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Multi-Sensor Fusion for Soil Monitoring

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Environmental Sensing".

Deadline for manuscript submissions: closed (20 March 2023) | Viewed by 17947

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Special Issue Editors

Dr. Dongyun Xu

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Guest Editor

College of Resources and Environment, Shandong Agricultural University, Taian 271000, China
Interests: soil spectra and modelling; sensor fusion

Dr. Wenjun Ji

E-Mail Website
Guest Editor

College of Land Science and Technology, China Agricultural University, Beijing 100193, China
Interests: sensor-data fusion; soil spectroscopy; proximal soil sensing; digital soil mapping; sustainable agriculture
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Fast and accurate measurement of soil properties is important for precision agriculture and food safety. A variety of soil sensors have been applied in order to determine soil properties rapidly during the past several decades. Spectroscopy in particular has increased in popularity because it is rapid, timely, cost-effective, non-destructive and straightforward. With the development of sensors, more and more portable sensors has been applied alone or in combination for soil monitoring.

In this Special Issue, we are finding and collecting original manuscripts that fusing proximal soil sensors including vis-NIR, mid-IR, PXRF etc. for soil properties estimation. We are also seeking manuscripts that improve estimation accuracy based on field conditions or use machine or deep learning methods for soil estimation.

Dr. Dongyun Xu
Dr. Wenjun Ji
Guest Editors

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Keywords

proximal soil sensing
soil properties
multi-sensor fusion
spectroscopy
machine learning

Published Papers (9 papers)

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Research

17 pages, 5288 KiB

Open AccessArticle

Comparison of Depth-Specific Prediction of Soil Properties: MIR vs. Vis-NIR Spectroscopy

by Zhan Shi, Jianxin Yin, Baoguo Li, Fujun Sun, Tianyu Miao, Yan Cao, Zhou Shi, Songchao Chen, Bifeng Hu and Wenjun Ji

Sensors 2023, 23(13), 5967; https://doi.org/10.3390/s23135967 - 27 Jun 2023

Viewed by 1696

Abstract

The prediction of soil properties at different depths is an important research topic for promoting the conservation of black soils and the development of precision agriculture. Mid-infrared spectroscopy (MIR, 2500–25000 nm) has shown great potential in predicting soil properties. This study aimed to explore the ability of MIR to predict soil organic matter (OM) and total nitrogen (TN) at five different depths with the calibration from the whole depth (0–100 cm) or the shallow layers (0–40 cm) and compare its performance with visible and near-infrared spectroscopy (vis-NIR, 350–2500 nm). A total of 90 soil samples containing 450 subsamples (0–10 cm, 10–20 cm, 20–40 cm, 40–70 cm, and 70–100 cm depths) and their corresponding MIR and vis-NIR spectra were collected from a field of black soil in Northeast China. Multivariate adaptive regression splines (MARS) were used to build prediction models. The results showed that prediction models based on MIR (OM: RMSE_p = 1.07–3.82 g/kg, RPD = 1.10–5.80; TN: RMSE_p = 0.11–0.15 g/kg, RPD = 1.70–4.39) outperformed those based on vis-NIR (OM: RMSE_p = 1.75–8.95 g/kg, RPD = 0.50–3.61; TN: RMSE_p = 0.12–0.27 g/kg; RPD = 1.00–3.11) because of the higher number of characteristic bands. Prediction models based on the whole depth calibration (OM: RMSE_p = 1.09–2.97 g/kg, RPD = 2.13–5.80; TN: RMSE_p = 0.08–0.19 g/kg, RPD = 1.86–4.39) outperformed those based on the shallow layers (OM: RMSE_p = 1.07–8.95 g/kg, RPD = 0.50–3.93; TN: RMSE_p = 0.11–0.27 g/kg, RPD = 1.00–2.24) because the soil sample data of the whole depth had a larger and more representative sample size and a wider distribution. However, prediction models based on the whole depth calibration might provide lower accuracy in some shallow layers. Accordingly, it is suggested that the methods pertaining to soil property prediction based on the spectral library should be considered in future studies for an optimal approach to predicting soil properties at specific depths. This study verified the superiority of MIR for soil property prediction at specific depths and confirmed the advantage of modeling with the whole depth calibration, pointing out a possible optimal approach and providing a reference for predicting soil properties at specific depths. Full article

