@article{MTMT:34743732, title = {Three-dimensional space and time mapping reveals soil organic matter decreases across anthropogenic landscapes in the Netherlands}, url = {https://m2.mtmt.hu/api/publication/34743732}, author = {Helfenstein, Anatol and Mulder, Vera L. and Heuvelink, Gerard B. M. and Hack-ten Broeke, Mirjam J. D.}, doi = {10.1038/s43247-024-01293-y}, journal-iso = {COMMUN EARTH ENVIRON}, journal = {COMMUNICATIONS EARTH & ENVIRONMENT}, volume = {5}, unique-id = {34743732}, abstract = {For restoring soil health and mitigating climate change, information of soil organic matter is needed across space, depth and time. Here we developed a statistical modelling platform in three-dimensional space and time as a new paradigm for soil organic matter monitoring. Based on 869 094 soil organic matter observations from 339,231 point locations and the novel use of environmental covariates variable in three-dimensional space and time, we predicted soil organic matter and its uncertainty annually at 25 m resolution between 0–2 m depth from 1953–2022 in the Netherlands. We predicted soil organic matter decreases of more than 25% in peatlands and 0.1–0.3% in cropland mineral soils, but increases between 10–25% on reclaimed land due to land subsidence. Our analysis quantifies the substantial variations of soil organic matter in space, depth, and time, highlighting the inadequacy of evaluating soil organic matter dynamics at point scale or static mapping at a single depth for policymaking.}, year = {2024}, eissn = {2662-4435}, orcid-numbers = {Helfenstein, Anatol/0000-0003-2432-2672; Heuvelink, Gerard B. M./0000-0003-0959-9358} } @article{MTMT:34546469, title = {Including soil depth as a predictor variable increases prediction accuracy of SOC stocks}, url = {https://m2.mtmt.hu/api/publication/34546469}, author = {Li, Jiaying and Liu, Feng and Shi, Wenjiao and Du, Zhengping and Deng, Xiangzheng and Ma, Yuxin and Shi, Xiaoli and Zhang, Mo and Li, Qiquan}, doi = {10.1016/j.still.2024.106007}, journal-iso = {SOIL TILL RES}, journal = {SOIL & TILLAGE RESEARCH}, volume = {238}, unique-id = {34546469}, issn = {0167-1987}, year = {2024}, eissn = {1879-3444} } @article{MTMT:34775474, title = {Machine Learning for Modeling Soil Organic Carbon as Affected by Land Cover Change in the Nebraska Sandhills, USA}, url = {https://m2.mtmt.hu/api/publication/34775474}, author = {Li, Lidong and Liang, Wanwan and Awada, Tala and Hiller, Jeremy and Kaiser, Michael}, doi = {10.1007/s10666-024-09973-x}, journal-iso = {ENVIRON MODEL ASSESS}, journal = {ENVIRONMENTAL MODELING & ASSESSMENT}, volume = {2024}, unique-id = {34775474}, issn = {1420-2026}, year = {2024}, eissn = {1573-2967} } @article{MTMT:34694088, title = {Digital soil mapping in the Russian Federation: A review}, url = {https://m2.mtmt.hu/api/publication/34694088}, author = {Suleymanov, A. and Arrouays, D. and Savin, I.}, doi = {10.1016/j.geodrs.2024.e00763}, journal-iso = {GEODERMA REG}, journal = {GEODERMA REGIONAL}, volume = {36}, unique-id = {34694088}, issn = {2352-0094}, keywords = {machine learning; Russia; soil science; Spatial modelling; cartography; Digital soil mapping; SCORPAN; environmental covariates}, year = {2024}, eissn = {2352-0094} } @article{MTMT:34702607, title = {Using a Multifunctional Approach for Cartographic Modeling of Organic Carbon Content in Natural and Arable Soils of the Central Caucasus}, url = {https://m2.mtmt.hu/api/publication/34702607}, author = {Tembotov, R. Kh.}, doi = {10.1134/S001095252370065X}, journal-iso = {COSMIC RES+}, journal = {COSMIC RESEARCH}, volume = {61}, unique-id = {34702607}, issn = {0010-9525}, year = {2024}, eissn = {1608-3075}, pages = {S71-S79} } @article{MTMT:34414098, title = {Using geostatistics and machine learning models to analyze the influence of soil nutrients and terrain attributes on lead prediction in forest soils}, url = {https://m2.mtmt.hu/api/publication/34414098}, author = {Ahado, Samuel Kudjo and Agyeman, Prince Chapman and Borůvka, Luboš and Kanianska, Radoslava and Nwaogu, Chukwudi}, doi = {10.1007/s40808-023-01890-4}, journal-iso = {MESE}, journal = {MODELING EARTH SYSTEMS AND ENVIRONMENT}, volume = {2023}, unique-id = {34414098}, issn = {2363-6203}, year = {2023}, eissn = {2363-6211} } @article{MTMT:33634184, title = {Regional-scale assessment of soil functions and resilience indicators: Accounting for change of support to estimate primary soil properties and their uncertainty}, url = {https://m2.mtmt.hu/api/publication/33634184}, author = {Allocca, C. and Castrignanò, A. and Nasta, P. and Romano, N.}, doi = {10.1016/j.geoderma.2023.116339}, journal-iso = {GEODERMA}, journal = {GEODERMA}, volume = {431}, unique-id = {33634184}, issn = {0016-7061}, year = {2023}, eissn = {1872-6259}, orcid-numbers = {Castrignanò, A./0000-0001-5488-9501; Nasta, P./0000-0001-9654-566X; Romano, N./0000-0001-7276-6994} } @article{MTMT:33709099, title = {A multivariate approach for mapping a soil quality index and its uncertainty in southern France}, url = {https://m2.mtmt.hu/api/publication/33709099}, author = {Angelini, M. E. and Heuvelink, G. B. M. and Lagacherie, P.