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Estimation and Mapping of Soil Properties Based on Multi-Source Data Fusion

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Agriculture and Vegetation".

Deadline for manuscript submissions: closed (30 September 2020) | Viewed by 63226

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Special Issue Editors

Prof. Dr. Abdul M. Mouazen

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Guest Editor

Precision Soil and Crop Engineering (Precision Scoring), Faculty of Bioscience Engineering, Ghent University, Coupure Links 653, Blok B, 1st Floor, 9000 Gent, Belgium
Interests: proximal soil sensing; soil and water management; soil dynamics; tillage; traction; compaction; mechanical weeding; soil remediation and management and precision agriculture
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Zhou Shi

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Guest Editor

College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China
Interests: soil sensing; data fusion; soil spectroscopy; digital soil mapping; optical remote sensing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Recent advances in remote and proximal sensing technologies are valuable for enriching our geo-datasets of soil properties, which are necessary for soil management and the precision application of farming input resources. This is because of the advantages of these modern technologies that provide a high sampling density enabling the exploration of the spatial variability of soil characteristics with high resolution and are fast and cost-effective compared to the traditional laboratory analysis methods. However, soils are complex, and measurements of key soil properties or processes in soils might not be achievable by the use of a single sensor. This necessitates new approaches that present innovative solutions beyond the single-sensor approach and can be implemented in situ in either stationary or on-line measurement modes. In the last few years, several studies on multi-sensor and data fusion approaches have been reported in the literature, although this research area is still at its early stages of development. The integration of different data—multi-source data fusion—has greatly benefited many applications that require more extensive temporal and spatial information than that contained in any individual dataset provided by a single sensor. At the same time, the major progresses that have been made in different aspects of digital soil mapping (DSM) make DSM increasingly mature and operable than ever before. The integration of multi-sensor source data fusion with the DSM technique will provide a better understanding of soil processes and enable a more accurate estimation of soil properties at various spatial and temporal scales. It will also provide new insights into processes occurring in soils and sources of variabilities linked to soil dynamics in different scenarios of land management practices, environmental pollution, and climate change.

In this Special Issue, we are seeking original scientific contributions on new methods for the estimation and mapping of biological, physical, and chemical soil properties based on multi-source spatio-temporal data fusion techniques. The Special Issue is open to all scientists working in related fields, and submissions relevant to the topics listed below are welcome:

Proximal soil sensing for the measurement and spatial modelling of soil properties (e.g., fertility, physical, chemical, contaminants)
Remote sensing for the measurement and spatial modelling of soil properties (e.g., fertility, physical, chemical, contaminants)
Modelling approaches for deriving new indices to estimate soil properties and/or soil processes
The potential of multi-sensor techniques for deriving information on soils including decision-support tools
Data-fusion approaches applied to proximal and remote sensing of soils
Estimating and mapping soil-related yield limiting factors, including yield prediction
The use of proximal and remote sensing in precision agriculture
Measurement and mapping of soil contaminations including heavy metals and hydrocarbon contaminations.

Prof. Dr. Abdul M. Mouazen
Prof. Dr. Zhou Shi
Guest Editors

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

Proximal soil sensing
Remote sensing
Soil property
Sensor fusion
Data fusion
Digital soil mapping

Published Papers (13 papers)

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Editorial

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4 pages, 170 KiB

Open AccessEditorial

Estimation and Mapping of Soil Properties Based on Multi-Source Data Fusion

by Abdul Mounem Mouazen and Zhou Shi

Remote Sens. 2021, 13(5), 978; https://doi.org/10.3390/rs13050978 - 04 Mar 2021

Cited by 9 | Viewed by 2365

Abstract

Recent advances in remote and proximal sensing technologies provide a valuable source of information for enriching our geo-datasets, which are necessary for soil management and the precision application of farming input resources [...] Full article

(This article belongs to the Special Issue Estimation and Mapping of Soil Properties Based on Multi-Source Data Fusion)

Research

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25 pages, 4331 KiB

Open AccessArticle

Soil Moisture Mapping Based on Multi-Source Fusion of Optical, Near-Infrared, Thermal Infrared, and Digital Elevation Model Data via the Bayesian Maximum Entropy Framework

by Leran Han, Chunmei Wang, Qiyue Liu, Gengke Wang, Tao Yu, Xingfa Gu and Yunzhou Zhang