(This article belongs to the Special Issue Multi-Sensor Fusion for Soil Monitoring)

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18 pages, 7465 KiB

Open AccessArticle

Investigating the Shear Strength of Granitic Gneiss Residual Soil Based on Response Surface Methodology

by Hao Zou, Shu Zhang, Jinqi Zhao, Liuzhi Qin and Hao Cheng

Sensors 2023, 23(9), 4308; https://doi.org/10.3390/s23094308 - 26 Apr 2023

Viewed by 1137

Abstract

The shear strength of granitic gneiss residual soil (GGRS) determines the stability of colluvial landslides in the Huanggang area, China. It depends on several parameters that represent its structure and state as well as their interactions, and therefore requires accurate assessment. For an effective evaluation of shear strength parameters of GGRS based on these factors and their interactions, three parameters, namely, moisture content, bulk density, and fractal dimension of grain size, were selected as influencing factors in this study based on a thorough investigation of the survey data and physical property tests of landslides in the study area. The individual effects and interaction of the factors were then incorporated by implementing a series of direct shear tests employing the response surface methodology (RSM) into the regression model of the shear parameters. The results indicate that the factors affecting shear parameters in the order of greater to lower are bulk density, moisture content, and fractal dimension, and their interactions are insignificant. The proposed model was validated by applying it to soil specimens from other landslide sites with the same parent bedrock, showing the validity of the strength regression model. This study demonstrates that RSM can be applied for parameter estimation of soils and provide reliable performance, and is also significant for conducting landslide investigation, evaluation, and regional risk assessment. Full article

(This article belongs to the Special Issue Multi-Sensor Fusion for Soil Monitoring)

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22 pages, 4296 KiB

Open AccessArticle

Evaluation of Mid-Infrared and X-ray Fluorescence Data Fusion Approaches for Prediction of Soil Properties at the Field Scale

by Isabel Greenberg, Michael Vohland, Michael Seidel, Christopher Hutengs, Rachel Bezard and Bernard Ludwig

Sensors 2023, 23(2), 662; https://doi.org/10.3390/s23020662 - 06 Jan 2023

Cited by 6 | Viewed by 1842

Abstract

Previous studies investigating multi-sensor fusion for the collection of soil information have shown variable improvements, and the underlying prediction mechanisms are not sufficiently understood for spectrally-active and -inactive properties. Our objective was to study prediction mechanisms and benefits of model fusion by measuring mid-infrared (MIR) and X-ray fluorescence (XRF) spectra, texture, total and labile organic carbon (OC) and nitrogen (N) content, pH, and cation exchange capacity (CEC) for n = 117 soils from an arable field in Germany. Partial least squares regression models underwent a three-fold training/testing procedure using MIR spectra or elemental concentrations derived from XRF spectra. Additionally, two sequential hybrid and two high-level fusion approaches were tested. For the studied field, MIR was superior for organic properties (ratio of prediction to interquartile distance of validation (RPIQV) for total OC = 7.7 and N = 5.0)), while XRF was superior for inorganic properties (RPIQV for clay = 3.4, silt = 3.0, and sand = 1.8). Even the optimal fusion approach brought little to no accuracy improvement for these properties. The high XRF accuracy for clay and silt is explained by the large number of elements with variable importance in the projection scores >1 (Fe ≈ Ni > Si ≈ Al ≈ Mg > Mn ≈ K ≈ Pb (clay only) ≈ Cr) with strong spearman correlations (±0.57 < r_s < ±0.90) with clay and silt. For spectrally-inactive properties relying on indirect prediction mechanisms, the relative improvements from the optimal fusion approach compared to the best single spectrometer were marginal for pH (3.2% increase in RPIQV versus MIR alone) but more pronounced for labile OC (9.3% versus MIR) and CEC (12% versus XRF). Dominance of a suboptimal spectrometer in a fusion approach worsened performance compared to the best single spectrometer. Granger-Ramanathan averaging, which weights predictions according to accuracy in training, is therefore recommended as a robust approach to capturing the potential benefits of multiple sensors. Full article

