TY - JOUR AU - Gadal, Sébastien AU - Oukhattar, Mounir AU - Keller, Catherine AU - Houmma, Ismaguil Hanadé TI - Spatiotemporal Modelling of Soil Organic Carbon Stocks in a Semi-Arid Region Using a Multilayer Perceptron Algorithm JF - SN COMPUTER SCIENCE J2 - SN COMP SCI VL - 5 PY - 2024 IS - 5 SN - 2662-995X DO - 10.1007/s42979-024-02872-8 UR - https://m2.mtmt.hu/api/publication/34865024 ID - 34865024 LA - English DB - MTMT ER - TY - JOUR AU - Helfenstein, Anatol AU - Mulder, Vera L. AU - Hack‐ten Broeke, Mirjam J. D. AU - Breman, Bas C. TI - A nature‐inclusive future with healthy soils? Mapping soil organic matter in 2050 in the Netherlands JF - EUROPEAN JOURNAL OF SOIL SCIENCE J2 - EUR J SOIL SCI VL - 75 PY - 2024 IS - 4 SN - 1351-0754 DO - 10.1111/ejss.13529 UR - https://m2.mtmt.hu/api/publication/35142206 ID - 35142206 AB - Nature‐inclusive scenarios of the future can help address numerous societal challenges related to soil health. As nature‐inclusive scenarios imply sustainable management of natural systems and resources, land use and soil health are assumed to be mutually beneficial in such scenarios. However, the interplay between nature‐inclusive land use scenarios and soil health has never been modelled using digital soil mapping. We predicted soil organic matter (SOM), an important indicator of soil health, in 2050, based on a recently developed nature‐inclusive scenario and machine learning in 3D space and time in the Netherlands. By deriving dynamic covariates related to land use and the occurrence of peat for 2050, we predicted SOM and its uncertainty in 2050 and assessed SOM changes between 2022 and 2050 from 0 to 2 m depth at 25 m resolution. We found little changes in the majority of mineral soils. However, SOM decreases of up to 5% were predicted in grasslands used for animal‐based production systems in 2022, which transitioned into croplands for plant‐based production systems by 2050. Although increases up to 25% SOM were predicted between 0 and 40 cm depth in rewetted peatlands, even larger decreases, on reclaimed land even surpassing 25% SOM, were predicted on non‐rewetted land in peat layers below 40 cm depth. There were several limitations to our approach, mostly due to predicting future trends based on historic data. Furthermore, nuanced nature‐inclusive practices, such as the adoption of agroecological farming methods, were too complex to incorporate in the model and would likely affect SOM spatial variability. Nonetheless, 3D‐mapping of SOM in 2050 created new insights and raised important questions related to soil health behind nature‐inclusive scenarios. Using machine learning explicit in 3D space and time to predict the impact of future scenarios on soil health is a useful tool for facilitating societal discussion, aiding policy making and promoting transformative change. LA - English DB - MTMT ER - TY - JOUR AU - Helfenstein, Anatol AU - Mulder, Vera L. AU - Hack-ten Broeke, Mirjam J. D. AU - van Doorn, Maarten AU - Teuling, Kees AU - Walvoort, Dennis J. J. AU - Heuvelink, Gerard B. M. TI - BIS-4D: mapping soil properties and their uncertainties at 25 m resolution in the Netherlands JF - EARTH SYSTEM SCIENCE DATA J2 - EARTH SYST SCI DATA VL - 16 PY - 2024 IS - 6 SP - 2941 EP - 2970 PG - 30 SN - 1866-3508 DO - 10.5194/essd-16-2941-2024 UR - https://m2.mtmt.hu/api/publication/35086743 ID - 35086743 AB - Abstract. In response to the growing societal awareness of the critical role of healthy soils, there has been an increasing demand for accurate and high-resolution soil information to inform national policies and support sustainable land management decisions. Despite advancements in digital soil mapping and initiatives like GlobalSoilMap, quantifying soil variability and its uncertainty across space, depth and time remains a challenge. Therefore, maps of key soil properties are often still missing on a national scale, which is also the case in the Netherlands. To meet this challenge and fill this data gap, we introduce BIS-4D, a high-resolution soil modeling and mapping platform for the Netherlands. BIS-4D delivers maps of soil texture (clay, silt and sand content), bulk density, pH, total nitrogen, oxalate-extractable phosphorus, cation exchange capacity and their uncertainties at 25 m resolution between 0 and 2 m depth in 3D space. Additionally, it provides maps of soil organic matter and its uncertainty in 3D space and time between 1953 and 2023 at the same resolution and depth range. The statistical model uses machine learning informed by soil observations amounting to between 3815 and 855 950, depending on the soil property, and 366 environmental covariates. We assess the accuracy of mean and median predictions using design-based statistical inference of a probability sample and location-grouped 10-fold cross validation (CV) and prediction uncertainty using the prediction interval coverage probability. We found that the accuracy of clay, sand and pH maps was the highest, with the model efficiency coefficient (MEC) ranging between 0.6 and 0.92 depending on depth. Silt, bulk density, soil organic matter, total nitrogen and cation exchange