Effect of the accuracy of topographic data on improving digital soil mapping predictions with limited soil data: An application to the Iranian loess plateau

Maleki, Sedigheh; Khormali, Farhad; Mohammadi, J.; Bogaert, Pierre-Maurice; Bodaghabadi, Mohsen Bagheri

doi:10.1016/j.catena.2020.104810

Cited by 34 publications

(13 citation statements)

References 56 publications

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“…Many other studies confirmed the significant role of topography and its derivatives (elevation, slope and TWI) in the spatial distribution of SAS (Cantón et al, 2009;Tang et al, 2010;Nsabimana et al, 2020). Furthermore, we cannot forget the effect of the topography on other soil properties (Kumar and Singh, 2016;Li et al, 2020;Maleki et al, 2020;Tajik el al., 2020) and (Celik, 2005;Jin et al, 2008;Mahmoudabadi et al, 2017;Maynard and Levi, 2017), which will undoubtedly affect the distribution of soil stability. Besides all above results, we cannot forget the role of remote sensing parameters, which have shown their importance during this study.…”

Section: Determining the Relative Importance Of Variablesmentioning

confidence: 74%

Soil Aggregate Stability Mapping Using Remote Sensing and GIS-Based Machine Learning Technique

Bouslıhım

Rochdi

Aboutayeb³

et al. 2021

Front. Earth Sci.

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Soil aggregate stability (SAS) is a critical parameter of soil quality and its mapping can help determine erosion hotspots. Despite this importance, SAS is less documented in available literature due to limited number of analyzes besides being a time consuming. For this reason, many researchers have turned to alternative methods that often use readily available variables such as soil parameters or remote sensing indices to estimate this variable. In that framework, the aim of the present study focused on the investigation of the feasibile use of adapted Leo Breiman’s random forest algorithm (RF) to mapping different mean weight diameter (MWD) tests as an index of SAS (mechanical breakdown (MWDmb), slow wetting (MWDsw), fast wetting (MWDfw) and the mean of the three tests (MWDmean)). The model was built with 77 samples distributed in the three watersheds of the study area located at Settat Ben-Ahmed, in Morocco and with the use of several environmental variables such as soil parameters (organic matter and clay), remote sensing indices (band 2, band 3, band 4, band 5, normalized difference vegetation index (NDVI) and transformed normalized difference vegetation index (TNDVI)), topography (elevation, slope, curvature plane and the topographic wetness index (TWI)) along with additional categorical variables as geological maps, land use and soil classes. The results showed a good level of accuracy for the training phase (75% of samples) for the different tests (R2 > 0.92, RMSE and MAE < 0.15) and were satisfactory for the testing phase (25% of samples, R2 > 0.65, RMSE and MAE < 0.31). Also, organic matter, topography and geology were the most important parameters in the spatial prediction of SAS. Finally, the maps build during this study could be of great use to identify areas of less stable soils in the perspective for taking the necessary measures to improve their quality.

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Section: Determining the Relative Importance Of Variablesmentioning

confidence: 74%

Soil Aggregate Stability Mapping Using Remote Sensing and GIS-Based Machine Learning Technique

Bouslıhım

Rochdi

Aboutayeb³

et al. 2021

Front. Earth Sci.

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“…Among the three soil properties, the clay content model had poor performance compared with pH and SOC, with the lowest MEC value. This could be attributed to one or a combination of the following: (a) the spatial resolution of some of the environmental variables was not detailed enough to capture the variation (Maleki et al, 2020); (b) the set of covariates retained was not suitable and other environmental variables need to be included; or (c) the sampling protocol was too clustered to capture variability across the study area, since observations were taken from three main clusters and various processes operate at different spatial scales (Hendriks et al, 2021).…”

Section: Discussionmentioning

confidence: 99%

Accounting for analytical and proximal soil sensing errors in digital soil mapping

