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Equipment and Industrial download Condensed work capital c. Saadat et al. They found that the spectral information in the RS data increased the possibility of distinguishing topographically similar landforms and subsequently improved the classification. Dobos et al. Compared to the terrain derivatives, the spectral information in the AVHRR bands were noted to have contributed more to the accurate delineation of soil types. Hahn and Gloaguen [ 17 ] underscored the importance of remotely sensed terrain variables e.

In a regional scale analysis, Scudiero et al. Other studies also demonstrated the contribution of RS data in mapping soil properties such as sand, silt, clay and soil organic carbon SOC based on reasonable correlations between soil properties and reflectance spectra [ 14 , 19 , 20 ]. Despite many advances, further exploration of the application of RS data to soil mapping is required, especially in data poor regions such as West Africa. This is in light of the increasing availability of RS data, some of which are provided free of charge e. Research on the potential of RS data to improve digital soil mapping in West Africa is sparse [ 22 ].

Recent digital mapping initiatives on the continent e.

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However, the spatial resolution of these studies is still coarse ca. The derivation of digital soil data at local scales is important for assessing landscape scale resource needs and subsequently aid in regional, national and global soil and agricultural monitoring efforts [ 4 , 6 ]. Moreover, the success of digital soil mapping is to a large extent dependent on the availability, quality and timing of RS data acquisitions [ 24 ].

Land surface characteristics, especially on agricultural lands, are subject to temporal changes and it is not always clear which periods of the year are suitable for acquiring RS data for accurate soil property prediction. The use of multi-temporal images permits an investigation on the impact of the temporal window of RS data acquisition on prediction accuracies. This paper reports findings of a digital soil mapping effort that integrated RS data and conventionally analysed soil samples to map the spatial distribution of soil properties sand, silt, clay, cation exchange capacity, SOC and nitrogen in a km 2 agricultural watershed in south-western Burkina Faso.

High spatial resolution multi-temporal RapidEye and Landsat imagery together with ASTER Global DEM terrain derivatives were tested to determine their suitability for improving the availability and accuracy of spatial soil information in rural African landscapes. Since typical farm sizes in West Africa are small i. However, such studies, to the best of our knowledge, are rare.

Four statistical methods which have proved their suitability for digital soil mapping in previous studies—multiple linear MLR , random forest regression RFR , support vector machine SVM and stochastic gradient boosting SGB [ 26 — 29 ] were explored to ascertain the most suitable method for high resolution RS data in the study region. Comparison of the traditional regression method MLR and different machine learning methods to spatially predict soil properties in West Africa are scarce.

The research questions that the study addresses are: 1 which regression method offers the best accuracy for predicting soil properties? The study was conducted in a rural watershed that falls in the Ioba province in south-western Burkina Faso South-west Region. Detailed soil sampling was carried out in a sub-watershed which is about one-quarter of the watershed Fig 1.

The watershed has a uni-modal rainfall distribution May-October , with an annual rainfall average of about mm [ 30 ] while daily temperature ranges between The lithology is composed of partly volcanic formations from the middle precambrian period and is made up mainly of andesic rocks with massive texture, basalt, diabase, gabbro and quartz-rich andesites. Representative soil units were chosen for sampling based on existing soil [ 33 ], land use [ 34 ] and DEM [ 31 ] data of the watershed.

The focus of the sampling was a sub-watershed see Fig 1.

A total of soil samples in sub-watershed and outside coming mainly from the topsoil 0—30 cm , were considered in this study. They were taken from the topsoil of 35 profiles along with intensive auger sampling carried out from July to October and from July to October At each auger sampling point, composite samples were taken from the topsoil 0—30 cm. Because of the high number of soil samples, we analysed only samples conventionally for the soil properties under study i.

For the rest of the sample set, we used mid infrared spectroscopy MIRS to predict the above mentioned soil properties.