Remote Sens. 2020, 12(23), 3916; https://doi.org/10.3390/rs12233916 - 29 Nov 2020

Cited by 14 | Viewed by 2513

Abstract

This paper proposes a combined approach wherein the optical, near-infrared, and thermal infrared data from the Landsat 8 satellite and the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) global digital elevation model (GDEM) data are fused for soil moisture mapping under sparse sampling conditions, based on the Bayesian maximum entropy (BME) framework. The study was conducted in three stages. First, based on the maximum entropy principle of the information theory, a Lagrange multiplier was introduced to construct general knowledge, representing prior knowledge. Second, a principal component analysis (PCA) was conducted to extract three principal components from the multi-source data mentioned above, and an innovative and operable discrete probability method based on a fuzzy probability matrix was used to approximate the probability relationship. Thereafter, soft data were generated on the basis of the weight coefficients and coordinates of the soft data points. Finally, by combining the general knowledge with the prior information, hard data (HD), and soft data (SD), we completed the soil moisture mapping based on the Bayesian conditioning rule. To verify the feasibility of the combined approach, the ordinary kriging (OK) method was taken as a comparison. The results confirmed the superiority of the soil moisture map obtained using the BME framework. The map revealed more detailed information, and the accuracies of the quantitative indicators were higher compared with that for the OK method (the root mean squared error (RMSE) = 0.0423 cm³/cm³, mean absolute error (MAE) = 0.0399 cm³/cm³, and Pearson correlation coefficient (PCC) = 0.7846), while largely overcoming the overestimation issue in the range of low values and the underestimation issue in the range of high values. The proposed approach effectively fused inexpensive and easily available multi-source data with uncertainties and obtained a satisfactory mapping accuracy, thus demonstrating the potential of the BME framework for soil moisture mapping using multi-source data. Full article

(This article belongs to the Special Issue Estimation and Mapping of Soil Properties Based on Multi-Source Data Fusion)

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26 pages, 4556 KiB

Open AccessArticle

Improved Mapping of Potentially Toxic Elements in Soil via Integration of Multiple Data Sources and Various Geostatistical Methods

by Fang Xia, Bifeng Hu, Youwei Zhu, Wenjun Ji, Songchao Chen, Dongyun Xu and Zhou Shi

Remote Sens. 2020, 12(22), 3775; https://doi.org/10.3390/rs12223775 - 17 Nov 2020

Cited by 16 | Viewed by 3154

Abstract

Soil pollution by potentially toxic elements (PTEs) has become a core issue around the world. Knowledge of the spatial distribution of PTEs in soil is crucial for soil remediation. Portable X-ray fluorescence spectroscopy (p-XRF) provides a cost-saving alternative to the traditional laboratory analysis of soil PTEs. In this study, we collected 293 soil samples from Fuyang County in Southeast China. Subsequently, we used several geostatistical methods, such as inverse distance weighting (IDW), ordinary kriging (OK), and empirical Bayesian kriging (EBK), to estimate the spatial variability of soil PTEs measured by the laboratory and p-XRF methods. The final maps of soil PTEs were outputted by the model averaging method, which combines multiple maps previously created by IDW, OK, and EBK, using both lab and p-XRF data. The study results revealed that the mean PTE content measured by the laboratory methods was as follows: Zn (127.43 mg kg⁻¹) > Cu (31.34 mg kg⁻¹) > Ni (20.79 mg kg⁻¹) > As (10.65 mg kg⁻¹) > Cd (0.33 mg kg⁻¹). p-XRF measurements showed a spatial prediction accuracy of soil PTEs similar to that of laboratory analysis measurements. The spatial prediction accuracy of different PTEs outputted by the model averaging method was as follows: Zn (R² = 0.71) > Cd (R² = 0.68) > Ni (R² = 0.67) > Cu (R² = 0.62) > As (R² = 0.50). The prediction accuracy of the model averaging method for five PTEs studied herein was improved compared with that of the laboratory and p-XRF methods, which utilized individual geostatistical methods (e.g., IDW, OK, EBK). Our results proved that p-XRF was a reliable alternative to the traditional laboratory analysis methods for mapping soil PTEs. The model averaging approach improved the prediction accuracy of the soil PTE spatial distribution and reduced the time and cost of monitoring and mapping PTE soil contamination. Full article

