Optimal scaling of predictors for digital mapping of soil properties

Dornik, Andrei; Cheţan, Marinela Adriana; Drăguţ, Lucian; Dicu, D.; Iliuță, A.

doi:10.1016/j.geoderma.2021.115453

Cited by 16 publications

(6 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…While below scale level L = 3 the multi-scale attribute variant NH 4 dominates, above scale level L = 3 other NH or VDC variants appear. This underlines the scale dependence of the soil-related processes, for which scale-specific parameters have to be identified as optimal for prediction [28,83,84]. It is finally noticeable that the one-dimensional attribute FILL has the highest explanatory power.…”

Section: Scale-specific Optimizationmentioning

confidence: 78%

“…This is also evident in the comparison of the modeling results based on the filter-specific parameterization (Section 3.1), which represents a static window-based aggregation procedure. In contrast to changes in grid resolution of terrain attributes [26,28], the segmentation-based aggregation considers both parameter-specific and spatial data variability. In this way, a more precise delineation of the reference units can be made.…”

Section: Scale-specific Optimizationmentioning

confidence: 99%

“…Using a sub-area within Bavaria as an example, we extend the modeling approach and compare the predictive single and mutual capabilities of Landsat-based SRCs and multi-scale terrain attributes. The latter should take into account the fact that soil properties and soil-forming processes are an expression of complex relationships between soil forming factors and landforms, which occur on different scales [21][22][23][24][25][26][27][28]. Multi-scale terrain attributes enable the consideration of contextual information, which can improve the prediction accuracy of soil properties [24].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Scale-specific Prediction of Topsoil Organic Carbon Contents using Terrain Attributes and SCMaP Soil Reflectance Composites

Möller¹,

Zepp²,

Wiesmeier³

et al. 2022

Preprint

View full text Add to dashboard Cite

There is a growing need for an area-wide knowledge of SOC contents in agricultural soils at field scale for food security, monitoring long-term changes related to soil health and climate change. In Germany, large-scale SOC maps are mostly available with a spatial resolution of 250 m to 1 km2. 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 Landsat-based multi-temporal soil reflectance composites (SRC) as explanatory variables. On the example of an Bavarian test of about 8000 km2, the scale-specific dependencies between the representativeness of 220 soil samples and 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, 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 lead to a significant model improvement at large field-related 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 R2=0.84 and RMSE=1.99.

show abstract

Section: Scale-specific Optimizationmentioning

confidence: 78%

Section: Scale-specific Optimizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Scale-specific Prediction of Topsoil Organic Carbon Contents using Terrain Attributes and SCMaP Soil Reflectance Composites

Möller¹,

Zepp²,

Wiesmeier³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Digital maps provide an opportunity to calculate the average values of the pixels located across the study area whereas conventional soil mapping was not capable of providing enough data (Dornik et al, 2022). This superiority has opened a pathway to distinguish the Homosoil at any area under the DSM umbrella for quantitative extrapolation of soil information (Malone et al, 2016).…”

Section: Resultsmentioning

confidence: 99%

Digital assessments of soil organic carbon storage using digital maps provided by static and dynamic environmental covariates

