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Agronomy
Special Issues
Soil Sensing and Landscape Modeling for Agronomic Application
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Soil Sensing and Landscape Modeling for Agronomic Application

A special issue of Agronomy (ISSN 2073-4395). This special issue belongs to the section "Precision and Digital Agriculture".

Deadline for manuscript submissions: closed (20 May 2023) | Viewed by 10929

Special Issue Editor

Dr. Wanderson de Sousa Mendes

E-Mail Website
Guest Editor
Leibniz Centre for Agricultural Landscape Research (ZALF), Muncheberg, Germany
Interests: spatial analysis; mapping; satellite image analysis; geoinformation; digital mapping; satellite image processing; land use planning; soil; agriculture; environment

Special Issue Information

Dear Colleagues,

Soil sensing and landscape modeling can be defined as the use of proximal and/or remote sensing combined with computer and soil analyses to map and monitor soil and landscape processes. In this sense, several disciplines in soil science (e.g., digital soil mapping, soil spectroscopy, pedometrics) have helped farmers and scientists to move towards an agriculture of the future. Those disciplines combine machine learning, geostatistics, soil sampling methods, and different proximal and remote sensors to achieve such a crucial goal of developing agriculture, environmental policies, and food security. Therefore, these two aspects have been a major concern for humankind since the emergence and identification of climate change. The current Special Issue aims to bring together research papers, communications, and review papers on recent developments in soil sensing and landscape modeling for agronomic applications. We strongly encourage contributions covering the disciplines of digital soil mapping, landscape modeling, soil spectroscopy, and integrated proximal and remote sensing.       

Dr. Wanderson de Sousa Mendes
Guest Editor

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. Agronomy is an international peer-reviewed open access monthly 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

  • digital soil mapping
  • soil spectroscopy
  • landscape modeling
  • satellite images
  • proximal sensors

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Published Papers (4 papers)

