Digital soil mapping: A brief history and some lessons

Agronomy

Land use change and soil organic carbon stock (SOCS) depletion over time is one of the predominant worldwide environmental problems related to global warming and the need to secure food production for an increasing world population. In our research, satellite images from 1988 and 2018 were analyzed for a 177.48 km2 region in Kurdistan Province, Iran. Across the study area. 186 disturbed and undisturbed soil samples were collected at two depths (0–20 cm and 20–50 cm). Bulk density (BD), soil organic carbon (SOC), rock fragments (RockF) and SOCS were measured. Random forest was used to model the spatial variability of SOCS. Land use was mapped with supervised classification and maximum likelihood approaches. The Kappa index and overall accuracy of the supervised classification and maximum likelihood land use maps varied between 83% and 88% and 78% and 85%, respectively. The area of forest and high-quality rangeland covered 5286 ha in 1988 and decreased by almost 30% by 2018. Most of t...

Applied Sciences

Development of a geotechnical database for the young capital of Kazakhstan, Nur-Sultan city, became a paramount concern of both the scientific community and industry. The creation of the Kazakhstan model of the geotechnical database was based on a fixed dataset which included a city map with determined X, Y, and H coordinates and the user generated data obtained from the materials of engineering and geological surveys from more than 2000 boreholes. Based on the assessment of the built-up area of the city, six main engineering-geological elements (EGE) were identified. The territory of the city was regrouped into eight zones instead of the previous six based on the geological origin and stratigraphy interposition of the EGE. Engineering properties of the soils were considered for physical and mechanical characteristics, where the plasticity limits demonstrated an accurate correlation to the elasticity modulus E and distribution boundaries between the alluvial and eluvial types of the...

Bárbara pereira christofaro Silva , Marx Leandro naves Silva, fabio Arnaldo pomar Avalos, Michele Duarte de Menezes & nilton curi this study aimed to evaluate the performance of three spatial association models used in digital soil mapping and the effects of additional point sampling in a steep-slope watershed (1,200 ha). A soil survey was carried out and 74 soil profiles were analyzed. The tested models were: Multinomial logistic regression (MLR), C5 decision tree (C5-DT) and Random forest (RF). In order to reduce the effects of an imbalanced dataset on the accuracy of the tested models, additional sampling retrieved by photointerpretation was necessary. Accuracy assessment was based on aggregated data from a proportional 5-fold cross-validation procedure. Extrapolation assessment was based on the multivariate environmental similarity surface (MESS). The RF model including additional sampling (RF*) showed the best performance among the tested models (overall accuracy = 49%, kappa index = 0.33). the Rf* allowed to link soil mapping units (SMU) and, in the case of less-common soil classes in the watershed, to set specific conditions of occurrence on the space of terrain-attributes. MESS analysis showed reliable outputs for 82.5% of the watershed. SMU distribution across the watershed was: Typic Rhodudult (56%), Typic Hapludult* (13%), Typic Dystrudept (10%), Typic Endoaquent + fluventic Dystrudept (10%), Typic Hapludult (9.5%) and Rhodic Hapludox + Typic Hapludox (2%). Knowledge of the geographic distribution of soils allows the assessment of environment-soil relationships at the landscape level 1. Such information is essential for agronomic assessment, soil and water management and land use planning 1-3. Digital soil mapping (DSM) techniques are supported by a well-known and widely accepted model in soil science: the factors of soil formation. Early efforts to bring a quantitative solution to this model could be traced back to the work of Jenny 4. Currently, with the development of geographic information technology and data processing, a comprehensive digital framework could be applied for the production of digital soil maps 1,5,6. The scorpan model, as formalized by McBratney et al. 5 , serves as a route for the production of digital soil maps. It establishes a quantitative model for spatial association between soil forming factors, evaluated as covariate maps, and the occurrence of soil classes or properties. However, since the spatial association complexity is highly variable among landscapes, there is still no consensus or a pre-established function or operational model (spatial inference models) that could relate soilscape features and the occurrence of soil classes. Hence, model selection still represents a challenge in DSM studies. Spatial inference models could be divided into data-driven (pedometric approach) and knowledge-driven approaches 7. The pedometric approach (automatic and quantitative, includes statistics, geostatistics, machine learning, and data mining) gives a predictive accuracy that is generally related to a dense sampling scheme 8. In the knowledge-driven approach, the knowledge of pedologists is incorporated into spatial prediction 9 and the soil maps are viewed as a representation of the pedologist's understanding of the soils 10. Such tacit knowledge is mostly based on a paradigm of conceptual soil-landscape model, under the hypothesis that the location and distribution of soils in the landscape is predictable 11. Thus, the use of spatial inference models as fuzzy logics 12 , or bayesian inference 13 , to name a few, take advantage of such concepts in predictive maps.

