Digital Soil Mapping for Agri-Environmental Management and Sustainability

A special issue of Land (ISSN 2073-445X). This special issue belongs to the section "Land Innovations – Data and Machine Learning".

Deadline for manuscript submissions: 26 April 2024 | Viewed by 10800

Special Issue Editors

Dale Bumpers Small Farms Research Center, United States Department of Agriculture, Booneville, AR 72927, USA
Interests: precision agriculture; digital soil mapping; soil property prediction errors and cost benefit analysis; sustainability of small farms with limited resources
Grassland, Soil & Water Research Laboratory, United States Department of Agriculture, Temple, TX 76502, USA
Interests: digital soil mapping; precision agriculture; soil–landscape modelling; soil health; sustainable soil management
Special Issues, Collections and Topics in MDPI journals
Soil Science Department, Universidade Federal de Lavras, Lavras 37200-900, MG, Brazil
Interests: digital soil mapping; remote and proximal sensing; precision agriculture; applied pedology
ICAR-National Bureau of Soil Survey and Land Use Planning, Regional Centre, Bangalore 560 024, India
Interests: digital soil mapping; remote sensing in natural resources management; land degradation; desertification; vulnerability modelling

Special Issue Information

Dear Colleagues,

Since its debut in the 1990s, Digital Soil Mapping (DSM) has matured as a soil science discipline and has become one of the major tools used by soil mappers throughout the world. Still major challenges remain, especially on improving the tool sets used by DSM. Incorporating cost-effective sampling, applying machine learning and deep learning algorithms for prediction and for robust uncertainty assessments, and providing interpretation and utilization of DSM products can benefit sustainable land management and policy decisions. New challenges are emerging on how to relate the uncertainty of mapping soil functions and interpretations to management and beneficial uses though cost benefit and risk assessment analysis.

The Journal Land is excited to announce the special issue on “Digital Soil Mapping for Agri-environmental Management and sustainability”. The major focuses of this special issue are:

  • Advanced techniques in soil mapping and monitoring
  • High-resolution soil mapping for precision agriculture / precision conservation applications;
  • Novel approaches in sampling, and prediction uncertainty assessment;
  • Remote sensing/proximal soil sensing, and UAVs for high resolution soil mapping;
  • Soil functional mapping, monitoring, and mapping dynamic soil properties;
  • Interpretations and utilization of DSM products for informed decisions;
  • Risk assessment and cost-benefit analysis for farm management decisions

Dr. Zamir Libohova
Dr. Kabindra Adhikari
Dr. Michele Duarte De Menezes
Dr. Subramanian Dharumarajan
Guest Editors

Manuscript Submission Information

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Keywords

  • soil mapping
  • machine learning
  • soil properties
  • soil functions and interpretations
  • precision agriculture
  • uncertainty predictions and cost benefit risk assessment

Published Papers (6 papers)