(This article belongs to the Special Issue Multi-Sensor Fusion for Soil Monitoring)

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18 pages, 4243 KiB

Open AccessArticle

Digital Mapping of Soil Organic Carbon Based on Machine Learning and Regression Kriging

by Changda Zhu, Yuchen Wei, Fubin Zhu, Wenhao Lu, Zihan Fang, Zhaofu Li and Jianjun Pan

Sensors 2022, 22(22), 8997; https://doi.org/10.3390/s22228997 - 21 Nov 2022

Cited by 9 | Viewed by 2332

Abstract

In the last two decades, machine learning (ML) methods have been widely used in digital soil mapping (DSM), but the regression kriging (RK) model which combines the advantages of the ML and kriging methods has rarely been used in DSM. In addition, due to the limitation of a single-model structure, many ML methods have poor prediction accuracy in undulating terrain areas. In this study, we collected the SOC content of 115 soil samples in a hilly farming area with continuous undulating terrain. According to the theory of soil-forming factors in pedogenesis, we selected 10 topographic indices, 7 vegetation indices, and 2 soil indices as environmental covariates, and according to the law of geographical similarity, we used ML and RK methods to mine the relationship between SOC and environmental covariates to predict the SOC content. Four ensemble models—random forest (RF), Cubist, stochastic gradient boosting (SGB), and Bayesian regularized neural networks (BRNNs)—were used to fit the trend of SOC content, and the simple kriging (SK) method was used to interpolate the residuals of the ensemble models, and then the SOC and residual were superimposed to obtain the RK prediction result. Moreover, the 115 samples were divided into calibration and validation sets at a ratio of 80%, and the tenfold cross-validation method was used to fit the optimal parameters of the model. From the results of four ensemble models: RF performed best in the calibration set (R²_c = 0.834) but poorly in the validation set (R²_v = 0.362); Cubist had good accuracy and stability in both the calibration and validation sets (R²_c = 0.693 and R²_v = 0.445); SGB performed poorly (R²_c = 0.430 and R²_v = 0.336); and BRNN had the lowest accuracy (R²_c = 0.323 and R²_v = 0.282). The results showed that the R² of the four RK models in the validation set were 0.718, 0.674, 0.724, and 0.625, respectively. Compared with the ensemble models without superimposed residuals, the prediction accuracy was improved by 0.356, 0.229, 0.388, and 0.343, respectively. In conclusion, Cubist has high prediction accuracy and generalization ability in areas with complex topography, and the RK model can make full use of trends and spatial structural factors that are not easy to mine by ML models, which can effectively improve the prediction accuracy. This provides a reference for soil survey and digital mapping in complex terrain areas. Full article

(This article belongs to the Special Issue Multi-Sensor Fusion for Soil Monitoring)

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14 pages, 3562 KiB

Open AccessArticle

An Innovative Fusion-Based Scenario for Improving Land Crop Mapping Accuracy

by Solmaz Fathololoumi, Mohammad Karimi Firozjaei and Asim Biswas

Sensors 2022, 22(19), 7428; https://doi.org/10.3390/s22197428 - 30 Sep 2022

Cited by 4 | Viewed by 1138

Abstract

The accuracy of land crop maps obtained from satellite images depends on the type of feature selection algorithm and classifier. Each of these algorithms have different efficiency in different conditions; therefore, developing a suitable strategy for combining the capabilities of different algorithms in preparing a land crop map with higher accuracy can be very useful. The objective of this study was to develop a fusion-based framework for improving land crop mapping accuracy. First, the features were retrieved using the Sentinel 1, Sentinel 2, and Landsat-8 imagery. Then, training data and various feature selection algorithms including recursive feature elimination (RFE), random forest (RF), and Boruta were used for optimal feature selection. Various classifiers, including artificial neural network (ANN), support vector machine (SVM), and RF, were implemented to create maps of land crops relying on optimal features and training data. After that, in order to increase the result accuracy, maps of land crops derived from several scenarios were fused using a fusion-based voting strategy at the level of decision, and new maps of land crops and classification uncertainty maps were prepared. Subsequently, the performance of different scenarios was evaluated and compared. Among the feature selection algorithms, RF accuracy was higher than RFE and Boruta. Moreover, the efficiency of RF was higher than SVM and ANN. The overall accuracy of the voting scenario was higher than all other scenarios. The finding of this research demonstrated that combining the features’ capabilities extracted from sensors in different spectral ranges, different feature selection algorithms, and classifiers improved the land crop classification accuracy. Full article