2020

Soils are a nonrenewable resource that support a wide array of ecosystem functions. The scope of these functions depends on the nature and properties of the soil at a given location on the Earth. Demand for better soil information has been growing since the development of soil science in the nineteenth century. This recent interest is driven by an increasing recognition of the ecological, economic, and societal benefits of understanding soil properties and the value of that knowledge for realizing management objectives for agriculture, grazing, forestry, and other land uses. Soil surveys are one method for amassing soil data and mapping the extent of various soil types. The Federal Government has singularly been a long-term sponsor of soil surveys in the United States. The history of these surveys is richly documented and illustrated by Helms et al. (2008). Soil surveys describe horizontal (e.g., soil series) and vertical (e.g., horizon depth) properties of soils. Soil mapping enhan...

Agronomy

The soil profile and its spatial distribution are two essential aspects for promoting sustainable agriculture, with precise inputs in quantity, space, and time. This work’s objective was to elaborate a digital map of soil fertility considering the complete profile for the accurate management of amendments and fertilizers. For the preparation of the soil fertility map, the following inputs were used: a digital elevation model, information from 44 soil profiles, the conversion of the properties of the soil profiles into surface units, geostatistical analysis of the soil properties, and the preparation of the final map with a geographic information system. The best spatial models were achieved with CEC, pH, Ca, Mg, Na, and K. The map of the soil fertility classes was produced considering CEC and the pH value. The soil fertility classes presented the following sequence of occupied surfaces: very low, medium, very high, high, and low. A process was generated to elaborate digital maps (ge...

Global Change Biology

This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Water Resources Research

In this synthesis, we assess present research and anticipate future development needs in modeling water quality in watersheds. We first discuss areas of potential improvement in the representation of freshwater systems pertaining to water quality, including representation of environmental interfaces, in-stream water quality and process interactions, soil health and land management, and (peri-)urban areas. In addition, we provide insights into the contemporary challenges in the practices of watershed water quality modeling, including quality control of monitoring data, model parameterization and calibration, uncertainty management, scale mismatches, and provisioning of modeling tools. Finally, we make three recommendations to provide a path forward for improving watershed water quality modeling science, infrastructure, and practices. These include building stronger collaborations between experimentalists and modelers, bridging gaps between modelers and stakeholders, and cultivating and applying procedural knowledge to better govern and support water quality modeling processes within organizations.

Remote Sensing

Cropmarks are a major factor in the effectiveness of traditional aerial archaeology. Identified almost 100 years ago, the positive and negative features shown by cropmarks are now well understood, as are the role of the different cultivated plants and the importance of precipitation and other elements of the physical environment. Generations of aerial archaeologists are in possession of empirical knowledge, allowing them to find as many cropmarks as possible every year. However, the essential analyses belong mostly to the predigital period, while the significant growth of datasets in the last 30 years could open a new chapter. This is especially true in the case of Hungary, as scholars believe it to be one of the most promising cropmark areas in Europe. The characteristics of soil formed of Late Quaternary alluvial sediments are intimately connected to the young geological/geomorphological background. The predictive soil maps elaborated within the framework of renewed data on Hungar...

Carbon Balance and Management, 2021

Background Legacy data are unique occasions for estimating soil organic carbon (SOC) concentration changes and spatial variability, but their use showed limitations due to the sampling schemes adopted and improvements may be needed in the analysis methodologies. When SOC changes is estimated with legacy data, the use of soil samples collected in different plots (i.e., non-paired data) may lead to biased results. In the present work, N = 302 georeferenced soil samples were selected from a regional (Sicily, south of Italy) soil database. An operational sampling approach was developed to spot SOC concentration changes from 1994 to 2017 in the same plots at the 0–30 cm soil depth and tested. Results The measurements were conducted after computing the minimum number of samples needed to have a reliable estimate of SOC variation after 23 years. By applying an effect size based methodology, 30 out of 302 sites were resampled in 2017 to achieve a power of 80%, and an α = 0.05. A Wilcoxon te...