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Research

21 pages, 4537 KiB  
Article
Time-Lapse Electromagnetic Conductivity Imaging for Soil Salinity Monitoring in Salt-Affected Agricultural Regions
by Mohamed G. Eltarabily, Abdulrahman Amer, Mohammad Farzamian, Fethi Bouksila, Mohamed Elkiki and Tarek Selim
Land 2024, 13(2), 225; https://doi.org/10.3390/land13020225 - 11 Feb 2024
Viewed by 646
Abstract
In this study, the temporal variation in soil salinity dynamics was monitored and analyzed using electromagnetic induction (EMI) in an agricultural area in Port Said, Egypt, which is at risk of soil salinization. To assess soil salinity, repeated soil apparent electrical conductivity (EC [...] Read more.
In this study, the temporal variation in soil salinity dynamics was monitored and analyzed using electromagnetic induction (EMI) in an agricultural area in Port Said, Egypt, which is at risk of soil salinization. To assess soil salinity, repeated soil apparent electrical conductivity (ECa) measurements were taken using an electromagnetic conductivity meter (CMD2) and inverted (using a time-lapse inversion algorithm) to generate electromagnetic conductivity images (EMCIs), representing soil electrical conductivity (σ) distribution. This process involved converting EMCI data into salinity cross-sections using a site-specific calibration equation that correlates σ with the electrical conductivity of saturated soil paste extract (ECe) for the collected soil samples. The study was performed from August 2021 to April 2023, involving six surveys during two agriculture seasons. The results demonstrated accurate prediction ability of soil salinity with an R2 value of 0.81. The soil salinity cross-sections generated on different dates observed changes in the soil salinity distribution. These changes can be attributed to shifts in irrigation water salinity resulting from canal lining, winter rainfall events, and variations in groundwater salinity. This approach is effective for evaluating agricultural management strategies in irrigated areas where it is necessary to continuously track soil salinity to avoid soil fertility degradation and a decrease in agricultural production and farmers’ income. Full article
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18 pages, 5275 KiB  
Article
Utilisation of Intrinsic and Extrinsic Soil Information to Derive Soil Nutrient Management Zones for Banana Production in a Smallholder Farm
by Seome Michael Swafo and Phesheya Eugine Dlamini
Land 2023, 12(9), 1651; https://doi.org/10.3390/land12091651 - 23 Aug 2023
Viewed by 925
Abstract
In South Africa (SA), smallholder farmers contribute significantly to food production and play an essential role in the nation’s food and nutritional security. However, there is a lack of basic understanding of the spatial variability of soil nutrients and their controlling factors in [...] Read more.
In South Africa (SA), smallholder farmers contribute significantly to food production and play an essential role in the nation’s food and nutritional security. However, there is a lack of basic understanding of the spatial variability of soil nutrients and their controlling factors in these smallholdings, which subsequently hinders their agricultural production. In this work, we assessed the spatial variability and structure of key soil nutrients required by banana fruit, identified their factors of control, and delineated management zones in a smallholder farm. We used a regular grid (50 m × 50 m) to collect a total of 27 composite samples from the 0–30 cm depth interval and analysed for soil physicochemical properties. Our classical statistics results indicated that phosphorus (P), potassium (K), calcium (Ca) and zinc (Zn) varied highly, while magnesium (Mg) and total nitrogen (TN) varied moderately across the plantation. On the other hand, geostatistics revealed that P and K were strongly spatially dependent (implying a good structure), while Mg and Zn were moderately spatially dependent (indicating a moderate structure) across the banana plantation. Soil Ca and TN contents were found to be weakly spatially dependent (meaning there was no structure) across the farm. The spatial prediction maps showed that P, Mg and Zn contents were high in the northeast part (underlain by Valsrivier) and low in the northwest part (underlain by Westleigh) of the banana plantation farm. Similarly, K and Ca were low in the northwest part (underlain by Westleigh), but they were high in the south to southwest portion (underlain by Glenrosa) of the farm. Soil TN was high in the west part (underlain by Westleigh) and low in the east-northeast part (underlain by Valsrivier) across the plantation. Three management zones (MZs) were delineated for soil P, K and Ca, while for other nutrients (Mg, Zn and TN), two MZs were delineated. The results of this study provide baseline information for site-specific management of fertilisers to supplement soil nutrients in the field to improve banana productivity. Full article
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12 pages, 1574 KiB  
Article
