Cokriging Research Articles

Assessing soil fungal diversity under different sampling schemes in conjunction with remote sensing technologies in a subtropical forest

Fungi, serving as real-time bioindicators to environmental changes and stressors, are crucial for effective forest conservation and management practices under ongoing global change. However, the large-scale assessment of soil fungi still encounters challenges in striking a balance between the extensive sampling costs and the limited accuracy of minimal sampling. In this study, we analyzed 1,606 soil samples collected from 625 quadrats (20 m × 20 m) within a 25-ha subtropical forest dynamic plot in East China. Our primary objective was to explore the impact of different sampling schemes, in conjunction with remote sensing (RS) technologies, on the interpolation of soil fungal diversity using Ordinary Kriging (OK) and Co-kriging (CoK) models. Our findings suggested that a sampling scheme including points at 0 m (the base points) and 8 m within each quadrat, totaling to 26 points/ha, would be a sufficient scheme. This scheme with OK model yielded comparable results to those of more intensive schemes (at 0, 2, 5 and 8 m), but required the fewest sampling points. Upon incorporating each RS variable separately into the CoK models, including two vegetation indices (normalized difference vegetation index and transformed chlorophyll absorption ratio index 2), three terrain attributes (Elevation, Aspect and Slope), and the synthesis of these RS variables, the accuracy of the predicted results was further improved for each sampling scheme. By leveraging high-precision soil DNA sequencing in conjunction with cost-effective RS technologies, this study proposes a rapid and affordable approach for monitoring soil fungal diversity on a large scale. This will facilitate data collection for understanding responses of forest soil fungi to ongoing global change.

Prediction of ionospheric TEC during the occurrence of earthquakes in Indonesia using ARMA and CoK models

Predicting ionospheric Total Electron Content (TEC) variations associated with seismic activity is crucial for mitigating potential disruptions in communication networks, particularly during earthquakes. This research investigates applying two modelling techniques, Autoregressive Moving Average (ARMA) and Cokriging (CoK) based models to forecast ionospheric TEC changes linked to seismic events in Indonesia. The study focuses on two significant earthquakes: the December 2004 Sumatra earthquake and the August 2012 Sulawesi earthquake. GPS TEC data from a BAKO station near Indonesia and solar and geomagnetic data were utilized to assess the causes of TEC variations. The December 2004 Sumatra earthquake, registering a magnitude of 9.1–9.3, exhibited notable TEC variations 5 days before the event. Analysis revealed that the TEC variations were weakly linked to solar and geomagnetic activities. Both ARMA and CoK models were employed to predict TEC variations during the Earthquakes. The ARMA model demonstrated a maximum TEC prediction of 50.92 TECU and a Root Mean Square Error (RMSE) value of 6.15, while the CoK model predicted a maximum TEC of 50.68 TECU with an RMSE value of 6.14. The August 2012 Sulawesi earthquake having a magnitude of 6.6, revealed TEC anomalies 6 days before the event. For both the Sumatra and Sulawesi earthquakes, the GPS TEC variations showed weak associations with solar and geomagnetic activities but stronger correlations with the earthquake-induced electric field for the considered two stations. The ARMA model predicted a maximum TEC of 54.43 TECU with an RMSE of 3.05, while the CoK model predicted a maximum TEC of 52.90 TECU with an RMSE of 7.35. Evaluation metrics including RMSE, Mean Absolute Deviation (MAD), Relative Error, and Normalized RMSE (NRMSE) were employed to assess the accuracy and reliability of the prediction models. The results indicated that while both models captured the general trend in TEC variations, nuances emerged in their responses to seismic events. The ARMA model demonstrated heightened sensitivity to seismic disturbances, particularly evident on the day of the earthquake, whereas the CoK model exhibited more consistent performance across pre- and post-earthquake periods.

