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

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: 31 March 2020.

Special Issue Editors

Prof. Abdul M. Mouazen
E-Mail Website
Guest Editor
Group Leader Precision Soil and Crop Engineering (Precision SCoRing), Faculty of Bioscience Engineering, Ghent University, Coupure Links 653, Blok B, 1st Floor 9000 Gent, Belgium
Tel. + 32 9 264 6037; Fax: + 32 9 264 6247
Interests: proximal soil sensing; precision agriculture; soil analysis
Prof. Zhou Shi
E-Mail Website
Guest Editor
Institute of Applied Remote Sensing and Information Technology, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China
Interests: soil sensing; data fusion; soil spectroscopy; digital soil mapping; pedometrics

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. Abdul M. Mouazen
Prof. Zhou Shi
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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 1800 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 (3 papers)

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Research

Open AccessArticle
Visible and Near-Infrared Reflectance Spectroscopy Analysis of a Coastal Soil Chronosequence
Remote Sens. 2019, 11(20), 2336; https://doi.org/10.3390/rs11202336 - 09 Oct 2019
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 [...] Read more.
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
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Open AccessArticle
Prediction of Soil Organic Carbon based on Landsat 8 Monthly NDVI Data for the Jianghan Plain in Hubei Province, China
Remote Sens. 2019, 11(14), 1683; https://doi.org/10.3390/rs11141683 - 16 Jul 2019
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 [...] Read more.
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 (RMSEP) and coefficient of determination of prediction (R2P) 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 RMSEP of 3.718 and highest R2P of 0.391, followed by SVM (RMSEP = 3.753, R2P = 0.361) and PLSR (RMSEP = 4.087, R2P = 0.283). The SLR model was the worst model, with the lowest R2P of 0.281 and highest RMSEP of 3.930. ANN and SVM were better than OK (RMSEP = 3.727, R2P = 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
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Open AccessArticle
Soil Texture Estimation Using Radar and Optical Data from Sentinel-1 and Sentinel-2
Remote Sens. 2019, 11(13), 1520; https://doi.org/10.3390/rs11131520 - 27 Jun 2019
Cited by 1
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 [...] Read more.
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
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