Shirazi

Shahbazi

Rezaei

et al. 2023

Soil Use and Management

View full text Add to dashboard Cite

Understanding the vertical and lateral distribution of soil organic carbon (SOC) and soil organic carbon density (SOCD) is indispensable for soil use and environmental management because of their vital role in soil quality assessments. Primarily, they are needed in calculating soil organic carbon storage (SOCS). The objective of this research was to provide digital maps of SOC and SOCD variation as well as their uncertainties at multiple standardized depths (H1: 0–5, H2: 5–15, H3: 15–30, H4: 30–60 and H5: 60–100 cm) using a parsimonious model with optimized terrain‐related attributes and satellite‐derived data. SOCS were evaluated at soil subgroup levels. An area of about 808 km2 with varying elevation, plant cover and lithology from the Miandoab region, West Azerbaijan Province, Iran was selected as a case study area. A total of 386 soil samples were collected from 104 profiles comprising various soil genetic horizons. A continuous spline function was then fitted to the target properties in advance of creating a dataset at five standard depth intervals (following the GlobalSoilMap project). These were then grouped into three classes including top (H1), middle (H2, H3 and H4) and bottom (H5) depths to ease interpretation. Static and dynamic covariates (30‐m resolution) were derived from a digital elevation model (DEM) and a suite of Landsat‐8 spectral imageries, respectively. Four candidate models including stepwise multiple linear regression (SMLR), random forest (RF), cubist (CU) and extreme gradient boosting (XGBoost) Tree were tested in this study. Finally, the digital maps at 30‐m resolution of SOC and SOCD and their uncertainties were prepared using the best‐fit model and the bootstrapping method, respectively. Four soil subgroups (Gypsic Haploxerepts, Typic Calcixerepts, Typic Haploxerepts and Xeric Haplocalcids) were identified across the study area. The covariates had variable contributions on the evaluated models. The XGBoost Tree model generally outperformed other models for prediction of SOC and SOCD (R2 = 0.60, on average). Regardless of soil subgroups, the uncertainty analysis showed that the SOCD map had a low prediction interval range value indicating high accuracy. Additionally, the highest SOCS and SOCD was observed at the top followed by middle and bottom depths in the study area. All subgroups exhibited a decreasing trend of SOCD with increasing depth. A similar trend was also observed for SOCS. The highest SOCD (on average) was observed in Gypsic Haploxerepts (4.71 kg C/m2) followed by Typic Calcixerepts (4.46 kg C/m2), Typic Haploxerepts (4.45 kg C/m2) and Xeric Haplocalcids (4.40 kg C/m2). Overall, the SOCS normalized by area within soil order boundaries was greater in Inceptisols than Aridisols across the study area. The findings of this study provide critical information for sustainable management of soil resources in the area for agricultural production and environmental health in the Miandoab region of Iran.

show abstract

“…This downscaling has the side effect that it removes noise from the image by smoothing out short-range artifacts, e.g., caused by BRDF effects due to surface roughness as discussed above [36]. Dornik et al [72] found that in DSM, the optimal scale of predictor variables improved the accuracy of prediction models. Thus, resampling helped to improve the prediction accuracy at the cost of a centimeter-scale resolution.…”

Section: Potential Of Som Mapping In Agriculturementioning

confidence: 99%

Fine-Scale Mapping of Soil Organic Matter in Agricultural Soils Using UAVs and Machine Learning

2022

View full text Add to dashboard Cite

The fine-scale mapping of soil organic matter (SOM) in croplands is vital for the sustainable management of soil. Traditionally, SOM mapping relies on laboratory methods that are labor-intensive and costly. Recent advances in unmanned aerial vehicles (UAVs) afford new opportunities for rapid and low-cost SOM mapping at the field scale. However, the conversion from UAV measurements to SOM maps requires specific transfer models that still rely on local sampling. This study aimed to develop a method for predicting topsoil SOM at a high resolution on the field scale based on soil color information gained from low-altitude UAV imagery and machine learning. For this, we performed a UAV survey in cropland within the German loess belt. We used two fields, one for training and one for validation of the model, to test the model transferability. We analyzed 91 soil samples for SOM in the laboratory for the model calibration and 8 additional samples for external model validation. A random forest model (RF) showed good performance for the prediction of SOM based on UAV-derived color information with an RMSE of 0.13% and with an RPIQ of 2.42. The RF model was used to predict SOM at a point-support of 1 × 1 m. The SOM map revealed spatial patterns within the fields with a uniform spread of the prediction uncertainty. The validation of the model performed similarly to the calibration with an RMSE of 0.12% and an RPIQ of 2.05, albeit with a slight bias of 0.05%. This validation using external data showed that prediction models are transferable to neighboring fields, thus permitting the prediction on larger scale farms or enabling carbon monitoring over time.

show abstract

Optimal scaling of predictors for digital mapping of soil properties

Cited by 16 publications

References 32 publications

Scale-specific Prediction of Topsoil Organic Carbon Contents using Terrain Attributes and SCMaP Soil Reflectance Composites

Scale-specific Prediction of Topsoil Organic Carbon Contents using Terrain Attributes and SCMaP Soil Reflectance Composites

Digital assessments of soil organic carbon storage using digital maps provided by static and dynamic environmental covariates

Fine-Scale Mapping of Soil Organic Matter in Agricultural Soils Using UAVs and Machine Learning

Contact Info

Product

Resources

About