scholarly journals Soil mapping in “Pochvovedenie” journal (review of publications since 1899)

2021 ◽  
pp. 139-179
Author(s):  
M. I. Gerasimova ◽  
M. D. Bogdanova
Keyword(s):  
Large Scale ◽  
Soil Mapping ◽  
Digital Soil Mapping ◽  
Soil Science ◽  
Science Journal ◽  
Soil Maps ◽  
Journal Review ◽  
Post War ◽  
Soil Surveys ◽  
High Diversity

The overview of publications on soil cartography in “Pochvovedenie”/“Eurasian Soil Science” journal for the period 1899–2020 demonstrates a high diversity of themes and certain trends in the number and dynamics of papers. Their total number (365), calculated per 5-year-long intervals, was distributed rather evenly among these 121 years: approximately 10–15 papers in each interval, although three maximums are rather clear. The first one fell on the post-war interval and was followed by 1965–1970 and 2010–2015 maximums. Discussion of large-scale maps dominated the early publications, many of them tackled soil surveys and applied problems; in the mid-century papers, soil maps of various regions of the country were described since it was time of extensive terrain investigations; numerous were also papers concerning methodology of soil mapping. New approaches and technique were actively discussed in the papers at the turn of centuries, such as remote sensing or digital soil mapping. Along with map compilation issues, there are publications on applying information provided by soil maps for both traditional and novel purposes: schemes of zoning in the former case and development of prognostic maps or assessment of pedodiversity in the latter case. The majority of papers on zoning, concern soil-geographical (later soil-ecological) schemes, whereas the derived types of zoning, for example, ameliorative or erosional, are discussed in few papers. The performed overview may be regarded as summing up the results of traditional soil mapping development with emphasizing its most valuable achievements, as well as indicating the initial signs of new trends.

scholarly journals Progress in soil geography I: Reinvigoration

2019 ◽  
Vol 43 (6) ◽  
pp. 827-854 ◽  
Author(s):  
Bradley A Miller ◽  
Eric C Brevik ◽  
Paulo Pereira ◽  
Randall J Schaetzl
Keyword(s):  
Land Use Planning ◽  
Precision Agriculture ◽  
Critical Role ◽  
Soil Mapping ◽  
Digital Soil Mapping ◽  
Soil Science ◽  
Accurate Information ◽  
Soil Maps ◽  
Geospatial Technologies ◽  
Soil Geography

The geography of soil is more important today than ever before. Models of environmental systems and myriad direct field applications depend on accurate information about soil properties and their spatial distribution. Many of these applications play a critical role in managing and preparing for issues of food security, water supply, and climate change. The capability to deliver soil maps with the accuracy and resolution needed by land use planning, precision agriculture, as well as hydrologic and meteorologic models is, fortunately, on the horizon due to advances in the geospatial revolution. Digital soil mapping, which utilizes spatial statistics and data provided by modern geospatial technologies, has now become an established area of study for soil scientists. Over 100 articles on digital soil mapping were published in 2018. The first and second generations of soil mapping thrived from collaborations between Earth scientists and geographers. As we enter the dawn of the third generation of soil maps, those collaborations remain essential. To that end, we review the historical connections between soil science and geography, examine the recent disconnect between those disciplines, and draw attention to opportunities for the reinvigoration of the long-standing field of soil geography. Finally, we emphasize the importance of this reinvigoration to geographers.

Efficiency Comparison of Conventional and Digital Soil Mapping for Updating Soil Maps

