Prediction of the Soil Organic Matter (SOM) Content from Moist Soil Using Synchronous Two-Dimensional Correlation Spectroscopy (2D-COS) Analysis

Wang, Shifang; Xu, Cheng; Zheng, Decong; Song, Haiyan; Han, Ping; Yuen, Peter W. T.

doi:10.3390/s20174822

Cited by 9 publications

(3 citation statements)

References 71 publications

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“…On the other hand, as the basic attribute and essential characteristic of soil, the soil fertility is the ability of soil to supply and coordinate nutrients, water, air, and heat for plant growth, and is a comprehensive response of soil physical, chemical, and biological properties. Soil organic matter promotes the formation of soil structure, improves soil physical properties, improves soil fertility retention and buffering performance (Tsakiridis et al., 2019; Wang et al., 2020). Therefore, it is very important to monitor the SOM content quickly and accurately.…”

Section: Introductionmentioning

confidence: 99%

Organic matter estimation of surface soil using successive projection algorithm

et al. 2021

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Soil organic matter (SOM) affects circulation and stability of the soil C pool, and plays a key role in global C change and local ecological balance. Timely and accurate acquisition of large‐scale farmland soil organic carbon (SOC) information is of great significance to the study of SOC reserves and distribution, farmland soil ecological security, and scientific crop management. In this study, we have evaluated the potentiality of visible‐near‐infrared spectroscopy to along with machine learning algorithms to predict SOM content. At present, soil characterization is mainly focused on hyperspectral data measured by remote‐sensing spectral index and spectrometer. This article aims to establish a statistical machine‐learning model to study the efficiency of spectral data to comprehensively evaluate SOM. The results show that after transforming the different forms of the original spectrum and combining with successive projection algorithm (SPA), the characteristic wavelength of organic matter at 350–2,500 nm can be effectively extracted. A combination of spectroscopy and chemometrics techniques can be used as a practical, rapid, low‐cost, and quantitative method to evaluate SOM. Based on SPA, partial least squares regression, support vector machine, random forest, a SOM prediction model with optimal characteristic wavelength is constructed. The best spectral processing method is continual removal combined with SPA, the validation coefficient of determination, root mean square error prediction, and ratio of performance deviation values are 0.82, 1.12, and 2.87, respectively.

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Section: Introductionmentioning

confidence: 99%

Organic matter estimation of surface soil using successive projection algorithm

et al. 2021

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“…The correlation of determination (R 2 ), root mean square error (RMSE), and relative error (RE) are selected as the evaluation criteria [ 56 ]. As the R 2 value increases and the values of RMSE and RE decrease, the prediction model performs better.…”

Section: Methodsmentioning

confidence: 99%

Applying spectral fractal dimension index to predict the SPAD value of rice leaves under bacterial blight disease stress

Cao

Song

et al. 2022

Plant Methods

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Background The chlorophyll content is a vital indicator for reflecting the photosynthesis ability of plants and it plays a significant role in monitoring the general health of plants. Since the chlorophyll content and the soil–plant analysis development (SPAD) value are positively correlated, it is feasible to predict the SPAD value by calculating the vegetation indices (VIs) through hyperspectral images, thereby evaluating the severity of plant diseases. However, current indices simply adopt few wavelengths of the hyperspectral information, which may decrease the prediction accuracy. Besides, few researches explored the applicability of VIs over rice under the bacterial blight disease stress. Methods In this study, the SPAD value was predicted by calculating the spectral fractal dimension index (SFDI) from a hyperspectral curve (420 to 950 nm). The correlation between the SPAD value and hyperspectral information was further analyzed for determining the sensitive bands that correspond to different disease levels. In addition, a SPAD prediction model was built upon the combination of selected indices and four machine learning methods. Results The results suggested that the SPAD value of rice leaves under different disease levels are sensitive to different wavelengths. Compared with current VIs, a stronger positive correlation was detected between the SPAD value and the SFDI, reaching an average correlation coefficient of 0.8263. For the prediction model, the one built with support vector regression and SFDI achieved the best performance, reaching R2, RMSE, and RE at 0.8752, 3.7715, and 7.8614%, respectively. Conclusions This work provides an in-depth insight for accurately and robustly predicting the SPAD value of rice leaves under the bacterial blight disease stress, and the SFDI is of great significance for monitoring the chlorophyll content in large-scale fields non-destructively.

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“…The correlation of determination (R 2 ), Root Mean Square Error (RMSE), and Relative Error (RE) are selected as the evaluation criteria [56]. As the R 2 value increases and the values of RMSE and RE decrease, the SPAD value prediction performed better.…”

Section: Tab5 Definition Of Current Vismentioning

confidence: 99%

Applying Spectral Fractal Dimension to Predict the SPAD Value of Rice Leaves Under Disease Stress

Cao

Song

et al. 2021

Preprint

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BackgroundThe chlorophyll content is a vital indicator for reflecting the photosynthesis ability of plants and it plays a significant role in monitoring the general health of plants. Since the chlorophyll content and the soil-plant analysis development (SPAD) value are positively correlated, it is feasible to estimate the SPAD value by calculating the vegetation indices (VIs) through hyperspectral images, thereby estimating the chlorophyll content. However, current indices simply adopted few wavelengths of the hyperspectral information, which may decrease the estimation accuracy. Besides, few researches explored the applicability of VIs over plant leaves under disease stress.MethodsIn this study, the SPAD value was estimated by calculating the fractal dimension of hyperspectral curves, ranging from 420 to 950 nm. The correlation between the SPAD value and wavelengths under disease stress was analyzed. In addition, a SPAD prediction model was built upon the combination of selected indices and 4 machine learning methods, including decision tree (DT), partial least square regression (PLSR), support vector regression (SVR), and back propagation neural network (BPNN). The performance of these models was compared through the correlation of determination, root mean square error, and relative error.ResultsThe results suggested that the SPAD value of rice leaves under different disease levels were sensitive to different wavelengths, meaning that the fixed wavelength selection in current indices may achieve poor estimation results. Compared with current VIs, a stronger positive correlation was detected between the SPAD value and our proposal, reaching an average correlation coefficient of 0.8263. For the prediction model, the one built with our proposal and SVR achieved the best performance, reaching R2, RMSE, and RE at 0.8752, 3.7715, and 7.8614%, respectively.ConclusionsThis work provides an in-depth insight for accurately and robustly estimating the SPAD value of rice leaves under disease stress, and our proposal is of great significance for monitoring the chlorophyll content in large-scale fields non-destructively.

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Prediction of the Soil Organic Matter (SOM) Content from Moist Soil Using Synchronous Two-Dimensional Correlation Spectroscopy (2D-COS) Analysis

Cited by 9 publications

References 71 publications

Organic matter estimation of surface soil using successive projection algorithm

Organic matter estimation of surface soil using successive projection algorithm

Applying spectral fractal dimension index to predict the SPAD value of rice leaves under bacterial blight disease stress

Applying Spectral Fractal Dimension to Predict the SPAD Value of Rice Leaves Under Disease Stress

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