Land-surface segmentation as a method to create strata for spatial sampling and its potential for digital soil mapping

Drăguţ, Lucian; Dornik, Andrei

doi:10.1080/13658816.2015.1131828

Cited by 15 publications

(6 citation statements)

References 36 publications

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“…Improving prediction accuracy might require partitioning the landscape into relatively homogeneous areas based on the covariates used as predictors. The latter could be carried out by considering land surface segmentation [100] or the conditioned Latin hypercube (cLHS) [101] using the key variables driving each soil property. Moreover, though the present study considered a vast array of predictors including topographical and spectral data, a single analysis scale was applied as generally carried out in DSM.…”

Section: Resultsmentioning

confidence: 99%

High Resolution Mapping of Soil Properties Using Remote Sensing Variables in South-Western Burkina Faso: A Comparison of Machine Learning and Multiple Linear Regression Models

et al. 2017

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Accurate and detailed spatial soil information is essential for environmental modelling, risk assessment and decision making. The use of Remote Sensing data as secondary sources of information in digital soil mapping has been found to be cost effective and less time consuming compared to traditional soil mapping approaches. But the potentials of Remote Sensing data in improving knowledge of local scale soil information in West Africa have not been fully explored. This study investigated the use of high spatial resolution satellite data (RapidEye and Landsat), terrain/climatic data and laboratory analysed soil samples to map the spatial distribution of six soil properties–sand, silt, clay, cation exchange capacity (CEC), soil organic carbon (SOC) and nitrogen–in a 580 km2 agricultural watershed in south-western Burkina Faso. Four statistical prediction models–multiple linear regression (MLR), random forest regression (RFR), support vector machine (SVM), stochastic gradient boosting (SGB)–were tested and compared. Internal validation was conducted by cross validation while the predictions were validated against an independent set of soil samples considering the modelling area and an extrapolation area. Model performance statistics revealed that the machine learning techniques performed marginally better than the MLR, with the RFR providing in most cases the highest accuracy. The inability of MLR to handle non-linear relationships between dependent and independent variables was found to be a limitation in accurately predicting soil properties at unsampled locations. Satellite data acquired during ploughing or early crop development stages (e.g. May, June) were found to be the most important spectral predictors while elevation, temperature and precipitation came up as prominent terrain/climatic variables in predicting soil properties. The results further showed that shortwave infrared and near infrared channels of Landsat8 as well as soil specific indices of redness, coloration and saturation were prominent predictors in digital soil mapping. Considering the increased availability of freely available Remote Sensing data (e.g. Landsat, SRTM, Sentinels), soil information at local and regional scales in data poor regions such as West Africa can be improved with relatively little financial and human resources.

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Section: Resultsmentioning

confidence: 99%

High Resolution Mapping of Soil Properties Using Remote Sensing Variables in South-Western Burkina Faso: A Comparison of Machine Learning and Multiple Linear Regression Models

et al. 2017

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“…In the FDR spectra, positive correlations were found in the green-red region (508-676 nm) with some peaks in the NIR region (1000, 1410 and 2133 nm), while negative correlations were observed in the 800-1850 nm region with some peaks in the 2200-2400 nm region. Soil reflectance in visible regions (400-700 nm) is primarily associated with absorption in minerals containing Fe [51][52][53] and organic matter [54,55]. NIR regions (700-2500 nm) are dominated by absorption related to water (1400 and 1900 nm), minerals (1300-1400, 1800-1900, and 2200-2500 nm) and organic matter (1100, 1600, 1700-1800, 2000, and 2200-2400 nm) [56].…”

Section: Soil Spectral Response and Its Correlation To Oxalate-extracmentioning

confidence: 99%

Laboratory Visible and Near-Infrared Spectroscopy with Genetic Algorithm-Based Partial Least Squares Regression for Assessing the Soil Phosphorus Content of Upland and Lowland Rice Fields in Madagascar

