Examining the influence of bare soil UAV imagery combined with auxiliary datasets to estimate and map soil organic carbon distribution in an erosion-prone agricultural field.

Soil organic content (SOC), an indicator of soil fertility, can be estimated quickly and accurately with remote sensing (RS) datasets; however, the issue of vegetation cover on the field still remains a major concern. In order to minimize the effects of vegetation cover, studies relating reflectance spectra to SOC may require bare soil. However, acquiring satellite images devoid of vegetation is still an enormous challenge for RS techniques. This is because the area that may have been accurately predicted at a targeted date is sometimes limited since many pixels are covered by vegetation. The study goal was to assess the impact of using UAV-borne imagery coupled with auxiliary datasets, which include spectral indices (SPIs) and terrain attributes (TAs) (at 20 cm and 30 m resolution), singly or merged, to estimate and map SOC in an erosion-prone agricultural field. Both field samples and UAV imagery were acquired while the fields were bare. Using a grid sampling design, 133 soil surface samples were collected. The models used include partial least square regression (PLSR), extreme gradient boosting (EGB), multivariate adaptive regression splines (MARS), and regularised random forest (RFF). The models were evaluated using the root mean squared error (RMSE), the coefficient of determination (R2), ratio of performance to interquartile distance (RPIQ), and the mean absolute error (MAE). For prediction, the three merged datasets (R2val = 0.86, RMSEval = 0.13, MAEval = 0.11, RPIQval = 4.19) outperformed the best separate dataset (R2val = 0.82, RMSEval = 0.15, MAEval = 0.10, RPIQval = 2.08). Though all datasets detected both low and high estimates of soil SOC, the three merged datasets with EGB showed a less extreme prediction error. This study demonstrated that SOC can be estimated with high accuracy using completely bare soil UAV imagery with other auxiliary data, and it is thus highly recommended.

Can in situ spectral measurements under disturbance-reduced environmental conditions help improve soil organic carbon estimation?

In situ visible and near-infrared (Vis-NIR) spectroscopy has proven to be a reliable tool for determining soil organic carbon (SOC) content with a small loss of precision as compared to laboratory measurements. The loss of precision is a result of disturbing external environmental factors that disrupt spectral measurements. For example, roughness, changes in weather conditions, humidity, temperature, human factors, spectral noise and especially soil water. It has been assumed that, in situ predictive capability could be improved if some of these factors are either minimized or eliminated during the in situ measurement. For this study, the prediction of SOC was carried out under two different in situ measurement conditions; less favourable environmental conditions (with disturbances) and more favourable site-specific conditions (disturbance-reduced conditions). The primary goal is to determine whether the estimate of SOC can be improved under more favourable site-specific conditions, as well as the impact of pre-treatment algorithms on both less and more favourable disturbed conditions. The study employed a large range of pretreatment algorithms and their combinations. Three separate multivariate models were used to predict SOC, namely Cubist, support vector machine regression (SVMR), and partial least squares regression (PLSR). The result clearly shows that reduced disturbing factors (i.e., drier and unploughed soil as well as noise reduction) result in an improvement of SOC prediction with in situ Vis-NIR spectroscopy. The best overall result was achieved with SVMR (R2CV = 0.72, RMSEPcv = 0.21, RPIQ = 2.34). Although the combination of pre-treatment algorithms resulted in an improvement, overall, these pre-treatment algorithms could not compensate for the factors affecting the measured spectra with disturbance. Though the obtained result is promising, further study is still needed to disentangle the impacts and interactions of various disturbing factors for different soil types.

Using an ensemble model coupled with portable X-ray fluorescence and visible near-infrared spectroscopy to explore the viability of mapping and estimating arsenic in an agricultural soil.