Hyperspectral Bare Soil Index (HBSI): Mapping Soil Using an Ensemble of Spectral Indices in Machine Learning Environment
by Eric Ariel L. Salas and Sakthi Subburayalu Kumaran
Land 2023, 12(7), 1375; https://doi.org/10.3390/land12071375 - 10 Jul 2023
Viewed by 1905
Abstract
Spectral remote-sensing indices based on visible, NIR, and SWIR wavelengths are useful in predicting spatial patterns of bare soil. However, identifying an effective combination of informative wavelengths or spectral indices for mapping bare soil in a complex urban/agricultural region is still a challenge. [...] Read more.
Spectral remote-sensing indices based on visible, NIR, and SWIR wavelengths are useful in predicting spatial patterns of bare soil. However, identifying an effective combination of informative wavelengths or spectral indices for mapping bare soil in a complex urban/agricultural region is still a challenge. In this study, we developed a new bare-soil index, the Hyperspectral Bare Soil Index (HBSI), to improve the accuracy of bare-soil remote-sensing mapping. We tested the HBSI using the high-spectral-resolution AVIRIS-NG and Sentinel-2 multispectral images. We applied an ensemble modeling approach, consisting of random forest (RF) and support vector machine (SVM), to classify bare soil. We found that the HBSI outperformed other existing bare-soil indices with over 91% accuracy for Sentinel-2 and AVIRIS-NG. Furthermore, the combination of the HBSI and the normalized difference vegetation index (NDVI) showed a better performance in bare-soil classification, with >92% accuracy for Sentinel-2 and >97% accuracy for AVIRIS-NG images. Also, the RF-SVM ensemble surpassed the performance of the individual models. The novelty of HBSI is due to its development, since it utilizes the blue band in addition to the NIR and SWIR2 bands from the high-spectral-resolution data from AVIRIS-NG to improve the accuracy of bare-soil mapping. Full article
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20 pages, 8123 KiB  
Article
Spatial Prediction of Soil Particle-Size Fractions Using Digital Soil Mapping in the North Eastern Region of India
by Roomesh Kumar Jena, Pravash Chandra Moharana, Subramanian Dharumarajan, Gulshan Kumar Sharma, Prasenjit Ray, Partha Deb Roy, Dibakar Ghosh, Bachaspati Das, Amnah Mohammed Alsuhaibani, Ahmed Gaber and Akbar Hossain
Land 2023, 12(7), 1295; https://doi.org/10.3390/land12071295 - 27 Jun 2023
Cited by 2 | Viewed by 1027
Abstract
Numerous applications in agriculture, climate, ecology, hydrology, and the environment are severely constrained by the lack of detailed information on soil texture. The purpose of this study was to predict soil particle-size fractions (PSF) in the Ri-Bhoi district of Meghalaya state, India, using [...] Read more.
Numerous applications in agriculture, climate, ecology, hydrology, and the environment are severely constrained by the lack of detailed information on soil texture. The purpose of this study was to predict soil particle-size fractions (PSF) in the Ri-Bhoi district of Meghalaya state, India, using a random forest model (RF). For the modeling of soil particle-size fractions, we employed 95 soil profiles (456 depth-wise layers) gathered from a recent national land resource inventory as well as currently accessible environmental variables. Sand, silt, and clay content were predicted using the Random Forest model at varied depths of 0–5, 5–15, 30–60, 60–100, and 100–200 cm. Our results showed the R2 for sand was found to be 0.30 (0–5 cm), 0.28 (5–15 cm), and 0.21 (15–30 cm). For the sand, silt, and clay fractions, respectively, the concordance correlation coefficient (CCC) was found to be greater in the 0–30 cm, 0–60 cm, and 0–15 cm depths. When there is a reasonably close monitoring of the coverage probability with a confidence level along the 1:1 line, prediction interval coverage probability (PICP) gives a decent indicator of what to anticipate. The most crucial variables for the prediction of sand and silt were channel network base level (CNBL) and LS-Factor, whereas Min Temperature of Coldest Month (°C) (BIO6) was discovered for clay prediction. For all three soil texture fractions, the range between the 5% lower and 95% higher prediction bounds was large, indicating that the existing spatial predictions may be improved. The maps of soil texture were significantly more precise, and they accurately depicted the spatial variations of particle-size fractions. Additionally, there is still a need to investigate novel methodologies for extensive digital soil mapping, which will be very advantageous for many international initiatives. Full article
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26 pages, 7927 KiB  
Article
Comparison of Machine Learning-Based Prediction of Qualitative and Quantitative Digital Soil-Mapping Approaches for Eastern Districts of Tamil Nadu, India
by Ramalingam Kumaraperumal, Sellaperumal Pazhanivelan, Vellingiri Geethalakshmi, Moorthi Nivas Raj, Dhanaraju Muthumanickam, Ragunath Kaliaperumal, Vishnu Shankar, Athira Manikandan Nair, Manoj Kumar Yadav and Thamizh Vendan Tarun Kshatriya