2012 ◽  
Vol 76 (6) ◽  
pp. 2097-2115 ◽  
Author(s):  
Bas Kempen ◽  
Dick J. Brus ◽  
Jetse J. Stoorvogel ◽  
Gerard B.M. Heuvelink ◽  
Folkert de Vries
Keyword(s):  
Soil Mapping ◽  
Digital Soil Mapping ◽  
Soil Maps ◽  
Efficiency Comparison

scholarly journals SOIL MAPPING ACCURACY FROM BLACK AND WHITE, COLOR, AND INFRARED AERIAL PHOTOGRAPHY

1971 ◽  
Vol 51 (3) ◽  
pp. 461-469 ◽  
Author(s):  
K. W. G. VALENTINE ◽  
T. M. LORD ◽  
W. WATT ◽  
A. L. BEDWANY
Keyword(s):  
Aerial Photography ◽  
Soil Mapping ◽  
Photographic Film ◽  
Soil Maps ◽  
Mapping Accuracy ◽  
Color Film ◽  
Black And White ◽  
White Film ◽  
Soil Surveys ◽  
Terrain Features

The accuracy obtainable from four types of aerial photographic film in the mapping and description of soil and terrain features was measured. Black and white film gave a soil mapping accuracy of 72% and was just as good as the color or infrared films for the description of specific terrain features in mountain lands. The accuracy of the soil map in the mountain lands and the description of terrain features in an alluvial valley increased to over 80% with the color film. Infrared film, both color and black and white, gave slightly more accurate soil maps in the valley. The use of a film like Kodak Special Ektachrome MS Aerographic Type SO-151 is recommended for future soil surveys. Black and white prints and color prints and transparencies can all be obtained from the same roll of this film type.

Covariates selection assessment for field scale digital soil mapping in the context of precision fertilization management

2020 ◽  
Author(s):  
Nada Mzid ◽  
Stefano Pignatti ◽  
Irina Veretelnikova ◽  
Raffaele Casa
Keyword(s):  
Soil Properties ◽  
Precision Agriculture ◽  
Prediction Models ◽  
Central Italy ◽  
Soil Mapping ◽  
Digital Soil Mapping ◽  
Soil Maps ◽  
Field Scale ◽  
Umbria Region ◽  
Digital Soil Maps

<p>The application of digital soil mapping in precision agriculture is extremely important, since an assessment of the spatial variability of soil properties within cultivated fields is essential in order to optimize agronomic practices such as fertilization, sowing, irrigation and tillage. In this context, it is necessary to develop methods which rely on information that can be obtained rapidly and at low cost. In the present work, an assessment is carried out of what are the most useful covariates to include in the digital soil mapping of field-scale properties of agronomic interest such as texture (clay, sand, silt), soil organic matter and pH in different farms of the Umbria Region in Central Italy. In each farm a proximal sensing-based mapping of the apparent soil electrical resistivity was carried out using the EMAS (Electro-Magnetic Agro Scanner) sensor. Soil sampling and subsequent analysis in the laboratory were carried out in each field. Different covariates were then used in the development of digital soil maps: apparent resistivity, high resolution Digital Elevation Model (DEM) from Lidar data, and bare soil and/or vegetation indices derived from Sentinel-2 images of the experimental fields. The approach followed two steps: (i) estimation of the variables using a Multiple Linear Regression (MLR) model, (ii) spatial interpolation via prediction models (including regression kriging and block kriging). The validity of the digital soil maps results was assessed both in terms of the accuracy in the estimation of soil properties and in terms of their impact on the fertilization prescription maps for nitrogen (N), phosphorus (P) and potassium (K).</p>

Digital soil mapping of organic carbon and it’s spatial distribution uncertainty in field scale

2020 ◽  
Author(s):  
Yan Guo ◽  
Ting Liu ◽  
Zhou Shi ◽  
Laigang Wang
Keyword(s):  
Standard Deviation ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Conditional Simulation ◽  
Soil Mapping ◽  
Digital Soil Mapping ◽  
Soil Maps ◽  
Distance Weighting ◽  
Fine Resolution ◽  
Inverse Distance