Multi-scale geographically weighted regression estimation of carbon storage on coniferous forests considering residual distribution using remote sensing data

The Above-Ground Carbon (AGC) is an important indicator reflecting the carbon sink function of forest ecosystems. The area of coniferous forests in China accounts for about 50 % of the national forest area. Accurately estimating the AGC of coniferous forests plays an important role in evaluating forest carbon sink function. This study takes coniferous forests in Chun’an, Zhejiang Province as an example, using Landsat 8 OLI as the remote sensing data source, and innovatively proposes a multi-scale Geographically Weighted Regression model (MGWR) combined with Co-Kriging (COK), namely MGWR-COK and Ordinary Kriging (OK), namely MGWR-OK, for AGC estimation. This method first processes Landsat8 OLI remote sensing data, extracts and screens variables, and then constructs an MGWR model with AGC survey samples to estimate AGC and calculate residuals; Afterwards, two models, COK and OK, were used to spatially estimate the residuals; Finally, the AGC estimation results are overlaid with the residual estimation results to obtain the spatial distribution of AGC in coniferous forests. The study indicates that: (1) The selected remote sensing feature variables such as Optimized Soil-Adjusted Vegetation Index (OSAVI), Normalized Difference Vegetation Index (NDVI), Entropy (B3_ent_1 and B2_ent_2), Correlation (B4_corr and B5_corr_2) of texture information, B754 with band combination and elevation have a significant impact on AGC estimation. (2) The AGC accuracy R2 estimated by MGWR-OK and MGWR-COK are 0.837 and 0.857, respectively, which are 6 % and 8 % higher than the MGWR model, and the root mean square error is also reduced by 10 % and 12 %, respectively. This indicates that the combination of MGWR and Kriging interpolation can effectively reduce its spatial estimation error. (3) The range of AGC values estimated by the MGWR-COK model is 0.189–62.591 Mg ∙ hm−2, with a mean of 28.795 Mg ∙ hm−2. The spatial distribution shows a characteristic of high in the southeast and low in the northwest, which is consistent with the actual situation in the study area.

Spatial Downscaling of Forest Above-Ground Biomass Distribution Patterns Based on Landsat 8 OLI Images and a Multiscale Geographically Weighted Regression Algorithm

Forest above-ground biomass (AGB) is an excellent indicator for the health status and carbon sink potential of forest ecosystems, as well as the effectiveness of sustainable forest management practices. However, due to the strong heterogeneity of forest structures, acquiring high-accuracy and high-resolution AGB distributions over wide regions is often prohibitively expensive. To fill the resulting gap, this paper uses part of Lishui city, Zhejiang province as the study area, based on 168 forest sample observations, and proposes a novel integrated framework that combines a multi-scale geographically weighted regression (MGWR) with the co-kriging algorithm to refine the spatial downscaling of AGB. Specifically, optimal predictor variable sets identified by random forest importance ranking, multiple stepwise regression, and Pearson VIF methods were first assessed based on their total explanatory power (R square), followed by reconfirmation of the optimal predictor variable set based on the non-stationarity impact of each variable’s action scale (bandwidth) on the output pattern of AGB downscaling. The AGB downscaling statistical algorithms included MGWR, GWR, random forest (RF), and the ordinary least square (OLS), and their downscaling performances were quantitatively compared to determine the best downscaling method. Ultimately, the downscaled AGB pattern was produced using the best method, which was further refined by considering the spatial autocorrelation in AGB samples by implementing a co-kriging interpolation analysis of the predicted AGB downscaling residuals. The results indicated that the variable set selected by random forest importance ranking had the strongest explanatory power, with a validation R square of 0.58. This was further confirmed by the MGWR analysis which showed that the set of variables produced a more spatially smooth downscaled AGB pattern. Among the set of optimal variables, elevation and aspect affected AGB at local scales, representing a strong spatial heterogeneity. Some textural features and spectral features showed a smooth action scale relative to AGB, showing insignificant spatial scale processes. In the study area with complex terrain, using aspect as a covariant, the co-kriging (CK) model achieved a higher simulation accuracy for the MGWR-predicted AGB residuals than the ordinary kriging model. Overall, the proposed MGWR-CK model, with a final validation R square value of 0.62, effectively improved the spatial distribution characteristics and textural details of AGB mapping without the additional costs of procuring finer satellite images and GIS-based features. This will contribute to the accurate assessment of carbon sinks and carbon stock changes in subtropical forest ecosystems globally.