capacity (MEC of 0.27 to 0.78), and especially oxalate-extractable phosphorus (MEC of −0.11 to 0.38) were more difficult to predict. One of the main limitations of BIS-4D is that prediction maps cannot be used to quantify the uncertainty in spatial aggregates. We provide an example of good practice to help users decide whether BIS-4D is suitable for their intended purpose. An overview of all maps and their uncertainties can be found in the Supplement. Openly available code and input data enhance reproducibility and help with future updates. BIS-4D prediction maps can be readily downloaded at https://doi.org/10.4121/0c934ac6-2e95-4422-8360-d3a802766c71 (Helfenstein et al., 2024a). BIS-4D fills the previous data gap of the national-scale GlobalSoilMap product in the Netherlands and will hopefully facilitate the inclusion of soil spatial variability as a routine and integral part of decision support systems. LA - English DB - MTMT ER - TY - JOUR AU - Helfenstein, Anatol AU - Mulder, Vera L. AU - Heuvelink, Gerard B. M. AU - Hack-ten Broeke, Mirjam J. D. TI - Three-dimensional space and time mapping reveals soil organic matter decreases across anthropogenic landscapes in the Netherlands JF - COMMUNICATIONS EARTH & ENVIRONMENT J2 - COMMUN EARTH ENVIRON VL - 5 PY - 2024 IS - 1 SN - 2662-4435 DO - 10.1038/s43247-024-01293-y UR - https://m2.mtmt.hu/api/publication/34743732 ID - 34743732 AB - 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. LA - English DB - MTMT ER - TY - JOUR AU - Kakhani, Nafiseh AU - Rangzan, Moien AU - Jamali, Ali AU - Attarchi, Sara AU - Alavipanah, Seyed Kazem AU - Mommert, Michael AU - Tziolas, Nikolaos AU - Scholten, Thomas TI - SSL-SoilNet: A Hybrid Transformer-based Framework with Self-Supervised Learning for Large-scale Soil Organic Carbon Prediction JF - IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING J2 - IEEE T GEOSCI REMOTE VL - 2024 PY - 2024 SP - 1 EP - 1 PG - 1 SN - 0196-2892 DO - 10.1109/TGRS.2024.3446042 UR - https://m2.mtmt.hu/api/publication/35180788 ID - 35180788 LA - English DB - MTMT ER - TY - JOUR AU - Li, Jiaying AU - Liu, Feng AU - Shi, Wenjiao AU - Du, Zhengping AU - Deng, Xiangzheng AU - Ma, Yuxin AU - Shi, Xiaoli AU - Zhang, Mo AU - Li, Qiquan TI - Including soil depth as a predictor variable increases prediction accuracy of SOC stocks JF - SOIL & TILLAGE RESEARCH J2 - SOIL TILL RES VL - 238 PY - 2024 SN - 0167-1987 DO - 10.1016/j.still.2024.106007 UR - https://m2.mtmt.hu/api/publication/34546469 ID - 34546469 N1 - Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China Hebei Key Laboratory of Environmental Change and Ecological Construction, School of Geographical Sciences, Hebei Normal University, Shijiazhuang, 050024, China Hebei Technology Innovation Center for Remote Sensing Identification of Environmental Change, Geocomputation and Planning Center, Hebei Normal University, Shijiazhuang, 050024, China Institute of Soil Science Chinese Academy of Sciences, State Key Laboratory of Soil and Sustainable Agriculture, Nanjing, 210008, China College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, 100049, China State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China Landcare Research, Private Bag 11052, Manawatu Mail Centre, Palmerston North, 4442, New Zealand College of Resources, Sichuan Agricultural University, Chengdu, 611130, China Export Date: 29 February 2024 CODEN: SOTRD Correspondence Address: Shi, W.; Institute of Geographic Sciences and Natural Resources Research, 11 A, Datun Road, Chaoyang District, China; email: shiwj@lreis.ac.cn LA - English DB - MTMT ER - TY - JOUR AU - Li, Lidong AU - Liang, Wanwan AU - Awada, Tala AU - Hiller, Jeremy AU - Kaiser, Michael TI - Machine Learning for Modeling Soil Organic Carbon as Affected by Land Cover Change in the Nebraska Sandhills, USA JF - ENVIRONMENTAL MODELING & ASSESSMENT J2 - ENVIRON MODEL ASSESS VL - 2024 PY - 2024 SN - 1420-2026 DO - 10.1007/s10666-024-09973-x UR - https://m2.mtmt.hu/api/publication/34775474 ID - 34775474 LA - English DB - MTMT ER - TY - THES AU - Llanos, Sánchez Grecia Ximena TI - Dinámica del stock del carbono orgánico del suelo en los ecosistemas del área de conservación privada Tilacancha, Amazonas, Perú PB - Universidad Nacional Agraria La Molina PY - 2024 UR - https://m2.mtmt.hu/api/publication/34846023 ID - 34846023 LA - Spanish DB - MTMT ER - TY - JOUR AU - Suleymanov, A. AU - Arrouays, D. AU - Savin, I. TI - Digital soil mapping in the Russian Federation: A review JF - GEODERMA REGIONAL J2 - GEODERMA REG VL - 36 PY - 2024 SN - 2352-0094 DO - 10.1016/j.geodrs.2024.e00763 UR - https://m2.mtmt.hu/api/publication/34694088 ID - 34694088 N1 - Laboratory of Soil Science, Ufa Institute of Biology, Ufa Federal Research Centre, Russian Academy of Sciences, Ufa, 450054, Russian Federation INRAE, Info&Sols, Orléans, 45075, France Department of Environmental Protection and Prudent Exploitation of Natural Resources, Ufa State Petroleum Technological University, Ufa, 450064, Russian Federation V.V. Dokuchaev Soil Science Institute, Moscow, 119017, Russian Federation Institute of Environmental Engineering, Peoples Friendship