How well does digital soil mapping represent soil geography?  An investigation from the USA

Rossiter, David G.; Poggio, Laura; Beaudette, Dylan; Libohova, Zamir

doi:10.5194/soil-8-559-2022

Cited by 15 publications

(9 citation statements)

References 39 publications

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“…These studies used the available water storage layer from the gridded Soil Survey Geographic Database (gSSURGO) as a metric for the water table depth. This method provides easily accessible information, but the scale (∼30 m) of these data are approximated from digital map data ( Soil Survey Staff, 2022b ; Rossiter et al, 2022 ). While this allows for projections of state- and nation-wide soil data, our results suggest these approximations, in a predictive model context, are too coarse to determine important burrowing crayfish habitat needs.…”

Section: Discussionmentioning

confidence: 99%

“…Our results further support the importance of boots-on-the-ground to collect fine-scale habitat data to understand a species’ habitat needs. Lidar data and other course-scale data should not replace field surveys to create predictive habitat models, instead work in combination with field surveys to generate accurate predictions ( Rossiter et al, 2022 ). Given the predicted impacts on biodiversity from global threats such as climate change and human population expansion, additional research into habitat associations will help to advise and understand the relationship between organisms and their landscapes.…”

Section: Discussionmentioning

confidence: 99%

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Variation in fine-scale water table depth drives abundance of a unique semi-terrestrial crayfish species

Carlson,

Swedberg,

Miernicki

et al. 2024

PeerJ

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With anthropogenic changes altering the environment and the subsequent decline of natural habitats, it can be challenging to predict essential habitats for elusive and difficult to study taxa. Primary burrowing crayfish are one such group due to the complexity in sampling their semi-terrestrial, subterranean habitat. Sampling burrows usually requires a labor-intensive, time-consuming excavation or trapping process. However, limited information on burrowing crayfish suggests that fine-scale habitat variation may drive burrowing crayfish habitat choice. This project aimed to evaluate the fine-scale habitat characteristics that influence burrowing crayfish presence and abundance at a large, restored-remnant grassland preserve in north-central Illinois. We documented burrow abundance and quadrat-specific habitat variables such as root biomass, canopy cover, apparent seasonal high-water table (water table) depth and dominant vegetation at sites with and without burrowing crayfish populations. Data was recorded at every quadrat and analyzed using generalized linear mixed models. A total of 21 models were created to determine what habitat variables affected burrow presence and abundance. We found that the water table depth was a significant driver of burrow presence and abundance. Root biomass and vegetation cover were not significant drivers, although they did show up in the final models, explaining the data. These findings demonstrate empirical support for previous observations from other burrowing crayfish research and demonstrate the influence of fine-scale habitat when modeling elusive taxa requirements.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Variation in fine-scale water table depth drives abundance of a unique semi-terrestrial crayfish species

Carlson,

Swedberg,

Miernicki

et al. 2024

PeerJ

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“…Visual inspection of the DSM output over the terrain was used to identify abnormalities and assess how effectively it depicts landscape components (Rossiter et al, 2022). For this, we employed an expert-based qualitative assessment of the model output.…”

Section: Expert Evaluation Of Spatial Patterns Of the Beta-version So...mentioning

confidence: 99%

“…Expert knowledge of soil-landscape relations and soil distribution remains important for evaluating the predictive soil mapping results and assessing whether the predicted spatial patterns make sense from a pedological viewpoint (Hengl et al, 2017;Poggio et al, 2021;Rossiter et al, 2022). An important step in qualitative model evaluation is, therefore, expert assessment, whereby professionals with broad experience in soil survey and mapping can evaluate and improve the quality of the soil resource map.…”

Section: Expert Validation Of the Soil Mapmentioning

confidence: 99%

Reference soil groups map of Ethiopia based on legacy data and machine learning-technique: EthioSoilGrids 1.0