(This article belongs to the Special Issue Multi-Sensor Fusion for Soil Monitoring)

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13 pages, 6722 KiB

Open AccessArticle

Estimation of Soil Organic Carbon Using Vis-NIR Spectral Data and Spectral Feature Bands Selection in Southern Xinjiang, China

by Zijin Bai, Modong Xie, Bifeng Hu, Defang Luo, Chang Wan, Jie Peng and Zhou Shi

Sensors 2022, 22(16), 6124; https://doi.org/10.3390/s22166124 - 16 Aug 2022

Cited by 21 | Viewed by 3101

Abstract

Soil organic carbon (SOC) plays an important role in the global carbon cycle and soil fertility supply. Rapid and accurate estimation of SOC content could provide critical information for crop production, soil management and soil carbon pool regulation. Many researchers have confirmed the feasibility and great potential of visible and near-infrared (Vis-NIR) spectroscopy in evaluating SOC content rapidly and accurately. Here, to evaluate the feasibility of different spectral bands variable selection methods for SOC prediction, we collected a total of 330 surface soil samples from the cotton field in the Alar Reclamation area in the southern part of Xinjiang, which is located in the arid region of northwest China. Then, we estimated the SOC content using laboratory Vis-NIR spectral. The Particle Swarm optimization (PSO), Competitive adaptive reweighted sampling (CARS) and Ant colony optimization (ACO) were adopted to select SOC feature bands. The partial least squares regression (PLSR), random forest (RF) and convolutional neural network (CNN) inversion models were constructed by using full-bands (400–2400 nm) spectra (R) and feature bands, respectively. And we also analyzed the effects of spectral feature band selection methods and modeling methods on the prediction accuracy of SOC. The results indicated that: (1) There are significant differences in the feature bands selected using different methods. The feature bands selected methods substantially reduced the spectral variable dimensionality and model complexity. The models built by the feature bands selected by CARS, PSO and ACO methods showed the different potential of improvement in model accuracy compared with the full-band models. (2) The CNN model had the best performance for predicting SOC. The R² of the optimal CNN model is 0.90 in the validation, which was improved by 0.05 and 0.04 in comparison with the PLSR and RF model, respectively. (3) The highest prediction accuracy was archived by the CNN model using the feature bands selected by CARS (validation set R² = 0.90, RMSE = 0.97 g kg⁻¹, RPD = 3.18, RPIQ = 3.11). This study indicated that using the CARS method to select spectral feature bands, combined with the CNN modeling method can well predict SOC content with higher accuracy. Full article

(This article belongs to the Special Issue Multi-Sensor Fusion for Soil Monitoring)

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11 pages, 3628 KiB

Open AccessArticle

Assessment of the Effect of Soil Sample Preparation, Water Content and Excitation Time on Proximal X-ray Fluorescence Sensing

by Shuo Li, Jiali Shen, Thomas F. A. Bishop and Raphael A. Viscarra Rossel

Sensors 2022, 22(12), 4572; https://doi.org/10.3390/s22124572 - 17 Jun 2022

Cited by 2 | Viewed by 2033

Abstract

X-ray fluorescence (XRF) spectroscopy offers a fast and efficient method for analysing soil elemental composition, both in the laboratory and the field. However, the technique is sensitive to spectral interference as well as physical and chemical matrix effects, which can reduce the precision of the measurements. We systematically assessed the XRF technique under different sample preparations, water contents, and excitation times. Four different soil samples were used as blocks in a three-way factorial experiment, with three sample preparations (natural aggregates, ground to ≤2 mm and ≤1 mm), three gravimetric water contents (air-dry, 10% and 20%), and three excitation times (15, 30 and 60 s). The XRF spectra were recorded and gave 540 spectra in all. Elemental peaks for Si, K, Ca, Ti, Fe and Cu were identified for analysis. We used analysis of variance (anova) with post hoc tests to identify significant differences between our factors and used the intensity and area of the elemental peaks as the response. Our results indicate that all of these factors significantly affect the XRF spectrum, but longer excitation times appear to be more defined. In most cases, no significant difference was found between air-dry and 10% water content. Moisture has no apparent effect on coarse samples unless ground to 1 mm. We suggested that the XRF measurements that take 60 s from dry samples or only slightly moist ones might be an optimum option under field conditions. Full article