University of Russia (RUDN University), Moscow, 117198, Russian Federation Export Date: 29 February 2024 Correspondence Address: Suleymanov, A.; Laboratory of Soil Science, Russian Federation; email: filpip@yandex.ru LA - English DB - MTMT ER - TY - JOUR AU - Suleymanov, Azamat AU - Richer-de-Forges, Anne C. AU - Saby, Nicolas P.A. AU - Arrouays, Dominique AU - Martin, Manuel P. AU - Bispo, Antonio TI - National-scale digital soil mapping performances are related to covariates and sampling density: Lessons from France JF - GEODERMA REGIONAL J2 - GEODERMA REG VL - 37 PY - 2024 SP - e00801 SN - 2352-0094 DO - 10.1016/j.geodrs.2024.e00801 UR - https://m2.mtmt.hu/api/publication/34843507 ID - 34843507 N1 - Export Date: 21 June 2024 Correspondence Address: Suleymanov, A.; INRAE, France; email: azamat.suleymanov@inrae.fr LA - English DB - MTMT ER - TY - JOUR AU - Tembotov, R. Kh. TI - Using a Multifunctional Approach for Cartographic Modeling of Organic Carbon Content in Natural and Arable Soils of the Central Caucasus JF - COSMIC RESEARCH J2 - COSMIC RES+ VL - 61 PY - 2024 IS - S1 SP - S71 EP - S79 SN - 0010-9525 DO - 10.1134/S001095252370065X UR - https://m2.mtmt.hu/api/publication/34702607 ID - 34702607 LA - English DB - MTMT ER - TY - JOUR AU - Wang, Jie AU - Filippi, Patrick AU - Haan, Sebastian AU - Pozza, Liana AU - Whelan, Brett AU - Bishop, Thomas FA TI - Gaussian process regression for three-dimensional soil mapping over multiple spatial supports JF - GEODERMA J2 - GEODERMA VL - 446 PY - 2024 SN - 0016-7061 DO - 10.1016/j.geoderma.2024.116899 UR - https://m2.mtmt.hu/api/publication/34865002 ID - 34865002 N1 - School of Life and Environmental Sciences, The University of Sydney, Sydney, NSW 2006, Australia Sydney Informatics Hub, The University of Sydney, Sydney, NSW 2006, Australia Export Date: 26 August 2024 CODEN: GEDMA Correspondence Address: Wang, J.; School of Life and Environmental Sciences, Australia; email: jie.wang@sydney.edu.au LA - English DB - MTMT ER - TY - JOUR AU - Xu, Limin AU - Green, E. C. R. AU - Kelly, C. TI - Inferring fault structures and overburden depth in 3D from geophysical data using machine learning algorithms – A case study on the Fenelon gold deposit, Quebec, Canada JF - GEOPHYSICAL PROSPECTING J2 - GEOPHYS PROSPECT VL - 2024 PY - 2024 SN - 0016-8025 DO - 10.1111/1365-2478.13589 UR - https://m2.mtmt.hu/api/publication/35180785 ID - 35180785 AB - We apply a machine learning approach to automatically infer two key attributes – the location of fault or shear zone structures and the thickness of the overburden – in an 18 km 2 study area within and surrounding the Archean Fenelon gold deposit in Quebec, Canada. Our approach involves the inversion of carefully curated borehole lithological and structural observations truncated at 480 m below the surface, combined with magnetic and Light Detection and Ranging survey data. We take a computationally low‐cost approach in which no underlying model for geological consistency is imposed. We investigated three contrasting approaches: (1) an inferred fault model, in which the borehole observations represent a direct evaluation of the presence of fault or shear zones; (2) an inferred overburden model, using borehole observations on the overburden‐bedrock contact; (3) a model with three classes – overburden, faulted bedrock and unfaulted bedrock, which combines aspects of (1) and (2). In every case, we applied all 32 standard machine learning algorithms. We found that Bagged Trees, fine K ‐nearest neighbours and weighted K ‐nearest neighbour were the most successful, producing similar accuracy, sensitivity and specificity metrics. The Bagged Trees algorithm predicted fault locations with approximately 80% accuracy, 70% sensitivity and 73% specificity. Overburden thickness was predicted with 99% accuracy, 77% sensitivity and 93% specificity. Qualitatively, fault location predictions compared well to independently construct geological interpretations. Similar methods might be applicable in other areas with good borehole coverage, providing that criteria used in borehole logging are closely followed in devising classifications for the machine learning training set and might be usefully supplemented with a variety of geophysical survey data types. LA - English DB - MTMT ER - TY - JOUR AU - Zanardini, Loreci AU - Marins, Araceli Ciotti de AU - Secco, Deonir AU - Dalposso, Gustavo Henrique AU - Messa, Vinicius Rigueiro AU - Bassegio, Doglas TI - Resiliência de um Latossolo argiloso com diferentes teores de matéria orgânica JF - Revista Caribeña de Ciencias Sociales J2 - Revista Caribeña de Ciencias Sociales VL - 13 PY - 2024 IS - 7 SN - 2254-7630 DO - 10.55905/rcssv13n7-022 UR - https://m2.mtmt.hu/api/publication/35148590 ID - 35148590 AB - Os Latossolos argilosos sob plantio direto são susceptíveis à compactação por ações naturais e antropogênicas e sua capacidade de recuperação, conhecida como resiliência, é função da matéria orgânica e dos ciclos de umedecimento e secagem do solo. Esse trabalho objetivou avaliar essa capacidade regenerativa das deformações que o solo sofre sob tráfego de máquinas e