Revista Brasileira de Ciência do Solo, 2018

Digital soil mapping (DSM) has been increasingly used to provide quick and accurate spatial information to support decision-makers in agricultural and environmental planning programs. In this study, we used a DSM approach to map soils in western Haiti and compare the performance of the Multinomial Logistic Regression (MLR) with Random Forest (RF) to classify the soils. The study area of 4,300 km 2 is mostly composed of diverse limestone rocks, alluvial deposits, and, to a lesser extent, basalt. A soil survey was conducted whereby soils were described and classified at 258 sites. Soil samples were collected and subjected to physical and chemical analyses. Recursive Feature Elimination (RFE) was used to select the most important covariates from auxiliary data, such as climate, lithology, and morphometric properties to describe the soil-landscape relationship. Mapping performance was assessed by the Kappa index and overall accuracy derived from a confusion matrix generated using a 5-fold cross validation process. In addition, an external mapping validation was carried out using an independent soil dataset. Accordingly, the soil dataset was split into 80 % and 20 % for training and validation of the models, respectively. No significant statistical difference (Z = 0.56< |1.96|) was found between maps generated with both classifiers (Kappa index 0.45 for MLR and 0.42 for RF). Based on the Kappa values, the classification performance can be characterized as moderate for both algorithms. Surprisingly, the RF classifier outperformed MLR in the validation process (Kappa values of 0.55 and 0.33, respectively). These results suggest a higher generalization ability of RF. However, no significant statistical difference (Z = 1.83< |1.96|) was observed. The soil map derived from RF indicated the occurrence of Leptosols (48.5 %), Gleysols (19.6 %), Chernozems (8 %), and Fluvisols (6.6 %) in most of the study area. The DSM approaches proved suitable for mapping soils in western Haiti and could be used in other parts of the country, thereby closing information gaps with regard to Haitian soils.

Soil Use and Management, 2018

The study of soil-landscape relationships at a detailed scale (1:10 000) and its use for soil management was less common in developing countries. The study was conducted in western Ethiopia with the aim to explain the soil variability across landscapes, classify soils into mapping units and produce a map of these soils. This study was performed based on a discrete model of spatial variation. Five soil reference groups: Vertisols, Cambisols, Fluvisols, Luvisols and Leptosols were identified in the study site. Distribution of the soil reference groups was determined by landscape position. Variation in soil texture, colour, pH, exchangeable acidity, organic carbon, total nitrogen, available phosphorus (av. P), carbon to nitrogen ratio (C/N), exchangeable potassium (K), calcium (Ca), magnesium (Mg), sodium (Na) and cation exchange capacity (CEC) was observed within and among soil mapping units (SMUs). Variability was considerably high for exchangeable Ca and CEC. Factor analysis result indicated that variation in soil properties within land unit was comparatively highest in Leptosols of SMU9 (88.87%) and lowest in Vertisols of SMU1 (60.82%). Moderate-to-fine scale mapping of soil properties helps to build detail information for soil management. Grouping fields into mapping units that require more or less similar management measure would be an important soil-landscape concept. As a result, mapping units could be used as cost-effective means of treating variable field so as to optimize the forecasted benefits.

Remote Sensing, 2016

There is a consensus about the necessity to achieve a quick soil spatial information with few human resources. Remote/proximal sensing and pedotransference are methods that can be integrated into this approach. On the other hand, there is still a lack of strategies indicating on how to put this in practice, especially in the tropics. Thus, the objective of this work was to suggest a strategy for the spatial prediction of soil classes by using soil spectroscopy from ground laboratory spectra to space images platform, as associated with terrain attributes and spectral libraries. The study area is located in São Paulo State, Brazil, which was covered by a regular grid (one per ha), with 473 boreholes collected at top and undersurface. All soil samples were analyzed in laboratory (granulometry and chemical), and scanned with a VIS-NIR-SWIR (400-2500 nm) spectroradiometer. We developed two traditional pedological maps with different detail levels for comparison: TFS-1 regarding orders and subgroups; and TFS-2 with additional information such as color, iron and fertility. Afterwards, we performed a digital soil map, generated by models, which used the following information: (i) predicted soil attributes from undersurface layer (diagnostic horizon), obtained by using a local spectral library; (ii) spectral reflectance of a bare soil surface obtained by Landsat image; and (iii) derivative of terrain attributes. Thus, the digital map was generated by a combination of three variables: remote sensing (Landsat data), proximal sensing (laboratory spectroscopy) and relief. Landsat image with bare soil was used as a first observation of surface. This strategy assisted on the location of topossequences to achieve soil variation in the area. Afterwards, spectral undersurface information from these locations was used to modelize soil attributes quantification (156 samples). The model was used to quantify samples in the entire area by spectra (other 317 samples). Since the surface was bare soil, it was sampled by image spectroscopy. Indeed, topsoil spectral laboratory information presented great similarity with satellite spectra. We observed angle variation on spectra from clayey to sandy soils as differentiated by intensity. Soil lines between bands 3/4 and 5/7 were helpful on the link between laboratory and satellite data. The spectral models of soil attributes (i.e., clay, sand, and iron) presented a high predictive performance (R 2 0.71 to 0.90) with low error. The spatial prediction of these attributes also presented a high performance (validations with R 2 > 0.78). The models increased spatial resolution of soil weathering information using a known spectral library. Elevation (altitude) improved mapping due to correlation with soil attributes

