Application of Ordinary Kriging in Mapping Soil Organic Carbon in Zambia

Chabala, Lydia M.; Mulolwa, Augustine; Lungu, Obed I.

doi:10.1016/s1002-0160(17)60321-7

Cited by 45 publications

(21 citation statements)

References 13 publications

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“…The extremely low R 2 values, as estimated for correlating training and validating measurements and concerning all the estimated parameters (SOM, CaCO 3 , and K-factor), highlighted the need for developing a new coherent methodology for explicitly mapping soil parameters with the use of EO data. These results are merely in accordance with similar studies [49] that applied the Ordinary kriging method to estimate SOM.…”

Section: Resultssupporting

confidence: 93%

Integrated Use of Satellite Remote Sensing, Artificial Neural Networks, Field Spectroscopy, and GIS in Estimating Crucial Soil Parameters in Terms of Soil Erosion

et al. 2019

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Soil erosion is one of the main causes of soil degradation among others (salinization, compaction, reduction of organic matter, and non-point source pollution) and is a serious threat in the Mediterranean region. A number of soil properties, such as soil organic matter (SOM), soil structure, particle size, permeability, and Calcium Carbonate equivalent (CaCO3), can be the key properties for the evaluation of soil erosion. In this work, several innovative methods (satellite remote sensing, field spectroscopy, soil chemical analysis, and GIS) were investigated for their potential in monitoring SOM, CaCO3, and soil erodibility (K-factor) of the Akrotiri cape in Crete, Greece. Laboratory analysis and soil spectral reflectance in the VIS-NIR (using either Landsat 8, Sentinel-2, or field spectroscopy data) range combined with machine learning and geostatistics permitted the spatial mapping of SOM, CaCO3, and K-factor. Synergistic use of geospatial modeling based on the aforementioned soil properties and the Revised Universal Soil Loss Equation (RUSLE) erosion assessment model enabled the estimation of soil loss risk. Finally, ordinary least square regression (OLSR) and geographical weighted regression (GWR) methodologies were employed in order to assess the potential contribution of different approaches in estimating soil erosion rates. The derived maps captured successfully the SOM, the CaCO3, and the K-factor spatial distribution in the GIS environment. The results may contribute to the design of erosion best management measures and wise land use planning in the study region.

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Section: Resultssupporting

confidence: 93%

Integrated Use of Satellite Remote Sensing, Artificial Neural Networks, Field Spectroscopy, and GIS in Estimating Crucial Soil Parameters in Terms of Soil Erosion

et al. 2019

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“…The spatial variability of SOC through field soil sampling cannot be obtained. Numerous studies on the digital mapping of SOC have been conducted to resolve this issue on the basis of the spatial autocorrelation of soil [13][14][15][16]. Many studies have proven that soil properties exhibit strong spatial dependence between neighboring regions, and trend surface analysis, inverse distance weighted, and geostatistical models have been successfully used in soil mapping [17][18][19].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Soil Organic Carbon based on Landsat 8 Monthly NDVI Data for the Jianghan Plain in Hubei Province, China

