BIS-4D: Mapping soil properties and their uncertainties at 25 m resolution in the Netherlands DOI Creative Commons
Anatol Helfenstein, Vera Leatitia Mulder,

M.J.D. Hack‐ten Broeke

et al.

Published: Feb. 1, 2024

Abstract. In response to the growing societal awareness of critical role healthy soils, there is an increasing demand for accurate and high-resolution soil information inform national policies support sustainable land management decisions. Despite advancements in digital mapping initiatives like GlobalSoilMap, quantifying variability its uncertainty across space, depth, time remains a challenge. Therefore, maps key properties are often still missing on scale, which also case Netherlands. To meet this challenge fill data gap, we introduce BIS-4D, high resolution modelling platform BIS-4D delivers texture (clay, silt sand content), bulk density, pH, total nitrogen, oxalate-extractable phosphorus, cation exchange capacity their uncertainties at 25 m between 0–2 depth 3D space. Additionally, it provides organic matter space 1953–2023 same range. The statistical model uses machine learning informed by observations numbering 3815–855 950, depending property, 366 environmental covariates. We assess accuracy mean median predictions using design-based inference probability sample location-grouped 10-fold cross-validation, prediction interval coverage probability. found that clay, pH was highest, with efficiency coefficient (MEC) ranging 0.6–0.92 depth. Silt, matter, nitrogen (MEC = 0.27–0.78), especially phosphorus −0.11–0.38), were more difficult predict. One main limitations cannot be used quantify spatial aggregates. A step-by-step manual helps users decide whether suitable intended purpose, overview allmaps can supplementary (SI), openly available code input enhance reproducibility future updates, easily downloaded https://doi.org/10.4121/0c934ac6-2e95-4422-8360-d3a802766c71 (Helfenstein et al., 2024a). fills previous gap scale GlobalSoilMap product Netherlands will hopefully facilitate inclusion as routine integral part decision systems.

Language: Английский

Why make inverse modeling and which methods to use in agriculture? A review DOI Creative Commons
Yulin Zhang, Léo Pichon, Sébastien Roux

et al.

Computers and Electronics in Agriculture, Journal Year: 2024, Volume and Issue: 217, P. 108624 - 108624

Published: Jan. 13, 2024

Inverse modeling (IM) is a valuable tool in agriculture for estimating model parameters that aid decision-making. It particularly useful when cannot be directly measured or easily estimated due to logistical constraints agricultural settings. Unlike other estimation methods, IM combines mechanistic with observations of its outputs derive the interest, allowing integration various sources knowledge. The availability numerous data sources, such as remote sensing and crowdsourcing, high spatial temporal resolution, has expanded potential agriculture. Practitioners can now incorporate footprint observational into parameter estimation. However, common techniques currently applied often struggle account effectively variability. Relevant methods address these challenges are usually isolated within specific developer user communities not well known community. There lack comprehensive reviews focusing on suitable handling In parallel, process conducting remains under-formalized. Typically, chosen combinations models types data, but rationale behind their selection rarely explained publications. relationship between models, unclear, making it overwhelming new practitioners choose an appropriate method. This complex problem, along diversity yet adequately addressed while taking specificities applications. To challenges, this review aims provide structured classification based practical needs examines wide range inversion agriculture-related domains covers four key topics: i) essential elements general IM, ii) main families characteristics, iii) circumstances which prefer using over approaches, motivations, iv) guidance choosing method family operational criteria. help readers develop clear understanding practice inverse modeling, gain insights make informed choices selecting

Language: Английский

Citations

5
National-scale digital soil mapping performances are related to covariates and sampling density: Lessons from France DOI Creative Commons
Azamat Suleymanov, Anne C Richer-De-Forges, Nicolas Saby

et al.