et al. 2019

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As a laboratory proximal sensing technique, the capability of visible and near-infrared (Vis-NIR) diffused reflectance spectroscopy with partial least squares (PLS) regression to determine soil properties has previously been demonstrated. However, the evaluation of the soil phosphorus (P) content—a major nutrient constraint for crop production in the tropics—is still a challenging task. PLS regression with waveband selection can improve the predictive ability of a calibration model, and a genetic algorithm (GA) has been widely applied as a suitable method for selecting wavebands in laboratory calibrations. To develop a laboratory-based proximal sensing method, this study investigated the potential to use GA-PLS regression analyses to estimate oxalate-extractable P in upland and lowland soils from laboratory Vis-NIR reflectance data. In terms of predictive ability, GA-PLS regression was compared with iterative stepwise elimination PLS (ISE-PLS) regression and standard full-spectrum PLS (FS-PLS) regression using soil samples collected in 2015 and 2016 from the surface of upland and lowland rice fields in Madagascar (n = 103). Overall, the GA-PLS model using first derivative reflectance (FDR) had the best predictive accuracy (R2 = 0.796) with a good prediction ability (residual predictive deviation (RPD) = 2.211). Selected wavebands in the GA-PLS model did not perfectly match wavelengths of previously known absorption features of soil nutrients, but in most cases, the selected wavebands were within 20 nm of previously known wavelength regions. Bootstrap procedures (N = 10,000 times) using selected wavebands also confirmed the improvements in accuracy and robustness of the GA-PLS model compared to those of the ISE-PLS and FS-PLS models. These results suggest that soil oxalate-extractable P can be predicted from Vis-NIR spectroscopy and that GA-PLS regression has the advantage of tuning optimum bands for PLS regression, contributing to a better predictive ability.

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“…They found that categorical predictions at each soil landscape unit could better explain SOC heterogeneity. Uniform predictions of fields do not consider spatial variability, which is often not the most effective mapping strategy [41]. The accuracy of zoning modelling in this study was better than the accuracy of entering the zoning methods into the model as covariates.…”

Section: Performance Of Zoning Modellingmentioning

confidence: 68%

Zoning Prediction and Mapping of Three-Dimensional Forest Soil Organic Carbon: A Case Study of Subtropical Forests in Southern China

Zhang

Zhao

et al. 2023

Forests

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Accurate soil organic carbon (SOC) maps are helpful for guiding forestry production and management. Different ecological landscape areas within a large region may have different soil–landscape relationships, so models specifically for these areas may capture these relationships more accurately than the global model for the entire study area. The aim of this study was to investigate the role of zonal modelling in predicting forest SOC and to produce highly accurate forest SOC distribution maps. The prediction objects were SOC at five soil depths (0–20, 20–40, 40–60, 60–80, and 80–100 cm). First, the forest type map and soil texture class map were used to divide the relative homogeneous regions in Shaoguan City, Guangdong Province, China. Second, seven terrain variables derived from a 12.5-m digital elevation model (DEM) and five vegetation variables generated from 10-m Sentinel-2 remote sensing images were used as predictors to develop regional artificial neural network (ANN) models for each homogeneous region, as well as a global ANN model for the entire study area (1000 sample points). Finally, 10-fold cross-validation was used to assess the ANN prediction model performance, and independent validation was used to evaluate the produced forest SOC prediction maps (194 additional samples). The cross-validation results showed that the accuracies of the regional models were better than that of the global model. Independent validation results also showed that the precision (R2) of 0- to 100-cm forest SOC maps generated by forest type modelling had an improvement of 0.05–0.15, and that by soil texture class modelling had an improvement of 0.07–0.13 compared to the map generated by the global model. In conclusion, delineating relatively homogeneous regions via simple methods can improve prediction accuracy when undertaking soil predictions over large areas, especially with complex forest landscapes. In addition, SOC in the study area is generally more abundant in broadleaf forest and clay areas, with overall levels decreasing with soil depth. Accurate SOC distribution information can provide references for fertilization and planting. Plants with particularly high soil fertility requirements may perhaps be planted in broadleaf forests or clay areas, and plants with particularly developed roots may require furrow application of a small amount of SOC.

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Land-surface segmentation as a method to create strata for spatial sampling and its potential for digital soil mapping

Cited by 15 publications

References 36 publications

High Resolution Mapping of Soil Properties Using Remote Sensing Variables in South-Western Burkina Faso: A Comparison of Machine Learning and Multiple Linear Regression Models

High Resolution Mapping of Soil Properties Using Remote Sensing Variables in South-Western Burkina Faso: A Comparison of Machine Learning and Multiple Linear Regression Models

Laboratory Visible and Near-Infrared Spectroscopy with Genetic Algorithm-Based Partial Least Squares Regression for Assessing the Soil Phosphorus Content of Upland and Lowland Rice Fields in Madagascar

Zoning Prediction and Mapping of Three-Dimensional Forest Soil Organic Carbon: A Case Study of Subtropical Forests in Southern China

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