A study on the hyperspectral signatures of sandy soils with varying texture and water content

Divya, Y.; Sanjeevi, S.; Ilamparuthi, K.

doi:10.1007/s12517-013-1015-1

Cited by 11 publications

(3 citation statements)

References 20 publications

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“…According to Lambert’s absorption law, the spectral properties vary with the composition or structure of the material, and the spectra from different groups vary in regard to the position and intensity of the absorption peaks [ 36 ]. It is also affected by the nonuniform distribution of soil surface texture, density, and internal composition, making it difficult to eliminate all redundant information, such as overlap, from the spectral data [ 37 , 38 , 39 ]. Thus, to reduce spectral noise, baseline drift, and interference from other backgrounds, as well as to distinguish overlapping peaks for the purpose of qualitative or quantitative analysis of complex mixtures, five different preprocessing methods were used in this study to analyze the spectral information [ 40 ].…”

Section: Methodsmentioning

confidence: 99%

Research on the Effects of Drying Temperature for the Detection of Soil Nitrogen by Near-Infrared Spectroscopy

Zhou,

Yao,

et al. 2023

Molecules

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Nitrogen nitrates play a significant role in the soil’s nutrient cycle, and near-infrared spectroscopy can efficiently and accurately detect the content of nitrate–nitrogen in the soil. Accordingly, it can provide a scientific basis for soil improvement and agricultural productivity by deeply examining the cycle and transformation pattern of nutrients in the soil. To investigate the impact of drying temperature on NIR soil nitrogen detection, soil samples with different N concentrations were dried at temperatures of 50 °C, 65 °C, 80 °C, and 95 °C, respectively. Additionally, soil samples naturally air-dried at room temperature (25 °C) were used as a control group. Different drying times were modified based on the drying temperature to completely eliminate the impact of moisture. Following data collection with an NIR spectrometer, the best preprocessing method was chosen to handle the raw data. Based on the feature bands chosen by the RFFS, CARS, and SPA methods, two linear models, PLSR and SVM, and a nonlinear ANN model were then established for analysis and comparison. It was found that the drying temperature had a great effect on the detection of soil nitrogen by near-infrared spectroscopy. In the meantime, the SPA-ANN model simultaneously yielded the best and most stable accuracy, with Rc2 = 0.998, Rp2 = 0.989, RMSEC = 0.178 g/kg, and RMSEP = 0.257 g/kg. The results showed that NIR spectroscopy had the least effect and the highest accuracy in detecting nitrogen at 80 °C soil drying temperature. This work provides a theoretical foundation for agricultural production in the future.

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Section: Methodsmentioning

confidence: 99%

Research on the Effects of Drying Temperature for the Detection of Soil Nitrogen by Near-Infrared Spectroscopy

Zhou,

Yao,

et al. 2023

Molecules

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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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“…With the rise of remote sensing technology, the application of hyperspectral remote sensing techniques to monitor soil salinity not only saves time and effort, but also exhibits the characteristics of rapid deployment, macroscopic and dynamic features, and a superior performance that cannot be achieved by other means. In recent years, numerous scholars have studied soil salinity estimation for different soil types based on visible-near-infrared (NIR) spectroscopy [13][14][15][16][17][18][19][20][21]. The background, environment, measuring instruments, etc.…”

Section: Introductionmentioning

confidence: 99%

Salinity Monitoring at Saline Sites with Visible–Near-Infrared Spectral Data

Liu

Bao

et al. 2021

Minerals

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To address the global phenomenon of the salinisation of large land areas, a quantitative inversion model of the salinity of saline soils and soil visible–near-infrared (NIR) spectral data was developed by considering saline soils in Zhenlai County, Jilin Province, China as the research object. The original spectral data were first subjected to Savitzky–Golay (SG) smoothing, multiplicative scattering correction (MSC) pre-processing, and a combined transformation technique. The pre-processed spectral data were then analysed to construct the difference index (DI), ratio index (RI), and normalised difference index (NDI), and the Spearman rank correlation coefficient (r) between these three spectral indices and the salt content in the samples was calculated, while a combined spectral index (r > 0.8) was eventually selected as a sensitive spectral index. Finally, a quantitative inversion model for the salinity of saline soils was developed, and the model’s accuracy was evaluated based on partial least squares regression (PLSR), the random forest (RF) algorithm, and the radial basis function (RBF) neural network algorithm. The results indicated that the inversion of soil salt content using the selected combination of spectral indices based on the RBF neural network algorithm was the most effective, with the prediction model yielding an R2 value of 0.950, a root mean square error (RMSE) of 1.014, and a relative percentage deviation (RPD) of 4.479, which suggested a good prediction effect.

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A study on the hyperspectral signatures of sandy soils with varying texture and water content

Cited by 11 publications

References 20 publications

Research on the Effects of Drying Temperature for the Detection of Soil Nitrogen by Near-Infrared Spectroscopy

Research on the Effects of Drying Temperature for the Detection of Soil Nitrogen by Near-Infrared Spectroscopy

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

Salinity Monitoring at Saline Sites with Visible–Near-Infrared Spectral Data

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