}, doi = {10.1111/ejss.13345}, journal-iso = {EUR J SOIL SCI}, journal = {EUROPEAN JOURNAL OF SOIL SCIENCE}, volume = {74}, unique-id = {33709099}, issn = {1351-0754}, year = {2023}, eissn = {1365-2389}, orcid-numbers = {Angelini, M. E./0000-0002-3815-4377; Heuvelink, G. B. M./0000-0003-0959-9358} } @article{MTMT:34657352, title = {Predictive performance of machine learning model with varying sampling designs, sample sizes, and spatial extents}, url = {https://m2.mtmt.hu/api/publication/34657352}, author = {Bouasria, Abdelkrim and Bouslihim, Yassine and Gupta, Surya and Taghizadeh-Mehrjardi, Ruhollah and Hengl, Tomislav}, doi = {10.1016/j.ecoinf.2023.102294}, journal-iso = {ECOL INFORM}, journal = {ECOLOGICAL INFORMATICS}, volume = {78}, unique-id = {34657352}, issn = {1574-9541}, keywords = {random forest; Simple random sampling; Model complexity; sampling design; Conditioned Latin Hypercube; Spatial extents; Spatial predictive mapping}, year = {2023}, eissn = {1878-0512}, orcid-numbers = {Bouasria, Abdelkrim/0000-0002-9101-6520} } @article{MTMT:34075531, title = {Combining machine learning and environmental covariates for mapping of organic carbon in soils of Russia}, url = {https://m2.mtmt.hu/api/publication/34075531}, author = {Chinilin, Andrey and Savin, Igor Yu.}, doi = {10.1016/j.ejrs.2023.07.007}, journal-iso = {EGYPT J REMOTE SENS}, journal = {Egyptian Journal of Remote Sensing and Space Science}, volume = {26}, unique-id = {34075531}, issn = {1110-9823}, year = {2023}, eissn = {2090-2476}, pages = {666-675}, orcid-numbers = {Chinilin, Andrey/0000-0002-4237-7995; Savin, Igor Yu./0000-0002-8739-5441} } @{MTMT:33710967, title = {Uncertainty assessment of spatial soil information}, url = {https://m2.mtmt.hu/api/publication/33710967}, author = {Heuvelink, Gerard B.M. and Webster, Richard}, booktitle = {Encyclopedia of Soils in the Environment}, doi = {10.1016/B978-0-12-822974-3.00174-9}, unique-id = {33710967}, year = {2023} } @article{MTMT:33060704, title = {Optimized modelling of countrywide soil organic carbon levels via an interpretable decision tree}, url = {https://m2.mtmt.hu/api/publication/33060704}, author = {Kebonye, Ndiye M. and Agyeman, Prince C. and Biney, James K.M.}, doi = {10.1016/j.atech.2022.100106}, journal-iso = {SMART AGRICULT TECHN}, journal = {SMART AGRICULTURAL TECHNOLOGY}, volume = {3}, unique-id = {33060704}, year = {2023}, eissn = {2772-3755}, orcid-numbers = {Kebonye, Ndiye M./0000-0001-9246-1987} } @article{MTMT:33734155, title = {Improving the Spatial Prediction of Soil Organic Carbon Content Using Phenological Factors: A Case Study in the Middle and Upper Reaches of Heihe River Basin, China}, url = {https://m2.mtmt.hu/api/publication/33734155}, author = {Liu, Xinyu and Wang, Jian and Song, Xiaodong}, doi = {10.3390/rs15071847}, journal-iso = {REMOTE SENS-BASEL}, journal = {REMOTE SENSING}, volume = {15}, unique-id = {33734155}, abstract = {The accurate mapping of soil organic carbon (SOC) distribution is important for carbon sequestration and land management strategies, contributing to mitigating climate change and ensuring agricultural productivity. The Heihe River Basin in China is an important region that has immense potential for SOC storage. Phenological variables are effective indicators of vegetation growth, and hence are closely related to SOC. However, few studies have incorporated phenological variables in SOC prediction, especially in alpine areas such as the Heihe River Basin. This study used random forest (RF) and extreme gradient boosting (XGBoost) to study the effects of phenological variables (e.g., Greenup, Dormancy, etc.) obtained from MODIS (i.e., Moderate Resolution Imaging Spectroradiometer) product (MCD12Q2) on SOC content prediction in the middle and upper reaches of Heihe River Basin. The current study also identified the dominating variables in SOC prediction and compared model performance using a cross validation procedure. The results indicate that: (1) when phenological variables were considered, the R2 (coefficient of determination) of RF and XGBoost were 0.68 and 0.56, respectively, and RF consistently outperforms XGBoost in various cross validation experiments; (2) the environmental variables MAT, MAP, DEM and NDVI play the most important roles in SOC prediction; (3) the phenological variables can account for 32–39% of the spatial variability of SOC in both the RF and XGBoost models, and hence were the most important factor among the five categories of predictive variables. This study proved that the introduction of phenological variables can significantly improve the performance of SOC prediction. They should be used as indispensable variables for accurately modeling SOC in related studies.}, year = {2023}, eissn = {2072-4292}, pages = {1847} } @article{MTMT:34095706, title = {Analysis of Soil Carbon Stock Dynamics by Machine Learning—Polish Case Study}, url = {https://m2.mtmt.hu/api/publication/34095706}, author = {Łopatka, Artur and Siebielec, Grzegorz and Kaczyński, Radosław and Stuczyński, Tomasz}, doi = {10.3390/land12081587}, journal-iso = {LAND-BASEL}, journal = {LAND (BASEL)}, volume = {12}, unique-id = {34095706}, abstract = {A simplified differential equation for the dynamics of soil organic carbon (SOC) that describes the rate of SOC change (dSOC/dt) was constructed using the LASSO regression—a regularized linear regression machine learning method. This method selects the best predefined explanatory variables and empirically evaluates the relevant parameters of the equation. The result, converted into a formula for the long-term equilibrium level of soil carbon, indicates the existence of carbon sequestration potential in the studied regions of Poland. In particular, the model predicts high SOC content in regions with a high Topographic Wetness Index (TWI), such as river valleys or areas with high cattle density, as expected.