Strategies for the efficient estimation of soil organic matter in salt-affected soils through Vis-NIR spectroscopy: Optimal band combination algorithm and spectral degradation

Zhang, Zipeng; Ding, Jianli; Zhu, Chuanmei; Wang, Jingzhe; Ma, Guolin; Ge, Xiangyu; Li, Zhenshan; Han, Lijing

doi:10.1016/j.geoderma.2020.114729

Cited by 62 publications

(20 citation statements)

References 61 publications

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“…A wavelength was considered significant if b exceeded a threshold. Coefficient values above the threshold were considered significant, and the threshold was set as the standard deviation of its value [ 27 , 28 ]. To assess the relative importance of each wavelength used in each PLSR model in the projection, a VIP score was used to determine the important wavelengths used in the PLSR calibration.…”

Section: Methodsmentioning

confidence: 99%

Estimation of Soil Organic Matter in Arid Zones with Coupled Environmental Variables and Spectral Features

Zheng

Ding

Zhang

2022

Sensors

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The soil organic matter (SOM) content is a key factor affecting the function and health of soil ecosystems. For measurements of land reclamation and soil fertility, SOM monitoring using visible and near-infrared spectroscopy (Vis-NIR) is one approach to quantifying soil quality, and Vis-NIR is important for monitoring the SOM content in a broad and nondestructive manner. To investigate the influence of environmental factors and Vis-NIR spectroscopy in estimating SOM, 249 soil samples were collected from the Werigan–Kuqa oasis in Xinjiang, China, and their spectral reflectance, SOM content and soil salinity were measured. To classify and improve the prediction accuracy, we also take into account the soil salinity content as a variable indicator. Relevant environmental variables were extracted using remote sensing datasets (land-use/land-cover (LULC), digital elevation model (DEM), World Reference Base for Soil Resources (WRB), and soil texture). On the basis of Savitzky–Golay (S-G) smoothing and first derivative (FD) preprocessing of the original spectrum, three clusters were obtained by K-means clustering through the use of Vis-NIR and used as spectral classification variables. Using Vis-NIR as Model 1, Vis-NIR combined with spectral classification as Model 2, environmental variables as Model 3, and the combination of all the above variables (Vis-NIR, spectral classification, environmental variables, and soil salinity) as Model 4, a SOM content estimation model was constructed using partial least squares regression (PLSR). Using the 249 soil samples, the modeling set contained 166 samples and the validation set contained 83 samples. The results showed that Model 2 (validation r2 = 0.78) was better than Model 1 (validation r2 = 0.76). The prediction accuracy for Model 4 (validation r2 = 0.85) was better than Model 2 (validation r2 = 0.78). Among these, Model 3 was the worst (validation r2 = 0.39). Therefore, the combination of environmental variables with Vis-NIR spectroscopy to estimate SOM content is an important method and has important implications for improving the accuracy of SOM predictions in arid regions.

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Section: Methodsmentioning

confidence: 99%

Estimation of Soil Organic Matter in Arid Zones with Coupled Environmental Variables and Spectral Features

Zheng

Ding

Zhang

2022

Sensors

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“…For spectral remote sensing instruments, the spectral response function of its band generally presents a curve shape similar to Gaussian function. Therefore, the spectral response function can be simulated by Gaussian function [23]. The spectral reflectance after resampling can be expressed as:…”

Section: Spectral Resamplingmentioning

confidence: 99%

Strategies for the Efficient Estimation of Soil Moisture through Spectroscopy: Sensitive Wavelength Algorithm, Spectral Resampling and Signal-to-Noise Ratio Selection

Yuan

et al. 2022

Applied Sciences

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It is found that the remote sensing parameters such as spectral range, spectral resolution and signal-to-noise ratio directly affect the estimation accuracy of soil moisture content. However, the lack of research on the relationship between the parameters and estimation accuracy restricts the prolongation of application. Therefore, this study took the demand for this application as the foothold for developing spectrometry. Firstly, a method based on sensitivity analysis of soil radiative transfer model-successive projection algorithm (SA-SPA) was proposed to select sensitive wavelengths. Then, the spectral resampling method was used to select the best spectral resolution in the corresponding sensitive wavelengths. Finally, the noise-free spectral data simulated by the soil radiative transfer model was added with Gaussian random noise to change the signal-to-noise ratio, so as to explore the influence of signal-to-noise ratio on the estimation accuracy. The research results show that the estimation accuracy obtained through the SA-SPA (RMSEP < 12.1 g kg−1) is generally superior to that from full-spectrum data (RMSEP < 14 g kg−1). At selected sensitive wavelengths, the best spectral resolution is 34 nm, and the applicable signal-to-noise ratio ranges from 150 to 350. This study provides technical support for the efficient estimation of soil moisture content and the development of spectrometry, which comprehensively considers the common influence of spectral range, spectral resolution and signal-to-noise ratio on the estimation accuracy of soil moisture content.

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“…With the extensive application of spectral technology in the detection of soil nutrient content, there has been a significant improvement made to the level of automation and efficiency for soil dynamic monitoring, which enables the real-time variable rate fertilization. In practice, however, soil spectral signal can be affected by a range of external environmental parameters, such as soil moisture content [12], soil salt content [13], soil particle size [14], soil texture [15], and outdoor temperature, and especially soil moisture content. It was revealed by a study that soil hyperspectral detection can be severely disrupted by soil moisture content in the water absorption bands (1400, 1900, and 2200 nm) [16], thus resulting in a poor applicability of the prediction model [17].…”

Section: Introductionmentioning

confidence: 99%

Construction and Evaluation of Prediction Model of Main Soil Nutrients Based on Spectral Information

Dong

et al. 2022

Applied Sciences

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The rapid and accurate detection of soil nutrient content through spectral technology is one of the requisite technologies for precision fertilization, which, however, is an unsolved issue. In order to achieve this purpose, a more robust and accurate model is established in this study. The regression algorithm is integrated with effective wavelength selection to construct the prediction model for total nitrogen, available phosphorus, and available potassium (N, P, and K), which removes the need for complex pretreatment and algorithm constraints. According to the research results, with regard to the prediction of soil nitrogen, phosphorus, and potassium contents, the joint interval support vector regression (Si-SVR) model performed best in modeling, with the root mean square error of prediction (RMSEP) limited to 0.0231%, 1.0554%, and 3.4225%, respectively. In addition, the relative percentage deviation (RPD) values were restricted to 2.68, 2.12, and 2.37, respectively. As indicated by the prediction results obtained for the above three nutrient contents, the RPD values of the Si-SVR model prediction accuracy evaluation indicators exceeded 2.0, which evidences a high level of prediction accuracy. This method makes it possible for spectral data to be applied in practical production, and these results provide a valuable reference for the effective detection of major soil nutrients.

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Strategies for the efficient estimation of soil organic matter in salt-affected soils through Vis-NIR spectroscopy: Optimal band combination algorithm and spectral degradation

Cited by 62 publications

References 61 publications

Estimation of Soil Organic Matter in Arid Zones with Coupled Environmental Variables and Spectral Features

Estimation of Soil Organic Matter in Arid Zones with Coupled Environmental Variables and Spectral Features

Strategies for the Efficient Estimation of Soil Moisture through Spectroscopy: Sensitive Wavelength Algorithm, Spectral Resampling and Signal-to-Noise Ratio Selection

Construction and Evaluation of Prediction Model of Main Soil Nutrients Based on Spectral Information

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