Gerard Heuvelink

Gerard Heuvelink
Wageningen University & Research | WUR · Department of Soil Geography and Landscape

PhD

About

414
Publications
168,442
Reads
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18,313
Citations
Additional affiliations
September 2011 - present
ISRIC - World Soil Information
Position
  • Senior Researcher

Publications

Publications (414)
Article
Understanding the spatial-temporal dynamics of crop nitrogen (N) use efficiency (NUE) and the relationship with explanatory environmental variables can support land-use management and policymaking. Nevertheless, the application of statistical models for evaluating the explanatory variables of space-time variation in crop NUE is still under-research...
Article
Full-text available
Soil compaction is a severe threat to agricultural productivity, as it can lead to yield losses ranging from 5% to 40%. Quantification of the state of compaction can help farmers and land managers to determine the optimal management to avoid these losses. Bulk density is often used as an indicator for compaction. It is a costly and time-consuming m...
Article
Full-text available
Mapping of environmental variables often relies on map accuracy assessment through cross-validation with the data used for calibrating the underlying mapping model. When the data points are spatially clustered, conventional cross-validation leads to optimistically biased estimates of map accuracy. Several papers have promoted spatial cross-validati...
Article
Full-text available
Accurate and high resolution spatial soil information is essential for efficient and sustainable land use, management and conservation. Since the establishment of digital soil mapping (DSM) and the goals set by the GlobalSoilMap (GSM) working group, great advances have been made to attain spatial soil information worldwide. Highly populated areas s...
Article
Along with land consolidation and the recent increase in the scale of farming in Japan, it is important to assess the relationships between soil properties, topography before land consolidation, and crop characteristics within fields through on-farm research. The objectives of this study were to evaluate the impacts of soil properties and presence/...
Article
Full-text available
For the better part of the 20th century pedologists mapped soil by drawing boundaries between different classes of soil which they identified from survey on foot or by vehicle, supplemented by air-photo interpretation, and backed by an understanding of landscape and the processes by which soil is formed. Its limitations for representing gradual spa...
Article
Full-text available
Digital soil mapping (DSM) approaches provide soil information by utilising the relationship between soil properties and environmental variables. Calibration of DSM models requires measurements that may often have substantial measurement errors which propagate to the DSM outputs and need to be accounted for. This study applied a geostatistical‐base...
Article
Full-text available
This paper presents a two-stage maximum likelihood framework to deal with measurement errors in digital soil mapping (DSM) when using a machine learning (ML) model. The framework is implemented with random forest and projection pursuit regression to illustrate two different areas of machine learning, i.e. ensemble learning with trees and feature-le...
Article
Full-text available
Many national and international initiatives rely on spatially explicit information on soil organic carbon (SOC) stock change at multiple scales to support policies aiming at land degradation neutrality and climate change mitigation. In this study, we used regression cokriging with random forest and spatial stochastic cosimulation to predict the SOC...
Article
Full-text available
Pedometrics, the application of mathematical and statistical methods to the study of the distribution and genesis of soils, has broadened its scope over the past two decades. The primary focus of pedometricians has traditionally been on spatial and spatio-temporal soil inventories with numerical soil classification, geostatistical modelling of spat...
Article
Nematodes are indicators of soil quality and soil health. Knowledge of the relationships between nematode-based soil quality indices and environmental properties is beneficial for assessing environmental threats on soil biota. This study evaluated the spatial distribution of nematode-based soil quality indices in a 23-ha heavy metal-polluted nature...
Article
Full-text available
For decades scientists have produced maps of biological, ecological and environmental variables. These studies commonly evaluate the map accuracy through cross-validation with the data used for calibrating the underlying mapping model. Recent studies, however, have argued that cross-validation statistics of most mapping studies are optimistically b...
Article
Full-text available
SoilGrids produces maps of soil properties for the entire globe at medium spatial resolution (250 m cell size) using state-of-the-art machine learning methods to generate the necessary models. It takes as inputs soil observations from about 240 000 locations worldwide and over 400 global environmental covariates describing vegetation, terrain morph...
