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Soil Sensing and Mapping for a Sustainable Future
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Soil Sensing and Mapping for a Sustainable Future

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

Deadline for manuscript submissions: 15 August 2024 | Viewed by 4376

Special Issue Editors

Prof. Dr. Zhou Shi

E-Mail Website
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
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
Dr. Songchao Chen

E-Mail Website
Guest Editor
1. ZJU-Hangzhou Global Scientific and Technological Innovation Center, Hangzhou 311200, China
2. Institute of Agricultural Remote Sensing and Information Technology Application, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China
Interests: pedometrics; digital soil mapping; proximal soil sensing; soil spectroscopy; spatial predictive modelling; soil biogeochemical modelling
Special Issues, Collections and Topics in MDPI journals
Dr. Yongsheng Hong

E-Mail Website1 Website2
Guest Editor
State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, China
Interests: remote sensing; digital soil mapping; pedometrics; biogeochemical modeling
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The accurate mapping and monitoring of soil information is of great importance for decision making in soil management, precision farming, and food security to achieve relevant sustainable development goals. The increasingly available remote (e.g., optical remote sensing, passive microwave, and radar) and proximal sensing (e.g., visible-near-infrared, mid-infrared, portable X-ray fluorescence, and laser-induced breakdown spectroscopy) data offer a valuable basis for monitoring and updating soil information. In the framework of digital soil mapping, these data can be combined with other environmental covariates (e.g., topography, climate, parent material, vegetation, human activity) to map the soil properties with high spatial resolution (from 10 to 90 m) at scales ranging from fields to landscapes, regions, countries, continents, and the globe.

In this Special Issue, seek original scientific contributions on new methods for the estimation and mapping of biological, physical, and chemical soil properties based on multisource spatial–temporal data. Potential topics include but are not limited to:

  • Visible-near-infrared spectroscopy;
  • Mid-infrared spectroscopy;
  • Proximal, airborne, and satellite remote sensing;
  • Sensor-data fusion;
  • Space–time modeling;
  • Measuring and mapping of soil quality or threats;
  • Quality assessment of the digital soil-mapping products;
  • Novel methods and models for evaluating soil quality and health potential;
  • Applications of data mining in soil-related ecosystem monitoring and soil management.

Prof. Dr. Zhou Shi
Dr. Wenjun Ji
Dr. Songchao Chen
Dr. Yongsheng Hong
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 submissions that pass pre-check are 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. Sensors 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 2600 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

  • soil monitoring
  • digital soil mapping
  • remote sensing
  • proximal soil sensing
  • spatial modeling
  • machine/deep learning
  • sustainable management
  • climate change

Published Papers (5 papers)