Scientific Reports, 2022

Soil pollution is a big issue caused by anthropogenic activities. The spatial distribution of potentially toxic elements (PTEs) varies in most urban and peri-urban areas. As a result, spatially predicting the PTEs content in such soil is difficult. A total number of 115 samples were obtained from Frydek Mistek in the Czech Republic. Calcium (Ca), magnesium (Mg), potassium (K), and nickel (Ni) concentrations were determined using Inductively Coupled Plasma Optical Emission Spectroscopy. The response variable was Ni, while the predictors were Ca, Mg, and K. The correlation matrix between the response variable and the predictors revealed a satisfactory correlation between the elements. The prediction results indicated that support vector machine regression (SVMR) performed well, although its estimated root mean square error (RMSE) (235.974 mg/kg) and mean absolute error (MAE) (166.946 mg/kg) were higher when compared with the other methods applied. The hybridized model of empirical bay...

Remote Sensing

Soil organic carbon (SOC) plays a critical role in major ecosystem processes, agriculture, and climate mitigation. Accurate SOC predictions are challenging due to natural variation, as well as variation in data sources, sampling design, and modeling approaches. The goal of this study was to (i) understand SOC stock distribution due to land use (restored prairie grass—PG; lawn grass—LG; and forest—F), and local topography, and (ii) assess the scalability of SOC stock predictions from the study site in North Carolina (Lat: 36°7′ N; Longitude: 80°16′ W) to the geographic extension of the Fairview soil series based on the US Soil Survey Geographic (gSSURGO) database. Overall, LG had the highest SOC stock (82 Mg ha−1) followed by PG (79 Mg ha−1) and forest (73.1 Mg ha−1). SOC stock decreased with the depth for LG and PG, which had about 60% concentrated on the surface horizon (0–23 cm), while forest had only 40%. The differences between measured SOC stocks and those estimated by gSSURGO ...

Agriculture

Predicting soil chemical properties such as soil organic carbon (SOC) and available phosphorus (Ava-P) content is critical in areas where different land uses exist. The distribution of SOC and Ava-P is influenced by both natural and anthropogenic factors. This study aimed at (1) predicting SOC and Ava-P in a piedmont plain of Northeast Iran using the Random Forests (RF) and Cubist mathematical models and hybrid models (Regression Kriging), (2) comparing the models’ results, and (3) identifying the key variables that influence the spatial dynamics of soil SOC and Ava-P under different agricultural practices. The machine learning models were trained with 201 composite surface soil samples and 24 ancillary data, including climate (C), organism (O), topography- relief (R), parent material (P) and key soil features (S) according to the SCORPAN digital soil mapping framework, which can predictively represent soil formation factors spatially. Clay, one of the most critical soil properties ...