(This article belongs to the Special Issue Estimation and Mapping of Soil Properties Based on Multi-Source Data Fusion)

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22 pages, 32595 KiB

Open AccessArticle

Utilization of Multi-Temporal Microwave Remote Sensing Data within a Geostatistical Regionalization Approach for the Derivation of Soil Texture

by Philip Marzahn and Swen Meyer

Remote Sens. 2020, 12(16), 2660; https://doi.org/10.3390/rs12162660 - 18 Aug 2020

Cited by 9 | Viewed by 3091

Abstract

Land Surface Models (LSM) have become indispensable tools to quantify water and nutrient fluxes in support of land management strategies or the prediction of climate change impacts. However, the utilization of LSM requires soil and vegetation parameters, which are seldom available in high spatial distribution or in an appropriate temporal frequency. As shown in recent studies, the quality of these model input parameters, especially the spatial heterogeneity and temporal variability of soil parameters, has a strong effect on LSM simulations. This paper assesses the potential of microwave remote sensing data for retrieving soil physical properties such as soil texture. Microwave remote sensing is able to penetrate in an imaged media (soil, vegetation), thus being capable of retrieving information beneath such a surface. In this study, airborne remote sensing data acquired at 1.3 GHz and in different polarization is utilized in conjunction with geostatistics to retrieve information about soil texture. The developed approach is validated with in-situ data from different field campaigns carried out over the TERENO test-site “North-Eastern German Lowland Observatorium”. With the proposed approach a high accuracy of the retrieved soil texture with a mean RMSE of 2.42 (Mass-%) could be achieved outperforming classical deterministic and geostatistical approaches. Full article

(This article belongs to the Special Issue Estimation and Mapping of Soil Properties Based on Multi-Source Data Fusion)

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21 pages, 13370 KiB

Open AccessArticle

Mapping of Peat Thickness Using a Multi-Receiver Electromagnetic Induction Instrument

by Amélie Beucher, Triven Koganti, Bo V. Iversen and Mogens H. Greve

Remote Sens. 2020, 12(15), 2458; https://doi.org/10.3390/rs12152458 - 31 Jul 2020

Cited by 16 | Viewed by 5259

Abstract

Peatlands constitute extremely valuable areas because of their ability to store large amounts of soil organic carbon (SOC). Investigating different key peat soil properties, such as the extent, thickness (or depth to mineral soil) and bulk density, is highly relevant for the precise calculation of the amount of stored SOC at the field scale. However, conventional peat coring surveys are both labor-intensive and time-consuming, and indirect mapping methods based on proximal sensors appear as a powerful supplement to traditional surveys. The aim of the present study was to assess the use of a non-invasive electromagnetic induction (EMI) technique as an augmentation to a traditional peat coring survey that provides localized and discrete measurements. In particular, a DUALEM-421S instrument was used to measure the apparent electrical conductivity (EC_a) over a 10-ha field located in Jutland, Denmark. In the study area, the peat thickness varied notably from north to south, with a range from 3 to 730 cm. Simple and multiple linear regressions with soil observations from 110 sites were used to predict peat thickness from (a) raw EC_a measurements (i.e., single and multiple-coil predictions), (b) true electrical conductivity (

σ

) estimates calculated using a quasi-three-dimensional inversion algorithm and (c) different combinations of EC_a data with environmental covariates (i.e., light detection and ranging (LiDAR)-based elevation and derived terrain attributes). The results indicated that raw EC_a data can already constitute relevant predictors for peat thickness in the study area, with single-coil predictions yielding substantial accuracies with coefficients of determination (R²) ranging from 0.63 to 0.86 and root mean square error (RMSE) values between 74 and 122 cm, depending on the measuring DUALEM-421S coil configuration. While the combinations of EC_a data (both single and multiple-coil) with elevation generally provided slightly higher accuracies, the uncertainty estimates for single-coil predictions were smaller (i.e., smaller 95% confidence intervals). The present study demonstrates a high potential for EMI data to be used for peat thickness mapping. Full article

(This article belongs to the Special Issue Estimation and Mapping of Soil Properties Based on Multi-Source Data Fusion)

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19 pages, 14777 KiB

Open AccessArticle

The Generation of Soil Spectral Dynamic Feedback Using Landsat 8 Data for Digital Soil Mapping

by Canying Zeng, Lin Yang and A-Xing Zhu

Remote Sens. 2020, 12(10), 1691; https://doi.org/10.3390/rs12101691 - 25 May 2020