Geoderma Regional, Journal Year: 2024, Volume and Issue: 37, P. e00801 - e00801

Published: April 20, 2024

Accurate soil property and class predictions through spatial modelling necessitate a thoughtful selection of explanatory variables sample size, as their choice greatly impacts model performance. Within the framework Global Soil Nutrient Budgets maps (GSNmap), FAO Partnership (GSP) launched country-driven digital mapping (DSM) approach. The GSP asked countries if they could implement DSM prediction ten properties, using national point data set widely available covariates (GSP_Cov). In this study, we examined effect including additional national-based observations on performance models mainland France pilot. learning dataset was based systematic 16-to-16 km grid. For subset also assessed repeated k-fold cross-validation adding to many other irregularly spread measurements. GSP_Cov included common that represented information about terrain, climate, organisms. second consisted GSP_Cov, extended extra at level, such previously existing maps, geological remote sensing products others. Random Forest approach in combination with Boruta method employed for properties: organic carbon (SOC), pH (water), total nitrogen (N), phosphorus (P), potassium (K), cation exchange capacity (CEC), bulk density (BD), texture (clay, silt, sand). results revealed noteworthy enhancements more than half although, some them, improvements were negligible. most significant obtained pH, CEC texture, where previous map significantly contributed increase accuracy. Adding numerous points (around 25,000) improved particle-size fractions predictions. By broadening spectrum better covering feature geographical spaces considered models, research underscores importance implementing diverse range scale densifying enlarge multidimensional soil/covariates combinations. This should be taken into account continental endeavours.

Language: Английский

Citations

5
Optimizing Subsurface Geotechnical Data Integration for Sustainable Building Infrastructure DOI Creative Commons
Nauman Ijaz, Zain Ijaz,

Nianqing Zhou

et al.

Buildings, Journal Year: 2025, Volume and Issue: 15(1), P. 140 - 140

Published: Jan. 5, 2025

Sustainable building construction encounters challenges stemming from escalating expenses and time delays associated with geotechnical assessments. Developing optimizing soil maps (SMs) using existing data across heterogeneous formations offer strategic dynamic solutions. This approach facilitates economical prompt site evaluations, offers preliminary ground models, enhancing efficient sustainable foundation design. In this framework, paper aimed to develop SMs for the first in rapidly growing district of Gujrat optimal interpolation technique (OIT). The subsurface conditions were evaluated standard penetration test (SPT) N-values classification including seismic wave velocity account effects. Among different geostatistical geospatial inverse distance weighting (IDW) model based on an optimized spatial analyst yielded minimum error a higher association field understudy region. Overall, IDW root mean square (RMSE), absolute (MAE), correlation coefficient (CC) ranges between 0.57 0.98. Furthermore, analytical depth-dependent models developed SPT-N values assess bearing capacity, demonstrating R2 > 0.95. Moreover, study area was divided into three zones average values. Comprehensive validation strata evaluation type-based revealed that RMSE MAE ranged 0.36–1.65 0.30–0.59, while CC 0.93 0.98 at multiple depths. allowable capacity (ABC) spread footings determined by evaluating shear, settlement, factors. insights regional variations along shallow design guidelines practitioners researchers working similar conditions.

Language: Английский

Citations

0
Spatial assessment of soil erosion using the revised universal soil loss equation (RUSLE) model for sustainable marine ecosystems in the coastal of northern part, Aceh Province DOI Creative Commons
Muhammad Nanda, Muhammad Alwan,

Muhammad Ghufran

et al.

BIO Web of Conferences, Journal Year: 2025, Volume and Issue: 156, P. 02010 - 02010

Published: Jan. 1, 2025

Coastal erosion presents a significant danger to sustainable marine ecosystems, especially in the northern coastal area of Aceh Province, Indonesia. This research combines Revised Universal Soil Loss Equation (RUSLE) model with GIS and remote sensing provide an innovative spatial evaluation soil risks. study produces high-resolution maps risk sediment yield by integrating precipitation patterns, properties, topography, land use data. The results indicate substantial areas that contribute accumulation regions, which may affect ecosystems increase land-sea connectivity issues. methodology enhances utilization RUSLE environments offers practical guidance for mitigation management. highlights significance mitigating as important factor attaining SDG 14 (Life Below Water), emphasizing necessity integrated policies reduce degradation its subsequent effects on ecosystems. findings highlight geospatial tools encourage evidence- based decision-making management resources.

Language: Английский

Citations

0
Validation of Three - Horizon Soil Maps Based on Different Soil Texture Datasets for Denmark DOI
Alireza Motevalli, Bo V. Iversen, Charles Pesch

et al.