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Research

24 pages, 6800 KiB  
Article
Spectral Data Processing for Field-Scale Soil Organic Carbon Monitoring
by Javier Reyes and Mareike Ließ
Sensors 2024, 24(3), 849; https://doi.org/10.3390/s24030849 - 28 Jan 2024
Viewed by 727
Abstract
Carbon sequestration in soils under agricultural use can contribute to climate change mitigation. Spatial–temporal soil organic carbon (SOC) monitoring requires more efficient data acquisition. This study aims to evaluate the potential of spectral on-the-go proximal measurements to serve these needs. The study was [...] Read more.
Carbon sequestration in soils under agricultural use can contribute to climate change mitigation. Spatial–temporal soil organic carbon (SOC) monitoring requires more efficient data acquisition. This study aims to evaluate the potential of spectral on-the-go proximal measurements to serve these needs. The study was conducted as a long-term field experiment. SOC values ranged between 14 and 25 g kg−1 due to different fertilization treatments. Partial least squares regression models were built based on the spectral laboratory and field data collected with two spectrometers (site-specific and on-the-go). Correction of the field data based on the laboratory data was done by testing linear transformation, piecewise direct standardization, and external parameter orthogonalization (EPO). Different preprocessing methods were applied to extract the best possible information content from the sensor signal. The models were then thoroughly interpreted concerning spectral wavelength importance using regression coefficients and variable importance in projection scores. The detailed wavelength importance analysis disclosed the challenge of using soil spectroscopy for SOC monitoring. The use of different spectrometers under varying soil conditions revealed shifts in wavelength importance. Still, our findings on the use of on-the-go spectroscopy for spatial–temporal SOC monitoring are promising. Full article
(This article belongs to the Special Issue Soil Sensing and Mapping for a Sustainable Future)
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23 pages, 4799 KiB  
Article
Basin Scale Soil Moisture Estimation with Grid SWAT and LESTKF Based on WSN
by Ying Zhang, Jinliang Hou and Chunlin Huang
Sensors 2024, 24(1), 35; https://doi.org/10.3390/s24010035 - 20 Dec 2023
Cited by 2 | Viewed by 780
Abstract
This research utilized in situ soil moisture observations in a coupled grid Soil and Water Assessment Tool (SWAT) and Parallel Data Assimilation Framework (PDAF) data assimilation system, resulting in significant enhancements in soil moisture estimation. By incorporating Wireless Sensor Network (WSN) data (WATERNET), [...] Read more.
This research utilized in situ soil moisture observations in a coupled grid Soil and Water Assessment Tool (SWAT) and Parallel Data Assimilation Framework (PDAF) data assimilation system, resulting in significant enhancements in soil moisture estimation. By incorporating Wireless Sensor Network (WSN) data (WATERNET), the method captured and integrated local soil moisture characteristics, thereby improving regional model state estimations. The use of varying observation search radii with the Local Error-subspace Transform Kalman Filter (LESTKF) resulted in improved spatial and temporal assimilation performance, while also considering the impact of observation data uncertainties. The best performance (improvement of 0.006 m3/m3) of LESTKF was achieved with a 20 km observation search radii and 0.01 m3/m3 observation standard error. This study assimilated wireless sensor network data into a distributed model, presenting a departure from traditional methods. The high accuracy and resolution capabilities of WATERNET’s regional soil moisture observations were crucial, and its provision of multi-layered soil temperature and moisture observations presented new opportunities for integration into the data assimilation framework, further enhancing hydrological state estimations. This study’s implications are broad and relevant to regional-scale water resource research and management, particularly for freshwater resource scheduling at small basin scales. Full article
(This article belongs to the Special Issue Soil Sensing and Mapping for a Sustainable Future)
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19 pages, 2923 KiB  
Article
Soil Organic Matter Estimation Model Integrating Spectral and Profile Features
by Shaofang He, Siqiao Tan, Luming Shen and Qing Zhou
Sensors 2023, 23(24), 9868; https://doi.org/10.3390/s23249868 - 16 Dec 2023
Cited by 1 | Viewed by 649
Abstract
The accurate measurement of soil organic matter (SOM) is vital for maintaining soil quality. We present an innovative model for SOM prediction by integrating spectral and profile features. We use PCA, Lasso, and SCARS methods to extract important spectral features and combine them [...] Read more.
The accurate measurement of soil organic matter (SOM) is vital for maintaining soil quality. We present an innovative model for SOM prediction by integrating spectral and profile features. We use PCA, Lasso, and SCARS methods to extract important spectral features and combine them with profile data. This hybrid approach significantly improves SOM prediction across various models, including Random Forest, ExtraTrees, and XGBoost, boosting the coefficient of determination (R2) by up to 26%. Notably, the ExtraTrees model, enriched with PCA-extracted features, achieves the highest accuracy with an R2 of 0.931 and an RMSE of 0.068. Compared with single-feature models, this approach improves the R2 by 17% and 26% for PCA features of full-band spectra and profile features, respectively. Our findings highlight the potential of feature integration, especially the ExtraTrees model with PCA-extracted features and profile features, as a stable and accurate tool for SOM prediction in extensive study areas. Full article