Remote Sensing, 2016

The determination of soil texture and organic carbon across agricultural areas provides important information to derive soil condition. Precise digital soil maps can help to till agricultural fields with more accuracy, greater cost-efficiency and better environmental protection. In the present study, the laboratory analysis of sand, silt, clay and soil organic carbon (SOC) content was combined with hyperspectral image data to estimate the distribution of soil texture and SOC across an agricultural area. The aim was to identify regions with similar soil properties and derive uniform soil regions based on this information. Soil parameter data and corresponding laboratory spectra were used to calibrate cross-validated (leave-one-out) partial least squares regression (PLSR) models, resulting in robust models for sand (R 2 = 0.77, root-mean-square error (RMSE) = 5.37) and SOC (R 2 = 0.89, RMSE = 0.27), as well as moderate models for silt (R 2 = 0.62, RMSE = 5.46) and clay (R 2 = 0.53, RMSE = 2.39). The regression models were applied to Airborne Imaging Spectrometer for Applications DUAL (aisaDUAL) hyperspectral image data to spatially estimate the concentration of these parameters. Afterwards, a decision tree, based on the Food and Agriculture Organization (FAO) soil texture classification scheme, was developed to determine the soil texture for each pixel of the hyperspectral airborne data. These soil texture regions were further refined with the spatial SOC estimations. The developed method is useful to identify spatial regions with similar soil properties, which can provide a vital information source for an adapted treatment of agricultural fields in terms of the necessary amount of fertilizers or water. The approach can also be adapted to wider regions with a larger sample size to create detailed digital soil maps (DSMs). Further, the presented method should be applied to future hyperspectral satellite missions like Environmental Mapping and Analysis Program (EnMap) and Hyperspectral Infrared Imager (HyspIRI) to cover larger areas in shorter time intervals. Updated DSMs on a regular basis could particularly support precision farming aspects.

Sustainability

With the emergence of machine learning methods during the past decade, alternatives to conventional geostatistical methods for soil mapping are becoming increasingly more sophisticated. To provide a complete overview of their performance, this study performed cost–benefit analysis of four soil mapping methods based on five criteria: accuracy, processing time, robustness, scalability and applicability. The evaluated methods were ordinary kriging (OK), regression kriging (RK), random forest (RF) and ensemble machine learning (EML) for the prediction of total soil carbon and nitrogen. The results of these mechanisms were objectively standardized using the linear scaling method, and their relative importance was quantified using the analytic hierarchy process (AHP). EML resulted in the highest cost–benefit score of the tested methods, with maximum values of accuracy, robustness and scalability, achieving a 55.6% higher score than the second-ranked RF method. The two geostatistical metho...

Geoderma, 2022

European Journal of Soil Science, 2020

Digital soil mapping (DSM) is an effective mapping technique that supports the increased need for quantitative soil data. In DSM, soil properties are correlated with environmental characteristics using statistical models such as regression. However, many of these relationships are explicitly described in mechanistic simulation models. Therefore, the mechanistic relationships can, in theory, replace the statistical relationships in DSM. This study aims to develop a mechanistic model to predict soil organic matter (SOM) stocks in Natura2000 areas of the Cantabria region (Spain). The mechanistic model is established in four steps: (a) identify major processes that influence SOM stocks, (b) review existing models describing the major processes and the respective environmental data that they require, (c) establish a database with the required input data, and (d) calibrate the model with field observations. The SOM stocks map resulting from the mechanistic model had a mean error (ME) of −2 t SOM ha −1 and a root mean square error (RMSE) of 66 t SOM ha −1. The Lin's concordance correlation coefficient was 0.47 and the amount of variance explained (AVE) was 0.21. The results of the mechanistic model were compared to the results of a statistical model. It turned out that the correlation coefficient between the two SOM stock maps was 0.8. This study illustrated that mechanistic soil models can be used for DSM, which brings new opportunities. Mechanistic models for DSM should be considered for mapping soil characteristics that are difficult to predict by statistical models, and for extrapolation purposes. Highlights • Theoretically, mechanistic models can replace the statistical relationships in digital soil mapping.

Geoderma, 2021

The understanding of attributes and magnetic susceptibility (χ) at soil surface, mainly subsurface, is crucial due to their role to identify climate changes, soil degradation, soil classification systems, soil fertility, and pedogenesis. The integration of proximal sensing (PS) and remote sensing (RS) data sources could increase the efficiency of Digital Soil Mapping. Nevertheless, products of this integration need to be evaluated in hybrid, stochastic, and deterministic models to predict soil attributes and χ at surface and subsurface. This study investigates the PS and RS integration by applying four deterministic (e.g. Bayesian Regularised Neural Network, Generalised Linear Model, Random Forest and Cubist) and hybrid models (e.g. Regression Kriging of residuals of the best-fitted model) to create a new environmental variable, the Best Synthetic Soil Image (BSSI), at three soil depths (e.g. 0-20, 40-60 and 80-100 cm) that quantitatively represent the soil spectral signature. We also used the BSSI in a comparison with bare soil surface (e.g. SYSI-Synthetic Soil Image) to predict soil attributes and χ by performing the deterministic and hybrid models. We hypothesize that the BSSI, which integrates PS and RS data, enhances soil modelling predictions at subsurface by selecting the best model approach. The BSSI demonstrated original and valuable contribution to increase the predictive model power at deeper layers, while SYSI was effective at upper layers. The PS and RS integration helped to identify the main soil patterns horizontally and vertically, which traditional soil surveys have not been capable of representing.