Article
Full-text available
There is a growing demand for high quality soil data. However, soil measurements are subject to many error sources. We aimed to quantify uncertainties in synthetic and real‐world wet chemistry soil data through a linear mixed‐effects model, including batch and laboratory effects. The use of synthetic data allowed us to investigate how accurately th...
Article
Full-text available
Spatial soil applications frequently involve binomial variables. If relevant environmental covariates are available, using a Bayesian generalized linear model (BGLM) might be a solution for mapping such discrete soil properties. The geostatistical extension, a Bayesian generalized linear geostatistical model (BGLGM), adds spatial dependence and is...
Article
With increasing discrepancies between population growth and food production in China, the monitoring of crop yield is essential to support food security policies. However, current studies about spatio-temporal variation of yield mainly focus on the influence of climatic factors on grain crops, and do not explore the contributions of agricultural, e...
Presentation
Since the establishment of Digital Soil Mapping (DSM) as a research field, the main focus has been on implementing new methods to improve the predictive performance of soil maps. However, considerably less effort has been invested in investigating the best way to communicate the quality of soil mapping products with users. This is essential for soi...
Article
Full-text available
It is vital for farmers to know if their land is suitable for the crops that they plan to grow. An increasing number of studies have used machine learning models based on land use data as an efficient means for mapping land suitability. This approach relies on the assumption that farmers grow their crops in the best-suited areas, but no studies hav...
Article
Full-text available
Uncertainty is often ignored in urban water systems modelling. Commercial software used in engineering practice often ignores the uncertainties of input variables and their propagation because of a lack of user-friendly implementations. This can have serious consequences, such as the wrong dimensioning of urban drainage systems (UDSs) and the inacc...
Article
Full-text available
As topography is a key factor controlling soil genesis and strongly influences physical and chemical soil properties, terrain attributes are routinely used in digital soil mapping to spatially predict soil properties. Forests on flysch sediments along the northern slopes of the Swiss Alps often have a strong microrelief. The dominant soil types are...
Preprint
Full-text available
SoilGrids produces maps of soil properties for the entire globe at medium spatial resolution (250 metres cell size) using state-of-the-art machine learning methods to generate the necessary models. It takes as inputs soil observations from about 240 000 locations worldwide and over 400 global environmental covariates describing vegetation, terrain...
Article
Full-text available
The Surface Soil Moisture (SSM) products derived from microwave remote sensing have a coarse spatial resolution, therefore downscaling is required to obtain accurate SSM at high spatial resolution. An effective way to handle the stratified heterogeneity is to model for various stratifications, however the number of samples is often limited under ea...
Article
Full-text available
River discharges are often predicted based on a calibrated rainfall-runoff model. The major sources of uncertainty, namely input, parameter and model structural uncertainty must all be taken into account to obtain realistic estimates of the accuracy of discharge predictions. Over the past years, Bayesian calibration has emerged as a suitable method...
Preprint
Full-text available
Uncertainty is often ignored in urban water systems modelling. Commercial software used in engineering practice often ignores uncertainties of input variables and their propagation because of a lack of user-friendly implementations. This can have serious consequences, such as the wrong dimensioning of urban drainage systems (UDS) and the inaccurate...
Article
Identifying poverty determinants in a region is crucial for taking effective poverty reduction measures. This paper utilizes two variable importance analysis methods to identify the relative importance of different geographic factors to explain the spatial distribution of poverty: the Lindeman, Merenda, and Gold (LMG) method used in multiple linear...
Article
Full-text available
For many decades, kriging and deterministic interpolation techniques, such as inverse distance weighting and nearest neighbour interpolation, have been the most popular spatial interpolation techniques. Kriging with external drift and regression kriging have become basic techniques that benefit both from spatial autocorrelation and covariate inform...
Conference Paper
Full-text available
>>> See online platform at https://soilgrids.org/ <<< Soil information is fundamen8tal for many global applications, such as food security, land degradation, water resources, hydrology, climate change and ecological conservation. To address these diverse needs, it is important to provide free, consistent, easily accessible and standardized soil in...