Cited by 6 | Viewed by 2823

Abstract

The soil spectral dynamic feedback captured from high temporal resolution remote sensing data, such as MODIS, during the soil drying process after a rainfall could assist with digital soil mapping. However, this method is ineffective in utilizing the images with a relatively high spatial resolution. There are an insufficient number of images in the soil drying process since those high spatial resolution images tend to have a low temporal resolution. This study is aimed at generating soil spectral dynamic feedback by integrating the feedback captured from the images with a high spatial resolution during the process of multiple drying after different rainfall events. The Landsat 8 data with a temporal resolution of 16 day was exemplified. Each single spectral feedback obtained from Landsat 8 was first adjusted to eliminate the impact of different rainfall magnitudes. Then, the soil spectral dynamic feedback was reorganized and generated based on the adjusted feedback. Finally, the soil spectral dynamic feedback generated based on Landsat 8 was used for mapping topsoil texture and compared with the mapping results based on the MODIS data and the fusion data of MODIS and Landsat 8. As revealed by the results, not only could the generated soil spectral dynamic feedback based on Landsat 8 data improve the details of the spatial distribution of soil texture, but it also enhances the accuracy of mapping. The mapping accuracy based on Landsat 8 data is higher than that based on the MODIS data and fusion data. The improvements of accuracy are more obvious in the areas with more complex surface conditions. This study widens the scope of application for soil spectral dynamic feedback and provides support for large-scale and high-precision digital soil mapping. Full article

(This article belongs to the Special Issue Estimation and Mapping of Soil Properties Based on Multi-Source Data Fusion)

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15 pages, 5858 KiB

Open AccessArticle

Rapid Determination of Soil Class Based on Visible-Near Infrared, Mid-Infrared Spectroscopy and Data Fusion

by Hanyi Xu, Dongyun Xu, Songchao Chen, Wanzhu Ma and Zhou Shi

Remote Sens. 2020, 12(9), 1512; https://doi.org/10.3390/rs12091512 - 09 May 2020

Cited by 25 | Viewed by 4075

Abstract

Wise soil management requires detailed soil information, but conventional soil class mapping in a rather coarse spatial resolution cannot meet the demand for precision agriculture. With the advantages of non-destructiveness, rapid cost-efficiency, and labor savings, the spectroscopic technique has proved its high potential for success in soil classification. Previous studies mainly focused on predicting soil classes using a single sensor. In this study, we attempted to compare the predictive ability of visible near infrared (vis-NIR) spectra, mid-infrared (MIR) spectra, and their fused spectra for soil classification. A total of 146 soil profiles were collected from Zhejiang, China, and the soil properties and spectra were measured by their genetic horizons. Along with easy-to-measure auxiliary soil information (soil organic matter, soil texture, color and pH), four spectral data, including vis-NIR, MIR, their simple combination (vis-NIR-MIR), and outer product analysis (OPA) fused spectra, were used for soil classification using a multiple objectives mixed support vector machine model. The independent validation results showed that the classification model using MIR (accuracy of 64.5%) was slightly better than that using vis-NIR (accuracy of 64.2%). The predictive model built on vis-NIR-MIR did not improve the classification accuracy, having the lowest accuracy of 61.1%, which likely resulted from an over-fitting problem. The model based on OPA fused spectra performed best with an accuracy of 68.4%. Our results prove the potential of fusing vis-NIR and MIR using OPA for improving prediction ability for soil classification. Full article

(This article belongs to the Special Issue Estimation and Mapping of Soil Properties Based on Multi-Source Data Fusion)

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21 pages, 3446 KiB

Open AccessArticle

Effect of X-Ray Tube Configuration on Measurement of Key Soil Fertility Attributes with XRF

by Tiago Rodrigues Tavares, José Paulo Molin, Lidiane Cristina Nunes, Elton Eduardo Novais Alves, Fábio L. Melquiades, Hudson Wallace Pereira de Carvalho and Abdul Mounem Mouazen

