Construction of the Calibration Set through Multivariate Analysis in Visible and Near-Infrared Prediction Model for Estimating Soil Organic Matter

Wang, Xiaomi; Chen, Yiyun; Guo, Long; Liu, Leilei

doi:10.3390/rs9030201

Cited by 11 publications

(5 citation statements)

References 35 publications

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“…Liu et al further combined the Kennard-Stone algorithm and spectral pretreatment to choose representative calibration samples, and achieved an RPD of 1.85, which was still poorer than that obtained in the current study [59]. Wang et al proposed the MVARC-R-KS method to select representative calibration samples (not spectral variables as in the current study), which has resulted in good accuracy of PLSR models [61]. They reported that the best RPD was 1.81, which was also lower than the RPD in this study.…”

Section: The Effect Of Spectral Variable Selection Techniques On Model Accuracymentioning

confidence: 60%

Estimation of Organic Carbon in Anthropogenic Soil by VIS-NIR Spectroscopy: Effect of Variable Selection

Hong

et al. 2020

Remote Sensing

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Visible and near-infrared reflectance (VIS-NIR) spectroscopy is widely applied to estimate soil organic carbon (SOC). Intense and diverse human activities increase the heterogeneity in the relationships between SOC and VIS-NIR spectra in anthropogenic soil. This fact results in poor performance of SOC estimation models. To improve model accuracy and parsimony, we investigated the performance of two variable selection algorithms, namely competitive adaptive reweighted sampling (CARS) and random frog (RF), coupled with five spectral pretreatments. A total of 108 samples were collected from Jianghan Plain, China, with the SOC content and VIS-NIR spectra measured in the laboratory. Results showed that both CARS and RF coupled with partial least squares regression (PLSR) outperformed PLSR alone in terms of higher model accuracy and less spectral variables. It revealed that spectral variable selection could identify important spectral variables that account for the relationships between SOC and VIS-NIR spectra, thereby improving the accuracy and parsimony of PLSR models in anthropogenic soil. Our findings are of significant practical value to the SOC estimation in anthropogenic soil by VIS-NIR spectroscopy.

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Section: The Effect Of Spectral Variable Selection Techniques On Model Accuracymentioning

confidence: 60%

Estimation of Organic Carbon in Anthropogenic Soil by VIS-NIR Spectroscopy: Effect of Variable Selection

Hong

et al. 2020

Remote Sensing

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“…They [10] also considered geographical zones and spectral similarity, and observed homogeneous clusters. Other researchers performed clustering by using other variables, such as soil humidity, slope, parent material, and unsupervised Ward's Euclidian distance [25,28,31]. Clustering aims to correctly allocate the validation samples to the most similar group, and the SOC model based on that group can estimate the SOC of validation samples as accurately as possible.…”

Section: Effects Of Stratifying Samples By Soil Type In Soc Estimatiomentioning

confidence: 99%

“…Clustering aims to correctly allocate the validation samples to the most similar group, and the SOC model based on that group can estimate the SOC of validation samples as accurately as possible. In most cases, the model that estimates soil properties is improved by clustering the samples into homogeneous groups [10,25,41].…”

Section: Effects Of Stratifying Samples By Soil Type In Soc Estimatiomentioning

confidence: 99%

“…How to properly employ a large soil library to estimate soil properties has become a hot topic. The most commonly utilized strategies to facilitate the application of large libraries include powerful regression approaches [19][20][21], optimal spectral transformations [22][23][24], representative calibration sample selection [10,17,[25][26][27][28], ancillary data integration [4,14,15,[29][30][31][32][33], subset spiking, and extra weighting [34][35][36][37]. A large soil library usually consists of various samples in terms of geographical origins, minerals, parent materials, environmental conditions, and land-use types.…”

