Bridging the gap between soil spectroscopy and traditional laboratory: Insights for routine implementation

Poppiel, Raúl Roberto; Paiva, Ariane Francine da Silveira; Demattê, José Alexandre Melo

doi:10.1016/j.geoderma.2022.116029

Cited by 27 publications

(6 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These methods include (i) standardizing the size of soil particles to ensure consistency, (ii) repeating the spectrum collection process multiple times to verify data reliability, and (iii) utilizing machine learning algorithms to select representative soil samples. Together, these strategies contribute to the precision and accuracy of soil spectral analysis [9,10]. Previous studies have shown that the use of near-infrared spectroscopy (NIR, 780-2500 nm) can accurately characterize the component information of soils [11][12][13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Advanced Soil Organic Matter Prediction with a Regional Soil NIR Spectral Library Using Long Short-Term Memory–Convolutional Neural Networks: A Case Study

Miao,

Ji,

et al. 2024

Remote Sensing

View full text Add to dashboard Cite

Soil analysis using near-infrared spectroscopy has shown great potential to be an alternative to traditional laboratory analysis, and there is continuously increasing interest in building large-scale soil spectral libraries (SSLs). However, due to issues such as high non-linearity in soil spectral data and complexity in soil spatial variation, the establishment of robust prediction models for soil spectral libraries remains a challenge. This study aimed to investigate the performance of deep learning algorithms, including long short-term memory (LSTM) and LSTM–convolutional neural networks (LSTM–CNN) integrated models, to predict the soil organic matter (SOM) of a provincial-scale SSL, and compare it to the normally used local weighted regression (LWR) model. The Hebei soil spectral library (HSSL) contains 425 topsoil samples (0–20 cm), of which every 3 soil samples were collected from dry land, irrigated land, and paddy fields, respectively, in different counties of Hebei Province, China. The results show that the accuracy of the validation dataset rank as follows: LSTM–CNN (R2p = 0.96, RMSEp = 1.66 g/kg) > LSTM (R2p = 0.83, RMSEp = 3.42 g/kg) > LWR (R2p = 0.82, RMSEp = 3.79 g/kg). The LSTM–CNN model performed the best, mainly due to its comprehensive ability to effectively extract spatial and temporal features. Meanwhile, the LSTM model achieved higher accuracy than the LWR model, owing to its built-in memory unit and its advantage of faster feature band extraction. Thus, it was suggested to use deep learning algorithms for SOM predictions in SSLs. However, their performance on larger-scale SSLs such as continental/global SSLs still needs to be further investigated.

show abstract

Section: Introductionmentioning

confidence: 99%

Advanced Soil Organic Matter Prediction with a Regional Soil NIR Spectral Library Using Long Short-Term Memory–Convolutional Neural Networks: A Case Study

Miao,

Ji,

et al. 2024

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…Vis-NIR has been the most studied spectral range for pedometrics [6,12,13], and many efforts for improving its accuracy have been made [14][15][16]. However, MIR contains more information related to soil properties than vis-NIR, since vis-NIR reflects the internal octave and ensemble frequency vibrations of matters, with a large overlap between spectral features and difficulty in the extraction [17].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Soil Properties in a Field in Typical Black Soil Areas Using in situ MIR Spectra and Its Comparison with vis-NIR Spectra

