A geostatistical approach to data harmonization – Application to radioactivity exposure data

Baume, Olivier; Skøien, Jon Olav; Heuvelink, G.B.M.; Pebesma, Edzer; Melles, Stephanie J.

doi:10.1016/j.jag.2010.09.002

Cited by 25 publications

(7 citation statements)

References 30 publications

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“…The usual reported pre‐processing tasks include the filtering of obvious recording errors (Schillaci et al, 2019), the screening for possible inconsistencies and outliers using automated and visual procedures (Batjes et al, 2017; Schillaci et al, 2019) or the harmonization of soil profile locations and observation depths (Sulaeman et al, 2013). More rarely, soil data pre‐processing included the corrections of some soil properties values to harmonize different soil datasets, either by simple regression (Velmurugan et al, 2009) or by geostatistical techniques (Baume et al, 2011; Ciampalini et al, 2013). However, none of these pre‐processing tasks were reported in the literature and, to our knowledge, none of those involved in the current DSM applications included a control sampling as the one performed in this study.…”

Section: Discussionmentioning

confidence: 99%

Evaluating the quality of soil legacy data used as input of digital soil mapping models

Lagacherie,

Arregui,

Fages

2024

European J Soil Science

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Most of the Digital Soil Mapping (DSM) products now available across the globe have been developed from the deposits of punctual soil observations inherited from several decades of soil survey activity. By using these legacy data as inputs for calibrating our DSM models, we implicitly make the assumption that these legacy soil data are accurate and therefore do not affect significantly our DSM products. However, this assumption has never been tested. The objectives of this study were to evaluate the accuracies of soil property measurements retrieved from legacy soil profiles, to analyse the different sources of error that may affect these measurements and to examine their impacts on the soil property predictions delivered by DSM models. The study was focused on a control sampling within the coastal plain of Languedoc (Southern France) at 129 locations where legacy measured soil profiles were collected between 1955 and 1992. At each location, four topsoil (0–20 cm) samples were collected at increasing distances (0, 5, 25 and 100 m) to characterize the local variabilities of the soil properties. Six soil properties—Clay, Silt, Sand, Soil Organic Carbon, Calcium Carbonate contents and Cation Exchange Capacity—were determined for each sample using certified soil laboratory methods. The results revealed that legacy soil property values had large overall errors and large biases. Biases likely induced by differences in soil analysis protocols could be corrected by linear functions calibrated onto the reference data obtained from the control sampling. The contributions of the errors propagated from the manual geo‐referencing errors (mean = 31 m) represented on average 52% of the errors after analytical bias corrections. These errors exhibited large variations from one property to another due to differences in the short‐range spatial variations (0–100 m) of these soil properties. A DSM exercise conducted on our control sampling revealed that the errors of legacy soil data were propagated to the soil property predictions provided by the DSM models. However, this propagation could be largely mitigated by applying the above‐evoked corrections. This study highlights the need to better control the quality of the legacy soil data used in DSM and to account for this source of uncertainty in the DSM models.

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Section: Discussionmentioning

confidence: 99%

Evaluating the quality of soil legacy data used as input of digital soil mapping models

Lagacherie,

Arregui,

Fages

2024

European J Soil Science

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“…Another way may be to use a scoring function to develop separate rankings region by region, with the prerequisite that a single standard analytical method is applied in each region: applying the same ranking (or relative scoring) method can result in similar and comparable values making it possible to compare across countries and regions, even if different methods are applied in each region (Fine et al, 2017, Nunes et al, 2021). Other kinds of geostatistical and statistical methods for posterior combination of soil datasets obtained with different soil sampling protocols and analytical standards have been tested by researchers in recent studies (Baume et al, 2011; Ciampalini et al, 2013).…”

Section: Discussion: Consequences Of the Soil Data Situation In Europ...mentioning

confidence: 99%

National soil data in EU countries, where do we stand?