implementos agrícolas, principalmente no espaço e no tempo, com técnicas geoestatísticas como a krigagem. Foram gerados mapas do índice de rugosidade superficial obtidos com o uso de um perfilômetro formado por 21 varetas de alumínio espaçadas de 5 em 5 com e com 10 avanços de 10 cm cada que monitoraram as elevações e depressões na superfície do solo em uma área de 1 m2 antes e após o solo sofrer deformação por compactação e após cada ciclo de umedecimento e secamento do solo. Foram analisadas duas áreas distintas, uma no NEEA (núcleo experimental de engenharia agrícola) da UNIOESTE, em Cascavel – PR, Oeste do Paraná, Brasil que possui teor de matéria orgânica em torno de 3% e outra no IDR (Instituto de Desenvolvimento Rural do Paraná) localizada em Santa Tereza Oeste do Paraná, Brasil com cerca de 4.5% de matéria orgânica. Foram tomadas as medidas de índice de rugosidade superficial do solo antes e após estabelecer os níveis compactação no solo pela passagem do conjunto trator-pulverizador (0; 1; 3 e 5 passadas do conjunto) e após cada ciclo de umedecimento e secagem ao longo do ciclo de cultivo da cultura da soja. Além disto, foram avaliados a densidade e a macroporosidade do solo. Observou-se que o índice de rugosidade superficial tem decaimento com os ciclos de umedecimento e secagem do solo, a densidade do solo aumentou após compactação e se recuperou após a colheita da soja, enquanto a macroporosidade diminuiu com a compactação e aumentou após a colheita da soja. LA - Portuguese DB - MTMT ER - TY - JOUR AU - Ahado, Samuel Kudjo AU - Agyeman, Prince Chapman AU - Borůvka, Luboš AU - Kanianska, Radoslava AU - Nwaogu, Chukwudi TI - Using geostatistics and machine learning models to analyze the influence of soil nutrients and terrain attributes on lead prediction in forest soils JF - MODELING EARTH SYSTEMS AND ENVIRONMENT J2 - MESE VL - 2023 PY - 2023 SN - 2363-6203 DO - 10.1007/s40808-023-01890-4 UR - https://m2.mtmt.hu/api/publication/34414098 ID - 34414098 N1 - Department of Soil Science and Soil Protection, Faculty of Agrobiology, Food and Natural Resources, Czech University of Life Sciences Prague, Prague, 16500, Czech Republic Institute for Environmental Studies and SOWA RI, Faculty of Science, Charles University, Benátská 2, Praha, 12800, Czech Republic Faculty of Natural Sciences, Matej Bel University Banská Bystrica, Tajovského 40, Banská Bystrica, 974 01, Slovakia Department of Environmental Management, School of Environmental Sciences, Federal university of Technology, Owerri, Nigeria Export Date: 29 February 2024 Correspondence Address: Ahado, S.K.; Department of Soil Science and Soil Protection, Czech Republic; email: ahados@af.czu.cz LA - English DB - MTMT ER - TY - JOUR AU - Allocca, C. AU - Castrignanò, A. AU - Nasta, P. AU - Romano, N. TI - Regional-scale assessment of soil functions and resilience indicators: Accounting for change of support to estimate primary soil properties and their uncertainty JF - GEODERMA J2 - GEODERMA VL - 431 PY - 2023 SN - 0016-7061 DO - 10.1016/j.geoderma.2023.116339 UR - https://m2.mtmt.hu/api/publication/33634184 ID - 33634184 N1 - Dept. of Agricultural Sciences, Division of Agricultural, Forest and Biosystems Engineering (AFBE), University of Naples Federico II, Portici (Naples), Italy Dept. of Engineering and Geology (InGeo), University “G. d'Annunzio” of Chieti-Pescara, Chieti, Italy The Interdepartmental Center for Environmental Research (C.I.R.AM.), University of Naples Federico II, Naples, Italy Cited By :2 Export Date: 28 February 2024 CODEN: GEDMA Correspondence Address: Romano, N.; Department of Agricultural Sciences, Via Università n. 100, Italy; email: nunzio.romano@unina.it LA - English DB - MTMT ER - TY - JOUR AU - Angelini, M. E. AU - Heuvelink, G. B. M. AU - Lagacherie, P. TI - A multivariate approach for mapping a soil quality index and its uncertainty in southern France JF - EUROPEAN JOURNAL OF SOIL SCIENCE J2 - EUR J SOIL SCI VL - 74 PY - 2023 IS - 2 SN - 1351-0754 DO - 10.1111/ejss.13345 UR - https://m2.mtmt.hu/api/publication/33709099 ID - 33709099 N1 - Soil Institute, National Institute of Agricultural Technology (INTA), Hurlingham, Argentina LISAH, University of Montpellier, INRAE, IRD, Montpellier SupAgro, Montpellier, France Soil Geography and Landscape Group, Wageningen University, Wageningen, Netherlands ISRIC – World Soil Information, Wageningen, Netherlands Cited By :1 Export Date: 29 February 2024 CODEN: ESOSE Correspondence Address: Angelini, M.E.; Soil Institute, N. Repetto y Los Reseros s/n, Argentina; email: angelini75@gmail.com LA - English DB - MTMT ER - TY - JOUR AU - Bouasria, Abdelkrim AU - Bouslihim, Yassine AU - Gupta, Surya AU - Taghizadeh-Mehrjardi, Ruhollah AU - Hengl, Tomislav TI - Predictive performance of machine learning model with varying sampling designs, sample sizes, and spatial extents JF - ECOLOGICAL INFORMATICS J2 - ECOL INFORM VL - 78 PY - 2023 PG - 14 SN - 1574-9541 DO - 10.1016/j.ecoinf.2023.102294 UR - https://m2.mtmt.hu/api/publication/34657352 ID - 34657352 LA - English DB - MTMT ER - TY - JOUR AU - Chinilin, Andrey AU - Savin, Igor Yu. TI - Combining machine