Interpolation of rainfall observations during extreme rainfall events in complex mountainous terrain

AbstractThe representation of rainfall in space is important for hydrological modelling. Accurate estimation of rainfall is particularly challenging in mountainous regions where observations are often sparse relative to the spatial variability of rainfall. In these regions, orographic processes lead to complex patterns of rainfall enhancement and rain shadow depletion. This study testsNatural Neighbour Interpolation (NNI),ordinary kriging (OK)andordinary cokriging (CK), to determine ifCKimproves rainfall interpolation during three extreme rainfall events. Three different elevation indices were considered as secondary variables forCK. Preliminary analysis using long‐term annual average rainfall totals, including additional high elevation rainfall observations, showed thatCKwith an effective elevation index (a directionally smoothed elevation, corrected for degree of ‘orographic processing’ and shifted to account for ‘wind‐drift’ of rainfall) as a secondary variable performed better thanNNIandOKwith an overall improvement of around 40%. Using rainfall totals for long‐term wind direction and wind speed rainfall classes,CKperformance was variable but provided an improvement of approximately 15% for wind direction classeswithoutan easterly wind component. For 15‐min timesteps during extreme rainfall events, there were comparatively small differences (cross‐validation usingRMSE) between interpolation methods, partly attributed to having only relatively low elevation rainfall observations, providing weak constraint. Using cross‐validation andmean biasdid, however, show an improvement for both high and low elevation observation classes. Importantly, cross‐variogram estimation provided differing cross‐validation results when estimated for different rainfall accumulation periods: 15‐min, hourly, daily and long‐term. Variograms and cross variograms estimated at a 15‐min timestep frequency were robust for many timesteps, but were difficult to fit automatically for others. Variograms estimated from longer periods were more reliably estimated, but tended to have lower variance and cross‐variance and longer correlation ranges producing a smoother interpolated rainfall field. Given the weak cross‐validation constraint, care must be taken in identifying the most appropriate method and variogram estimation period.

An interpolation method incorporating the pollution diffusion characteristics for soil heavy metals - taking a coke plant as an example

The existing spatial interpolation methods in the prediction of soil heavy metal distribution are generally based on spatial auto correlation theory, rarely considering the pollution patterns. By contrast, in polluted sites, heavy metals have a strong heterogeneity even within a very small area, which is not exactly in line with auto correlation theory. This contradiction may lead to inaccuracy in spatial prediction. Atmospheric diffusion and deposition are one of the main sources of soil heavy metal pollution caused by coal-related production activities. To improve the prediction accuracy, the diffusion patterns of pollutants were considered in this paper by integrating Geodetector, Co-Kriging (COK), and partition interpolation. Geodetector was used to identify the main driving factors of soil pollution, based on which, the main driving factors were used as covariates introduced into the interpolation method (COK). Specifically, the amount of particulate matter deposition obtained by a pollutant diffusion model (AERMOD) was used as a covariate. For comparison, the distances to quenching, coke oven, and ammonium sulfate section were also used as covariates. Compared with the Ordinary Kriging method, the method COK-AERMOD established here decreased the root mean square error values of As (2.05 reduced to 1.89), Cd (0.18 reduced to 0.16), Cr (19.07 reduced to 12.97), Cu (6.92 reduced to 4.72), Hg (0.32 reduced to 0.28), Ni (16.92 reduced to 16.10), Pb (18.29 reduced to 16.62), and Zn (159.68 reduced to 153.66). This method in this paper is informative for the interpolation of soil elements in contaminated areas with known pollution source and diffusion patterns.

Apportionment and Spatial Pattern Analysis of Soil Heavy Metal Pollution Sources Related to Industries of Concern in a County in Southwestern China.