}, year = {2023}, eissn = {2073-445X}, orcid-numbers = {Łopatka, Artur/0000-0002-6977-4464; Siebielec, Grzegorz/0000-0001-8089-6123} } @article{MTMT:34353654, title = {Hand-feel soil texture observations to evaluate the accuracy of digital soil maps for local prediction of soil particle size distribution: A case study in Central France}, url = {https://m2.mtmt.hu/api/publication/34353654}, author = {Richer-De-Forges, Anne C. and Arrouays, Dominique and Poggio, Laura and Chen, Songchao and Lacoste, Marine and Minasny, Budiman and Libohova, Zamir and Roudier, Pierre and Mulder, Vera L. and Nedelec, Herve and Martelet, Guillaume and Lemercier, Blandine and Lagacherie, Philippe and Bourennane, Hocine}, doi = {10.1016/j.pedsph.2022.07.009}, journal-iso = {PEDOSPHERE}, journal = {PEDOSPHERE}, volume = {33}, unique-id = {34353654}, issn = {1002-0160}, abstract = {Digital maps of soil properties are now widely available. End-users now can access several digital soil mapping (DSM) products of soil properties, produced using different models, calibration/training data, and covariates at various spatial scales from global to local. Therefore, there is an urgent need to provide easy-to-understand tools to communicate map uncertainty and help end-users assess the reliability of DSM products for use at local scales. In this study, we used a large amount of hand-feel soil texture (HFST) data to assess the performance of various published DSM products on the prediction of soil particle size distribution in Central France. We tested four DSM products for soil texture prediction developed at various scales (global, continental, national, and regional) by comparing their predictions with approximately 3 200 HFST observations realized on a 1:50 000 soil survey conducted after release of these DSM products. We used both visual comparisons and quantitative indicators to match the DSM predictions and HFST observations. The comparison between the low-cost HFST observations and DSM predictions clearly showed the applicability of various DSM products, with the prediction accuracy increasing from global to regional predictions. This simple evaluation can determine which products can be used at the local scale and if more accurate DSM products are required.}, keywords = {Spatial extent; visual assessment; Prediction performance; Map uncertainty; digital soil mapping products; easy-to-understand tool; hand-feel observation; local use}, year = {2023}, eissn = {2210-5107}, pages = {731-743}, orcid-numbers = {Chen, Songchao/0000-0003-1245-0482} } @article{MTMT:34039155, title = {Assessment of the Spatial Variability and Uncertainty of Shreddable Pruning Biomass in an Olive Grove Based on Canopy Volume and Tree Projected Area}, url = {https://m2.mtmt.hu/api/publication/34039155}, author = {Rodríguez-Lizana, Antonio and Ramos, Alzira and Pereira, María João and Soares, Amílcar and Ribeiro, Manuel Castro}, doi = {10.3390/agronomy13071697}, journal-iso = {AGRONOMY-BASEL}, journal = {AGRONOMY (BASEL)}, volume = {13}, unique-id = {34039155}, abstract = {Olive pruning residues are a by-product that can be applied to soil or used for energy production in a circular economy model. Its benefits depend on the amount of pruning, which varies greatly within farms. This study aimed to investigate the spatial variability of shreddable olive pruning in a traditional olive grove in Córdoba (Spain) with an area of 15 ha and trees distanced 12.5 m from each other. To model the spatial variability of shreddable olive pruning, geostatistical methods of stochastic simulation were applied to three correlated variables measured on sampled trees: the crown projected area (n = 928 trees), the crown volume (n = 167) and the amount of shreddable pruning (n = 59). Pearson’s correlation between pairs of variables varied from 0.71 to 0.76. The amount of pruning showed great variability, ranging from 7.6 to 76 kg tree−1, with a mean value of 37 kg tree−1. Using exponential and spherical variogram models, the spatial continuity of the variables under study was established. Shreddable dry pruning weight values showed spatial autocorrelation up to 180 m. The spatial uncertainty of the estimation was obtained using sequential simulation algorithms. Stochastic simulation algorithms provided 150 possible images of the amount of shreddable pruning on the farm, using tree projected area and crown volume as secondary information. The interquartile range and 90% prediction interval were used as indicators of the uncertainty around the mean value. Uncertainty validation was performed using accuracy plots and the associated G-statistic. Results indicate with high confidence (i.e., low uncertainty) that shreddable dry pruning weight in the mid-western area of the farm will be much lower than the rest of the farm. In the same way, results show with high confidence that dry pruning weight will be much higher in a small area in the middle east of the farm. The values of the G-statistic ranged between 0.89 and 0.90 in the tests performed. The joint use of crown volume and projected areas is valuable in estimating the spatial variability of the amount of pruning. The study shows that the use of prediction intervals enables the evaluation of farm areas and informed management decisions with a low level of risk. The methodology proposed in this work can be extrapolated to other 3D crops without requiring modifications. On a larger scale, it can be useful for predicting optimal locations for biomass plants, areas with high potential as carbon sinks or areas requiring special soil protection measures.