learning and environmental covariates for mapping of organic carbon in soils of Russia JF - Egyptian Journal of Remote Sensing and Space Science J2 - EGYPT J REMOTE SENS VL - 26 PY - 2023 IS - 3 SP - 666 EP - 675 PG - 10 SN - 1110-9823 DO - 10.1016/j.ejrs.2023.07.007 UR - https://m2.mtmt.hu/api/publication/34075531 ID - 34075531 N1 - Cited By :1 Export Date: 29 February 2024 Correspondence Address: Chinilin, A.; V.V. Dokuchaev Soil Science InstituteRussian Federation; email: chinilin_av@esoil.ru LA - English DB - MTMT ER - TY - CHAP AU - Heuvelink, Gerard B.M. AU - Webster, Richard ED - Gross, Michael J. ED - Oliver, Margaret A. TI - Uncertainty assessment of spatial soil information T2 - Encyclopedia of Soils in the Environment, Second Edition PB - Elsevier Academic Press CY - Amsterdam SN - 9780128229743 PY - 2023 DO - 10.1016/B978-0-12-822974-3.00174-9 UR - https://m2.mtmt.hu/api/publication/33710967 ID - 33710967 LA - English DB - MTMT ER - TY - JOUR AU - Kebonye, Ndiye M. AU - Agyeman, Prince C. AU - Biney, James K.M. TI - Optimized modelling of countrywide soil organic carbon levels via an interpretable decision tree JF - SMART AGRICULTURAL TECHNOLOGY J2 - SMART AGRICULT TECHN VL - 3 PY - 2023 SN - 2772-3755 DO - 10.1016/j.atech.2022.100106 UR - https://m2.mtmt.hu/api/publication/33060704 ID - 33060704 N1 - Department of Geosciences, Chair of Soil Science and Geomorphology, University of Tübingen, Rümelinstr. 19-23, Tübingen, Germany DFG Cluster of Excellence “Machine Learning: New Perspectives for Science”, University of Tübingen, AI Research Building, Maria-von-Linden-Str. 6, Tübingen, 72076, Germany Department of Soil Science and Soil Protection, Faculty of Agrobiology, Food and Natural Resources, Czech University of Life Sciences Prague, Kamýcká 129, Suchdol, Prague, 165 00, Czech Republic Department of Landscape Ecology, The Silva Tarouca Research Institute for Landscape and Ornamental Gardening, Lidická 25/27, Brno, 602 00, Czech Republic Cited By :4 Export Date: 29 February 2024 Correspondence Address: Kebonye, N.M.; Department of Geosciences, Rümelinstr. 19-23, Germany; email: ndiyekeb@yahoo.com LA - English DB - MTMT ER - TY - JOUR AU - Liu, Xinyu AU - Wang, Jian AU - Song, Xiaodong TI - 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 JF - REMOTE SENSING J2 - REMOTE SENS-BASEL VL - 15 PY - 2023 IS - 7 SP - 1847 SN - 2072-4292 DO - 10.3390/rs15071847 UR - https://m2.mtmt.hu/api/publication/33734155 ID - 33734155 N1 - College of Earth Sciences, Chengdu University of Technology, Chengdu, 610059, China State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing, 210008, China Export Date: 29 February 2024 Correspondence Address: Wang, J.; College of Earth Sciences, China; email: jwang@cdut.edu.cn AB - 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. LA - English DB - MTMT ER - TY - JOUR AU - Łopatka, Artur AU - Siebielec, Grzegorz AU - Kaczyński, Radosław AU - Stuczyński, Tomasz TI - Analysis of Soil Carbon Stock Dynamics by Machine Learning—Polish Case Study JF - LAND (BASEL) J2 - LAND-BASEL VL - 12 PY - 2023 IS - 8 SN - 2073-445X DO - 10.3390/land12081587 UR - https://m2.mtmt.hu/api/publication/34095706 ID - 34095706 N1 - Department of Soil Science Erosion and Land Protection, Institute of Soil Science and Plant Cultivation—State Research Institute, Czartoryskich 8, Pulawy, 24-100, Poland Faculty of Science and Health, The John Paul II Catholic University of Lublin, Konstantynów 1 H, Lublin, 20-708, Poland Cited By :1 Export Date: 29 February 2024 Correspondence Address: Łopatka, A.; Department of Soil Science Erosion and Land Protection, Czartoryskich 8, Poland; email: artur@iung.pulawy.pl AB - 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. LA - English DB - MTMT ER - TY - JOUR AU - Richer-De-Forges, Anne C. AU - Arrouays, Dominique AU - Poggio, Laura AU - Chen, Songchao AU - Lacoste, Marine AU - Minasny, Budiman AU - Libohova, Zamir AU - Roudier, Pierre AU - Mulder, Vera L. AU - Nedelec, Herve AU - Martelet, Guillaume AU - Lemercier, Blandine AU - Lagacherie, Philippe AU - Bourennane, Hocine TI - 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 JF - PEDOSPHERE J2 - PEDOSPHERE VL - 33 PY - 2023 IS - 5 SP - 731 EP - 743 PG - 13 SN - 1002-0160 DO - 10.1016/j.pedsph.2022.07.009 UR - https://m2.mtmt.hu/api/publication/34353654 ID - 34353654 N1 - INRAE (Institut national de recherche pour l'agriculture, l'alimentation et l'environnement), Info&Sols Unit, Orléans, 45075, France ISRIC (International Soil Reference and Information Centre) Wageningen, P.O. Box 353, Wageningen, 6700, Netherlands Zhejiang University-Hangzhou Global Scientific and Technological Innovation Center, Hangzhou, 311200, China The University of Sydney, Sydney Institute of Agriculture, Eveleigh, NSW 2015, Australia The University of Sydney, School of Life and Environmental Sciences, Eveleigh, NSW 2015, Australia USDA-ARS (United States Department of Agriculture-Agricultural Research Service), Dale Bumpers Small Farms Research Center, 6883 S. State Hwy. 23, Booneville, AR 72927, United States Landcare Research-Manaaki Whenua, Palmerston North, 4442, New Zealand Soil