<p>     Soil organic carbon (SOC) is a key property that affects soil quality and the assessment of soil resources. However, the spatial distribution of SOC is very heterogeneous and existing soil maps have considerable uncertainty. Traditional polygon-based soil maps are less useful for fine-resolution soil maps modeling and monitoring because they do not adequately characterize and quantify the spatial variation of continuous soil properties. And recently, digital soil mapping of organic carbon is the main source of information to be used in natural resource assessment and soil management. In this study, we collected 100 soil samples on a 50 m grid to conduct soil maps of topsoil (0-20 cm) organic carbon in a 500×500m field and evaluate the uncertainty by spatial stochastic simulation. The map of soil organic carbon generated by inverse distance weighting interpolation indicated that the average topsoil SOC is 11.59±0.61g/kg with averaged standard deviation error is 0.61. In order to evaluate the uncertainties, numbers were defined as 50, 100, 200, 500, 1000, 5000, 10000 with interval of 2×2 m to conduct conditional simulation. The standard deviation error gradually declined from 0.74 to 0.51 g/kg. Then, the uncertainty of SOC was expressed as the range of the 95% confidence intervals of the standard deviation error. Maps of uncertainty showed fine spatial heterogeneity even the numbers of simulations reached 10000. Compared with inverse distance weighting interpolation method, conditional simulation approach can improve the fine-resolution SOC maps. For some points, the simulated values deviated from the averaged values while closed to the observed values. On the whole, the maps of uncertainty showed larger waves in the field-edge and different SOC contour border. Consideration of the sample distribution and sampling strategy, the uncertainty map provides a guide for decision-making in additional sampling.</p><p><strong>Key words</strong><strong>:</strong> Soil organic carbon (SOC); uncertainty assessment; conditional simulation; digital soil mapping</p><p><strong>Acknowledgements</strong></p><p>This material is based upon work funded by National Natural Science Foundation of China (No. 41601213), Major science and technology projects of Henan (171100110600), the Key Science and Technology Program of Henan (182102410024).</p>

HOW SOIL MAP UNITS AND DELINEATIONS CHANGE WITH SURVEY INTENSITY AND MAP SCALE

1981 ◽  
Vol 61 (4) ◽  
pp. 535-551 ◽  
Author(s):  
K. W. G. VALENTINE
Keyword(s):  
Large Scale ◽  
Small Scale ◽  
Soil Maps ◽  
Soil Map ◽  
Cartographic Generalization ◽  
Computer Based ◽  
Map Scale ◽  
Soil Surveys ◽  
Practical Implications ◽  
Different Soils

It is becoming common for soil surveys to be made of the same area at different intensities and published at different scales. The principles of cartographic generalization are discussed that control the relationships between the map units and delineations on maps made from such surveys. A study of two sets of maps showed that almost no lines were coincident. Up to 20% of the small scale delinations could be ’inliers’ of different soils and about 15% of the large scale delineations would be outside their small scale equivalents. The same discrepancies are to be expected between large scale soil maps and the smaller scale maps of physiography or vegetation that are often used to stratify soils. Reasons for these discrepancies are discussed under the headings of simplification and classification. Recommendations arc made to guide the preparation of maps and legends for different intensities and scales of survey in the same area. These recommendations have practical implications for the planning of surveys and the designs of computer-based autocartography systems.

scholarly journals Decision trees for digital soil mapping on subtropical basaltic steeplands

2011 ◽  
Vol 68 (2) ◽  
pp. 167-174 ◽  
Author(s):  
Elvio Giasson ◽  
Eliana Casco Sarmento ◽  
Eliseu Weber ◽  
Carlos Alberto Flores ◽  
Heinrich Hasenack
Keyword(s):  
Decision Tree ◽  
Decision Trees ◽  
Land Use Planning ◽  
Spatial Information ◽  
Soil Mapping ◽  
Soil Survey ◽  
Digital Soil Mapping ◽  
Tree Algorithms ◽  
Mapping Techniques ◽  
Soil Surveys

When soil surveys are not available for land use planning activities, digital soil mapping techniques can be of assistance. Soil surveyors can process spatial information faster, to assist in the execution of traditional soil survey or predict the occurrence of soil classes across landscapes. Decision tree techniques were evaluated as tools for predicting the ocurrence of soil classes in basaltic steeplands in South Brazil. Several combinations of types of decicion tree algorithms and number of elements on terminal nodes of trees were compared using soil maps with both original and simplified legends. In general, decision tree analysis was useful for predicting occurrence of soil mapping units. Decision trees with fewer elements on terminal nodes yield higher accuracies, and legend simplification (aggregation) reduced the precision of predictions. Algorithm J48 had better performance than BF Tree, RepTree, Random Tree, and Simple Chart.