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The estimation of soil properties is generated by calibrating spectral information against conventionally obtained data using multivariate statistical procedures such as partial least squares regression PLSR [ 36 ]. For spectra measurement, about 20 mg of the soil samples were set into microplates and compacted with a plunger to get a level and plain surface in five replicates. For each spectrum, scans were recorded from to cm -1 at a resolution of 4 cm -1 [ 37 ].

OPUS QUANT uses a routine that automatically tests combinations of varying spectral ranges and data treatments for the optimum prediction power of the model. For each soil parameter, we conducted calibration procedures employing a leave—one—out, full—cross validation as well as a test-set calibration for checking model robustness as described by Bornemann et al.

The quality of the different models for each soil property was assessed based on their predictive ability with the R 2 , ratio of performance to deviation RPD and the standard error of prediction SEP. For more technical information, readers are referred to [ 38 ].

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Multi-temporal data from two optical sensors, RapidEye and Landsat, were used in this study. The RapidEye data has five spectral channels blue, green, red, rededge and near infrared NIR and a spatial resolution of 5 m i. Six out of the eleven spectral channels of Landsat see Table 2 were used in the analysis. In addition to the original spectral bands, six soil and vegetation indices were calculated for each image. In all, twenty-one spectral bands and twenty-four spectral indices were derived i.

Table 2 provides further details of the spectral bands of RapidEye and Landsat as well as formulae and definitions of the spectral indices calculated. These spectral indices have been found to be useful in digital soil mapping [ 43 ].

Spatial Analysis, GIS and Remote Sensing: Applications in the Health Sciences

ASP [ 30 ]. Although the 30 m SRTM data has been made freely available, it came at a time that this manuscript was at an advanced development stage. The data was pre-processed to generate a depressionless DEM prior to the calculation of terrain variables. Climatic data i. In order to ensure integration with the RapidEye data, the DEM and climatic variables were resampled to 5 m resolution using the bilinear and bicubic interpolation methods, respectively.

Table 3 lists the 29 terrain and climatic variables that were used in this study together with the relevant references.

Linear regression models aim at explaining the spatial distribution of a dependent variable by means of a linear combination of predictors independent variables. The regression equation is used to predict the spatial distribution of the parameter of interest based on the independent variables. One soil property was modelled at a time as the response dependent variable with the developed matrix as the predictors.

For each model, the adjusted R 2 and residual standard error were recorded.

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A common limitation of regression models is the problem of multicollinearity, which occurs when there is significant correlation between the predictors. Since the number of predictors identified in this study are many seventy-four , and there could be high correlation between some of them, a stepwise regression analysis was first conducted to produce uncorrelated predictors needed to model each soil parameter and thereby minimize the problem of multicollinearity.

Stepwise regression identifies a subset of predictors based on the statistical significance of the predictors using stepwise, forward selection, or backward elimination [ 62 ]. For each soil parameter, a subset of uncorrelated predictors were identified for subsequent analysis. RF [ 64 ] belongs to the family of ensemble machine learning algorithms that predicts a response in this case the respective soil parameters from a set of predictors matrix of training data by creating multiple Decision Trees DTs and aggregating their results.

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Each tree in the forest is independently constructed using a unique bootstrap sample of the training data. Whereas other machine learning algorithms e. The introduction of this additional randomness decreases the correlation between trees in the forest, and consequently increases accuracy [ 66 ]. Additionally, RF requires no assumption of the probability distribution of the target predictors as with linear regression, and is robust against nonlinearity and overfitting, although overfitting may occur in instances where noisy data are being modelled [ 67 ].

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RF optionally provides information on the relative importance of the predictors variable importance used in the construction of the forest [ 63 ]. Then, the difference between the two cases is averaged over all trees and normalized by the standard deviation of the differences. The second measure IncNodePurity represents the total decrease in node impurity from splitting on a predictor in the tree construction process, averaged over all trees. In RFR, the node impurity is measured by the residual sum of squares [ 63 ].