Soil heavy metal pollution is frequent around areas with a high concentration of heavy industry enterprises. The integration of geostatistical and chemometric methods has been used to identify sources and the spatial patterns of soil heavy metals. Taking a county in southwestern China as an example, two subregions were analyzed. Subregion R1 mainly contained nonferrous mining, and subregion R2 was affected by smelting. Two factors (R1F1 and R1F2) associated with industry in R1 were extracted through positive matrix factorization (PMF) to obtain contributions to the soil As (64.62%), Cd (77.77%), Cu (53.10%), Pb (75.76%), Zn (59.59%), and Sb (32.66%); two factors (R2F1 and R2F2) also related to industry in R2 were extracted to obtain contributions to the As (53.35%), Cd (32.99%), Cu (53.10%), Pb (56.08%), Zn (67.61%), and Sb (42.79%). Combined with PMF results, cokriging (CK) was applied, and the z-score and root-mean square error were reduced by 11.04% on average due to the homology of heavy metals. Furthermore, a prevention distance of approximately 1800 m for the industries of concern was proposed based on locally weighted regression (LWR). It is concluded that it is necessary to define subregions for apportionment in area with different industries, and CK and LWR analyses could be used to analyze prevention distance.

Spatial variability of soil available potassium in rubber plantation based on coKriging

The coKriging method of selecting effective variables is helpful to improve the spatial prediction accuracy of soil available potassium in county-scale rubber plantation, which is of significance in precision fertilization management of rubber plantation. In this study, we analyzed the spatial variability characteristics of soil available potassium in 0-20 cm layer in the rubber plantation of Baisha County, Hainan Province, by geostatistics. The significant characteristic variables were screened by correlation analysis, and the spatial interpolation precisions of coKriging (COK) of different variables were compared. The results showed that the average soil available potassium content in the study area was 44.65 g·kg-1, generally at a state of shortage. The variable coefficient was 52.6%, which was a moderate intensity of variation. The nugget effect was 12.5%, with a strong spatial autocorrelation. The organic matter and elevation were closely related to soil available potassium content. The COK spatial interpolation prediction precisions of the three covariates of organic matter (COK1), elevation (COK2), and organic matter+elevation (COK3) were all higher than ordinary Kriging (OK), and the fitting precision of the cross-validation model was COK1>COK3>COK2>OK. The fitting precision was not proportional to the number of covariates selected. Selecting more correlated covariates was more conducive to reflecting the spatial heterogeneity of soil properties. The soil available potassium content was higher in the northwest and lower in the central and eastern regions, which provided a theoretical basis for the further development of soil potassium management in rubber plantations.

Spatial modeling of soil salinity using kriging interpolation techniques: A study case in the Great Hungarian Plain

The world’s current task is to ensure food security for an ever-growing population of 7.674 billion in 2019. Soil degradation threatens sustainable agriculture in arid and semi-arid climates, where evaporation rates outweigh precipitation. Soluble salts concentrated in the subsoil under certain climatic conditions influence soil physicochemical properties, leading to soil fertility and biodiversity losses. Hence, understanding salinity behavior and its spatial variation are crucial for natural resources management to achieve and maintain sustainability. This study aims to model soil salinity spatial distribution using four kriging interpolation methods, i.e., ordinary kriging (OK), empirical Bayesian kriging (EBK), co-kriging (CK), and indicator kriging (IK). Two hundred twenty-two soil samples were collected for this purpose during a field campaign conducted in the Hungarian Soil Monitoring System framework in 2016. The performance of kriging methods was assessed and compared using two cross-validations, i.e., leave-one-out cross-validation (LOOCV) and the holdout method. The Pearson correlation analysis has been used to expose a significant moderate correlation between salt content and cation exchange capacity (CEC) with a correlation coefficient of 0.4 and a p-value of 0.003. Thus, the spatial relationship between soil salinity content (SSC) and CEC was integrated into the model to enhance predictions in areas where no measurements were accessible. The study demonstrated co-kriging efficiency by reducing the mean squared error (MSE) of ordinary kriging (OK) from 0.8 g/kg and 0.85 g/kg for LOOCV and the holdout cross-validation to 0.3 g/kg.

