Spatial prediction of soil sand content at various sampling density based on geostatistical and machine learning algorithms in plain areas

Qu, Lili; Lu, Huizhong; Tian, Zhiyuan; Schoorl, J.M.; Huang, Biao; Liang, Yonghong; Qiu, Dan; Liang, Yin

doi:10.1016/j.catena.2023.107572

Cited by 9 publications

(3 citation statements)

References 62 publications

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“…On the other hand, altitude can influence sediment deposition and erosion, as higher areas may experience different patterns of precipitation and erosion. Mello et al (2022) corroborated the importance of topographic attributes linked to hydrological behavior in mapping sand and silt content, while Qu et al (2024) demonstrated the relevance of other topographic indices for obtaining more accurate maps of sand content spatial distribution. These findings underscore the need to consider a variety of topographic attributes for precise and detailed prediction of sediment distribution in the soil.…”

Section: Discussionmentioning

confidence: 59%

Looking for Optimal Maps of Soil Properties at the Regional Scale

Barrena-González,

Lavado Contador,

Repe

et al. 2024

Int J Environ Res

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Around 70% of surface in Extremadura, Spain, faces a critical risk of degradation processes, highlighting the necessity for regional-scale soil property mapping to monitor degradation trends. This study aimed to generate the most reliable soil property maps, employing the most accurate methods for each case. To achieve this, six different machine learning (ML) techniques were tested to map nine soil properties across three depth intervals (0–5, 5–10 and > 10 cm). Additionally, 22 environmental covariates were utilized as inputs for model performance. Results revealed that the Random Forest (RF) model exhibited the highest precision, followed by Cubist, while Support Vector Machine showed effectiveness with limited data availability. Moreover, the study highlighted the influence of sample size on model performance. Concerning environmental covariates, vegetation indices along with selected topographic indices proved optimal for explaining the spatial distribution of soil physical properties, whereas climatic variables emerged as crucial for mapping the spatial distribution of chemical properties and key nutrients at a regional scale. Despite providing an initial insight into the regional soil property distribution using ML, future work is warranted to ensure a robust, up-to-date, and equitable database for accurate monitoring of soil degradation processes arising from various land uses.

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Section: Discussionmentioning

confidence: 59%

Looking for Optimal Maps of Soil Properties at the Regional Scale

Barrena-González,

Lavado Contador,

Repe

et al. 2024

Int J Environ Res

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“…Despite these limitations, geological data could be used to help improve predictions in areas with lower data coverage, or higher uncertainty, via co-kriging, which allows relationships with additional variables to be used during the interpolation process 150,151 . Alternatively, a range of machine learning approaches are available which are also able to use additional information, such as geological data and other environmental covariates; these methods can perform better than traditional geostatistical methods for generating spatial interpolations 152,153 , particularly when the density of data points for the primary variable of interest is low 154 . However, they do not always generate more accurate interpolations; a combined approach, which uses an ensemble of outputs from different interpolation methods with spatially-varying weightings dependant on density of available data, may result in better overall accuracy 136 .…”

Section: Usage Notesmentioning

confidence: 99%

High-resolution freshwater dissolved calcium and pH data layers for Canada and the United States

Guerin,

Weise,

Chu

et al. 2024

Sci Data

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Freshwater ecosystems are biologically important habitats that provide many ecosystem services. Calcium concentration and pH are two key variables that are linked to multiple chemical processes in these environments, influence the biology of organisms from diverse taxa, and can be important factors affecting the distribution of native and non-native species. However, it can be challenging to obtain high-resolution data for these variables at regional and national scales. To address this data gap, water quality data for lakes and rivers in Canada and the continental USA were compiled and used to generate high-resolution (10 × 10 km) interpolated raster layers, after comparing multiple spatial interpolation approaches. This is the first time that such data have been made available at this scale and resolution, providing a valuable resource for research, including projects evaluating risks from environmental change, pollution, and invasive species. This will aid the development of conservation and management strategies for these vital habitats.

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“…These values suggest a relatively robust model, particularly for SOC, where the PIR is lower than 0.50. Dvorakova et al (2023) [87] report a higher PIR of 0.88 for SOC prediction using Sentinel-2 data in Belgium and the Netherlands, while Qu et al (2024) [88] found a PIR of about 0.50 for predicting sand content in eastern China using digital soil mapping techniques. It should be noted, however, that the scientific community is still not decided on which is the best uncertainty measure in digital soil mapping.…”

Section: General Overview and Comparison With Other Work And Productsmentioning

confidence: 99%

Soil Data Cube and Artificial Intelligence Techniques for Generating National-Scale Topsoil Thematic Maps: A Case Study in Lithuanian Croplands

Samarinas,

Tsakiridis,

Kokkas

et al. 2023

Remote Sensing

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There is a growing realization among policymakers that in order to pave the way for the development of evidence-based conservation recommendations for policy, it is essential to improve the capacity for soil-health monitoring by adopting multidimensional and integrated approaches. However, the existing ready-to-use maps are characterized mainly by a coarse spatial resolution (>200 m) and information that is not up to date, making their use insufficient for the EU’s policy requirements, such as the common agricultural policy. This work, by utilizing the Soil Data Cube, which is a self-hosted custom tool, provides yearly estimations of soil thematic maps (e.g., exposed soil, soil organic carbon, clay content) covering all the agricultural area in Lithuania. The pipeline exploits various Earth observation data such as a time series of Sentinel-2 satellite imagery (2018–2022), the LUCAS (Land Use/Cover Area Frame Statistical Survey) topsoil database, the European Integrated Administration and Control System (IACS) and artificial intelligence (AI) architectures to improve the prediction accuracy as well as the spatial resolution (10 m), enabling discrimination at the parcel level. Five different prediction models were tested with the convolutional neural network (CNN) model to achieve the best accuracy for both targeted indicators (SOC and clay) related to the R2 metric (0.51 for SOC and 0.57 for clay). The model predictions supported by the prediction uncertainties based on the PIR formula (average PIR 0.48 for SOC and 0.61 for clay) provide valuable information on the model’s interpretation and stability. The model application and the final predictions of the soil indicators were carried out based on national bare-soil-reflectance composite layers, generated by employing a pixel-based composite approach to the overlaid annual bare-soil maps and by using a combination of a series of vegetation indices such as NDVI, NBR2, and SCL. The findings of this work provide new insights for the generation of soil thematic maps on a large scale, leading to more efficient and sustainable soil management, supporting policymakers and the agri-food private sector.

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Spatial prediction of soil sand content at various sampling density based on geostatistical and machine learning algorithms in plain areas

Cited by 9 publications

References 62 publications

Looking for Optimal Maps of Soil Properties at the Regional Scale

Looking for Optimal Maps of Soil Properties at the Regional Scale

High-resolution freshwater dissolved calcium and pH data layers for Canada and the United States

Soil Data Cube and Artificial Intelligence Techniques for Generating National-Scale Topsoil Thematic Maps: A Case Study in Lithuanian Croplands

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