}, year = {2023}, eissn = {2073-4395}, orcid-numbers = {Rodríguez-Lizana, Antonio/0000-0003-1436-465X; Ramos, Alzira/0000-0003-3307-8253; Ribeiro, Manuel Castro/0000-0002-7890-7708} } @article{MTMT:33785679, title = {Random Forest Modeling of Soil Properties in Saline Semi-Arid Areas}, url = {https://m2.mtmt.hu/api/publication/33785679}, author = {Suleymanov, Azamat and Gabbasova, Ilyusya and Komissarov, Mikhail and Suleymanov, Ruslan and Garipov, Timur and Tuktarova, Iren and Belan, Larisa}, doi = {10.3390/agriculture13050976}, journal-iso = {AGRICULTURE-BASEL}, journal = {AGRICULTURE-BASEL}, volume = {13}, unique-id = {33785679}, abstract = {The problem of salinization/spreading of saline soils is becoming more urgent in many regions of the world, especially in context of climate change. The monitoring of salt-affected soils’ properties is a necessary procedure in land management and irrigation planning and is aimed to obtain high crop harvest and reduce degradation processes. In this work, a machine learning method was applied for modeling of the spatial distribution of topsoil (0–20 cm) properties—in particular: soil organic carbon (SOC), pH, and salt content (dry residue). A random forest (RF) machine learning approach was used in combination with environmental variables to predict soil properties in a semi-arid area (Trans-Ural steppe zone). Soil, salinity, and texture maps; topography attributes; and remote sensing data (RSD) were used as predictors. The coefficient of determination (R2) and the root mean square error (RMSE) were used to estimate the performance of the RF model. The cross-validation result showed that the RF model achieved an R2 of 0.59 and an RMSE of 0.68 for SOM; 0.36 and 0.65, respectively, for soil pH; and 0.78 and 1.21, respectively for dry residue prediction. The SOC content ranged from 0.8 to 2.8%, with an average value of 1.9%; soil pH ranged from 5.9 to 8.4, with an average of 7.2; dry residue varied greatly from 0.04 to 16.8%, with an average value of 1.3%. A variable importance analysis indicated that remote sensing variables (salinity indices and NDVI) were dominant in the spatial prediction of soil parameters. The importance of RSD for evaluating saline soils and their properties is explained by their absorption characteristics/reflectivity in the visible and near-infrared spectra. Solonchak soils are distinguished by a salt crust on the land surface and, as a result, reduced SOC contents and vegetation biomass. However, the change in saline and non-saline soils over a short distance with mosaic structure of soil cover requires high-resolution RSD or aerial images obtained from unmanned aerial vehicle/drones for successful digital mapping of soil parameters. The presented results provide an effective method to estimate soil properties in saline landscapes for further land management/reclamation planning of degraded soils in arid and semi-arid regions.}, year = {2023}, eissn = {2077-0472}, orcid-numbers = {Suleymanov, Azamat/0000-0001-7974-4931; Gabbasova, Ilyusya/0000-0002-9238-9011; Komissarov, Mikhail/0000-0001-6135-7212; Suleymanov, Ruslan/0000-0002-7754-0406; Garipov, Timur/0000-0003-4942-8203; Tuktarova, Iren/0000-0003-4731-1394} } @article{MTMT:34314847, title = {Digital mapping of soil organic carbon density in China using an ensemble model}, url = {https://m2.mtmt.hu/api/publication/34314847}, author = {Sun, Yi and Ma, Jin and Zhao, Wenhao and Qu, Yajing and Gou, Zilun and Chen, Haiyan and Tian, Yuxin and Wu, Fengchang}, doi = {10.1016/j.envres.2023.116131}, journal-iso = {ENVIRON RES}, journal = {ENVIRONMENTAL RESEARCH}, volume = {231}, unique-id = {34314847}, issn = {0013-9351}, abstract = {The soil organic carbon stock (SOCS) is considered as one of the largest carbon reservoirs in terrestrial ecosystems, and small changes in soil can cause significant changes in atmospheric CO2 concentration. Understanding organic carbon accumulation in soils is crucial if China is to meet its dual carbon target. In this study, the soil organic carbon density (SOCD) in China was digitally mapped using an ensemble machine learning (ML) model. First, based on SOCD data obtained at depths of 0-20 cm from 4356 sampling points (15 environmental covariates), we compared the performance of four ML models, namely random forest (RF), extreme gradient boosting (XGBoost), support vector machine (SVM), and artificial neural network (ANN) models, in terms of coefficient of determination (R2), mean absolute error (MAE), and root mean square error (RMSE) values. Then, we ensembled four models using Voting Regressor and the principle of stacking. The results showed that ensemble model (EM) accuracy was high (RMSE = 1.29, R2 = 0.85, MAE = 0.81), so that it could be a good choice for future research. Finally, the EM was used to predict the spatial distribution of SOCD in China, which ranged from 0.63 to 13.79 kg C/m2 (average = 4.09 (+/- 1.90) kg C/m2). The SOC storage amount in surface soil (0-20 cm) was 39.40 Pg C. This study developed a novel, ensemble ML model for SOC prediction, and improved our understanding of the spatial distribution of SOC in China.