Section: Introductionmentioning

confidence: 99%

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Application of Spectrally Derived Soil Type as Ancillary Data to Improve the Estimation of Soil Organic Carbon by Using the Chinese Soil Vis-NIR Spectral Library

et al. 2018

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Ancillary data, such as soil type, may improve the visible and near-infrared (vis-NIR) estimation of soil organic carbon (SOC); however, they require data collection or expert knowledge. The application of a national soil spectral library to local SOC estimations usually requires soil type information, because the relationships between vis-NIR spectra and SOC from different populations may vary. Using 515 samples of five soil types (genetic soil classification of China, GSCC) from the Chinese soil spectral library (CSSL), we compared three strategies in the vis-NIR estimation of SOC. Different regression models were calibrated using the entire dataset (Strategy I, without using soil type as ancillary data) and the subsets stratified by soil type from CSSL as ancillary data (strategies II and III). In Strategy II, the subsets were stratified by soil type from the CSSL for validation. In Strategy III, the subsets were stratified by spectrally derived soil type for validation. The results showed that 86.72% of the samples were successfully discriminated for the soil types by using the vis-NIR spectra. The coefficients of determination in the prediction (R 2 p ) of SOC estimation by strategies I, II, and III were 0.74, 0.83, and 0.82, respectively. The stratified calibration strategies (strategies II and III) improved the vis-NIR estimation of SOC. The misclassification of the soil type in the application of Strategy III slightly affected the SOC estimations. Nevertheless, this strategy is inexpensive and beneficial when expert knowledge on soil classification is lacking. We concluded that vis-NIR spectroscopy could be applied to distinguish some soil types in terms of GSCC, which further provided essential and easily accessible ancillary data for the application of stratified calibration strategies in the vis-NIR estimation of SOC.

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“…The performances were evaluated by R 2 p and RPD. The former value represents the quality of the data fitting when a given model is applied to another year, while the latter depicts the quality of the model transfer based on the prediction errors [34]. The results are shown in Figures 4 and 5. 0.7 for the other six years.…”

Section: Transferability Validationmentioning

confidence: 99%

Transferability of Economy Estimation Based on DMSP/OLS Night-Time Light

Cheng

et al. 2017

Remote Sensing

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Despite the fact that economic data are of great significance in the assessment of human socioeconomic development, the application of this data has been hindered partly due to the unreliable and inefficient economic censuses conducted in developing countries. The night-time light (NTL) imagery from the Defense Meteorological Satellite Program's Operational Linescan System (DMSP/OLS) provides one of the most important ways to evaluate an economy with low cost and high efficiency. However, little research has addressed the transferability of the estimation across years. Based on the entire DN series from 0 to 63 of NTL data and GDP data in 31 provinces of mainland China from 2000 to 2012, this paper aims to study the transferability of economy estimation across years, with four linear and non-linear data mining methods, including the Multiple Linear Regression (MLR), Local Weighted Regression (LWR), Partial Least Squares Regression (PLSR), and Support Vector Machine Regression (SVMR). We firstly built up the GDP estimation model based on the NTL data in each year with each method respectively, then applied each model to the other 12 years for the evaluation of the time series transferability. Results revealed that the performances of models differ greatly across years and methods: PLSR (mean of R 2 cv = 0.84) and SVMR (mean of R 2 cv = 0.86) are superior to MLR (mean of R 2 cv = 0.72) and LWR (mean of R 2 cv = 0.75) for model calibration; only PLSR (mean of R 2 p = 0.88, mean of RPD = 1.8) holds a strong transferability among different years; the frequency of three DN sections of (0-1), (4-16), and (57-63) are especially important for economy estimation. Such results are expected to provide a more comprehensive understanding of the NTL, which can be used for economy estimation across years.

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Construction of the Calibration Set through Multivariate Analysis in Visible and Near-Infrared Prediction Model for Estimating Soil Organic Matter

Cited by 11 publications

References 35 publications

Estimation of Organic Carbon in Anthropogenic Soil by VIS-NIR Spectroscopy: Effect of Variable Selection

Estimation of Organic Carbon in Anthropogenic Soil by VIS-NIR Spectroscopy: Effect of Variable Selection

Application of Spectrally Derived Soil Type as Ancillary Data to Improve the Estimation of Soil Organic Carbon by Using the Chinese Soil Vis-NIR Spectral Library

Transferability of Economy Estimation Based on DMSP/OLS Night-Time Light

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