Geography and Landscape Group, Wageningen University, P.O. Box 47, Wageningen, 6700, Netherlands Chambre d'Agriculture du Loiret, Orléans, F-45000, France Bureau de Recherches Géologiques et Minières, BP 36009, Orléans cedex 2, Orléans, 45060, France UMR SAS (Unité Mixte de Recherche “Sol Agro et hydrosystème Spatialisation”) Institut Agro, INRAE, Rennes, F-35000, France UMR LISAH (Unité Mixte de Recherche “Laboratoire d'Etude des Interactions entre Sol-Agrosystème-Hydrosystème”), INRAE, Institut Agro, IRD (Institut de Recherche pour le Développement), Montpellier, F-34000, France Cited By :5 Export Date: 29 February 2024 CODEN: PDOSE Correspondence Address: RICHER-de-FORGES, A.C.; INRAE (Institut national de recherche pour l'agriculture, France; email: anne.richer-de-forges@inrae.fr AB - 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. LA - English DB - MTMT ER - TY - JOUR AU - Rodríguez-Lizana, Antonio AU - Ramos, Alzira AU - Pereira, María João AU - Soares, Amílcar AU - Ribeiro, Manuel Castro TI - Assessment of the Spatial Variability and Uncertainty of Shreddable Pruning Biomass in an Olive Grove Based on Canopy Volume and Tree Projected Area JF - AGRONOMY (BASEL) J2 - AGRONOMY-BASEL VL - 13 PY - 2023 IS - 7 SN - 2073-4395 DO - 10.3390/agronomy13071697 UR - https://m2.mtmt.hu/api/publication/34039155 ID - 34039155 N1 - Department of Aerospace Engineering and Fluid Mechanics, Area of Rural Engineering, University of Seville, Ctra. de Utrera, Km. 1, Seville, 41013, Spain CERENA, DER, Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais, Lisbon, 1049-001, Portugal Cited By :1 Export Date: 28 February 2024 Correspondence Address: Rodríguez-Lizana, A.; Department of Aerospace Engineering and Fluid Mechanics, Ctra. de Utrera, Km. 1, Spain; email: arodriguez2@us.es AB - 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. LA - English DB - MTMT ER - TY - JOUR AU - Suleymanov, Azamat AU - Gabbasova, Ilyusya AU - Komissarov, Mikhail AU - Suleymanov, Ruslan AU - Garipov, Timur AU - Tuktarova, Iren AU - Belan, Larisa TI - Random Forest Modeling of Soil Properties in Saline Semi-Arid Areas JF - AGRICULTURE-BASEL J2 - AGRICULTURE-BASEL VL - 13 PY - 2023 IS - 5 SN - 2077-0472 DO - 10.3390/agriculture13050976 UR - https://m2.mtmt.hu/api/publication/33785679 ID - 33785679 N1 - Laboratory of Soil Science, Ufa Institute of Biology UFRC RAS, Ufa, 450054, Russian Federation Department of Environmental Protection and Prudent Exploitation of Natural Resources, Ufa State Petroleum Technological University, Ufa, 450064, Russian Federation Laboratory of Climate Change Monitoring and Carbon Ecosystems Balance, Ufa State Petroleum Technological University, Ufa, 450064, Russian Federation Department of Geology, Hydrometeorology and Geoecology, Ufa University of Science and Technology, Ufa, 450076, Russian Federation Cited By :1 Export Date: 12 June 2023 Correspondence Address: Suleymanov, A.; Laboratory of Soil Science, Russian Federation; email: filpip@yandex.ru AB - 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. LA - English DB - MTMT ER - TY - JOUR AU - Sun, Yi AU - Ma, Jin AU - Zhao, Wenhao AU - Qu, Yajing AU - Gou, Zilun AU - Chen, Haiyan AU - Tian, Yuxin AU - Wu, Fengchang TI - Digital mapping of soil organic carbon density in China using an ensemble model JF - ENVIRONMENTAL RESEARCH J2 - ENVIRON RES VL - 231 PY - 2023 PG - 9 SN - 0013-9351 DO - 10.1016/j.envres.2023.116131 UR - https://m2.mtmt.hu/api/publication/34314847 ID - 34314847 N1 - Export Date: 29 February 2024 CODEN: ENVRA Correspondence Address: Ma, J.; State Key Laboratory of Environmental Criteria and Risk Assessment, China; email: majin@craes.org.cn AB - 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. LA - English DB - MTMT ER - TY - JOUR AU - Szatmári, Gábor AU - Pásztor, László AU - Laborczi, Annamária AU - Illés, Gábor AU - Bakacsi, Zsófia AU - Zacháry, Dóra AU - Filep, Tibor AU - Szalai, Zoltán AU - Jakab, Gergely Imre TI - Countrywide mapping and assessment of organic carbon saturation in the topsoil using machine learning-based pedotransfer function with uncertainty propagation JF - CATENA J2 - CATENA VL - 227 PY - 2023 PG - 11 SN - 0341-8162 DO - 10.1016/j.catena.2023.107086 UR - https://m2.mtmt.hu/api/publication/33721644 ID - 33721644 N1 - Funding Agency and Grant Number: National Research, Development and Innovation Office (NKFIH) [K-123953, K- 131820]; Janos Bolyai Research Scholarship of the Hungarian Academy of Sciences Funding text: This research was funded by the National Research, Development and Innovation Office (NKFIH; grant numbers: K-123953 and K- 131820) , and the Janos Bolyai Research Scholarship of the Hungarian Academy of Sciences (G. Szatm ari) . LA - English DB - MTMT ER - TY - JOUR AU - Tembotov, R.Kh. TI - 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 JF - Sovremennye Problemy Distantsionnogo Zondirovaniya Zemli iz Kosmosa J2 - Sovremennye Problemy Distantsionnogo Zondirovaniya Zemli iz Kosmosa VL - 20 PY - 2023 IS - 3 SP - 193 EP - 206 PG - 14 SN - 2411-0280 DO - 10.21046/2070-7401-2023-20-3-193-206 UR - https://m2.mtmt.hu/api/publication/34071598 ID - 