SOIL, 2021

The status of the soil organic carbon (SOC) stock at any position in the landscape is subject to a complex interplay of soil state factors operating at different scales and regulating multiple processes resulting either in soils acting as a net sink or net source of carbon. Forest landscapes are characterized by high spatial variability, and key drivers of SOC stock might be specific for sub-areas compared to those influencing the whole landscape. Consequently, separately calibrating models for sub-areas (local models) that collectively cover a target area can result in different prediction accuracy and SOC stock drivers compared to a single model (global model) that covers the whole area. The goal of this study was therefore to (1) assess how global and local models differ in predicting the humus layer, mineral soil, and total SOC stock in Swedish forests and (2) identify the key factors for SOC stock prediction and their scale of influence. We used the Swedish National Forest Soil Inventory (NFSI) database and a digital soil mapping approach to evaluate the prediction performance using random forest models calibrated locally for the northern, central, and southern Sweden (local models) and for the whole of Sweden (global model). Models were built by considering (1) only site characteristics which are recorded on the plot during the NFSI, (2) the group of covariates (remote sensing, historical land use data, etc.) and (3) both site characteristics and group of covariates consisting mostly of remote sensing data. Local models were generally more effective for predicting SOC stock after testing on independent validation data. Using the group of covariates together with NFSI data indicated that such covariates have limited predictive strength but that site-specific covariates from the NFSI showed better explanatory strength for SOC stocks. The most important covariates that influence the humus layer, mineral soil (0-50 cm), and total SOC stock were related to the site-characteristic covariates and include the soil moisture class, vegetation type, soil type, and soil texture. This study showed that local calibration has the potential to improve prediction accuracy, which will vary depending on the type of available covariates.

Land

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...

Soil Use and Management, 2021

A systematic map is a summary of a research area based on systematic searches, screening and coding of scientific literature (James et al., 2016). It aims to give an overview of a subject area and identify trends, knowledge gaps and knowledge clusters in scientific reporting. In contrast to a systematic review, the aim is not to answer specific close-framed questions, but rather to describe the state of knowledge in the subject, in order to guide future research. Periodic reviewing of research areas can be a healthy exercise in order to discover trends in practices-good or bad-and guide future research, so that good trends can be continued, bad habits can be broken, and practice gaps can be filled. It also enables discussion on the implications of current practices for stakeholders such as policymakers and practitioners.

Boletim de Ciências Geodésicas, 2018

The use of remote sensing is increasing in agriculture and this raises questions about its efficiency over other usual methods. Thus, the purposes of this study were to compare methodologies for soil mapping, using field samplings and spectral data (from laboratory and from a simulated Landsat-TM), and to estimate their correlation. The soil samples were collected in a wetland with a great variety of soil classes. The distribution of soil classes in the maps was based on independence analysis by the Chi-square. Ten soil classes were determined in the study area, 6 in the first category level. The map of laboratory spectral data showed low correlation with conventional data. The map of spectral data that simulated wavelenghts corresponding the spectral bands of Landsat-TM sensor showed the same behavior of the previous map, with lower correlation with the conventional data. Thus, we verified that the mapping of paddy soils with spectral data shows low correlation with conventional data, however, still rather positive.

Open Geosciences

This article investigates creating digital maps for physical and geotechnical characteristics of soil based on Geographical Information Systems (GIS) technology and remote sensing for one of the most important areas in Egypt, namely, Delta Nile region, which is characterized by its agricultural and cultural resources. To create accurate digital maps for the soil characteristics of this area, data are collected mechanically, manually and in the laboratory and loaded up with the help of GIS technology using Modified Inverse Distance Weighted as a spatial interpolation technique throughout using 119 soil samples inside Kafr El-Sheikh Governorate, Egypt. A digital elevation map of the Delta region has been downloaded using remote sensing technology to obtain the reduced levels of the different points for the studied area. Data were analyzed and studied well to produce six digital maps describing the important physical and geotechnical characteristics of soil such as groundwater level, p...