}, keywords = {machine learning; Digital soil mapping; terrestrial ecosystem; Topsoil carbon storage}, year = {2023}, eissn = {1096-0953}, orcid-numbers = {Sun, Yi/0000-0003-4319-0281; Ma, Jin/0000-0002-0394-7238} } @article{MTMT:33721644, title = {Countrywide mapping and assessment of organic carbon saturation in the topsoil using machine learning-based pedotransfer function with uncertainty propagation}, url = {https://m2.mtmt.hu/api/publication/33721644}, author = {Szatmári, Gábor and Pásztor, László and Laborczi, Annamária and Illés, Gábor and Bakacsi, Zsófia and Zacháry, Dóra and Filep, Tibor and Szalai, Zoltán and Jakab, Gergely Imre}, doi = {10.1016/j.catena.2023.107086}, journal-iso = {CATENA}, journal = {CATENA}, volume = {227}, unique-id = {33721644}, issn = {0341-8162}, year = {2023}, eissn = {1872-6887}, orcid-numbers = {Szatmári, Gábor/0000-0003-3201-598X; Pásztor, László/0000-0002-1605-4412; Laborczi, Annamária/0000-0003-4095-7838; Szalai, Zoltán/0000-0001-5267-411X; Jakab, Gergely Imre/0000-0001-5424-1983} } @article{MTMT:34071598, title = {Использование мультифункционального подхода для картографического моделирования содержания органического углерода в естественных и пахотных почвах Центрального Кавказа = Using a multifunctional approach for cartographic modeling of organic carbon content in natural and arable soils of Central Caucasus}, url = {https://m2.mtmt.hu/api/publication/34071598}, author = {Tembotov, R.Kh.}, doi = {10.21046/2070-7401-2023-20-3-193-206}, journal-iso = {Sovremennye Problemy Distantsionnogo Zondirovaniya Zemli iz Kosmosa}, journal = {Sovremennye Problemy Distantsionnogo Zondirovaniya Zemli iz Kosmosa}, volume = {20}, unique-id = {34071598}, issn = {2411-0280}, year = {2023}, pages = {193-206} } @article{MTMT:33069168, title = {Global mapping of volumetric water retention at 100, 330 and 15 000 cm suction using the WoSIS database}, url = {https://m2.mtmt.hu/api/publication/33069168}, author = {Turek, Maria Eliza and Poggio, Laura and Batjes, Niels H. and Armindo, Robson André and de Jong van Lier, Quirijn and de Sousa, Luis and Heuvelink, Gerard B.M.}, doi = {10.1016/j.iswcr.2022.08.001}, journal-iso = {INT SOIL WATER CONS RES}, journal = {INTERNATIONAL SOIL AND WATER CONSERVATION RESEARCH}, volume = {11}, unique-id = {33069168}, issn = {2095-6339}, year = {2023}, eissn = {2589-059X}, pages = {225-239}, orcid-numbers = {Turek, Maria Eliza/0000-0002-3616-8695; Batjes, Niels H./0000-0003-2367-3067; Armindo, Robson André/0000-0003-4675-8872; de Sousa, Luis/0000-0002-5851-2071; Heuvelink, Gerard B.M./0000-0003-0959-9358} } @article{MTMT:33965037, title = {Uncertainty of spatial averages and totals of natural resource maps}, url = {https://m2.mtmt.hu/api/publication/33965037}, author = {Wadoux, Alexandre M. J. -C. and Heuvelink, Gerard B. M.}, doi = {10.1111/2041-210X.14106}, journal-iso = {METHODS ECOL EVOL}, journal = {METHODS IN ECOLOGY AND EVOLUTION}, volume = {14}, unique-id = {33965037}, issn = {2041-210X}, abstract = {1. Global, continental and regional maps of concentrations, stocks and fluxes of natural resources provide baseline data to assess how ecosystems respond to human disturbance and global warming. They are also used as input to numerous modelling efforts. But these maps suffer from multiple error sources and, hence, it is good practice to report estimates of the associated map uncertainty so that users can evaluate their fitness for use.2.We explain why quantification of uncertainty of spatial aggregates is more complex than uncertainty quantification at point support because it must account for spatial autocorrelation of the map errors. Unfortunately, this is not done in a number of recent high-profile studies. We describe how spatial autocorrelation of map errors can be accounted for with block kriging, a method that requires geostatistical expertise. Next, we propose a new, model-based approach that avoids the numerical complexity of block kriging and is feasible for large-scale studies where maps are typically made using machine learning. Our approach relies on Monte Carlo integration to derive the uncertainty of the spatial average or total from point support prediction errors. We account for spatial autocorrelation of the map error by geostatistical modelling of the standardized map error.3. We show that the uncertainty strongly depends on the spatial autocorrelation of the map errors. In a first case study, we used block kriging to show that the uncertainty of the predicted topsoil organic carbon in France decreases when the support increases. In a second case study, we estimated the uncertainty of spatial aggregates of a machine learning map of the above-ground biomass in Western Africa using Monte Carlo integration. We found that this uncertainty was small because of the weak spatial autocorrelation of the standardized map errors.4. We present a tool to get realistic estimates of the uncertainty of spatial averages and totals of natural resource maps. The method presented in this paper is essential for parties that need to evaluate whether differences in aggregated environmental variables or natural resources between regions or over time are statistically significant.