Comparison between artificial neural network and partial least squares for on-line visible and near infrared spectroscopy measurement of soil organic carbon, pH and clay content

Kuang, Boyan; Tekin, Yücel; Mouazen, Abdul Mounem

doi:10.1016/j.still.2014.11.002

Cited by 123 publications

(74 citation statements)

References 32 publications

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“…The Kappa indices in this study (0.64 and 0.67) were classified with a high level of agreement in according to the categorical data of Landis and Koch (1977). Comparing the laboratory particle-size analysis with DRS prediction for the clay content, Kuang et al (2015) found a Kappa index of 0.48. To predict the soil texture and other properties in a semi-arid area of northern Turkey, Bilgili et al (2010) used DRS.…”

Section: Classification Of Soil Samplesmentioning

confidence: 50%

Potential of Spectroradiometry to Classify Soil Clay Content

Dotto¹,

Dalmolin²,

Caten

et al. 2016

Rev. Bras. Ciênc. Solo

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Section: Classification Of Soil Samplesmentioning

confidence: 50%

Potential of Spectroradiometry to Classify Soil Clay Content

Dotto¹,

Dalmolin²,

Caten

et al. 2016

Rev. Bras. Ciênc. Solo

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“…The accuracy for models trained on dry spectra and tested on field moist spectra was improved. Kuang et al [107] compared the accuracy between PLSR and artificial neural networks (ANN) for online spectral measurements, and concluded that ANN provided better results in cross validation (R 2 = 0.83) compared to PLSR (R 2 = 0.71). Though using an independent dataset for calibration, the model's performance dropped significantly for both ANN and PLSR calibrations, with R 2 = 0.49 and R 2 = 0.46, respectively.…”

Section: Commercial Available In Situ Soil Sensorsmentioning

confidence: 99%

From Laboratory to Proximal Sensing Spectroscopy for Soil Organic Carbon Estimation—A Review

et al. 2020

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Rapid and cost-effective soil properties estimations are considered imperative for the monitoring and recording of agricultural soil condition for the implementation of site-specific management practices. Conventional laboratory measurements are costly and time-consuming, and, therefore, cannot be considered appropriate for large datasets. This article reviews laboratory and proximal sensing spectroscopy in the visible and near infrared (VNIR)–short wave infrared (SWIR) wavelength region for soil organic carbon and soil organic matter estimation as an alternative to analytical chemistry measurements. The aim of this work is to report the progress made in the last decade on data preprocessing, calibration approaches, and system configurations used for VNIR-SWIR spectroscopy of soil organic carbon and soil organic matter estimation. We present and compare the results of over fifty selective studies and discuss the factors that affect the accuracy of spectroscopic measurements for both laboratory and in situ applications.

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“…By contrast, the PLSR model performed slightly worse because it was based on traditional linear regression although the information of SOC was not. Kuang et al [80] demonstrated that the ANN model performs better compared with PLSR, which may be attributed to a nonlinear behavior documented for SOC [81,82] and appeared to be overcome by the nature of ANN in solving nonlinear problems.…”

Section: Comparisons Of Model Performance In Soc Predictionmentioning

confidence: 99%

Prediction of Soil Organic Carbon based on Landsat 8 Monthly NDVI Data for the Jianghan Plain in Hubei Province, China

et al. 2019

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High-precision maps of soil organic carbon (SOC) are beneficial for managing soil fertility and understanding the global carbon cycle. Digital soil mapping plays an important role in efficiently obtaining the spatial distribution of SOC, which contributes to precision agriculture. However, traditional soil-forming factors (i.e., terrain or climatic factors) have weak variability in low-relief areas, such as plains, and cannot reflect the spatial variation of soil attributes. Meanwhile, vegetation cover hinders the acquisition of the direct information of farmland soil. Thus, useful environmental variables should be utilized for SOC prediction and the digital mapping of such areas. SOC has an important effect on crop growth status, and remote sensing data can record the apparent spectral characteristics of crops. The normalized difference vegetation index (NDVI) is an important index reflecting crop growth and biomass. This study used NDVI time series data rather than traditional soil-forming factors to map SOC. Honghu City, located in the middle of the Jianghan Plain, was selected as the study region, and the NDVI time series data extracted from Landsat 8 were used as the auxiliary variables. SOC maps were estimated through stepwise linear regression (SLR), partial least squares regression (PLSR), support vector machine (SVM), and artificial neural network (ANN). Ordinary kriging (OK) was used as the reference model, while root mean square error of prediction (RMSE P ) and coefficient of determination of prediction (R 2 P ) were used to evaluate the model performance. Results showed that SOC had a significant positive correlation in July and August (0.17, 0.29) and a significant negative correlation in January, April, and December (−0.23, −0.27, and −0.23) with NDVI time series data. The best model for SOC prediction was generated by ANN, with the lowest RMSE P of 3.718 and highest R 2 P of 0.391, followed by SVM (RMSE P = 3.753, R 2 P = 0.361) and PLSR (RMSE P = 4.087, R 2 P = 0.283). The SLR model was the worst model, with the lowest R 2 P of 0.281 and highest RMSE P of 3.930. ANN and SVM were better than OK (RMSE P = 3.727, R 2 P = 0.372), whereas PLSR and SLR were worse than OK. Moreover, the prediction results using single-data NDVI or short time series NDVI showed low accuracy. The effect of the terrain factor on SOC prediction represented unsatisfactory results. All these results indicated that the NDVI time series data can be used for SOC mapping in plain areas and that the ANN model can maximally extract additional associated information between NDVI time series data and SOC. This study presented an effective method to overcome the selection of auxiliary variables for digital soil mapping in plain areas when the soil was covered with vegetation. This finding indicated that the time series characteristics of NDVI were conducive for predicting SOC in plains.

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Comparison between artificial neural network and partial least squares for on-line visible and near infrared spectroscopy measurement of soil organic carbon, pH and clay content

Cited by 123 publications

References 32 publications

Potential of Spectroradiometry to Classify Soil Clay Content

Potential of Spectroradiometry to Classify Soil Clay Content

From Laboratory to Proximal Sensing Spectroscopy for Soil Organic Carbon Estimation—A Review

Prediction of Soil Organic Carbon based on Landsat 8 Monthly NDVI Data for the Jianghan Plain in Hubei Province, China

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