Remote Sens. 2020, 12(6), 963; https://doi.org/10.3390/rs12060963 - 17 Mar 2020

Cited by 35 | Viewed by 4827

Abstract

The successful use of energy-dispersive X-ray fluorescence (ED-XRF) sensors for soil analysis requires the selection of an optimal procedure of data acquisition and a simple modelling approach. This work aimed at assessing the performance of a portable XRF (XRF) sensor set up with two different X-ray tube configurations (combinations of voltage and current) to predict nine key soil fertility attributes: (clay, organic matter (OM), cation exchange capacity (CEC), pH, base saturation (V), and extractable nutrients (P, K, Ca, and Mg). An XRF, operated at a voltage of 15 kV (and current of 23 μA) and 35 kV (and current of 7 μA), was used for analyzing 102 soil samples collected from two agricultural fields in Brazil. Two different XRF data analysis scenarios were used to build the predictive models: (i) 10 emission lines of 15 keV spectra (EL-15), and (ii) 12 emission lines of 35 keV spectra (EL-35). Multiple linear regressions (MLR) were used for model calibration, and the models’ prediction performance was evaluated using different figures of merit. The results show that although X-ray tube configuration affected the intensity of the emission lines of the different elements detected, it did not influence the prediction accuracy of the studied key fertility attributes, suggesting that both X-ray tube configurations tested can be used for future analyses. Satisfactory predictions with residual prediction deviation (RPD) ≥ 1.54 and coefficient of determination (R²) ≥ 0.61 were obtained for eight out of the ten studied soil fertility attributes (clay, OM, CEC, V, and extractable K, Ca, and Mg). In addition, simple MLR models with a limited number of emission lines was effective for practical soil analysis of the key soil fertility attributes (except pH and extractable P) using XRF. The simple and transparent methodology suggested also enables future researches that seek to optimize the XRF scanning time in order to speed up the XRF analysis in soil samples. Full article

(This article belongs to the Special Issue Estimation and Mapping of Soil Properties Based on Multi-Source Data Fusion)

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21 pages, 5308 KiB

Open AccessArticle

Estimation of Secondary Soil Properties by Fusion of Laboratory and On-Line Measured Vis–NIR Spectra

by Muhammad Abdul Munnaf, Said Nawar and Abdul Mounem Mouazen

Remote Sens. 2019, 11(23), 2819; https://doi.org/10.3390/rs11232819 - 28 Nov 2019

Cited by 37 | Viewed by 4371

Abstract

Visible and near infrared (vis–NIR) diffuse reflectance spectroscopy has made invaluable contributions to the accurate estimation of soil properties having direct and indirect spectral responses in NIR spectroscopy with measurements made in laboratory, in situ or using on-line (while the sensor is moving) platforms. Measurement accuracies vary with measurement type, for example, accuracy is higher for laboratory than on-line modes. On-line measurement accuracy deteriorates further for secondary (having indirect spectral response) soil properties. Therefore, the aim of this study is to improve on-line measurement accuracy of secondary properties by fusion of laboratory and on-line scanned spectra. Six arable fields were scanned using an on-line sensing platform coupled with a vis–NIR spectrophotometer (CompactSpec by Tec5 Technology for spectroscopy, Germany), with a spectral range of 305–1700 nm. A total of 138 soil samples were collected and used to develop five calibration models: (i) standard, using 100 laboratory scanned samples; (ii) hybrid-1, using 75 laboratory and 25 on-line samples; (iii) hybrid-2, using 50 laboratory and 50 on-line samples; (iv) hybrid-3, using 25 laboratory and 75 on-line samples, and (v) real-time using 100 on-line samples. Partial least squares regression (PLSR) models were developed for soil pH, available potassium (K), magnesium (Mg), calcium (Ca), and sodium (Na) and quality of models were validated using an independent prediction dataset (38 samples). Validation results showed that the standard models with laboratory scanned spectra provided poor to moderate accuracy for on-line prediction, and the hybrid-3 and real-time models provided the best prediction results, although hybrid-2 model with 50% on-line spectra provided equally good results for all properties except for pH and Na. These results suggest that either the real-time model with exclusively on-line spectra or the hybrid model with fusion up to 50% (except for pH and Na) and 75% on-line scanned spectra allows significant improvement of on-line prediction accuracy for secondary soil properties using vis–NIR spectroscopy. Full article

(This article belongs to the Special Issue Estimation and Mapping of Soil Properties Based on Multi-Source Data Fusion)