(This article belongs to the Special Issue Multi-Sensor Fusion for Soil Monitoring)

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16 pages, 7129 KiB

Open AccessArticle

Estimation of Soil Salt Content and Organic Matter on Arable Land in the Yellow River Delta by Combining UAV Hyperspectral and Landsat-8 Multispectral Imagery

by Mingyue Sun, Qian Li, Xuzi Jiang, Tiantian Ye, Xinju Li and Beibei Niu

Sensors 2022, 22(11), 3990; https://doi.org/10.3390/s22113990 - 25 May 2022

Cited by 5 | Viewed by 2005

Abstract

Rapid and large-scale estimation of soil salt content (SSC) and organic matter (SOM) using multi-source remote sensing is of great significance for the real-time monitoring of arable land quality. In this study, we simultaneously predicted SSC and SOM on arable land in the Yellow River Delta (YRD), based on ground measurement data, unmanned aerial vehicle (UAV) hyperspectral imagery, and Landsat-8 multispectral imagery. The reflectance averaging method was used to resample UAV hyperspectra to simulate the Landsat-8 OLI data (referred to as fitted multispectra). Correlation analyses and the multiple regression method were used to construct SSC and SOM hyperspectral/fitted multispectral estimation models. Then, the best SSC and SOM fitted multispectral estimation models based on UAV images were applied to a reflectance-corrected Landsat-8 image, and SSC and SOM distributions were obtained for the YRD. The estimation results revealed that moderately salinized arable land accounted for the largest proportion of area in the YRD (48.44%), with the SOM of most arable land (60.31%) at medium or lower levels. A significant negative spatial correlation was detected between SSC and SOM in most regions. This study integrates the advantages of UAV hyperspectral and satellite multispectral data, thereby realizing rapid and accurate estimation of SSC and SOM for a large-scale area, which is of great significance for the targeted improvement of arable land in the YRD. Full article

(This article belongs to the Special Issue Multi-Sensor Fusion for Soil Monitoring)

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23 pages, 6242 KiB

Open AccessArticle

A Phase-Dependent Effect That Enables Multi-Scale Moisture Measurements in Heterogeneous Substrates Using Tubular RH Sensors

by Detlef Lazik

Sensors 2022, 22(10), 3887; https://doi.org/10.3390/s22103887 - 20 May 2022

Cited by 1 | Viewed by 1242

Abstract

A knowledge of the moisture in soils/soil litter allows for the estimation of irrigation needs or the risk of forest fire. A membrane-based humidity sensor (MHS) can measure the relative humidity (

R H

) as an average value in such heterogeneous substrates [...] Read more.

A knowledge of the moisture in soils/soil litter allows for the estimation of irrigation needs or the risk of forest fire. A membrane-based humidity sensor (MHS) can measure the relative humidity (

R H

) as an average value in such heterogeneous substrates via its sensitive tubular silicone membrane. This

R H

corresponds to the moisture-dependent water potential of the substrate. For humid conditions in soil, however, the

R H

is already larger than 98% and hence is insensitively correlated with the water potential. For such conditions, a step-like response of the MHS was found, which occurs if the silicone membrane is wetted with water. This appears to correspond to oversaturated water vapor and must be attributed to a phase-dependent sorption mechanism of the membrane. This effect allows the expansion of the range of applications of the MHS in the detection of liquid water, such as in dew point detection. Based on this, the dependency of the measurement signal on the mean water saturation in a substrate along the tubular membrane is demonstrated. A comparison of the measurement signal with an internal reference signal according to the MHS measurement principle makes it possible to distinguish this new, saturation-dependent measurement scale from the one used for

R H

measurement. Full article

(This article belongs to the Special Issue Multi-Sensor Fusion for Soil Monitoring)

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