(This article belongs to the Special Issue Soil Sensing and Mapping for a Sustainable Future)
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15 pages, 4425 KiB  
Article
NIR Spectral Inversion of Soil Physicochemical Properties in Tea Plantations under Different Particle Size States
by Qinghai He, Haowen Zhang, Tianhua Li, Xiaojia Zhang, Xiaoli Li and Chunwang Dong
Sensors 2023, 23(22), 9107; https://doi.org/10.3390/s23229107 - 10 Nov 2023
Viewed by 613
Abstract
Soil fertility is vital for the growth of tea plants. The physicochemical properties of soil play a key role in the evaluation of soil fertility. Thus, realizing the rapid and accurate detection of soil physicochemical properties is of great significance for promoting the [...] Read more.
Soil fertility is vital for the growth of tea plants. The physicochemical properties of soil play a key role in the evaluation of soil fertility. Thus, realizing the rapid and accurate detection of soil physicochemical properties is of great significance for promoting the development of precision agriculture in tea plantations. In recent years, spectral data have become an important tool for the non-destructive testing of soil physicochemical properties. In this study, a support vector regression (SVR) model was constructed to model the hydrolyzed nitrogen, available potassium, and effective phosphorus in tea plantation soils of different grain sizes. Then, the successful projections algorithm (SPA) and least-angle regression (LAR) and bootstrapping soft shrinkage (BOSS) variable importance screening methods were used to optimize the variables in the soil physicochemical properties. The findings demonstrated that soil particle sizes of 0.25–0.5 mm produced the best predictions for all three physicochemical properties. After further using the dimensionality reduction approach, the LAR algorithm (R2C = 0.979, R2P = 0.976, RPD = 6.613) performed optimally in the prediction model for hydrolytic nitrogen at a soil particle size of 0.25~0.5. The models using data dimensionality reduction and those that used the BOSS method to estimate available potassium (R2C = 0.977, R2P = 0.981, RPD = 7.222) and effective phosphorus (R2C = 0.969, R2P = 0.964, RPD = 5.163) had the best accuracy. In order to offer a reference for the accurate detection of soil physicochemical properties in tea plantations, this study investigated the modeling effect of each physicochemical property under various soil particle sizes and integrated the regression model with various downscaling strategies. Full article
(This article belongs to the Special Issue Soil Sensing and Mapping for a Sustainable Future)
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15 pages, 3893 KiB  
Article
Regional Inversion of Soil Heavy Metal Cr Content in Agricultural Land Using Zhuhai-1 Hyperspectral Images
by Hongxu Guo, Kai Yang, Fan Wu, Yu Chen and Jinxiang Shen
Sensors 2023, 23(21), 8756; https://doi.org/10.3390/s23218756 - 27 Oct 2023
Cited by 2 | Viewed by 891
Abstract
With the development of hyperspectral imaging technology, the potential for utilizing hyperspectral images to accurately estimate heavy metal concentrations in regional soil has emerged. Currently, soil heavy metal inversion based on laboratory hyperspectral data has demonstrated a commendable level of accuracy. However, satellite [...] Read more.
With the development of hyperspectral imaging technology, the potential for utilizing hyperspectral images to accurately estimate heavy metal concentrations in regional soil has emerged. Currently, soil heavy metal inversion based on laboratory hyperspectral data has demonstrated a commendable level of accuracy. However, satellite images are susceptible to environmental factors such as atmospheric and soil background, presenting a significant challenge in the accurate estimation of soil heavy metal concentrations. In this study, typical chromium (Cr)-contaminated agricultural land in Shaoguan City, Guangdong Province, China, was taken as the study area. Soil sample collection, Cr content determination, laboratory spectral measurements, and hyperspectral satellite image collection were carried out simultaneously. The Zhuhai-1 hyperspectral satellite image spectra were corrected to match laboratory spectra using the direct standardization (DS) algorithm. Then, the corrected spectra were integrated into an optimal model based on laboratory spectral data and sample Cr content data for regional inversion of soil heavy metal Cr content in agricultural land. The results indicated that the combination of standard normal variate (SNV)+ uninformative variable elimination (UVE)+ support vector regression (SVR) model performed best with laboratory spectral data, achieving a high accuracy with an R2 of 0.97, RMSE of 5.87, MAE of 4.72, and RPD of 4.04. The DS algorithm effectively transformed satellite hyperspectral image data into spectra resembling laboratory measurements, mitigating the impact of environmental factors. Therefore, it can be applied for regional inversion of soil heavy metal content. Overall, the study area exhibited a low-risk level of Cr content in the soil, with the majority of Cr content values falling within the range of 36.21–76.23 mg/kg. Higher concentrations were primarily observed in the southeastern part of the study area. This study can provide useful exploration for the promotion and application of Zhuhai-1 image data in the regional inversion of soil heavy metals. Full article
(This article belongs to the Special Issue Soil Sensing and Mapping for a Sustainable Future)
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