SOIL

We present methods to evaluate the spatial patterns of the geographic distribution of soil properties in the USA, as shown in gridded maps produced by digital soil mapping (DSM) at global (SoilGrids v2), national (Soil Properties and Class 100 m Grids of the USA), and regional (POLARIS soil properties) scales and compare them to spatial patterns known from detailed field surveys (gNATSGO and gSSURGO). The methods are illustrated with an example, i.e. topsoil pH for an area in central New York state. A companion report examines other areas, soil properties, and depth intervals. A set of R Markdown scripts is referenced so that readers can apply the analysis for areas of their interest. For the test case, we discover and discuss substantial discrepancies between DSM products and large differences between the DSM products and legacy field surveys. These differences are in whole-map statistics, visually identifiable landscape features, level of detail, range and strength of spatial autocorrelation, landscape metrics (Shannon diversity and evenness, shape, aggregation, mean fractal dimension, and co-occurrence vectors), and spatial patterns of property maps classified by histogram equalization. Histograms and variogram analysis revealed the smoothing effect of machine learning models. Property class maps made by histogram equalization were substantially different, but there was no consistent trend in their landscape metrics. The model using only national points and covariates was not substantially different from the global model and, in some cases, introduced artefacts from a lithology covariate. Uncertainty (5 %-95 % confidence intervals) provided by SoilGrids and POLARIS were unrealistically wide compared to gNATSGO/gSSURGO low and high estimated values and show substantially different spatial patterns. We discuss the potential use of the DSM products as a (partial) replacement for field-based soil surveys. There is no substitute for actually examining and interpreting the soil-landscape relation, but despite the issues revealed in this study, DSM can be an important aid to the soil surveyor.

Ambio, 2018

Concentrations of phosphorus (P), the main limiting nutrient in freshwater ecosystems, need to be reduced, but this is difficult due to high spatial and temporal variations and limited resources. Reliable targeting of critical source areas, such as erosion-prone fields and parts of fields, is necessary to improve the cost efficiency of mitigation measures. We used high-resolution (2 m 9 2 m) distributed modelling to calculate erosion risk for a large area (202 279 km 2) covering [ 90% of Swedish arable land. Comparison of model results with independent farmers' observations in a pilot catchment showed high spatial agreement. The modelled worst case scenario produced reasonable quantitative results comparable to measured 90th percentile values of suspended sediment (SS) loads at both field and small catchment scale (R 2 = 0.81, p \ 0.001). Overall, loads of SS, especially during extreme episodes, strongly governed losses of unreactive P and total P at both field and catchment scale.

Geoderma, 2021

Soil is a three-dimensional volume with property variability in all three dimensions. In Digital Soil Mapping (DSM), the variation of soil properties down a profile is usually harmonised by the use of the equal-area spline depth function approach. Soil observations at various depth intervals are harmonised to predetermined depth intervals. To create maps of soil at the defined depth intervals, 2.5D model produces maps of individual depth intervals separately. Those maps can be reconstructed to produce a continuous depth function for each predicted location. More recently, several studies propose that soil property at any depth can be mapped using a model incorporating depth along with spatial covariates as predictor variables, creating a '3D' model. The aim of this study is to evaluate the proposition that soil properties can be predicted at any depth. This study compares the 2.5D model and 3D model in two areas. The first test is on a 1500 km 2 area in Edgeroi, New South Wales (NSW), Australia, mapping soil organic carbon (SOC, %), carbon storage (kg m − 2), pH (H 2 O), clay content (%), and cation exchange capacity (CEC, mg/kg) based on depth-interval observations. The second study area in the Lower Hunter Valley has SOC observations at every 2 cm increment from a 210 km 2 area. 2.5D and 3D models were tested in both study areas using four machine learning techniques: Cubist regression tree, Quantile Regression Forest (QRF), Artificial Neural Network (ANN), and 3D Generalised Additive Model (GAM). Results show that, in terms of R 2 and RMSE, 2.5D and 3D models using different machine learning models produce comparable results on the validation of depth interval observations. The 3D tree-based models produce "stepped" prediction of properties with depth. Results on the Hunter Valley area with point observations show that the 3D model cannot replicate field point observations. 3D soil mapping on point depth observation has lower accuracy and larger uncertainty compared to the 2.5D model. For future DSM studies, 3D soil mapping with depth as a covariate requires caution with respect to the prediction method and the requirements of the results.