Land 2022, 11(12), 2279; https://doi.org/10.3390/land11122279 - 13 Dec 2022
Cited by 5 | Viewed by 2337
Abstract
The soil–environmental relationship identified and standardised over the years has expedited the growth of digital soil-mapping techniques; hence, various machine learning algorithms are involved in predicting soil attributes. Therefore, comparing the different machine learning algorithms is essential to provide insights into the performance [...] Read more.
The soil–environmental relationship identified and standardised over the years has expedited the growth of digital soil-mapping techniques; hence, various machine learning algorithms are involved in predicting soil attributes. Therefore, comparing the different machine learning algorithms is essential to provide insights into the performance of the different algorithms in predicting soil information for Indian landscapes. In this study, we compared a suite of six machine learning algorithms to predict quantitative (Cubist, decision tree, k-NN, multiple linear regression, random forest, support vector regression) and qualitative (C5.0, k-NN, multinomial logistic regression, naïve Bayes, random forest, support vector machine) soil information separately at a regional level. The soil information, including the quantitative (pH, OC, and CEC) and qualitative (order, suborder, and great group) attributes, were extracted from the legacy soil maps using stratified random sampling procedures. A total of 4479 soil observations sampled were non-spatially partitioned and intersected with 39 environmental covariate parameters. The predicted maps depicted the complex soil–environmental relationships for the study area at a 30 m spatial resolution. The comparison was facilitated based on the evaluation metrics derived from the test datasets and visual interpretations of the predicted maps. Permutation feature importance analysis was utilised as the model-agnostic interpretation tool to determine the contribution of the covariate parameters to the model’s calibration. The R2 values for the pH, OC, and CEC ranged from 0.19 to 0.38; 0.04 to 0.13; and 0.14 to 0.40, whereas the RMSE values ranged from 0.75 to 0.86; 0.25 to 0.26; and 8.84 to 10.49, respectively. Irrespective of the algorithms, the overall accuracy percentages for the soil order, suborder, and great group class ranged from 31 to 67; 26 to 65; and 27 to 65, respectively. The tree-based ensemble random forest and rule-based tree models’ (Cubist and C5.0) algorithms efficiently predicted the soil properties spatially. However, the efficiency of the other models can be substantially increased by advocating additional parameterisation measures. The range and scale of the quantitative soil attributes, in addition to the sampling frequency and design, greatly influenced the model’s output. The comprehensive comparison of the algorithms can be utilised to support model selection and mapping at a varied scale. The derived digital soil maps will help farmers and policy makers to adopt precision information for making decisions at the farm level leading to productivity enhancements through the optimal use of nutrients and the sustainability of the agricultural ecosystem, ensuring food security. Full article
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23 pages, 6841 KiB  
Article
Topographic Wetness Index as a Proxy for Soil Moisture in a Hillslope Catena: Flow Algorithms and Map Generalization
by Hans Edwin Winzeler, Phillip R. Owens, Quentin D. Read, Zamir Libohova, Amanda Ashworth and Tom Sauer
Land 2022, 11(11), 2018; https://doi.org/10.3390/land11112018 - 11 Nov 2022
Cited by 8 | Viewed by 2408
Abstract
Topographic wetness index (TWI) is used as a proxy for soil moisture, but how well it performs across varying timescales and methods of calculation is not well understood. To assess the effectiveness of TWI, we examined spatial correlations between in situ soil volumetric [...] Read more.
Topographic wetness index (TWI) is used as a proxy for soil moisture, but how well it performs across varying timescales and methods of calculation is not well understood. To assess the effectiveness of TWI, we examined spatial correlations between in situ soil volumetric water content (VWC) and TWI values over 5 years in soils at 42 locations in an agroforestry catena in Fayetteville, Arkansas, USA. We calculated TWI 546 ways using different flow algorithms and digital elevation model (DEM) preparations. We found that most TWI algorithms performed poorly on DEMs that were not first filtered or resampled, but DEM filtration and resampling (collectively called generalization) greatly improved the TWI performance. Seasonal variation of soil moisture influenced TWI performance which was best when conditions were not saturated and not dry. Pearson correlation coefficients between TWI and grand mean VWC for the 5-year measurement period ranged from 0.18 to 0.64 on generalized DEMs and 0.15 to 0.59 for on DEMs that were not generalized. These results aid management of crop fields with variable moisture characteristics. Full article
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