Increasing concentrations of potentially toxic elements (PTE) in agricultural soils remain a major source of public concern. Monitoring PTEs in an agricultural field with no history of contaminants necessitate adequate analysis utilizing a robust model to accurately uncover hidden PTEs. Detecting and mapping the distribution of soil properties using portable X-ray fluorescence (pXRF) and proximal sensing techniques is not only rapid, but also relatively inexpensive. In this study, an ensemble model, consisting of partial least square regression (PLSR), support vector machine (SVM), random forest (RF) and cubist, was used for the prediction and mapping of soil As content in an agricultural field with no history of pollution. The datasets were collected using pXRF and field spectroscopy techniques. The main goal was to compare the ensemble model to each of the calibration techniques in terms of prediction accuracy of As content in such a field. Other components [e.g., soil organic carbon (SOC), Mn, S, soil pH, Fe] that are known to influence As levels in the soil were also retrieved to assess their correlation with soil As. The models were evaluated using the root mean squared error (RMSECV), the coefficient of determination (R2CV) and the ratio of performance to interquartile range (RPIQ). In terms of prediction accuracy, the ensemble model outperformed each of the individual techniques (R2CV = 0.80/0.75) and obtained the least error margin (RMSECV = 1.91/2.16). Overall, all the predictive techniques were able to detect both low and high estimated values of soil As within the study field, but with the ensemble model resembling the measurements better. The ensemble model, a promising tool as demonstrated by the current study, is highly recommended to be included in future studies for more accurate estimation of As and other PTEs in other agricultural fields.

Trend analysis of global usage of digital soil mapping models in the prediction of potentially toxic elements in soil/sediments: a bibliometric review.

The rising and continuous pollution of the soil from anthropogenic activities is of great concern. Owing to this concern, the advent of digital soil mapping (DSM) has been a tool that soil scientists use in this era to predict the potentially toxic element (PTE) content in the soil. The purpose of this paper was to conduct a review of articles, summarize and analyse the spatial prediction of potentially toxic elements, determine and compare the models' usage as well as their performance over time. Through Scopus, the Web of Science and Google Scholar, we collected papers between the year 2001 and the first quarter of 2019, which were tailored towards the spatial PTE prediction using DSM approaches. The results indicated that soil pollution emanates from diverse sources. However, it provided reasons why the authors investigate a piece of land or area, highlighting the uncertainties in mapping, number of publications per journal and continental efforts to research as well as published on trending issues regarding DSM. This paper reveals the complementary role machine learning algorithms and the geostatistical models play in DSM. Nevertheless, geostatistical approaches remain the most preferred model compared to machine learning algorithms.

Source apportionment, contamination levels, and spatial prediction of potentially toxic elements in selected soils of the Czech Republic.

The sustenance of humans and livestock depends on the protection of the soil. Consequently, the pollution of the soil with potentially toxic elements (PTEs) is of great concern to humanity. The objective of this study is to investigate the source apportionment, concentration levels and spatial distribution of PTEs in selected soils in Frýdek-Místek District of the Czech Republic. The total number of soil samples was 70 (topsoil 49 and 21 subsoils) and was analysed using a portable XRF machine. Contamination factor and the pollution index load were used for the assessment and interpreting the pollution and distribution of PTEs in the soils. The inverse distance weighting was used for the spatial evaluation of the PTEs. The results of the analysis showed that the area is composed of low-to-high pollution site. PTEs displayed spatial variation patterns. The average PTE concentration decreases in this Fe > Ti > Ba > Zr > Rb > Sr > Cr > Y>Cu > Ni > Th order for the topsoil and also decreases in this Fe > Ti > Zr > Ba > Rb > Sr > Cr > Y > Cu > Ni > and Th order for the subsoil. These PTEs Cr, Ni, Cu, Rb, Y, Zr, Ba, Th, and Fe were far above the baseline European average value and the World average value level, respectively. The source apportionment showed the dominance of Cr, Ni, Rb, Ti, Th, Zr, Cu, Fe in the topsoil, while the subsoil was dominated by all the PTEs (factor 1 to 6) except Ba. The study concludes that indiscriminate human activities have an enormous effect on soil pollution.