}, keywords = {machine learning; Geostatistics; Quantile regression forest; Change of support; Block kriging; mapping spatial aggregation}, year = {2023}, eissn = {2041-2096}, pages = {1320-1332} } @article{MTMT:34159359, title = {Soil carbon sequestration potential of cultivated lands and its controlling factors in China}, url = {https://m2.mtmt.hu/api/publication/34159359}, author = {Wang, Sh and Xu, L and Adhikari, K and He, N}, doi = {10.1016/j.scitotenv.2023.167292}, journal-iso = {SCI TOTAL ENVIRON}, journal = {SCIENCE OF THE TOTAL ENVIRONMENT}, volume = {905}, unique-id = {34159359}, issn = {0048-9697}, year = {2023}, eissn = {1879-1026} } @article{MTMT:34353656, title = {Uncertainty assessment of spatiotemporal distribution and variation in regional soil heavy metals based on spatiotemporal sequential Gaussian simulation}, url = {https://m2.mtmt.hu/api/publication/34353656}, author = {Yan, Yibo and Yang, Yong}, doi = {10.1016/j.envpol.2023.121243}, journal-iso = {ENVIRON POLLUT}, journal = {ENVIRONMENTAL POLLUTION}, volume = {322}, unique-id = {34353656}, issn = {0269-7491}, abstract = {Revealing the spatiotemporal (ST) distribution and changes in regional soil heavy metals is significant to soil pollution control and management. However, most of the ST analysis models in the existing studies ignore the uncertainty of ST changes in soil heavy metals, making their results unreliable. In this study, using soil Pb collected from 2016 to 2019 in a mining city in China as case data, an ST sequential Gaussian simulation (STSGS) is proposed to reveal the ST distribution and variation in heavy metals in regional soils and their uncertainties. Firstly, the ST variogram was analysed and fitted using a theoretical variogram model integrating the experi-mental variations at the ST scale. Secondly, 500 simulation realisations with random access path were generated by the ST Kriging method. Considering the obtained 500 simulation realisations, a series of ST analysis methods was proposed and employed to reveal the ST distribution and changes with uncertainty assessment of regional soil heavy metals. The main results are as follows. (1) For the whole study region, soil Pb content initially increased and then decreased from 2016 to 2019. The average probability of soil Pb exceeding 90 mg/kg was 0.121, 0.214, 0.312 and 0.291 in 2016, 2017, 2018 and 2019, respectively, whereas the average probability of always exceeding 90 mg/kg in the 4 years was only 0.032. (2) From 2016 to 2019, the area proportions of the increase and decrease of soil Pb content in the study area were 87.2% and 12.8%, respectively. However, ac-cording to the standardised statistic, only 0.161% and 8.72% of the total areas were determined to have a sig-nificant decrease and increase in soil Pb content from 2016 to 2019. (3) From 2016 to 2019, the areas with a greater than 0.6 probability of soil Pb concentration decreasing by more than 5 mg/kg and increasing by more than 20, 40 and 80 mg/kg accounted for 4.96%, 32.2%, 11.5% and only 1.91% of the total study region, respectively. The incremental high-probability areas were primarily those where Pb pollution was already serious. Finally, the advantages of the proposed STSGS method were summarised.}, keywords = {UNCERTAINTY; Geostatistics; Spatiotemporal; Soil heavy metal}, year = {2023}, eissn = {1873-6424} } @article{MTMT:33734152, title = {Effects of optical and radar satellite observations within Google Earth Engine on soil organic carbon prediction models in Spain}, url = {https://m2.mtmt.hu/api/publication/33734152}, author = {Zhou, Tao and Geng, Yajun and Lv, Wenhao and Xiao, Shancai and Zhang, Peiyu and Xu, Xiangrui and Chen, Jie and Wu, Zhen and Pan, Jianjun and Si, Bingcheng and Lausch, Angela}, doi = {10.1016/j.jenvman.2023.117810}, journal-iso = {J ENVIRON MANAGE}, journal = {JOURNAL OF ENVIRONMENTAL MANAGEMENT}, volume = {338}, unique-id = {33734152}, issn = {0301-4797}, year = {2023}, eissn = {1095-8630}, orcid-numbers = {Lv, Wenhao/0000-0002-5000-1437} } @article{MTMT:34175971, title = {КАРТОГРАФИРОВАНИЕ СОДЕРЖАНИЯ И ЗАПАСОВ ОРГАНИЧЕСКОГО УГЛЕРОДА ПОЧВ НА РЕГИОНАЛЬНОМ И ЛОКАЛЬНОМ УРОВНЯХ: АНАЛИЗ СОВРЕМЕННЫХ МЕТОДИЧЕСКИХ ПОДХОДОВ}, url = {https://m2.mtmt.hu/api/publication/34175971}, author = {Гопп, НВ and Мешалкина, ЮЛ and Нарыкова, АН and Плотникова, АС and Чернова, ОВ}, journal-iso = {FSI}, journal = {FOREST SCIENCE ISSUES}, volume = {6}, unique-id = {34175971}, year = {2023}, eissn = {2658-607X}, pages = {14-73} } @article{MTMT:33577346, title = {Оценка содержания органического углерода в почвах России с помощью ансамблевого машинного обучения}, url = {https://m2.mtmt.hu/api/publication/33577346}, author = {Чинилин, АВ and Савин, ИЮ}, journal-iso = {Vestnik Moskovskogo universiteta. Seriya 5: Geografiya}, journal = {MOSKOVSKII GOSUDERSTVENNYI UNIVERSITET. VESTNIK. SERIYA 5: GEOGRAFIYA}, volume = {2022}, unique-id = {33577346}, issn = {0579-9414}, year = {2023}, pages = {49-63} } @inproceedings{MTMT:33712776, title = {A LUCAS és TIM adatbázisok fizikai talajtulajdonságainak térbeli összehasonlítása}, url = {https://m2.mtmt.hu/api/publication/33712776}, author = {Benő, András and Kocsis, Mihály and Szatmári, Gábor and Laborczi, Annamária and Tóth, Brigitta and Bakacsi, Zsófia and Pásztor, László}, booktitle = {Az elmélet és gyakorlat találkozása a térinformatikában XIII.}, unique-id = {33712776}, year = {2022}, pages = {59-66}, orcid-numbers = {Szatmári, Gábor/0000-0003-3201-598X; Laborczi, Annamária/0000-0003-4095-7838; Tóth, Brigitta/0000-0003-1485-8908; Pásztor, László/0000-0002-1605-4412} } @article{MTMT:34694091, title = {ESTIMATION OF ORGANIC CARBON CONTENT IN RUSSIAN SOILS USING ENSEMBLE MACHINE LEARNING}, url = {https://m2.mtmt.hu/api/publication/34694091}, author = {Chinilin, A.V. and Savin, I.Yu.