Yin

Shi

et al. 2023

Remote Sensing

View full text Add to dashboard Cite

As a precious soil resource, black soils in Northeast China are currently facing severe land degradation. Visible and near-infrared spectroscopy (vis-NIR, 350–2500 nm) and mid-infrared spectroscopy (MIR, 2500–25,000 nm) have shown great potential to predict soil properties. However, there is still limited research on using MIR in situ. The aim of this study was to explore the feasibility of in situ MIR for the prediction of soil total nitrogen (TN) and total phosphorus (TP) and to compare its performance with the use of laboratory MIR, as well as the use of in situ and laboratory vis-NIR. A total of 450 samples from 90 soil profiles, along with their in situ and laboratory spectra of MIR and vis-NIR, were collected in a field with ten different tillage and management practices in a typical black soil area of Northeast China. Partial least square regression (PLSR), random forest (RF) and multivariate adaptive regression splines (MARS) were used to generate the calibrations between the spectra and the two properties. The results showed that both MIR and vis-NIR were able to predict the TN whether in laboratory or in situ conditions, but neither of them could predict the TP quantitatively since there was no sensitive band on both spectra regarding the TP. The prediction accuracy of the TN with laboratory spectra was higher than that with in situ spectra, for both vis-NIR and MIR. The optimal prediction accuracy of the TN with laboratory MIR (RMSE = 0.11 g/kg, RPD = 3.12) was higher than that of laboratory vis-NIR (RMSE = 0.14 g/kg, RPD = 2.45). The optimal prediction accuracy of in situ MIR (RMSE = 0.20 g/kg, RPD = 1.80) was lower than that of in situ vis-NIR (RMSE = 0.16 g/kg, RPD = 2.14). The prediction performance of the spectra followed laboratory MIR > laboratory vis-NIR > in situ vis-NIR > in situ MIR. The performance of in situ MIR was relatively poor, mainly due to the fact that MIR was more influenced by soil moisture. This study verified the feasibility of in situ MIR for soil property prediction and provided an approach for obtaining rapid soil information and a reference for soil research and management in black soil areas.

show abstract

“…The development of remote sensing technology has addressed these limitations, enabling real-time, rapid quantitative monitoring of soil properties. This capability holds greater importance for quickly obtaining soil information under the ongoing global climate change scenario [11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Identification and Evolution of Soil Organic Carbon Density Caused by Coastal Rapid Siltation Based on Imaging Spectroscopy

Wang

Zheng

Wei

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

View full text Add to dashboard Cite

This study aimed to investigate the feasibility of using imaging spectroscopy (IS) to predict soil organic carbon density (SOCD) either directly or indirectly through soil organic carbon (SOC). Three methods, partial least square regression (PLSR), support vector machine (SVM), and random forest (RF), were utilized to calibrate the models and map the SOCD. The results showed that direct prediction was better than indirect prediction and the best model SVM had high R 2 of 0.94 and 0.93 for calibration and validation, respectively. The measured SOCD was mainly concentrated in the surface soil layer from 0 to 40 cm, and the deeper layer tended to be gentle. The continuous depth variation trend of SOCD in the topsoil (0-40 cm) was relatively close to the measured values, while in the deeper layers (below 40 cm), it was much higher than the measured values. The study also found that the best fitting function of measured SOC stocks over time varied from linear to power and then to logarithmic with increasing depth, indicating less efficient accumulation of SOC in deep soil compared to topsoil, resulting in an overall decrease in SOC accumulation rate across soil depth. The best temporal functions for the predicted values differed from the measured values at each depth, but the changing trends of the three functions were basically consistent. It suggests that IS technology has the potential to quantitatively reveal the process of coastal soil evolution and offers a new insight for the rapid monitoring of soil property changes.

show abstract

Bridging the gap between soil spectroscopy and traditional laboratory: Insights for routine implementation

Cited by 27 publications

References 47 publications

Advanced Soil Organic Matter Prediction with a Regional Soil NIR Spectral Library Using Long Short-Term Memory–Convolutional Neural Networks: A Case Study

Advanced Soil Organic Matter Prediction with a Regional Soil NIR Spectral Library Using Long Short-Term Memory–Convolutional Neural Networks: A Case Study

Prediction of Soil Properties in a Field in Typical Black Soil Areas Using in situ MIR Spectra and Its Comparison with vis-NIR Spectra

Identification and Evolution of Soil Organic Carbon Density Caused by Coastal Rapid Siltation Based on Imaging Spectroscopy

Contact Info

Product

Resources

About