Ali,

Erkossa,

Gudeta

et al. 2024

SOIL

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Abstract. Up-to-date digital soil resource information and its comprehensive understanding are crucial to supporting crop production and sustainable agricultural development. Generating such information through conventional approaches consumes time and resources, and is difficult for developing countries. In Ethiopia, the soil resource map that was in use is qualitative, dated (since 1984), and small scaled (1 : 2 M), which limit its practical applicability. Yet, a large legacy soil profile dataset accumulated over time and the emerging machine-learning modeling approaches can help in generating a high-quality quantitative digital soil map that can provide better soil information. Thus, a group of researchers formed a Coalition of the Willing for soil and agronomy data-sharing and collated about 20 000 soil profile data and stored them in a central database. The data were cleaned and harmonized using the latest soil profile data template and 14 681 profile data were prepared for modeling. Random forest was used to develop a continuous quantitative digital map of 18 World Reference Base (WRB) soil groups at 250 m resolution by integrating environmental covariates representing major soil-forming factors. The map was validated by experts through a rigorous process involving senior soil specialists or pedologists checking the map based on purposely selected district-level geographic windows across Ethiopia. The map is expected to be of tremendous value for soil management and other land-based development planning, given its improved spatial resolution and quantitative digital representation.

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“…The Digital Soil Mapping (DSM) products now available across the globe at different scales are still strongly limited in accuracy (Lemercier et al, 2022; Rossiter et al, 2022), which hamper their use for making decisions, especially at the local level. An abundant literature has been devoted within these last years to the analysis of the causes of these inaccuracies and to the possible solutions to reduce them.…”

Section: Introductionmentioning

confidence: 99%

Evaluating the quality of soil legacy data used as input of digital soil mapping models

Lagacherie,

Arregui,

Fages

2024

European J Soil Science

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Most of the Digital Soil Mapping (DSM) products now available across the globe have been developed from the deposits of punctual soil observations inherited from several decades of soil survey activity. By using these legacy data as inputs for calibrating our DSM models, we implicitly make the assumption that these legacy soil data are accurate and therefore do not affect significantly our DSM products. However, this assumption has never been tested. The objectives of this study were to evaluate the accuracies of soil property measurements retrieved from legacy soil profiles, to analyse the different sources of error that may affect these measurements and to examine their impacts on the soil property predictions delivered by DSM models. The study was focused on a control sampling within the coastal plain of Languedoc (Southern France) at 129 locations where legacy measured soil profiles were collected between 1955 and 1992. At each location, four topsoil (0–20 cm) samples were collected at increasing distances (0, 5, 25 and 100 m) to characterize the local variabilities of the soil properties. Six soil properties—Clay, Silt, Sand, Soil Organic Carbon, Calcium Carbonate contents and Cation Exchange Capacity—were determined for each sample using certified soil laboratory methods. The results revealed that legacy soil property values had large overall errors and large biases. Biases likely induced by differences in soil analysis protocols could be corrected by linear functions calibrated onto the reference data obtained from the control sampling. The contributions of the errors propagated from the manual geo‐referencing errors (mean = 31 m) represented on average 52% of the errors after analytical bias corrections. These errors exhibited large variations from one property to another due to differences in the short‐range spatial variations (0–100 m) of these soil properties. A DSM exercise conducted on our control sampling revealed that the errors of legacy soil data were propagated to the soil property predictions provided by the DSM models. However, this propagation could be largely mitigated by applying the above‐evoked corrections. This study highlights the need to better control the quality of the legacy soil data used in DSM and to account for this source of uncertainty in the DSM models.

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How well does digital soil mapping represent soil geography? An investigation from the USA

Cited by 15 publications

References 39 publications

Variation in fine-scale water table depth drives abundance of a unique semi-terrestrial crayfish species

Variation in fine-scale water table depth drives abundance of a unique semi-terrestrial crayfish species

Reference soil groups map of Ethiopia based on legacy data and machine learning-technique: EthioSoilGrids 1.0

Evaluating the quality of soil legacy data used as input of digital soil mapping models

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