Comparative Analysis of Different Spatial Interpolation Methods Applied to Monthly Rainfall as Support for Landscape Management

Landscape management requires spatially interpolated data, whose outcomes are strictly related to models and geostatistical parameters adopted. This paper aimed to implement and compare different spatial interpolation algorithms, both geostatistical and deterministic, of rainfall data in New Zealand. The spatial interpolation techniques used to produce finer-scale monthly rainfall maps were inverse distance weighting (IDW), ordinary kriging (OK), kriging with external drift (KED), and ordinary cokriging (COK). Their performance was assessed by the cross-validation and visual examination of the produced maps. The results of the cross-validation clearly evidenced the usefulness of kriging in the spatial interpolation of rainfall data, with geostatistical methods outperforming IDW. Results from the application of different algorithms provided some insights in terms of strengths and weaknesses and the applicability of the deterministic and geostatistical methods to monthly rainfall. Based on the RMSE values, the KED showed the highest values only in April, whereas COK was the most accurate interpolator for the other 11 months. By contrast, considering the MAE, the KED showed the highest values in April, May, June and July, while the highest values have been detected for the COK in the other months. According to these results, COK has been identified as the best method for interpolating rainfall distribution in New Zealand for almost all months. Moreover, the cross-validation highlights how the COK was the interpolator with the best least bias and scatter in the cross-validation test, with the smallest errors.

An assessment of statistical interpolation methods suited for gridded rainfall datasets

AbstractAccurate spatial distribution information of rainfall is essential to rainfall‐induced hazard predictions and statistical interpolation methods may serve as a useful tool to produce a detailed distribution from coarse data sources. Although numerous comparison studies about different interpolation methods have been conducted on irregular rain‐gauge networks, there is a need to perform such work on the increasingly available gridded rainfall data. Carried out in the Emilia‐Romagna region (Italy) from 2008 to 2018, this study aims to examine accurate and appropriate interpolation methods to produce finer rainfall surface maps based on the 0.25° × 0.25° ERA5 gridded precipitation datasets. Five interpolation techniques, namely Thiessen polygons, Inverse Distance Weighting (IDW), Thin Plate Spline (TPS), Ordinary Kriging (OK) and ordinary Co‐Kriging (CoK), have been selected and compared at different time scales (annual, monthly and annual maximum daily precipitation). To assess the accuracy, the leave‐one‐out‐cross‐validation test was used by using the indexes of Bias, correlation coefficient, the Nash‐Sutcliffe Efficiency, Root Mean Square Error and the Kling‐Gupta Efficiency (KGE). Additionally, visual inspections are employed to evaluate the plausibility of the interpolated maps. Results show: (a) All five interpolation methods have certain capabilities to improve spatial resolution, but they fail on accuracy at the daily scale. The OK generally outperforms the other four methods, while TPS shows better performance through the visual inspection at the monthly scale; (b) Unlike in the case of the interpolation using conventional point‐based rain‐gauge data, the multivariate method CoK is inferior to the univariate ones and the p‐power of the IDW also differs and (c) Winter and spring results are better than those of summer and autumn. This study has provided useful guidance on choosing suitable rainfall interpolation methods for gridded datasets, which can be expanded in other regions and data sources to explore the generality of the conclusions.

Spatial variability of soil mineral fractions and bulk density in Northern Ireland: Assessing the influence of topography using different interpolation methods and fractal analysis

Understanding how topography affects the distribution of soil properties is essential in the management of landscape hydrology and establishment of sustainable soil management practices. This study investigated the impact of topography on the variation in particle size distribution, coarse fragments, and soil bulk density using different interpolation techniques and fractal analysis. It also evaluated the performance of various interpolation techniques in predicting and characterizing the distribution of soil properties. The study was conducted using data from 620 samples extracted from the Tellus and LUCAS databases in Eglinton and Castlederg counties, Northern Ireland. Terrain attributes were obtained at a 30 × 30 m resolution using a global digital elevation model (GDEM) reintroduced to the Universal Transverse Mercator (UTM) projection. Interpolation analyses were conducted using inverse distance weighting (IDW), ordinary kriging (OK), block kriging (BK) and co-kriging (CK). Among the terrain attributes, elevation was the most influential covariate for CK. In addition, fractal analysis was conducted to assess the self-similarity of the soil properties. Prediction accuracy of the interpolation methods was evaluated using the Nash-Sutcliffe efficiency, mean absolute error, index of agreement, and Pearson correlation coefficient. Spatial maps produced from the kriging techniques showed high accuracy in the prediction of soil particle size distribution and bulk density. The use of elevation as an auxiliary variable was successful in producing accurate soil property distribution maps with CK. The fractal parameters showed that the soil properties had short range spatial variability, anti-persistent nature, and strong spatial structure. Additionally, the fractal dimension was strongly correlated with sand, silt and clay contents and bulk density, and weakly correlated with the coarse fragments.