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15 pages, 4209 KiB

Open AccessArticle

A Comprehensive Study of Three Different Portable XRF Scanners to Assess the Soil Geochemistry of An Extensive Sample Dataset

by Ynse Declercq, Nele Delbecque, Johan De Grave, Philippe De Smedt, Peter Finke, Abdul M. Mouazen, Said Nawar, Dimitri Vandenberghe, Marc Van Meirvenne and Ann Verdoodt

Remote Sens. 2019, 11(21), 2490; https://doi.org/10.3390/rs11212490 - 24 Oct 2019

Cited by 27 | Viewed by 4503

Abstract

The assessment of soil elemental concentrations nowadays mainly occurs through conventional laboratory analyses. However, proximal soil sensing (PSS) techniques such as X-ray fluorescence (XRF) spectrometry are proving to reduce analysis time and costs, and thus offer a worthy alternative to laboratory analyses. Moreover, XRF scanners are non-destructive and can be directly employed in the field. Although the use of XRF for soil elemental analysis is becoming widely accepted, most previous studies were limited to one scanner, a few samples, a few elements, or a non-diverse sample database. Here, an extensive and diverse soil database was used to compare the performance of three different XRF scanners with results obtained through conventional laboratory analyses. Scanners were used in benchtop mode with built-in soil calibrations to measure the concentrations of 15 elements. Although in many samples Cu, S, P, and Mg concentrations were up to 6, 12, 13, and 5 times overestimated by XRF, and empirical recalibration is recommended, all scanners produced acceptable results, even for lighter elements. Unexpectedly, XRF performance did not seem to depend on soil characteristics such as CaCO3 content. While performances will be worse when expanding to the field, our results show that XRF can easily be applied by non-experts to measure soil elemental concentrations reliably in widely different environments. Full article

(This article belongs to the Special Issue Estimation and Mapping of Soil Properties Based on Multi-Source Data Fusion)

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19 pages, 7957 KiB

Open AccessArticle

Visible and Near-Infrared Reflectance Spectroscopy Analysis of a Coastal Soil Chronosequence

by Guanghui Zheng, Dongryeol Ryu, Caixia Jiao, Xianli Xie, Xuefeng Cui and Gang Shang

Remote Sens. 2019, 11(20), 2336; https://doi.org/10.3390/rs11202336 - 09 Oct 2019

Cited by 13 | Viewed by 3622

Abstract

The soil chronosequence is a useful method for investigating pedological theories. Soil chemical, physical and mineralogical properties in chronosequences change over time and exhibit systematic and time-dependent trends, which can be used to analyze the rates and directions of pedogenic changes. The potential of soil spectroscopy as an emerging, rapid and cost-effective technique for predicting soil properties has been widely accepted and has motivated the application of spectroscopic techniques to the analysis of soil chronosequence. We present a soil chronosequence derived from 1000-year-old calcareous marine sediments and examine changes in six soil properties over this period. We evaluated the utility of a soil spectroscopic method to detect soil property changes and to predict the pedogenic properties and soil ages of the chronosequence. The results show that some soil pedogenic processes, such as soil organic matter accumulation, CaCO₃ leaching and clay migration, can be identified in the millennium chronosequence. Power chronofunctions are formulated for soil organic matter (SOM) and Logarithmic chronofunctions are fitted for clay, CaCO₃ and pH. These pedogenic processes are identified in the reflectance intensity and absorption features of soil spectroscopy, and pedogenic properties can be calibrated via soil reflectance spectroscopy. Profile ages can also be predicted via pseudo multi-depth spectra of soil profiles, and soil spectral curves for 0–30 cm generated the best prediction results (RPD = 1.85). We conclude that soil properties, changing due to weathering and soil formation, act as a bridge linking spectroscopy and weathering levels/pedogenic processes. The results imply that applying spectroscopy techniques to chronosequence study and mapping the degree of soil development in certain areas should be possible. Full article

(This article belongs to the Special Issue Estimation and Mapping of Soil Properties Based on Multi-Source Data Fusion)

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26 pages, 6493 KiB

Open AccessArticle

Prediction of Soil Organic Carbon based on Landsat 8 Monthly NDVI Data for the Jianghan Plain in Hubei Province, China

by Yangchengsi Zhang, Long Guo, Yiyun Chen, Tiezhu Shi, Mei Luo, QingLan Ju, Haitao Zhang and Shanqin Wang

Remote Sens. 2019, 11(14), 1683; https://doi.org/10.3390/rs11141683 - 16 Jul 2019