Takoutsing

Heuvelink

Stoorvogel

et al. 2022

European J Soil Science

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Digital soil mapping (DSM) approaches provide soil information by utilising the relationship between soil properties and environmental variables. Calibration of DSM models requires measurements that may often have substantial measurement errors which propagate to the DSM outputs and need to be accounted for. This study applied a geostatistical-based DSM approach that incorporates measurement error variances in the covariance structure of the spatial model, weights measurements in accordance with their measurement accuracies and assesses the effects of measurement errors on the accuracies of DSM outputs. The method was applied in the Western Cameroon, where soil samples from 480 locations were collected and analysed for pH, clay and soil organic carbon (SOC) using conventional and mid-infrared spectroscopy methods. Variogram parameters and regression coefficients were estimated using residual maximum likelihood under two scenarios: with and without taking measurement errors into account. Performance of the spatial models in the two scenarios was compared using validation metrics obtained with three types of cross-validation. Acknowledging measurement errors impacted the regression coefficients and influenced the variogram parameters by reducing the nugget and sill variance for the three soil properties. Validation metrics including mean error, root mean square error and model efficiency coefficient were quite similar in both scenarios, but the prediction uncertainties were more realistically quantified by the models that account for measurement errors, as indicated by accuracy plots. There were relatively small absolute differences in predicted values of soil properties of up to 0.1 for pH, 1.6% for clay and 2 g/kg for SOC between the two scenarios. We emphasised the need of incorporating measurement errors in DSM approaches to improve uncertainty quantification, particularly when applying spectroscopy for estimating soil properties. Further development of the approach is the extension to non-linear machine learning regression methods.

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“…The first approach, at the field scale, uses in situ spectral reflectance and spectral covariables to predict SOC [14,[18][19][20][21]. At the field scale, the topographic indices derived from lidar sensors are also very commonly used to explain soil variability [22,23]. The second approach, commonly used at regional scales, considers a suite of covariables with or without spectral variables to predict SOC [24][25][26][27][28][29][30].…”

Section: Surface Soc Prediction Modeling Across Biomesmentioning

confidence: 99%

Remote Sensing of Surface and Subsurface Soil Organic Carbon in Tidal Wetlands: A Review and Ideas for Future Research

et al. 2022

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Tidal wetlands, widely considered the most extensive reservoir of soil organic carbon (SOC), can benefit from remote sensing studies enabling spatiotemporal estimation and mapping of SOC stock. We found that a majority of the remote-sensing-based SOC mapping efforts have been focused on upland ecosystems, not on tidal wetlands. We present a comprehensive review detailing the types of remote sensing models and methods used, standard input variables, results, and limitations for the handful of studies on tidal wetland SOC. Based on that synthesis, we pose several unexplored research questions and methods that are critical for moving tidal wetland SOC science forward. Among these, the applicability of machine learning and deep learning models for predicting surface SOC and the modeling requirements for SOC in subsurface soils (soils without a remote sensing signal, i.e., a soil depth greater than 5 cm) are the most important. We did not find any remote sensing study aimed at modeling subsurface SOC in tidal wetlands. Since tidal wetlands store a significant amount of SOC at greater depths, we hypothesized that surface SOC could be an important covariable along with other biophysical and climate variables for predicting subsurface SOC. Preliminary results using field data from tidal wetlands in the southeastern United States and machine learning model output from mangrove ecosystems in India revealed a strong nonlinear but significant relationship (r2 = 0.68 and 0.20, respectively, p < 2.2 × 10−16 for both) between surface and subsurface SOC at different depths. We investigated the applicability of the Soil Survey Geographic Database (SSURGO) for tidal wetlands by comparing the data with SOC data from the Smithsonian’s Coastal Blue Carbon Network collected during the same decade and found that the SSURGO data consistently over-reported SOC stock in tidal wetlands. We concluded that a novel machine learning framework that utilizes remote sensing data and derived products, the standard covariables reported in the limited literature, and more importantly, other new and potentially informative covariables specific to tidal wetlands such as tidal inundation frequency and height, vegetation species, and soil algal biomass could improve remote-sensing-based tidal wetland SOC studies.

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Effect of the accuracy of topographic data on improving digital soil mapping predictions with limited soil data: An application to the Iranian loess plateau

Cited by 34 publications

References 56 publications

Soil Aggregate Stability Mapping Using Remote Sensing and GIS-Based Machine Learning Technique

Soil Aggregate Stability Mapping Using Remote Sensing and GIS-Based Machine Learning Technique

Accounting for analytical and proximal soil sensing errors in digital soil mapping

Remote Sensing of Surface and Subsurface Soil Organic Carbon in Tidal Wetlands: A Review and Ideas for Future Research

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