Comparison of Different Interpolation Methods for Prediction of Soil Salinity in Arid Irrigation Region in Northern China

Numerous methods have been used in the spatial prediction of soil salinity. However, the most suitable method is still unknown in arid irrigation regions. In this paper, 78 locations were sampled in salt-affected land caused by irrigation in an arid area in northern China. The geostatistical characteristics of the soil pH, Sodium Adsorption Ratio (SAR), Total Salt Content (TSC), and Soil Organic Matter (SOM) of the surface (0–20 cm) and subsurface (20–40 cm) layers were analyzed. The abilities of the Inverse Distance Weighting (IDW), Ordinary Kriging (OK), and CoKriging (CK) interpolation methods were compared, and the Root Mean Square Error (RMSE) was used to justify the results of the methods. The results showed that the spatial distributions of the soil properties obtained using the different interpolation methods were similar. However, the surface layer exhibits more spatial heterogeneity than the subsurface layer. Based on the RSME, the nugget/sill value and range significantly affected which method was the most suitable. Lower nugget/sill values and lower ranges can be fitted using the IDW method, but higher nugget/sill values and higher ranges can be fitted using the OK method. These results provide a valuable reference for the prediction of soil salinity.

Remote Sensing Estimation of Bamboo Forest Aboveground Biomass Based on Geographically Weighted Regression

Bamboo forests are widespread in subtropical areas and are well known for their rapid growth and great carbon sequestration ability. To recognize the potential roles and functions of bamboo forests in regional ecosystems, forest aboveground biomass (AGB)—which is closely related to forest productivity, the forest carbon cycle, and, in particular, carbon sinks in forest ecosystems—is calculated and applied as an indicator. Among the existing studies considering AGB estimation, linear or nonlinear regression models are the most frequently used; however, these methods do not take the influence of spatial heterogeneity into consideration. A geographically weighted regression (GWR) model, as a spatial local model, can solve this problem to a certain extent. Based on Landsat 8 OLI images, we use the Random Forest (RF) method to screen six variables, including TM457, TM543, B7, NDWI, NDVI, and W7B6VAR. Then, we build the GWR model to estimate the bamboo forest AGB, and the results are compared with those of the cokriging (COK) and orthogonal least squares (OLS) models. The results show the following: (1) The GWR model had high precision and strong prediction ability. The prediction accuracy (R2) of the GWR model was 0.74, 9%, and 16% higher than the COK and OLS models, respectively, while the error (RMSE) was 7% and 12% lower than the errors of the COK and OLS models, respectively. (2) The bamboo forest AGB estimated by the GWR model in Zhejiang Province had a relatively dense spatial distribution in the northwestern, southwestern, and northeastern areas. This is in line with the actual bamboo forest AGB distribution in Zhejiang Province, indicating the potential practical value of our study. (3) The optimal bandwidth of the GWR model was 156 m. By calculating the variable parameters at different positions in the bandwidth, close attention is given to the local variation law in the estimation of the results in order to reduce the model error.