}, doi = {10.55959/MSU0579-9414-5-2022-6-49-63}, journal-iso = {Vestnik Moskovskogo universiteta. Seriya 5: Geografiya}, journal = {MOSKOVSKII GOSUDERSTVENNYI UNIVERSITET. VESTNIK. SERIYA 5: GEOGRAFIYA}, volume = {5}, unique-id = {34694091}, issn = {0579-9414}, keywords = {ALGORITHM; organic carbon; machine learning; Time series; spatial resolution; Mapping; Russian Federation; MODIS; digital elevation model; three-dimensional modeling; soil cover; Spatial modeling; Stacked regression; spatial cross-validation}, year = {2022}, pages = {49-63} } @article{MTMT:33096470, title = {Assessment of global, national and regional‐level digital soil mapping products at different spatial supports}, url = {https://m2.mtmt.hu/api/publication/33096470}, author = {Han, Si Yang and Filippi, Patrick and Singh, Kanika and Whelan, Brett M. and Bishop, Thomas F. A.}, doi = {10.1111/ejss.13300}, journal-iso = {EUR J SOIL SCI}, journal = {EUROPEAN JOURNAL OF SOIL SCIENCE}, volume = {73}, unique-id = {33096470}, issn = {1351-0754}, year = {2022}, eissn = {1365-2389}, orcid-numbers = {Han, Si Yang/0000-0003-0162-9401; Filippi, Patrick/0000-0003-3573-084X} } @article{MTMT:33041982, title = {High-Resolution Mapping and Assessment of Salt-Affectedness on Arable Lands by the Combination of Ensemble Learning and Multivariate Geostatistics}, url = {https://m2.mtmt.hu/api/publication/33041982}, author = {Hateffard, Fatemeh and Balog, Kitti and Tóth, Tibor and Mészáros, János and Árvai, Mátyás and Kovács, Zsófia Adrienn and Szűcs-Vásárhelyi, Nóra and Koós, Sándor and László, Péter and Novák, Tibor József and Pásztor, László and Szatmári, Gábor}, doi = {10.3390/agronomy12081858}, journal-iso = {AGRONOMY-BASEL}, journal = {AGRONOMY (BASEL)}, volume = {12}, unique-id = {33041982}, abstract = {Soil salinization is one of the main threats to soils worldwide, which has serious impacts on soil functions. Our objective was to map and assess salt-affectedness on arable land (0.85 km2) in Hungary, with high spatial resolution, using a combination of ensemble machine learning and multivariate geostatistics on three salt-affected soil indicators (i.e., alkalinity, electrical conductivity, and sodium adsorption ratio (n = 85 soil samples)). Ensemble modelling with five base learners (i.e., random forest, extreme gradient boosting, support vector machine, neural network, and generalized linear model) was carried out and the results showed that ensemble modelling outperformed the base learners for alkalinity and sodium adsorption ratio with R2 values of 0.43 and 0.96, respectively, while only the random forest prediction was acceptable for electrical conductivity. Multivariate geostatistics was conducted on the stochastic residuals derived from machine learning modelling, as we could reasonably assume that there is spatial interdependence between the selected salt-affected soil indicators. We used 10-fold cross-validation to check the performance of the spatial predictions and uncertainty quantifications, which provided acceptable results for each selected salt-affected soil indicator (for pH value, electrical conductivity, and sodium adsorption ratio, the root mean square error values were 0.11, 0.86, and 0.22, respectively). Our results showed that the methodology applied in this study is efficient in mapping and assessing salt-affectedness on arable lands with high spatial resolution. A probability map for sodium adsorption ratio represents sodic soils exceeding a threshold value of 13, where they are more likely to have soil structure deterioration and water infiltration problems. This map can help the land user to select the appropriate agrotechnical operation for improving soil quality and yield.}, year = {2022}, eissn = {2073-4395}, orcid-numbers = {Hateffard, Fatemeh/0000-0003-0265-4991; Balog, Kitti/0000-0001-9183-5529; Mészáros, János/0000-0003-2604-3052; Novák, Tibor József/0000-0002-5514-9035; Pásztor, László/0000-0002-1605-4412; Szatmári, Gábor/0000-0003-3201-598X} } @article{MTMT:32558650, title = {Tier 4 maps of soil pH at 25 m resolution for the Netherlands}, url = {https://m2.mtmt.hu/api/publication/32558650}, author = {Helfenstein, Anatol and Mulder, Vera L. and Heuvelink, Gerard B.M. and Okx, Joop P.}, doi = {10.1016/j.geoderma.2021.115659}, journal-iso = {GEODERMA}, journal = {GEODERMA}, volume = {410}, unique-id = {32558650}, issn = {0016-7061}, year = {2022}, eissn = {1872-6259} } @article{MTMT:32692390, title = {Spatial statistics and soil mapping: A blossoming partnership under pressure}, url = {https://m2.mtmt.hu/api/publication/32692390}, author = {Heuvelink, Gerard B.M. and Webster, Richard}, doi = {10.1016/j.spasta.2022.100639}, journal-iso = {SPAT STAT}, journal = {SPATIAL STATISTICS}, volume = {50}, unique-id = {32692390}, issn = {2211-6753}, year = {2022}, orcid-numbers = {Heuvelink, Gerard B.M./0000-0003-0959-9358} } @inproceedings{MTMT:34694092, title = {STUDY OF THE UNCERTAINTY OF THE AMOUNT OF PRUNING IN THE OLIVE GROVE USING GEOSTATISTICAL ALGORITHMS}, url = {https://m2.mtmt.hu/api/publication/34694092}, author = {Lizana, A.R. and Pereira, M.J. and Ramos, A. and Garcia, M.M. and Ribeiro, M.}, doi = {10.5593/sgem2022V/3.2/s14.50}, volume = {22}, unique-id = {34694092}, keywords = {SOILS; UNCERTAINTY; uncertainty analysis; Correlation methods; Soil conservation; Water conservation; Geostatistics; Olive grove; Direct sequential simulation; Direct sequential simulation; olive tree; olive tree; Geo-statistics; Projected area; Pruning residues; spatial uncertainty; Pruning residue; Sequential simulation; Spatial continuity}, year = {2022}, pages = {431-437} } @article{MTMT:34088995, title = {Talajökológiai egyensúly érdekében csökkenő élelmiszer-termelés: dilemma vagy szükségszerűség? • Decreasing Food Production in Quest to Restore Soil Ecological Balance: Dilemma or Necessity?