Cornu

Keesstra

Bispo

et al. 2023

European J Soil Science

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At European scale, soil characteristics are needed to evaluate soil quality, soil health and soil‐based ecosystem services in the context of the European Green Deal. While some soil databases exist at the European scale, a much larger wealth of data is present in individual European countries, allowing a more detailed soil assessment. There is thus an urgent and crucial need to combine these data at the European scale. In the frame of a large European Joint Programme on agricultural soils launched by the European Commission, a survey was conducted in the spring of 2020, in the 24 European participating countries to assess the existing soil data sources, focusing on agricultural soils. The survey will become a contribution to the European Soil Observatory, launched in December 2020, which aims to collect metadata of soil databases related to all kind of land uses, including forest and urban soils. Based upon a comprehensive questionnaire, 170 soil databases were identified at local, regional and national scales. Soil parameters were divided into five groups: 1. main soil parameters according to the Global Soil Map specifications; 2. other soil chemical parameters; 3. other physical parameters; 4. other pedological parameters; and 5. soil biological features. A classification based on the environmental zones of Europe was used to distinguish the climatic zones. This survey shows that while most of the main pedological and chemical parameters are included in more than 70 % of the country soil databases, water content, contamination with organic pollutants and biological parameters are the least frequently reported parameters. Such differences will have consequences when developing an EU policy on soil health as proposed under the EU soil strategy for 2023 and using the data to derive soil health indicators. Many differences in the methods used in collecting, preparing, and analysing the soils were found, thus requiring harmonisation procedures and more cooperation among countries and with the EU to use the data at the European scale Additionally, choosing harmonized and useful interpretation and threshold values for EU soil indicators may be challenging due to the different methods used and the wide variety of soil land‐use and climate combinations influencing possible thresholds. The temporal scale of the soil databases reported is also extremely wide, starting from the ‘20s of the 20th century.This article is protected by copyright. All rights reserved.

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“…Much of the performances limitations are due to bias of clay measurements that affect the legacy soil databases. This means that these legacy soil databases need to be standardized themselves before using them for such approach, as some attempts has been already done in that objective (Baume et al, 2011, Ciampalini, 2013 The standardisation applied in our study is composed of a boxcox transformation for normalization of the predicted data, and a scaling and centering of the normalized data. This standardisation process is comparable to one of the most widely used transfer methods for correcting predicted values which is the univariate slope and bias correction (SBC) (Bouveresse et al, 1996).…”

Section: Discussionmentioning

confidence: 99%

Using Legacy Soil Data for Standardizing Predictions of Topsoil Clay Content Obtained From Vnir/Swir Hyperspectral Airborne Images

Gomez

Gholizadeh

Borůvka

et al. 2015

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Mapping of topsoil properties using Visible, Near-Infrared and Short Wave Infrared (VNIR/SWIR) hyperspectral imagery requires large sets of ground measurements for calibrating the models that estimate soil properties. To avoid collecting such expensive data, we proposed a procedure including two steps that involves only legacy soil data that were collected over and/or around the study site: 1) estimation of a soil property using a spectral index of the literature and 2) standardisation of the estimated soil property using legacy soil data. This approach was tested for mapping clay contents in a Mediterranean region in which VNIR/SWIR AISA-DUAL hyperspectral airborne data were acquired. The spectral index was the one proposed by Levin et al (2007) using the spectral bands at 2209, 2133 and 2225 nm. Two legacy soil databases were tested as inputs of the procedure: the Focused-Legacy database composed of 67 soil samples collected in 2000 over the study area, and the No-Focused-Legacy database composed of 64 soil samples collected between 1973 and 1979 around but outside of the study area. The results were compared with those obtained from 120 soil samples collected over the study area during the hyperspectral airborne data acquisition, which were considered as a reference.Our results showed that: 1) the spectral index with no further standardisation offered predictions with high accuracy in term of coefficient of correlation r (0.71), but also high bias (-414 g/kg) and SEP (439 g/kg), 2) the standardisation using both legacy soil databases allowed an increase of accuracy (r = 0.76) and a reduction of bias and SEP and 3) a better standardisation was obtained by using the Focused-Legacy database rather than the No-Focused-Legacy database. Finally, the clay predicted map obtained with standardisation using the Focused-Legacy database showed pedologically-significant soil spatial structures with clear short-scale variations of topsoil clay contents in specific areas.This study, associated with the coming availability of a next generation of hyperspectral VNIR/SWIR satellite data for the entire globe, paves the way for inexpensive methods for delivering high resolution soil properties maps.

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A geostatistical approach to data harmonization – Application to radioactivity exposure data

Cited by 25 publications

References 30 publications

Evaluating the quality of soil legacy data used as input of digital soil mapping models

Evaluating the quality of soil legacy data used as input of digital soil mapping models

National soil data in EU countries, where do we stand?

Using Legacy Soil Data for Standardizing Predictions of Topsoil Clay Content Obtained From Vnir/Swir Hyperspectral Airborne Images

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