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Research

11 pages, 2628 KiB  
Article
Advancing Soil Organic Carbon and Total Nitrogen Modelling in Peatlands: The Impact of Environmental Variable Resolution and vis-NIR Spectroscopy Integration
by Wanderson de Sousa Mendes and Michael Sommer
Agronomy 2023, 13(7), 1800; https://doi.org/10.3390/agronomy13071800 - 6 Jul 2023
Cited by 2 | Viewed by 1914
Abstract
Visible and near-infrared (vis-NIR) spectroscopy has proven to be a straightforward method for sample preparation and scaling soil testing, while the increasing availability of high-resolution remote sensing (RS) data has further facilitated the understanding of spatial variability in soil organic carbon (SOC) and [...] Read more.
Visible and near-infrared (vis-NIR) spectroscopy has proven to be a straightforward method for sample preparation and scaling soil testing, while the increasing availability of high-resolution remote sensing (RS) data has further facilitated the understanding of spatial variability in soil organic carbon (SOC) and total nitrogen (TN) across landscapes. However, the impact of combining vis-NIR spectroscopy with high-resolution RS data for SOC and TN prediction remains an open question. This study evaluated the effects of incorporating a high-resolution LiDAR-derived digital elevation model (DEM) and a medium-resolution SRTM-derived DEM with vis-NIR spectroscopy for predicting SOC and TN in peatlands. A total of 57 soil cores, comprising 262 samples from various horizons (<2 m), were collected and analysed for SOC and TN content using traditional methods and ASD Fieldspec® 4. The 262 observations, along with elevation data from LiDAR and SRTM, were divided into 80% training and 20% testing datasets. By employing the Cubist modelling approach, the results demonstrated that incorporating high-resolution LiDAR data with vis-NIR spectra improved predictions of SOC (RMSE: 4.60%, RPIQ: 9.00) and TN (RMSE: 3.06 g kg−1, RPIQ: 7.05). In conclusion, the integration of LiDAR and soil spectroscopy holds significant potential for enhancing soil mapping and promoting sustainable soil management. Full article
(This article belongs to the Special Issue Soil Sensing and Landscape Modeling for Agronomic Application)
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17 pages, 4010 KiB  
Article
Digital Soil Mapping of Cadmium: Identifying Arable Land for Producing Winter Wheat with Low Concentrations of Cadmium
by Karl Adler, Kristin Persson, Mats Söderström, Jan Eriksson and Carl-Göran Pettersson
Agronomy 2023, 13(2), 317; https://doi.org/10.3390/agronomy13020317 - 20 Jan 2023
Cited by 6 | Viewed by 3082
Abstract
Intake of cadmium (Cd) via vegetable food poses a possible health risk. Cereals are one of the major sources of Cd, and the Cd concentration in the soil has a great effect on the levels in the grain. The aim of the study [...] Read more.
Intake of cadmium (Cd) via vegetable food poses a possible health risk. Cereals are one of the major sources of Cd, and the Cd concentration in the soil has a great effect on the levels in the grain. The aim of the study was to produce decision support for identification of areas suitable for low-Cd winter wheat production in the form of a detailed digital soil map covering an important agricultural region in southern Sweden. A two-step approach was used: (1) we increased the number of soil Cd observations by combining two sets of soil samples, one with laboratory Cd analyses (304 samples) and one with predicted Cd from a portable x-ray fluorescent (PXRF) sensor (2097 samples); and (2) a digital soil mapping (DSM) model (gradient boosting regression) was calibrated on all 2401 soil samples to create a soil Cd concentration map using a number of covariates, of which airborne gamma ray data was identified as the most important. In the first step, cross-validation of the PXRF model obtained a model efficiency (E) of 0.82 and mean absolute error (MAE) of 0.08 mg kg−1. The DSM model had an E of 0.69 and MAE of 0.11 mg kg−1. The map of predicted soil Cd concentrations were compared against 307 winter wheat (Triticum aestivum L.) grain samples with laboratory-analyzed Cd concentrations. Areas in the map with low soil Cd concentrations had a high frequency of lower grain Cd concentrations. The map thus seemed to have potential for finding areas suitable for production of low-Cd winter wheat; e.g., for baby food. Full article
(This article belongs to the Special Issue Soil Sensing and Landscape Modeling for Agronomic Application)
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15 pages, 4766 KiB  
Article
Impact of Field Topography and Soil Characteristics on the Productivity of Alfalfa and Rhodes Grass: RTK-GPS Survey and GIS Approach
by Rangaswamy Madugundu, Khalid A. Al-Gaadi, ElKamil Tola, Ahmed M. Zeyada, Ahmed A. Alameen, Mohamed K. Edrris, Haroon F. Edrees and Omer Mahjoop
Agronomy 2022, 12(12), 2918; https://doi.org/10.3390/agronomy12122918 - 23 Nov 2022
Cited by 7 | Viewed by 2843
Abstract
Understanding the spatial pattern of soil chemical properties along with the topologic indicators is essential for site-specific agriculture management. A study was conducted on a 50 ha field to investigate the effect of selected topographic indicators, including elevation (DEM), slope (SL), flow accumulation [...] Read more.
Understanding the spatial pattern of soil chemical properties along with the topologic indicators is essential for site-specific agriculture management. A study was conducted on a 50 ha field to investigate the effect of selected topographic indicators, including elevation (DEM), slope (SL), flow accumulation (FA) and Topographic Wetness Index (TWI) on forage crop production. The soil samples and yield data were obtained from the field inventory. Topographical parameters of elevation and slope were estimated with the use of a real-time kinematic global positioning system (RTK-GPS), and then the DEM was generated. The collected soil samples were analyzed for pH, EC, nitrogen and soil organic carbon. Sentinel-2 images were for the creation of yield maps of alfalfa and Rhodes grass. Subsequently, on the basis of DEM, the generated elevation, slope and FA model were then compared with the yield and soil chemical properties. Statistical analysis revealed that the SL, FA and TWI, which are associated with water distribution, were significantly related to crop yields. The FA showed a medium-to-non-significant correlation with the productivity of both alfalfa (R2 = 0.586; p = 0.015) and Rhodes grass (R2 = 0.578; p = 0.01). A significant inverse correlation was recorded between the SL and the yield of both crops (R2 = −0.591 to −0.617; p = 0.01). The yield map revealed that the majority of the area (37.56%) of the experimental field was occupied by the medium-yield class, followed by the high-yield class (33.03%). Full article
(This article belongs to the Special Issue Soil Sensing and Landscape Modeling for Agronomic Application)
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18 pages, 3691 KiB  
Article
Combining Proximal and Remote Sensors in Spatial Prediction of Five Micronutrients and Soil Texture in a Case Study at Farmland Scale in Southeastern Brazil
by Luiza Maria Pereira Pierangeli, Sérgio Henrique Godinho Silva, Anita Fernanda dos Santos Teixeira, Marcelo Mancini, Renata Andrade, Michele Duarte de Menezes, João José Marques, David C. Weindorf and Nilton Curi
Agronomy 2022, 12(11), 2699; https://doi.org/10.3390/agronomy12112699 - 31 Oct 2022
Cited by 4 | Viewed by 2403
Abstract
Despite the increasing adoption of proximal sensors worldwide, rare works have coupled proximal with remotely sensed data to spatially predict soil properties. This study evaluated the contribution of proximal and remotely sensed data to predict soil texture and available contents of micronutrients using [...] Read more.
Despite the increasing adoption of proximal sensors worldwide, rare works have coupled proximal with remotely sensed data to spatially predict soil properties. This study evaluated the contribution of proximal and remotely sensed data to predict soil texture and available contents of micronutrients using portable X-ray fluorescence (pXRF) spectrometry, magnetic susceptibility (MS), and terrain attributes (TA) via random forest algorithm. Samples were collected in Brazil from soils with high, moderate, and low weathering degrees (Oxisols, Ultisols, Inceptisols, respectively), and analyzed by pXRF and MS and for texture and available micronutrients. Seventeen TA were generated from a digital elevation model of 12.5 m spatial resolution. Predictions were made via: (i) TA; (ii) TA + pXRF; (iii) TA + MS; (iv) TA + MS + pXRF; (v) MS + pXRF; and (vi) pXRF; and validated via root mean square error (RMSE) and coefficient of determination (R2). The best predictions were achieved by: pXRF dataset alone for available Cu (R² = 0.80) and clay (R2 = 0.67) content; MS + pXRF dataset for available Fe (R2 = 0.68) and sand (R2 = 0.69) content; TA + pXRF + MS dataset for available Mn (R2 = 0.87) content. PXRF data were key to the best predictions. Soil property maps created from these predictions supported the adoption of sustainable soil management practices. Full article
(This article belongs to the Special Issue Soil Sensing and Landscape Modeling for Agronomic Application)
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