In traditional soil surveying, the spatial distribution of soil properties and soil bodies can be inferred from mental models, leading to manual delineations. Such methods can be considered subjective and therefore it is difficult or impossible to statistically evaluate the accuracy of such maps without additional field sampling. Traditional soil survey mapping also has limitations in a multi-topic GIS, related to the fact that it is often not applied consistently by different mappers, and is largely manual and difficult to automate. Most traditional soil maps are based on manual delineations of assumed soil bodies, to which soil attributes are then attached. With pedometric mapping, all results are based on rigorous statistical computing and are therefore reproducible.[4] Pedometric mapping relies heavily on extensive and detailed covariate layers, such as those derived from digital elevation model (DEM), remote sensing imagery, climate, land cover and geological GIS layers and images. Its evolution may be closely related to the emergence of new global and publicly available technologies and data sources, such as SRTM DEM, MODIS, ASTER and Landsat images, gamma radiometry and LiDAR images and new automated mapping methods.

Pedometric analyzes are strictly based on geostatistics, while digital soil mapping uses more traditional soil mapping concepts that are not strictly pedometric in nature. Also known as predictive soil mapping digital soil mapping relies on computer-aided inference of soil properties to produce digital maps of discrete soil types. Pedometric mapping does not produce maps that delineate discrete soil types.[5]​.

Pedometric mapping methods differ depending on the steps of soil survey data processing:.

1. • Sampling.

2. • Data screening.

3. • Preprocessing of soil covariates.

4. • Adjustment of the geostatistical model.

5. • spatial prediction.

6. • Cross validation/accuracy evaluation.

7. • Display of outputs.

One of the main theoretical bases for pedometric mapping is the universal model of soil variation:

...where the deterministic part of the soil variation, is the stochastic, spatially autocorrelated part of the variation, the remaining residual variation (measurement errors, short-range variability, etc.) that possibly also depends on, but is not modeled. This model was first introduced by French mathematician Georges Matheron and has proven to be the best unbiased linear predictor for spatial data. One way to use this model to produce predictions or simulations is through regression kriging (also known as universal kriging). With soil data, the deterministic component of the model is often based on the soil formation factors of climate, organism, relief, parent material (lithology), and time. This conceptual model, known as the CLORPT model, was introduced into soil and landscape modeling by Hans Jenny.

A special group of pedometric mapping techniques focuses on downscaling spatial information that can be continuous or area-based. Prediction of soil classes is also another subfield of pedometric mapping, where specific geostatistical methods are used to interpolate the factor types of variables.

Pedometric mapping also relies heavily on novel technologies to measure soil properties, also known as digital soil mapping techniques include:.

• - Soil spectroscopy and proximal soil detection (portable or vehicle-driven devices).

• - Remote sensing systems for soil mapping and monitoring (e.g. SMOS).

• - LiDAR technology for digital elevation models.

• - Precision agriculture technologies.

• - Pedometrics Commission of the International Union of Soil Sciences.

• - ISRIC — World Soil Information data center.

• - International Society for Geomorphometry.

• - Open Source tools for soil scientists by the California Soil Resource Lab.

• - Working Group on Digital Soil Mapping.

In traditional soil surveying, the spatial distribution of soil properties and soil bodies can be inferred from mental models, leading to manual delineations. Such methods can be considered subjective and therefore it is difficult or impossible to statistically evaluate the accuracy of such maps without additional field sampling. Traditional soil survey mapping also has limitations in a multi-topic GIS, related to the fact that it is often not applied consistently by different mappers, and is largely manual and difficult to automate. Most traditional soil maps are based on manual delineations of assumed soil bodies, to which soil attributes are then attached. With pedometric mapping, all results are based on rigorous statistical computing and are therefore reproducible.[4] Pedometric mapping relies heavily on extensive and detailed covariate layers, such as those derived from digital elevation model (DEM), remote sensing imagery, climate, land cover and geological GIS layers and images. Its evolution may be closely related to the emergence of new global and publicly available technologies and data sources, such as SRTM DEM, MODIS, ASTER and Landsat images, gamma radiometry and LiDAR images and new automated mapping methods.

Pedometric analyzes are strictly based on geostatistics, while digital soil mapping uses more traditional soil mapping concepts that are not strictly pedometric in nature. Also known as predictive soil mapping digital soil mapping relies on computer-aided inference of soil properties to produce digital maps of discrete soil types. Pedometric mapping does not produce maps that delineate discrete soil types.[5]​.

Pedometric mapping methods differ depending on the steps of soil survey data processing:.

1. • Sampling.

2. • Data screening.

3. • Preprocessing of soil covariates.

4. • Adjustment of the geostatistical model.

5. • spatial prediction.

6. • Cross validation/accuracy evaluation.

7. • Display of outputs.

One of the main theoretical bases for pedometric mapping is the universal model of soil variation:

...where the deterministic part of the soil variation, is the stochastic, spatially autocorrelated part of the variation, the remaining residual variation (measurement errors, short-range variability, etc.) that possibly also depends on, but is not modeled. This model was first introduced by French mathematician Georges Matheron and has proven to be the best unbiased linear predictor for spatial data. One way to use this model to produce predictions or simulations is through regression kriging (also known as universal kriging). With soil data, the deterministic component of the model is often based on the soil formation factors of climate, organism, relief, parent material (lithology), and time. This conceptual model, known as the CLORPT model, was introduced into soil and landscape modeling by Hans Jenny.

A special group of pedometric mapping techniques focuses on downscaling spatial information that can be continuous or area-based. Prediction of soil classes is also another subfield of pedometric mapping, where specific geostatistical methods are used to interpolate the factor types of variables.

Pedometric mapping also relies heavily on novel technologies to measure soil properties, also known as digital soil mapping techniques include:.

• - Soil spectroscopy and proximal soil detection (portable or vehicle-driven devices).

• - Remote sensing systems for soil mapping and monitoring (e.g. SMOS).

• - LiDAR technology for digital elevation models.

• - Precision agriculture technologies.

• - Pedometrics Commission of the International Union of Soil Sciences.

• - ISRIC — World Soil Information data center.

• - International Society for Geomorphometry.

• - Open Source tools for soil scientists by the California Soil Resource Lab.

• - Working Group on Digital Soil Mapping.