because MARS is a non-linear and flexible modeling method, capable of fitting complex and non-linear relationships and specifying the interaction effects as well as the linear combinations of variables [26,28,59]. Several studies have demonstrated that the prediction of high soil salinity levels implies some non-linear relationship between the measured soil salinity and the soil reflectance spectra [18,35,60].…”

Section: Soil Salinity Estimation Using the Plsr And Mars Modelsmentioning

confidence: 99%

Modeling and Mapping of Soil Salinity with Reflectance Spectroscopy and Landsat Data Using Two Quantitative Methods (PLSR and MARS)

et al. 2014

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Abstract:The monitoring of soil salinity levels is necessary for the prevention and mitigation of land degradation in arid environments. To assess the potential of remote sensing in estimating and mapping soil salinity in the El-Tina Plain, Sinai, Egypt, two predictive models were constructed based on the measured soil electrical conductivity (ECe) and laboratory soil reflectance spectra resampled to Landsat sensor's resolution. The models used were partial least squares regression (PLSR) and multivariate adaptive regression splines (MARS). The results indicated that a good prediction of the soil salinity can be made based on the MARS model (R 10814 salinity. The method developed in this paper can be used for other satellite data, like those provided by Landsat 8, and can be applied in other arid and semi-arid environments.

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Application of principal component analysis–multivariate adaptive regression splines for the simultaneous spectrofluorimetric determination of dialkyltins in micellar media

Cited by 24 publications

References 34 publications

Evaluation of Radiometric and Atmospheric Correction Algorithms for Aboveground Forest Biomass Estimation Using Landsat 5 TM Data

Evaluation of Radiometric and Atmospheric Correction Algorithms for Aboveground Forest Biomass Estimation Using Landsat 5 TM Data

Digital Mapping of Soil Properties Using Multivariate Statistical Analysis and ASTER Data in an Arid Region

Modeling and Mapping of Soil Salinity with Reflectance Spectroscopy and Landsat Data Using Two Quantitative Methods (PLSR and MARS)

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