Article
The soil organic carbon (SOC) pool is the largest terrestrial carbon (C) pool and is two to three times larger than the C stored in vegetation and the atmosphere. SOC is a crucial component within the C cycle, and an accurate baseline of SOC is required, especially for biogeochemical and earth system modelling. This baseline will allow better monit...
Article
In theory, two separate regions with the same soil-forming factors should develop similar soil conditions. This theoretical finding has been used in digital soil mapping (DSM) to extrapolate a model from one area to another, which usually does not work out well. One reason for failure could be that most of these studies used empirical methods. Stru...
Article
Full-text available
Spatially resolved estimates of change in soil organic carbon (SOC) stocks are necessary for supporting national and international policies aimed at achieving land degradation neutrality and climate change mitigation. In this work we report on the development, implementation and application of a data‐driven, statistical method for mapping SOC stock...
Article
Countrywide estimates of soil organic carbon stock (SOCS) are useful to set up national strategies for sustainable land use management as well as to enhance the accuracy of global SOCS inventories. We appraised the spatial distribution of SOCS at five depth layers (0–15 cm, 15–30 cm, 30–100 cm, 0–30 cm and 0–100 cm) in Cameroon at 100 m spatial res...
Article
Full-text available
Machine learning techniques are widely employed to generate digital soil maps. The map accuracy is partly determined by the number and spatial locations of the measurements used to calibrate the machine learning model. However, determining the optimal sampling design for mapping with machine learning techniques has not yet been considered in detail...
Article
Full-text available
Super-resolution mapping (SRM) is used to obtain fine-scale land cover maps from coarse remote sensing images. Spatial attraction, geostatistics, and using prior geographic information are conventional approaches used to derive fine-scale land cover maps. As the convolutional neural network (CNN) has been shown to be effective in capturing the spat...
Technical Report
Full-text available
A summary presenting the challenges for soil carbon sequestration research, hypothesis to be further tested and key research (and innovation) products.
Article
The importance of representing the spatial structure of rainfall accurately has been emphasized in various hydrological studies. It has also been widely acknowledged that there is a need to account for uncertainty in rainfall input. Common approaches focus on accounting for either point measurement or sampling uncertainty in rainfall estimation. We...
Poster
Full-text available
Soil is key in the realisation of a number of UN Sustainable Development Goals providing a variety of goods and services. Erosion, decline in soil organic carbon and loss of biodiversity can lead to soil and land degradation, a global challenge for sustainability. It is therefore important to link the ecosystem services approach with the multitude...
Article
Full-text available
p>Many environmental and geographical models, such as those used in land degradation, agroecological and climate studies, make use of spatially distributed inputs that are known imperfectly. The R package spup provides functions for examining the uncertainty propagation from input data and model parameters onto model outputs via the environmental m...
Conference Paper
Full-text available
The SoilGrids system (www.soilgrids.org) uses machine learning algorithms to predict soil type and basic soil properties at seven depths on global extent. These algorithms (i.e., random forests, gradient boosting) are trained with soil observations assembled from 150 000 locations across the globe as stored in WoSIS (ISRIC’s World Soil Information...
Presentation
Full-text available
Presentación del quinto capítulo de mi tesis sobre cómo incluir el modelo geoestadístico en los modelos de ecuaciones estructurales. El trabajo incluye el desarrollo matemático y la implementación en R.
Article
Full-text available
Research in the area of spatial decision support (SDS) and resource allocation has recently generated increased attention for integrating optimization techniques with GIS. In this paper we address the use of spatial optimization techniques for solving multi-site land-use allocation (MLUA) problems, where MLUA refers to the optimal allocation of mul...
Article
Full-text available
This paper aims to stimulate discussion based on the experiences derived from the QUICS project (Quantifying Uncertainty in Integrated Catchment Studies). First it briefly discusses the current state of knowledge on uncertainties in sub-models of integrated catchment models and the existing frameworks for analysing uncertainty. Furthermore, it comp...