}, url = {https://m2.mtmt.hu/api/publication/34088995}, author = {Rajkai, Kálmán László}, doi = {10.1556/2065.183.2022.10.2}, journal-iso = {MAGYAR TUDOMÁNY}, journal = {MAGYAR TUDOMÁNY}, volume = {183}, unique-id = {34088995}, issn = {0025-0325}, year = {2022}, eissn = {1588-1245}, pages = {1246-1254}, orcid-numbers = {Rajkai, Kálmán László/0000-0003-4095-774X} } @inproceedings{MTMT:33728124, title = {STUDY OF THE UNCERTAINTY OF THE AMOUNT OF PRUNING IN THE OLIVE GROVE USING GEOSTATISTICAL ALGORITHMS}, url = {https://m2.mtmt.hu/api/publication/33728124}, author = {Rodriguez Lizana, Antonio and Pereira, Maria Joao and Ramos, Alzira and Moreno Garcia, Manuel and Ribeiro, Manuel}, booktitle = {Proceedings of 22nd International Multidisciplinary Scientific GeoConference SGEM 2022}, doi = {10.5593/sgem2022V/3.2/s14.50}, unique-id = {33728124}, abstract = {Olive pruning mulch modifies the physical, chemical and biological properties of the soil. They are an efficient soil and water conservation system, while simultaneously improving the organic matter content of the soil. In any case, their effect on soil properties is a function of the densities provided. In any agricultural field, there can be significant variations in plant size, which can affect the amount of pruning obtained. In this research, a spatial sampling of pruning amount collected in olive trees (n=59) in a 13.1-ha traditional olive grove located in Cordoba (Spain), was conducted to estimate the mean pruning amount and assess its spatial uncertainty. In addition, the projected areas of all trees in the field (n=928) were determined. Tree projected area was found to be well correlated with the amount of pruning (Pearson correlation coefficient value of 0.74). The spatial continuity of the study variables was determined using isotropic variograms with nested spherical models. Direct sequential simulation and cosimulation algorithms were used to generate 125 realizations of each variable and map the spatial uncertainty of the amount of pruning in unsampled areas. The results indicate that pruning amounts exhibit spatial continuity. The projected area of the trees is a useful variable to improve estimates of total amount of pruning.}, year = {2022}, pages = {431-438}, orcid-numbers = {Rodriguez Lizana, Antonio/0000-0003-1436-465X; Pereira, Maria Joao/0000-0003-2580-6281; Ramos, Alzira/0000-0003-3307-8253; Ribeiro, Manuel/0000-0002-7890-7708} } @article{MTMT:32641881, title = {Joint Spatial Modeling of Nutrients and Their Ratio in the Sediments of Lake Balaton (Hungary): A Multivariate Geostatistical Approach. A Multivariate Geostatistical Approach}, url = {https://m2.mtmt.hu/api/publication/32641881}, author = {Szatmári, Gábor and Kocsis, Mihály and Makó, András and Pásztor, László and Bakacsi, Zsófia}, doi = {10.3390/w14030361}, journal-iso = {WATER-SUI}, journal = {WATER}, volume = {14}, unique-id = {32641881}, year = {2022}, eissn = {2073-4441}, orcid-numbers = {Szatmári, Gábor/0000-0003-3201-598X; Makó, András/0000-0002-6169-6393; Pásztor, László/0000-0002-1605-4412} } @CONFERENCE{MTMT:33071860, title = {Estimating soil organic carbon stock change at regional scales: Challenges and possible solutions. poster}, url = {https://m2.mtmt.hu/api/publication/33071860}, author = {Szatmári, Gábor and Laborczi, Annamária and Heuvelink, Gerard and Pirkó, Béla and Koós, Sándor and Bakacsi, Zsófia and Mészáros, János and Pásztor, László}, booktitle = {22nd World Congress of Soil Science : poster book of abstracts}, unique-id = {33071860}, year = {2022}, orcid-numbers = {Szatmári, Gábor/0000-0003-3201-598X; Laborczi, Annamária/0000-0003-4095-7838; Mészáros, János/0000-0003-2604-3052; Pásztor, László/0000-0002-1605-4412} } @article{MTMT:33232227, title = {Comparing the prediction performance, uncertainty quantification and extrapolation potential of regression kriging and random forest while accounting for soil measurement errors}, url = {https://m2.mtmt.hu/api/publication/33232227}, author = {Takoutsing, Bertin and Heuvelink, Gerard B.M.}, doi = {10.1016/j.geoderma.2022.116192}, journal-iso = {GEODERMA}, journal = {GEODERMA}, volume = {428}, unique-id = {33232227}, issn = {0016-7061}, year = {2022}, eissn = {1872-6259} } @article{MTMT:32505596, title = {Integration of a process-based model into the digital soil mapping improves the space-time soil organic carbon modelling in intensively human-impacted area}, url = {https://m2.mtmt.hu/api/publication/32505596}, author = {Xie, Enze and Zhang, Xiu and Lu, Fangyi and Peng, Yuxuan and Chen, Jian and Zhao, Yongcun}, doi = {10.1016/j.geoderma.2021.115599}, journal-iso = {GEODERMA}, journal = {GEODERMA}, volume = {409}, unique-id = {32505596}, issn = {0016-7061}, year = {2022}, eissn = {1872-6259} } @article{MTMT:32801420, title = {Identifying the scale-controlling factors of soil organic carbon in the cropland of Jilin Province, China}, url = {https://m2.mtmt.hu/api/publication/32801420}, author = {Zhang, Yue and Jiang, Yanyan and Jia, Zenghui and Qiang, Ruowen and Gao, Qiang}, doi = {10.1016/j.ecolind.2022.108921}, journal-iso = {ECOL INDIC}, journal = {ECOLOGICAL INDICATORS}, volume = {139}, unique-id = {32801420}, issn = {1470-160X}, year = {2022}, eissn = {1872-7034} }