Land

Salt meadows, protected within National Parks, cannot be directly surveyed, yet understanding their soil condition is crucial. Our study indirectly estimates soil parameters (Total Salt Content (TSC), Na, and pH) related to salinization/sodification/alkalinization using spectral indices and UAV survey-derived elevation model, focusing on continental lowland salt meadows. A vegetation map was created using 16 spectral indices and a Digital Elevation Model calculated from RGB orthophotos using photogrammetry. Field observations helped define habitat types based on the General National Habitat Classification System (Hungary), and quadrats with complete coverage of specific plant species were identified. Machine learning was employed on 84 training quadrats to develop a prediction algorithm for vegetation patterns. Five saline habitat types, representing variations in soil properties and topography, were identified. Spectral and topomorphometric indices derived from UAV were key to the ...

Agronomy, 2022

This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY

Sustainability, 2018

Recent estimates show that one third of the world’s land and water resources are highly or moderately degraded. Global economic losses from land degradation (LD) are as high as USD $10.6 trillion annually. These trends catalyzed a call for avoiding future LD, reducing ongoing LD, and reversing past LD, which has culminated in the adoption of Sustainable Development Goal (SDG) Target 15.3 which aims to achieve global land degradation neutrality (LDN) by 2030. The political momentum and increased body of scientific literature have led to calls for a ‘new science of LDN’ and highlighted the practical challenges of implementing LDN. The aim of the present study was to derive LDN soil organic carbon (SOC) stock baseline maps by comparing different digital soil mapping (DSM) methods and sampling densities in a case study (Otjozondjupa, Namibia) and evaluate each approach with respect to complexity, cost, and map accuracy. The mean absolute error (MAE) leveled off after 100 samples were in...

Arabian Journal of Geosciences, 2020

This article presents a method to produce accurate soil maps according to the numerical correlation of experimental data obtained from field description and soil laboratory analyses. At first, the diagnostic horizons and characteristics of all the 56 studied profiles were identified based on the standard definitions of the US soil classification system. In the next step, six thematic maps were prepared by interpolating the thickness of diagnostic horizons and/or the depth of the upper/lower boundaries of the soil layers with diagnostic characteristics. Afterward, the thematic maps were intersected to obtain the final soil map in this approach. The produced map with the present approach was compared with the conventional geopedologic soil map. The results showed that the geopedological approach could delineate 12 soil map units as consociation and 8 units as association while using the diagnostic layers as thematic maps were able to delineate all of the 20 soil map units as consociation. This approach introduced how the soil properties could be applied as a covariate in the SCORPAN model. Using interpolated soil, diagnostic layers could increase the precision and quality of soil maps.

Sensors

Terrain traversability is critical for developing Go/No-Go maps for ground vehicles, which significantly impact a mission’s success. To predict the mobility of terrain, one must understand the soil characteristics. In-situ measurements performed in the field are the current method of collecting this information, which is time-consuming, costly, and can be lethal for military operations. This paper investigates an alternative approach using thermal, multispectral, and hyperspectral remote sensing from an unmanned aerial vehicle (UAV) platform. Remotely sensed data combined with machine learning (linear, ridge, lasso, partial least squares (PLS), support vector machines (SVM), and k nearest neighbors (KNN)) and deep learning (multi-layer perceptron (MLP) and convolutional neural network (CNN)) are used to perform a comparative study to estimate the soil properties, such as the soil moisture and terrain strength, used to generate prediction maps of these terrain characteristics. This s...

Remote Sensing, 2022

There is a growing need for an area-wide knowledge of SOC contents in agricultural soils at the field scale for food security and monitoring long-term changes related to soil health and climate change. In Germany, SOC maps are mostly available with a spatial resolution of 250 m to 1 km 2 . The nationwide availability of both digital elevation models at various spatial resolutions and multi-temporal satellite imagery enables the derivation of multi-scale terrain attributes and (here: Landsat-based) multi-temporal soil reflectance composites (SRC) as explanatory variables. In the example of a Bavarian test of about 8000 km 2 , relations between 220 SOC content samples as well as different aggregation levels of the explanatory variables were analyzed for their scale-specific predictive power. The aggregation levels were generated by applying a region-growing segmentation procedure, and the SOC content prediction was realized by the Random Forest algorithm. In doing so, established approaches of (geographic) object-based image analysis (GEOBIA) and machine learning were combined. The modeling results revealed scale-specific differences. Compared to terrain attributes, the use of SRC parameters leads to a significant model improvement at fieldrelated scale levels. The joint use of both terrain attributes and SRC parameters resulted in further model improvements. The best modeling variant is characterized by an accuracy of R 2 = 0.84 and RMSE = 1.99.