The Response of the HydroGeoSphere Model to Alternative Spatial Precipitation Simulation Methods

This paper presents the simulation results obtained from a physically based surface-subsurface hydrological model in a 5730 km2 watershed and the runoff response of the physically based hydrological models for three methods used to generate the spatial precipitation distribution: Thiessen polygons (TP), Co-Kriging (CK) interpolation and simulated annealing (SA). The HydroGeoSphere model is employed to simulate the rainfall-runoff process in two watersheds. For a large precipitation event, the simulated patterns using SA appear to be more realistic than those using the TP and CK method. In a large-scale watershed, the results demonstrate that when HydroGeoSphere is forced by TP precipitation data, it fails to reproduce the timing, intensity, or peak streamflow values. On the other hand, when HydroGeoSphere is forced by CK and SA data, the results are consistent with the measured streamflows. In a medium-scale watershed, the HydroGeoSphere results show a similar response compared to the measured streamflow values when driven by all three methods used to estimate the precipitation, although the SA case is slightly better than the other cases. The analytical results could provide a valuable counterpart to existing climate-based drought indices by comparing multiple interpolation methods in simulating land surface runoff.

Improving the spatial prediction accuracy of soil alkaline hydrolyzable nitrogen using GWPCA‐GWRK

AbstractPrincipal component analysis‐multiple linear regression (PCA‐MLR) is usually used to weaken the multi‐collinearity effects among auxiliary variables in a regression prediction. However, both PCA and MLR in this model are only built on variable space rather than geographical space. When used in the spatial prediction of soil properties, PCA‐MLR usually cannot effectively capture the spatially non‐stationary structures among auxiliary variables and spatially non‐stationary relationships between the target variable and principal component scores. Moreover, PCA‐MLR may ignore the potentially valuable regression residual. To address these limitations, this study first proposed geographically weighted principal component analysis‐geographically weighted regression kriging (GWPCA‐GWRK) for the spatial prediction of soil alkaline hydrolyzable nitrogen (AN) in Shayang County, China. Then, the spatial prediction accuracy of GWPCA‐GWRK was compared with those of the following five models: ordinary kriging (OK), co‐kriging (CoK), PCA‐MLR, PCA‐graphically weighted regression (PCA‐GWR), and GWPCA‐GWR. Results showed that (i) eight variables were determined as auxiliary data by a geodetector; (ii) the spatially non‐stationary relationships among the eight auxiliary variables were revealed by the results of the local correlation analysis, Monte Carlo test, and GWPCA; (iii) GWPCA‐GWRK provided the lowest prediction error (RMSE = 18.80 mg kg−1, MAE = 12.79 mg kg−1) and highest Lin's concordance correlation coefficient (LCCC; 0.75); (iv) relative improvement accuracies over the traditionally‐used OK were 19.74% for GWPCA‐GWRK, 16.42% for GWPCA‐GWR, 8.09% for PCA‐GWR, −3.67% for PCA‐MLR, and 4.70% for CoK. It is concluded that the proposed GWPCA‐GWRK model is an effective spatial predictor, which can adequately extract the main information of the multiple auxiliary variables in a large‐scale area.

ESTIMATION OF SOIL ORGANIC CARBON DISTRIBUTION BY GEOSTATISTICAL AND DETERMINISTIC INTERPOLATION METHODS: A CASE STUDY OF THE SOUTHEASTERN SOILS OF NIGERIA

Soil organic carbon (SOC) plays a significant role in ecosystem protection and sustainable agriculture. The present study aims to estimate the spatial distribution of SOC using three different interpolation methods: ordinary kriging (OK), cokriging (COK), and inverse distance weighting (IDW). Sixty (n = 60) soil samples were collected from the depth of 0 30 cm and analyzed for SOC. The digital elevation model of the site was obtained from USGS explorer at 30 m spatial resolution and processed. Ten (10) terrain attributes were obtained, and a correlation matrix was conducted between SOC and terrain derivatives. The whole dataset was used to evaluate the model accuracy; root mean square error (RMSE) and mean error (ME) were the criteria adopted. Mean value of the SOC of the study area was generally low when compared to the standard rating for tropical soils (< 2%). SOC was significantly (p < 0.01) correlated with LS-factor (r = 0.34*), negatively correlated with elevation (r = 0.30*) and profile curvature (r = 0.30*). IDW performed better (RMSE = 0.75, ME = 0.004) followed by OK (RMSE= 0.78, ME = 0.004) and then COK (RMSE = 0.94, ME = 0.067). Conversely, COK produced the model with the smallest ME with terrain attributes (elevation, LS-factor, and profile curvature). The findings in the study showed that IDW is superior in SOC estimation. COK with the terrain attributes proved to have the capacity as a useful ancillary variable for improving the spatial structure of SOC maps of southeastern Nigeria.