Semi-supervised learning for increasing transferability of machine learning in digital soil mapping 

2021 ◽  
Author(s):  
Ruhollah Taghizadeh-Mehrjardi ◽  
Razieh Sheikhpour ◽  
Norair Toomanian ◽  
Thomas Scholten
Keyword(s):  
Machine Learning ◽  
Supervised Learning ◽  
Transfer Learning ◽  
Soil Mapping ◽  
Digital Soil Mapping ◽  
Soil Science ◽  
Target Area ◽  
Reference Area ◽  
Environmental Covariates ◽  
Soil Information

<p>The most critical aspect of application of digital soil mapping is its limited transferability. Modelling soil properties for regions where no or only sparse soil information is available is highly uncertain, when using the low-cost geo-spatial environmental covariates alone. To overcome this drawback, transfer learning has been introduced in different environmental sciences, including soil science. The general idea behind extrapolation of soil information with transfer learning in soil science is that the target area to transfer to is alike, e.g. in terms of soil-forming factors, and the same machine learning rules can be applied. Supervised machine learning, so far, has been used to transfer the soil information from the reference to the target areas with very similar environmental characteristics between both. Hence, it is unclear how machine learning can perform for other target regions with different environmental characteristics. Semi-supervised learning is an approach to machine learning that combines a small amount of labeled data (reference area) with a large amount of unlabeled data (target area) during training. In this study, we explored if semi-supervised learning could improve the transferability of digital soil mapping relative to supervised learning methods. Soil data for two arid regions and associated environmental covariates were obtained. Semi-supervised learning and supervised learning models were trained based on the data in the reference area and then tested based on the data in the target area. The results of this study indicated the higher power of semi-supervised learning for transferring soil information from one area to another in comparison to the supervised learning method.   </p>

Creation of detailed soil properties maps of the Czech Republic based on national legacy data and digital soil mapping

2020 ◽  
Author(s):  
Robert Minařík ◽  
Daniel Žížala ◽  
Anna Juřicová
Keyword(s):  
Czech Republic ◽  
Soil Properties ◽  
Large Scale ◽  
Agricultural Land ◽  
Prediction Models ◽  
External Validation ◽  
Soil Mapping ◽  
Digital Soil Mapping ◽  
The Czech Republic ◽  
Legacy Data

<p>Legacy soil data arising from traditional soil surveys are an important resource for digital soil mapping. In the Czech Republic, a large-scale (1:10 000) mapping of agricultural land was completed in 1970 after a decade of field investigation mapping. It represents a worldwide unique database of soil samples by its national extent and detail. This study aimed to create a detailed map of soil properties (organic carbon, ph, texture, soil unit) by using state-of-the-art digital soil mapping (DSM) methods. For this purpose we chose four geomorphologically different areas (2440 km<sup>2</sup> in total). A selected ensemble machine learning techniques based on bagging, boosting and stacking with random hyperparameters tuning were used to model each soil property. In addition to soil sample data, a DEM and its derivatives were used as common covariate layers. The models were evaluated using both internal repeated cross-validation and external validation. The best model was used for prediction of soil properties. The accuracy of prediction models is comparable with other studies. The resulting maps were also compared with the available original soil maps of the Czech Republic. The new maps reveal more spatial detail and natural variability of soil properties resulting from the use of DEM. This combination of high detailed legacy data with DSM results in the production of more spatially detailed and accurate maps, which may be particularly beneficial in supporting the decision-making of stakeholders.</p><p>The research has been supported by the project no. QK1820389 " Production of actual detailed maps of soil properties in the Czech Republic based on database of Large-scale Mapping of Agricultural Soils in Czechoslovakia and application of digital soil mapping" funding by Ministry of Agriculture of the Czech Republic.</p>

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