34071598 N1 - Export Date: 29 February 2024 Correspondence Address: Tembotov, R.Kh.; Tembotov Institute of Ecology of Mountain Territories RASRussian Federation; email: tembotov.rustam@mail.ru LA - Russian DB - MTMT ER - TY - JOUR AU - Turek, Maria Eliza AU - Poggio, Laura AU - Batjes, Niels H. AU - Armindo, Robson André AU - de Jong van Lier, Quirijn AU - de Sousa, Luis AU - Heuvelink, Gerard B.M. TI - Global mapping of volumetric water retention at 100, 330 and 15 000 cm suction using the WoSIS database JF - INTERNATIONAL SOIL AND WATER CONSERVATION RESEARCH J2 - INT SOIL WATER CONS RES VL - 11 PY - 2023 IS - 2 SP - 225 EP - 239 PG - 15 SN - 2095-6339 DO - 10.1016/j.iswcr.2022.08.001 UR - https://m2.mtmt.hu/api/publication/33069168 ID - 33069168 N1 - ISRIC - World Soil Information, Wageningen, Netherlands Graduate Program in Environmental Engineering, Federal University of Paraná, PR, Curitiba, Brazil Soil Geography and Landscape Group, Wageningen University, Wageningen, Netherlands Department of Physics, Federal University of Lavras, MG, Brazil CENA - University of São Paulo, SP, Piracicaba, Brazil Export Date: 20 January 2023 Correspondence Address: Turek, M.E.; ISRIC - World Soil InformationNetherlands; email: mariaeliza.turek@agroscope.admin.ch LA - English DB - MTMT ER - TY - JOUR AU - Wadoux, Alexandre M. J. -C. AU - Heuvelink, Gerard B. M. TI - Uncertainty of spatial averages and totals of natural resource maps JF - METHODS IN ECOLOGY AND EVOLUTION J2 - METHODS ECOL EVOL VL - 14 PY - 2023 IS - 5 SP - 1320 EP - 1332 PG - 13 SN - 2041-210X DO - 10.1111/2041-210X.14106 UR - https://m2.mtmt.hu/api/publication/33965037 ID - 33965037 N1 - Sydney Institute of Agriculture & School of Life and Environmental Sciences, The University of Sydney, Sydney, NSW, Australia Soil Geography and Landscape group, Wageningen University & Research, Wageningen, Netherlands ISRIC—World Soil Information, Wageningen, Netherlands Cited By :2 Export Date: 29 February 2024 Correspondence Address: Wadoux, A.M.J.C.; Sydney Institute of Agriculture & School of Life and Environmental Sciences, Australia; email: alexandre.wadoux@sydney.edu.au AB - 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. LA - English DB - MTMT ER - TY - JOUR AU - Wang, Sh AU - Xu, L AU - Adhikari, K AU - He, N TI - Soil carbon sequestration potential of cultivated lands and its controlling factors in China JF - SCIENCE OF THE TOTAL ENVIRONMENT J2 - SCI TOTAL ENVIRON VL - 905 PY - 2023 SN - 0048-9697 DO - 10.1016/j.scitotenv.2023.167292 UR - https://m2.mtmt.hu/api/publication/34159359 ID - 34159359 N1 - Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing, 100101, China College of Land and Environment, Shenyang Agricultural University, Liaoning Province, Shenyang, 110866, China College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, 100049, China Earth Critical Zone and Flux Research Station of Xing'an Mountains, Chinese Academy of Sciences, Daxing'anling, 165200, China USDA-ARS, Grassland, Soil and Water Research Laboratory, Temple, TX 76502, United States Export Date: 8 January 2024 CODEN: STEVA Correspondence Address: Xu, L.; Institute of Geographical Sciences and Natural Resources Research, 11A, Datun Road, Chaoyang District, China; email: xuli@igsnrr.ac.cn LA - English DB - MTMT ER - TY - JOUR AU - Yan, Yibo AU - Yang, Yong TI - Uncertainty assessment of spatiotemporal distribution and variation in regional soil heavy metals based on spatiotemporal sequential Gaussian simulation JF - ENVIRONMENTAL POLLUTION J2 - ENVIRON POLLUT VL - 322 PY - 2023 PG - 9 SN - 0269-7491 DO - 10.1016/j.envpol.2023.121243 UR - https://m2.mtmt.hu/api/publication/34353656 ID - 34353656 N1 - Cited By :2 Export Date: 29 February 2024 CODEN: ENPOE Correspondence Address: Yang, Y.; College of Resources and Environment, China; email: yangyong@mail.hzau.edu.cn AB - 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. LA - English DB - MTMT ER - TY - JOUR AU - Zhou, Tao AU - Geng, Yajun AU - Lv, Wenhao AU - Xiao, Shancai AU - Zhang, Peiyu AU - Xu, Xiangrui AU - Chen, Jie AU - Wu, Zhen AU - Pan, Jianjun AU - Si, Bingcheng AU - Lausch, Angela TI - Effects of optical and radar satellite observations within Google Earth Engine on soil organic carbon prediction models in Spain JF - JOURNAL OF ENVIRONMENTAL MANAGEMENT J2 - J ENVIRON MANAGE VL - 338 PY - 2023 SN - 0301-4797 DO - 10.1016/j.jenvman.2023.117810 UR - https://m2.mtmt.hu/api/publication/33734152 ID - 33734152 N1 - Ludong University, School of Resources and Environmental Engineering, Middle Hongqi Road 186, Yantai, 264025, China Humboldt-Universität zu Berlin, Department of Geography, Unter Den Linden 6, Berlin, 10099, Germany Helmholtz Centre for Environmental Research, Department of Computational Landscape Ecology, Permoserstraße 15, Leipzig, 04318, Germany Peking University, College of Urban and Environmental Sciences, Yiheyuan Road 5, Beijing, 100871, China Hunan Normal University, College of Geographical Sciences, Lushan Road 36, Changsha, 410081, China Zhejiang University City College, School of Spatial Planning and Design, Huzhou Street 51, Hangzhou, 31000, China Hunan