Estimation of surface soil moisture by integrating environmental data and remote-sensing satellites

Soil moisture (SM) or soil water content is a critical variable in the climate system and a key parameter in earth surface processes. This study aimed to assess citizen observatory (CO) data's suitability to develop a method to estimate surface SM distribution using Sentinel-1B and Landsat 8 data; acquired between January 2019 and June 2019. Three approaches were developed and compared using multiple linear regression (MLR), regression-kriging (RK) and cokriging (CK). MLR provided more realistic spatial patterns over the landscape, even in a data-poor environment. RK was found to be a potential tool to refine the results, while CO was found to be less effective. The obtained results showed that CO data harmonised with Sentinel-1B SAR, Landsat 8, and terrain data could estimate and map soil moisture content.

Comparison Quality of Interpolation Methods to Estimate Spatial Distribution of Soil Moisture Content

ABSTRACT Determining and predicting both the spatial and temporal variability of soil moisture content is a critical factor in crop production in semi-arid agricultural systems. The objective of this research was to predict the spatial distribution of soil moisture content using ordinary kriging (OK), regression kriging (RK) and cokriging (CK) models. For that purpose, the soil moisture was monitored in the wet period (March, April and May) and dry period (August, September and October) in Kadınhanı district of Konya Province, Turkey. In each monthly sampling period of the study, a total of 278 samples were collected from locations within an area of 29,608.6 ha. The coefficients of variation (CV) of the soil moisture in dry and wet soil conditions were 0.39 and 0.20, respectively. Soil texture, bulk density (BD) and land use were the most important factors influencing soil moisture. In addition, dry soil generally had a smaller nugget effect than wet soil. Overall, CK was able to better estimate the soil moisture content than OK and RK, as evidenced by the smaller mean absolute error (MAE) and mean square error (MSE). RK estimation is more vulnerable to error than CK and OK estimations because the low coefficients of determination in the regression equation lead to increased error and lower confidence in the estimates. The insights generated by the modeling utilized in this study can assist land use planners, land managers and farmers to more efficiently predict, manage and utilize soil moisture content and hence produce larger crops more sustainably.

Geostatistical models with the use of hyperspectral data and seasonal variation – A new approach for evaluating the risk posed by invasive plants

The general trend of ongoing plant invasion and the increasing number of species that may become invasive in the future, force seeking solutions that can improve the efficiency and economy of their management. Thus, we applied a novel approach combining the use of geostatistical interpolators such as ordinary kriging (OK) and co-kriging (CK) with environmental and hyperspectral data to evaluate the potential threat associated with the distribution of invasive plant species and to predict their probabilities of occurrence above the selected threshold of 10%. The specific spatial patterns of the probability of occurrence of Heracleum sosnowskyi and Fallopia spp. were modelled in two study areas in southern Poland. The significant achievement of this study was the application of geostatistical tools producing results characterized by a degree of precision quantified by cross-validation errors, and prediction errors after field verification. OK and CK returned root mean squared error (RMSE) values in a range from 0.21 to 0.51 and 0.21 to 0.47, respectively. For OK and CK, the prediction errors resulting from field verification in the following year were between 0.03–0.39, and 0.03–0.29, respectively. Additionally, the study provided the first prediction maps (2D) and Digital Prediction Models (DPMs) (3D) visualizations of the probability of occurrence of both invasive plants. Although the proposed approach is illustrated with real case studies related to Heracleum sosnowskyi and Fallopia spp., it could be extended to other species. This demonstrates the potential of an effective alternative strategy for evaluating the risk posed by invasive plants, that will be able to provide fast, low cost and effective prediction and monitoring of their spread. For institutions dealing with invasive plants, this may be beneficial and help to reduce the negative consequences of their improper management.