Article
Full-text available
In 1992 pedometrics as a concept became Pedometrics in the formal sense, with the establishment of a Working Group of the International Union of Soil Sciences (IUSS) and a first conference in Wageningen, the Netherlands (de Gruijter et al., 1994). To celebrate its 25th anniversary, the pedometrics community therefore convened again in Wageningen in...
Article
Full-text available
Drylands (hyperarid, arid, semiarid, and dry subhumid ecosystems) cover almost half of Earth’s land surface and are highly vulnerable to environmental pressures. Here we provide an inventory of soil properties including carbon (C), nitrogen (N), and phosphorus (P) stocks within the current boundaries of drylands, aimed at serving as a benchmark in...
Article
Full-text available
Random forest and similar Machine Learning techniques are already used to generate spatial predictions, but spatial location of points (geography) is often ignored in the modeling process. Spatial auto-correlation, especially if still existent in the cross-validation residuals, indicates that the predictions are maybe biased, and this is suboptimal...
Data
RFsp—Random Forest for spatial data (R tutorial)
Article
Simple and ordinary kriging assume a constant mean and variance of the soil variable of interest. This assumption is often implausible because the mean and/or variance are linked to terrain attributes, parent material or other soil forming factors. In kriging with external drift (KED) non-stationarity in the mean is accounted for by modelling it as...
Article
Full-text available
In rainfed crop production, root zone plant-available water holding capacity (RZ-PAWHC) of the soil has a large influence on crop growth and the yield response to management inputs such as improved seeds and fertilisers. However, data are lacking for this parameter in sub-Saharan Africa (SSA). This study produced the first spatially explicit, coher...
Preprint
Full-text available
Random forest and similar Machine Learning techniques are already used to generate spatial predictions, but spatial location of points (geography) is often ignored in the modeling process. Spatial auto-correlation, especially if still existent in the cross-validation residuals, indicates that the predictions are maybe biased, and this is suboptimal...
Preprint
Full-text available
Random forest and similar Machine Learning techniques are already used to generate spatial predictions, but spatial location of points (geography) is often ignored in the modeling process. Spatial auto-correlation, especially if still existent in the cross-validation residuals, indicates that the predictions are maybe biased, and this is suboptimal...
Article
Full-text available
Integrated environmental modelling requires coupling sub-models at different spatial and temporal scales, thus accounting for change of support procedures (aggregation and disaggregation). We introduce the R-package spatio-temporal Uncertainty Propagation across multiple scales, stUPscales, which constitutes a contribution to state-of-the-art open...
Article
Full-text available
Many complex urban drainage quality models are computationally expensive. Complexity and computing times may become prohibitive when these models are used in a Monte Carlo (MC) uncertainty analysis of long time series, in particular for practitioners. Computationally scalable and fast “surrogate” models may reduce the overall computation time for p...
Article
Land surface soil moisture (SSM) has important roles in the energy balance of the land surface and in the water cycle. Downscaling of coarse-resolution SSM remote sensing products is an efficient way for producing fine-resolution data. However, the downscaling methods used most widely require full-coverage visible/infrared satellite data as ancilla...
Article
In this paper, we analyze the methods that are used in The Netherlands to upscale in-situ groundwater measurements in time and in space, and how the selected combinations of upscaling methods affect the resulting groundwater characteristic. In The Netherlands, a three-step approach is used to obtain groundwater characteristics for a specific area:...
Preprint
Full-text available
Random forest and similar Machine Learning techniques are already used to generate spatial predictions, but spatial location of points (geography) is often ignored in the modeling process. Spatial auto-correlation, especially if still existent in the cross-validation residuals, indicates that the predictions are maybe biased, and this is suboptimal...
Article
Visual soil evaluation (VSE) is a simple and fast method to assess soil quality in situ, and is becoming increasingly popular. Besides soil structure assessment, also other soil properties can be assessed such as grass cover, roots and earthworms. Yet, the full set of visual observations has not been properly evaluated for reproducibility and corre...