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Soil Sensing and Mapping for a Sustainable Future

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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 (15 August 2024) | Viewed by 11861

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Special Issue Editors

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
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Dr. Wenjun Ji

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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
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Prof. Dr. Songchao Chen

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Guest Editor

Hangzhou Global Scientific and Technological Innovation Center, Zhejiang University, Hangzhou 311200, China
Interests: proximal soil sensing; soil spectroscopy; digital soil mapping; carbon sequestration; soil biogeochemical modeling
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Dr. Yongsheng Hong

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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
Prof. Dr. Songchao Chen
Dr. Yongsheng Hong
Guest Editors

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Keywords

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

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Published Papers (7 papers)

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Research

16 pages, 3302 KiB

Open AccessArticle

Performance of Soil Moisture Sensors at Different Salinity Levels: Comparative Analysis and Calibration

by Qiuju Qi, Hai Yang, Quanping Zhou, Xiaole Han, Zhengyang Jia, Yuehua Jiang, Zi Chen, Lili Hou and Shijia Mei

Sensors 2024, 24(19), 6323; https://doi.org/10.3390/s24196323 - 29 Sep 2024

Cited by 2 | Viewed by 2213

Abstract

Soil dielectric sensors have been widely used to obtain real-time soil moisture data, which are important for water resource management. However, soluble salts in the soil significantly affect the accuracy of these sensor measurements. Therefore, it is crucial to select suitable soil dielectric sensors for soil moisture measurements at different salinity levels. Eight mainstream sensors (EC-5, 5TE, Teros12, Hydra-probe II, TDR315L, TDR315H, TDR305H, and CS655) were selected and tested at four different soil salinity levels (EC_1:5 = 3.0, 1.5, 1.0, and 0.75 dS·m⁻¹). The measured values using the factory calibration formulas were compared at six soil moisture levels. The results showed that the measured soil moisture values from various sensors exhibited varying degrees of overestimation, which increased with increasing salinity. Only EC-5 did not exhibit distortion at high-salinity levels, with the measured values showing a good linear trend compared to the standard values. Mutational distortion of the measured apparent dielectric permittivity occurred in TDR315L, TDR315H, Hydra-probe II, and 5TE at EC_1:5 = 3.0 dS·m⁻¹. Insensitive distortion of the measured apparent dielectric permittivity occurred in Teros12 and TDR305H at EC_1:5 = 3.0 dS·m⁻¹ as well as in Teros12, TDR305H, 5TE and Hydra-probe II at EC_1:5 = 1.5 dS·m⁻¹. All tested sensors performed reasonably well at EC_1:5 ≤ 1.0 dS·m⁻¹. Seven sensors (excluding CS655) were calibrated within the distortion threshold. The soil moisture accuracy using the calibrated formulas could reach ±0.02 cm³·cm⁻³. At EC_1:5 ≤ 1.0 dS·m⁻¹, most sensors in this study could be applied with the factory calibration formulas. TDR series, EC-5, 5TE and Teros12 were recommended after calibration for EC_1:5 > 1.0 dS·m⁻¹. For extremely high soil salinity levels, the TDR series and EC-5 may be the best choices. Full article

(This article belongs to the Special Issue Soil Sensing and Mapping for a Sustainable Future)

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16 pages, 9685 KiB

Open AccessArticle

Monitoring Soil Copper in Urban Land Using Visibale and Near-Infrared Spectroscopy with Spatially Nearby Samples

by Yi Liu, Tiezhu Shi, Zeying Lan, Kai Guo, Chao Yang and Yiyun Chen

Sensors 2024, 24(17), 5612; https://doi.org/10.3390/s24175612 - 29 Aug 2024

Viewed by 1098

Abstract

Soil heavy metal contamination in urban land can affect biodiversity, ecosystem functions, and the health of city residents. Visible and near-infrared (Vis-NIR) spectroscopy is fast, inexpensive, non-destructive, and environmentally friendly compared to traditional methods of monitoring soil Cu, a common heavy metal found in urban soils. However, there has been limited research on using spatially nearby samples to build the Cu estimation model. Our study aims to investigate how spatially nearby samples influence the Cu estimation model. In our study, we collected 250 topsoil samples (0–20 cm) from China’s third-largest city and analyzed their spectra (350–2500 nm). For each unknown validation sample, we selected its spatially nearby samples to construct the Cu estimation model. The results showed that compared to the traditional method (

R_{p}^{2}

= 0.75, RMSEP = 8.56, RPD = 1.73), incorporating nearby samples greatly improved the model (

R_{p}^{2}

= 0.93, RMSEP = 4.02, RPD = 3.89). As the number of nearby samples increased, the performance of the Cu estimation model followed an inverted U-shaped curve—initially increasing and then declining. The optimal number of nearby samples is 125 (62.5% of the total), and the mean distance between validation and calibration samples is 17 km. Therefore, we conclude that using nearby samples significantly enhances the Cu estimation model. The optimal number of nearby samples should strike a balance, covering a moderate area without there being too few or too many. Full article

(This article belongs to the Special Issue Soil Sensing and Mapping for a Sustainable Future)

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24 pages, 6800 KiB

Open AccessArticle

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

Cited by 5 | Viewed by 1584

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 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

Open AccessArticle

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 5 | Viewed by 1497

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), 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

Open AccessArticle

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 1435

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 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

Open AccessArticle

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

Cited by 1 | Viewed by 1096

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 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 (R²_C = 0.979, R²_P = 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 (R²_C = 0.977, R²_P = 0.981, RPD = 7.222) and effective phosphorus (R²_C = 0.969, R²_P = 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

Open AccessArticle

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 8 | Viewed by 1850

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 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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