Estimating soil organic carbon stock change at multiple scales using machine learning and multivariate geostatistics

Szatmári, Gábor; Pásztor, László; Heuvelink, G.B.M.

doi:10.1016/j.geoderma.2021.115356

Cited by 52 publications

(27 citation statements)

References 59 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…If we compare the uncertainty of point-support predictions (Figure 5) with the uncertainty of spatial averages (Table 4, numbers in square brackets), it can be seen that the uncertainty associated with the spatial average is smaller. This can be attributed to the fact that negative and positive interpolation errors partially cancel out when values are aggregated, a phenomenon wellknown in geostatistics [26,39,52]. We should also highlight that in the Keszthely basin the prediction uncertainty of spatial averages is the highest for the N content and for the N:P ratio.…”

Section: Spatial Aggregationmentioning

confidence: 87%

“…Although it is also an attractive feature of multivariate geostatistics, it is still rarely exploited in practice. In addition, Szatmári et al [39] pointed out that an approach that does not account for the spatial cross-correlation between the variables under study will fail to characterize the uncertainty about the derived variable reliably.…”

Section: Discussionmentioning

confidence: 99%

“…Based on the fitted linear model of coregionalization, a multivariate geostatistical model was built to model the joint spatial variability of the nutrients N and P. We used sequential stochastic cosimulation instead of cokriging due to two reasons. First, there is no analytical geostatistical solution to predict a spatial average or total over a region in case a nonlinear transformation (e.g., normal score transformation) is applied prior to geostatistical modeling [39]. Second, analytical derivation of the ratio of correlated random variables could be problematic and often needs stringent assumptions made on the distribution of the random variables.…”

Section: Multivariate Geostatistical Modelingmentioning

confidence: 99%

“…We should note that block kriging may be problematic in case observations are transformed prior to geostatistical modeling, and predictions are back-transformed afterwards [25,38]. In such cases, stochastic simulation is often preferred [39].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Joint Spatial Modeling of Nutrients and Their Ratio in the Sediments of Lake Balaton (Hungary): A Multivariate Geostatistical Approach

Szatmári

Kocsis

Makó

et al. 2022

Water

Self Cite

View full text Add to dashboard Cite

Eutrophication, water quality, and environmental status of lakes is a global issue that depends not only on external loadings from industrial, agricultural, and municipal sources but often also on internal loadings from lake sediments. In the latter case, in addition to the quality and quantity of nutrients stored in sediments, their relative content may be an important factor. In the example of Lake Balaton, we jointly modeled the spatial distribution of the nutrients nitrogen (N) and phosphorus (P) and their ratio (i.e., N:P) in the sediments of the lake and then provided spatial predictions at different scales (i.e., point, basin, and entire lake) with the associated uncertainty. Our aim was to illustrate the merits of applying multivariate geostatistics when spatial modeling of more than one variable is targeted at various scales in water ecosystems. Variography confirmed that there is a spatial interdependence between the nutrients. The results revealed that multivariate geostatistics allows this interdependence to be taken into account and exploited to provide coherent and accurate spatial models. Additionally, stochastic realizations, reproducing the joint spatial variability, can be generated that allow providing spatially aggregated predictions with the associated uncertainty at various scales. Our study highlighted that it is worthy of applying multivariate geostatistics in case spatial modeling of two or more variables, which jointly vary in space, is targeted in water ecosystems.

show abstract

Section: Spatial Aggregationmentioning

confidence: 87%

Section: Discussionmentioning

confidence: 99%

Section: Multivariate Geostatistical Modelingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Joint Spatial Modeling of Nutrients and Their Ratio in the Sediments of Lake Balaton (Hungary): A Multivariate Geostatistical Approach

Szatmári

Kocsis

Makó

et al. 2022

Water

Self Cite

View full text Add to dashboard Cite

show abstract

“…Once the quality of map predictions has been assessed, a possible avenue to reduce the uncertainty is to predict over larger spatial supports, which have a long history in geostatistics in the form of block kriging (Burgess & Webster, 1980). A recent example of this is Szatmári et al (2021) who combined machine learning and geostatistics to predict change in carbon stocks for Hungary at spatial supports of point, 1 and 10 km – block, county and country level. Recent work by Bishop et al (2015) empirically demonstrated the reduction in uncertainty with increasing spatial support.…”

Section: Introductionmentioning

confidence: 99%

Assessment of global, national and regional‐level digital soil mapping products at different spatial supports

Han

Filippi

Singh

et al. 2022

European J Soil Science

View full text Add to dashboard Cite

Digital soil mapping provides estimates of soil properties and, in turn, an understanding of the spatial variation in soil across landscapes. In recent years, several digital soil mapping products have been released across the world. An end user in New South Wales (NSW), Australia, could choose between a global product (SoilGrids), a national product (Soil and Landscape Grid of Australia) and a state product (NSW digital soil maps). All predict at grid resolutions of 250 m or less, which approaches the required detail for within-field sitespecific management such as variable-rate application of agricultural inputs.In this study, the quality of clay and soil organic carbon (SOC) products (0-30 cm) from these three publicly available digital soil maps (DSM) were validated with an external dataset of 394 soil observations, collected from 14 farms across eastern Australia. Each farm was sampled using stratified random sampling, allowing for unbiased estimates of prediction quality at three spatial supports: point (soil core), strata (management zone) and farm. For the clay attribute, this study observed Lin's concordance correlation coefficient (LCCC) values of 0.22-0.37 at the point spatial support, far below reported values. This improved at the strata spatial support with LCCC values of 0.20-0.60 and the farm spatial support with values of 0.07-0.54. For SOC, LCCC values ranged from 0.25 to 0.45 for points, 0.31-0.55 for strata and 0.31-0.60 for farms. The grid resolution of DSMs depends largely on the resolution of the covariates used, and current efforts look to incorporate more recent, finer resolution covariates to create higher-resolution soil maps. However, the results of this study suggest that both the grid resolution and spatial support used in DSMs need further consideration. Rather than letting the resolution of covariates determine the resolution of DSMs, this should also be determined by the number of soil observations available for modelling, their distribution in geographic and attribute space and the prediction quality.

show abstract

A Path in Regression Random Forest Looking for Spatial Dependence: A Taxonomy and a Systematic Review

Patelli,

Cameletti,

Golini

et al. 2024

Advanced Statistical Methods in Process Monitoring, Finance, and Environmental Science

View full text Add to dashboard Cite

Estimating soil organic carbon stock change at multiple scales using machine learning and multivariate geostatistics

Cited by 52 publications

References 59 publications

Joint Spatial Modeling of Nutrients and Their Ratio in the Sediments of Lake Balaton (Hungary): A Multivariate Geostatistical Approach

Joint Spatial Modeling of Nutrients and Their Ratio in the Sediments of Lake Balaton (Hungary): A Multivariate Geostatistical Approach

Assessment of global, national and regional‐level digital soil mapping products at different spatial supports

A Path in Regression Random Forest Looking for Spatial Dependence: A Taxonomy and a Systematic Review

Contact Info

Product

Resources

About