Cited by 76 | Viewed by 8871

Abstract

High-precision maps of soil organic carbon (SOC) are beneficial for managing soil fertility and understanding the global carbon cycle. Digital soil mapping plays an important role in efficiently obtaining the spatial distribution of SOC, which contributes to precision agriculture. However, traditional soil-forming factors (i.e., terrain or climatic factors) have weak variability in low-relief areas, such as plains, and cannot reflect the spatial variation of soil attributes. Meanwhile, vegetation cover hinders the acquisition of the direct information of farmland soil. Thus, useful environmental variables should be utilized for SOC prediction and the digital mapping of such areas. SOC has an important effect on crop growth status, and remote sensing data can record the apparent spectral characteristics of crops. The normalized difference vegetation index (NDVI) is an important index reflecting crop growth and biomass. This study used NDVI time series data rather than traditional soil-forming factors to map SOC. Honghu City, located in the middle of the Jianghan Plain, was selected as the study region, and the NDVI time series data extracted from Landsat 8 were used as the auxiliary variables. SOC maps were estimated through stepwise linear regression (SLR), partial least squares regression (PLSR), support vector machine (SVM), and artificial neural network (ANN). Ordinary kriging (OK) was used as the reference model, while root mean square error of prediction (RMSE_P) and coefficient of determination of prediction (R²_P) were used to evaluate the model performance. Results showed that SOC had a significant positive correlation in July and August (0.17, 0.29) and a significant negative correlation in January, April, and December (−0.23, −0.27, and −0.23) with NDVI time series data. The best model for SOC prediction was generated by ANN, with the lowest RMSE_P of 3.718 and highest R²_P of 0.391, followed by SVM (RMSE_P = 3.753, R²_P = 0.361) and PLSR (RMSE_P = 4.087, R²_P = 0.283). The SLR model was the worst model, with the lowest R²_P of 0.281 and highest RMSE_P of 3.930. ANN and SVM were better than OK (RMSE_P = 3.727, R²_P = 0.372), whereas PLSR and SLR were worse than OK. Moreover, the prediction results using single-data NDVI or short time series NDVI showed low accuracy. The effect of the terrain factor on SOC prediction represented unsatisfactory results. All these results indicated that the NDVI time series data can be used for SOC mapping in plain areas and that the ANN model can maximally extract additional associated information between NDVI time series data and SOC. This study presented an effective method to overcome the selection of auxiliary variables for digital soil mapping in plain areas when the soil was covered with vegetation. This finding indicated that the time series characteristics of NDVI were conducive for predicting SOC in plains. Full article

(This article belongs to the Special Issue Estimation and Mapping of Soil Properties Based on Multi-Source Data Fusion)

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20 pages, 5443 KiB

Open AccessArticle

Soil Texture Estimation Using Radar and Optical Data from Sentinel-1 and Sentinel-2

by Safa Bousbih, Mehrez Zribi, Charlotte Pelletier, Azza Gorrab, Zohra Lili-Chabaane, Nicolas Baghdadi, Nadhira Ben Aissa and Bernard Mougenot

Remote Sens. 2019, 11(13), 1520; https://doi.org/10.3390/rs11131520 - 27 Jun 2019

Cited by 72 | Viewed by 12215

Abstract

This paper discusses the combined use of remotely sensed optical and radar data for the estimation and mapping of soil texture. The study is based on Sentinel-1 (S-1) and Sentinel-2 (S-2) data acquired between July and early December 2017, on a semi-arid area about 3000 km² in central Tunisia. In addition to satellite acquisitions, texture measurement samples were taken in several agricultural fields, characterized by a large range of clay contents (between 13% and 60%). For the period between July and August, various optical indicators of clay content Short-Wave Infrared (SWIR) bands and soil indices) were tested over bare soils. Satellite moisture products, derived from combined S-1 and S-2 data, were also tested as an indicator of soil texture. Algorithms based on the support vector machine (SVM) and random forest (RF) methods are proposed for the classification and mapping of clay content and a three-fold cross-validation is used to evaluate both approaches. The classifications with the best performance are achieved using the soil moisture indicator derived from combined S-1 and S-2 data, with overall accuracy (OA) of 63% and 65% for the SVM and RF classifications, respectively. Full article

(This article belongs to the Special Issue Estimation and Mapping of Soil Properties Based on Multi-Source Data Fusion)

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