et al. 2019

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High-precision maps of soil organic carbon (SOC) are beneficial for managing soil fertility and understanding the global carbon cycle. Digital soil mapping plays an important role in efficiently obtaining the spatial distribution of SOC, which contributes to precision agriculture. However, traditional soil-forming factors (i.e., terrain or climatic factors) have weak variability in low-relief areas, such as plains, and cannot reflect the spatial variation of soil attributes. Meanwhile, vegetation cover hinders the acquisition of the direct information of farmland soil. Thus, useful environmental variables should be utilized for SOC prediction and the digital mapping of such areas. SOC has an important effect on crop growth status, and remote sensing data can record the apparent spectral characteristics of crops. The normalized difference vegetation index (NDVI) is an important index reflecting crop growth and biomass. This study used NDVI time series data rather than traditional soil-forming factors to map SOC. Honghu City, located in the middle of the Jianghan Plain, was selected as the study region, and the NDVI time series data extracted from Landsat 8 were used as the auxiliary variables. SOC maps were estimated through stepwise linear regression (SLR), partial least squares regression (PLSR), support vector machine (SVM), and artificial neural network (ANN). Ordinary kriging (OK) was used as the reference model, while root mean square error of prediction (RMSE P ) and coefficient of determination of prediction (R 2 P ) were used to evaluate the model performance. Results showed that SOC had a significant positive correlation in July and August (0.17, 0.29) and a significant negative correlation in January, April, and December (−0.23, −0.27, and −0.23) with NDVI time series data. The best model for SOC prediction was generated by ANN, with the lowest RMSE P of 3.718 and highest R 2 P of 0.391, followed by SVM (RMSE P = 3.753, R 2 P = 0.361) and PLSR (RMSE P = 4.087, R 2 P = 0.283). The SLR model was the worst model, with the lowest R 2 P of 0.281 and highest RMSE P of 3.930. ANN and SVM were better than OK (RMSE P = 3.727, R 2 P = 0.372), whereas PLSR and SLR were worse than OK. Moreover, the prediction results using single-data NDVI or short time series NDVI showed low accuracy. The effect of the terrain factor on SOC prediction represented unsatisfactory results. All these results indicated that the NDVI time series data can be used for SOC mapping in plain areas and that the ANN model can maximally extract additional associated information between NDVI time series data and SOC. This study presented an effective method to overcome the selection of auxiliary variables for digital soil mapping in plain areas when the soil was covered with vegetation. This finding indicated that the time series characteristics of NDVI were conducive for predicting SOC in plains.

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“…The soil organic carbon was measured by wet oxidation with chromic acid and back titrated with ferrous ammonium sulfate following the standard Walkey and Black method (Nelson and Sommers, 1996). A histogram tool in Arc GIS software version 10.4 was used to identify the outliers of SOC, where a more isolated bar from the main group of bars in the histogram was taken to represent a possible outlier (Chabala et al, 2017). Therefore, in case if outliers observed, the laboratory analyses were repeated and any outliers recorded one more were removed from the samples set.…”

Section: Soil Sampling and Analysismentioning

confidence: 99%

Soil organic carbon mapping and prediction based on depth intervals using kriging technique: A case of study in alluvial soil from Sudan

Sulieman¹,

Algarni²

2019

Eurasian Journal of Soil Science (Ejss)

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Soil organic carbon plays a vital role in the arid and semiarid regions. This study aimed to predict and map soil organic carbon content at soil depth intervals of 0-0.3 m, 0.3-0.6 m, 0.6-0.9 m, and 0.9-1.2 m in alluvium soils along Blue Nile and River Nile, Sudan. Ordinary kriging (OK) technique was used as a geostatistical tool and applied to model the spatial variability of soil organic carbon in the study area. A total of 38 soil profiles were excavated in the study area and 152 samples from the four depths intervals were collected for determining organic carbon content. Results revealed that, the spatial autocorrelation for the different soil layers was moderate to weak with a nugget to sill ratios ranging from 0.21 to 0.86 suggesting their controlled by both intrinsic and extrinsic factors. The root mean square error standardized (RMSE) of the predictions ranging from 0.79 to 0.83 indicating that the model which generated by ordinary kriging was correctly estimating the variability of soil organic carbon in the study area.

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Application of Ordinary Kriging in Mapping Soil Organic Carbon in Zambia

Cited by 45 publications

References 13 publications

Integrated Use of Satellite Remote Sensing, Artificial Neural Networks, Field Spectroscopy, and GIS in Estimating Crucial Soil Parameters in Terms of Soil Erosion

Integrated Use of Satellite Remote Sensing, Artificial Neural Networks, Field Spectroscopy, and GIS in Estimating Crucial Soil Parameters in Terms of Soil Erosion

Prediction of Soil Organic Carbon based on Landsat 8 Monthly NDVI Data for the Jianghan Plain in Hubei Province, China

Soil organic carbon mapping and prediction based on depth intervals using kriging technique: A case of study in alluvial soil from Sudan

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