Published: Jan. 1, 2025

Language: Английский

Citations

0
Applicability of three remote sensing based soil moisture variables for mapping soil organic matter in areas with different vegetation densities DOI

Chenconghai Yang,

Lin Yang, Lei Zhang

et al.

Journal of Hydrology, Journal Year: 2025, Volume and Issue: unknown, P. 132980 - 132980

Published: Feb. 1, 2025

Language: Английский

Citations

0
Prediction and mapping of soil organic carbon stock via large datasets coupled with pedotransfer functions DOI
S. Dharumarajan, Kabindra Adhikari,

Ranabir Chakraborty

et al.

Earth Science Informatics, Journal Year: 2025, Volume and Issue: 18(3)

Published: March 1, 2025

Language: Английский

Citations

0
Does digital soil mapping prediction performance of soil texture improve when adding uncertain field texture estimates? A study based on clay content DOI Creative Commons
Anne C Richer-De-Forges, Songchao Chen, G.B.M. Heuvelink

et al.

Geoderma, Journal Year: 2025, Volume and Issue: 456, P. 117277 - 117277

Published: April 1, 2025

Language: Английский

Citations

0
Agronomic Biofortification of Zinc in Rice for Diminishing Malnutrition in South Asia DOI Open Access
Panneerselvam Peramaiyan, Peter Craufurd, Virender Kumar

et al.

Sustainability, Journal Year: 2022, Volume and Issue: 14(13), P. 7747 - 7747

Published: June 24, 2022

Zinc (Zn) is increasingly recognized as an essential trace element in the human diet that mediates a plethora of health conditions, including immune responses to infectious diseases. Interestingly, geographical distribution dietary Zn deficiency overlaps with soil deficiency. In South Asia, malnutrition high due excessive consumption rice low content. Interventions such diversification, food fortification, supplementation, and biofortification are followed address malnutrition. Among these, most encouraging, cost-effective, sustainable for Asia. Biofortification through conventional breeding transgenic approaches has been achieved cereals; however, if deficient Zn, then these not advantageous. Therefore, this article, we review strategies enhancing concentration agronomic timing, dose, method fertilizer application, how nitrogen phosphorus application well crop establishment methods influence rice. We also propose data-driven recommendations anticipate fertilization targeted policies support regions where high.

Language: Английский

Citations

13
Soil Data Cube and Artificial Intelligence Techniques for Generating National-Scale Topsoil Thematic Maps: A Case Study in Lithuanian Croplands DOI Creative Commons
Nikiforos Samarinas, Nikolaos Tsakiridis, Stylianos Kokkas

et al.

Remote Sensing, Journal Year: 2023, Volume and Issue: 15(22), P. 5304 - 5304

Published: Nov. 9, 2023

There is a growing realization among policymakers that in order to pave the way for development of evidence-based conservation recommendations policy, it essential improve capacity soil-health monitoring by adopting multidimensional and integrated approaches. However, existing ready-to-use maps are characterized mainly coarse spatial resolution (>200 m) information not up date, making their use insufficient EU’s policy requirements, such as common agricultural policy. This work, utilizing Soil Data Cube, which self-hosted custom tool, provides yearly estimations soil thematic (e.g., exposed soil, organic carbon, clay content) covering all area Lithuania. The pipeline exploits various Earth observation data time series Sentinel-2 satellite imagery (2018–2022), LUCAS (Land Use/Cover Area Frame Statistical Survey) topsoil database, European Integrated Administration Control System (IACS) artificial intelligence (AI) architectures prediction accuracy well (10 m), enabling discrimination at parcel level. Five different models were tested with convolutional neural network (CNN) model achieve best both targeted indicators (SOC clay) related R2 metric (0.51 SOC 0.57 clay). predictions supported uncertainties based on PIR formula (average 0.48 0.61 provide valuable model’s interpretation stability. application final carried out national bare-soil-reflectance composite layers, generated employing pixel-based approach overlaid annual bare-soil using combination vegetation indices NDVI, NBR2, SCL. findings this work new insights generation large scale, leading more efficient sustainable management, supporting agri-food private sector.

Language: Английский

Citations

7