Academy of Agricultural Sciences, Yuanda 2nd Road 560, Changsha, 410125, China Nanjing Agricultural University, College of Resources and Environmental Sciences, Weigang 1, Nanjing, 210095, China University of Saskatchewan, Department of Soil Science, Saskatoon, SK S7N 5A8, Canada Export Date: 29 February 2024 CODEN: JEVMA Correspondence Address: Si, B.; Ludong University, Middle Hongqi Road 186, China; email: bing.si@usask.ca LA - English DB - MTMT ER - TY - JOUR AU - Гопп, НВ AU - Мешалкина, ЮЛ AU - Нарыкова, АН AU - Плотникова, АС AU - Чернова, ОВ TI - КАРТОГРАФИРОВАНИЕ СОДЕРЖАНИЯ И ЗАПАСОВ ОРГАНИЧЕСКОГО УГЛЕРОДА ПОЧВ НА РЕГИОНАЛЬНОМ И ЛОКАЛЬНОМ УРОВНЯХ: АНАЛИЗ СОВРЕМЕННЫХ МЕТОДИЧЕСКИХ ПОДХОДОВ JF - FOREST SCIENCE ISSUES J2 - FSI VL - 6 PY - 2023 IS - 1 SP - 14 EP - 73 PG - 60 SN - 2658-607X UR - https://m2.mtmt.hu/api/publication/34175971 ID - 34175971 LA - English DB - MTMT ER - TY - JOUR AU - Чинилин, АВ AU - Савин, ИЮ TI - Оценка содержания органического углерода в почвах России с помощью ансамблевого машинного обучения JF - MOSKOVSKII GOSUDERSTVENNYI UNIVERSITET. VESTNIK. SERIYA 5: GEOGRAFIYA J2 - Vestnik Moskovskogo universiteta. Seriya 5: Geografiya VL - 2022 PY - 2023 IS - 6 SP - 49 EP - 63 PG - 15 SN - 0579-9414 UR - https://m2.mtmt.hu/api/publication/33577346 ID - 33577346 LA - Russian DB - MTMT ER - TY - CHAP AU - Benő, András AU - Kocsis, Mihály AU - Szatmári, Gábor AU - Laborczi, Annamária AU - Tóth, Brigitta AU - Bakacsi, Zsófia AU - Pásztor, László ED - Abriha-Molnár, Vanda Éva TI - A LUCAS és TIM adatbázisok fizikai talajtulajdonságainak térbeli összehasonlítása T2 - Az elmélet és gyakorlat találkozása a térinformatikában XIII. PB - Debreceni Egyetemi Kiadó CY - Debrecen SN - 9789636150396 PY - 2022 SP - 59 EP - 66 PG - 8 UR - https://m2.mtmt.hu/api/publication/33712776 ID - 33712776 LA - Hungarian DB - MTMT ER - TY - JOUR AU - Chinilin, A.V. AU - Savin, I.Yu. TI - ESTIMATION OF ORGANIC CARBON CONTENT IN RUSSIAN SOILS USING ENSEMBLE MACHINE LEARNING JF - MOSKOVSKII GOSUDERSTVENNYI UNIVERSITET. VESTNIK. SERIYA 5: GEOGRAFIYA J2 - Vestnik Moskovskogo universiteta. Seriya 5: Geografiya VL - 5 PY - 2022 IS - 6 SP - 49 EP - 63 PG - 15 SN - 0579-9414 DO - 10.55959/MSU0579-9414-5-2022-6-49-63 UR - https://m2.mtmt.hu/api/publication/34694091 ID - 34694091 N1 - Cited By :1 Export Date: 29 February 2024 LA - Russian DB - MTMT ER - TY - JOUR AU - Han, Si Yang AU - Filippi, Patrick AU - Singh, Kanika AU - Whelan, Brett M. AU - Bishop, Thomas F. A. TI - Assessment of global, national and regional‐level digital soil mapping products at different spatial supports JF - EUROPEAN JOURNAL OF SOIL SCIENCE J2 - EUR J SOIL SCI VL - 73 PY - 2022 IS - 5 SN - 1351-0754 DO - 10.1111/ejss.13300 UR - https://m2.mtmt.hu/api/publication/33096470 ID - 33096470 N1 - Precision Agriculture Laboratory, Sydney Institute of Agriculture, School of Life and Environmental Sciences, The University of Sydney, Sydney, NSW, Australia School of Life and Environmental Sciences, The University of Sydney, Sydney, NSW, Australia Export Date: 20 January 2023 CODEN: ESOSE Correspondence Address: Han, S.Y.; Precision Agriculture Laboratory, Australia; email: siyang.han@sydney.edu.au LA - English DB - MTMT ER - TY - JOUR AU - Hateffard, Fatemeh AU - Balog, Kitti AU - Tóth, Tibor AU - Mészáros, János AU - Árvai, Mátyás AU - Kovács, Zsófia Adrienn AU - Szűcs-Vásárhelyi, Nóra AU - Koós, Sándor AU - László, Péter AU - Novák, Tibor József AU - Pásztor, László AU - Szatmári, Gábor TI - High-Resolution Mapping and Assessment of Salt-Affectedness on Arable Lands by the Combination of Ensemble Learning and Multivariate Geostatistics JF - AGRONOMY (BASEL) J2 - AGRONOMY-BASEL VL - 12 PY - 2022 IS - 8 PG - 19 SN - 2073-4395 DO - 10.3390/agronomy12081858 UR - https://m2.mtmt.hu/api/publication/33041982 ID - 33041982 N1 - Department of Landscape Protection and Environmental Geography, University of Debrecen, Egyetem tér 1, Debrecen, H-4032, Hungary Institute for Soil Sciences, Centre for Agricultural Research, Herman Ottó út 15, Budapest, H-1022, Hungary Department of Physical Geography and Geoinformatics, University of Debrecen, Egyetem tér 1, Debrecen, H-4032, Hungary Cited By :1 Export Date: 16 January 2023 Correspondence Address: Balog, K.; Institute for Soil Sciences, Herman Ottó út 15, Hungary; email: balog.kitti@atk.hu AB - 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. LA - English DB - MTMT ER - TY - JOUR AU - Helfenstein, Anatol AU - Mulder, Vera L. AU - Heuvelink, Gerard B.M. AU - Okx, Joop P. TI - Tier 4 maps of soil pH at 25 m resolution for the Netherlands JF - GEODERMA J2 - GEODERMA VL - 410 PY - 2022 SN - 0016-7061 DO - 10.1016/j.geoderma.2021.115659 UR - https://m2.mtmt.hu/api/publication/32558650 ID - 32558650 N1 - Soil Geography and Landscape Group, Wageningen University, PO Box 47, AA Wageningen, 6700, Netherlands Soil, Water and Land Use Team, Wageningen Environmental Research, Droevendaalsesteeg 3, RC Wageningen, 6708, Netherlands Export Date: 6 January 2022 CODEN: GEDMA Correspondence Address: Helfenstein, A.; Soil Geography and Landscape Group, PO Box 47, Netherlands; email: anatol.helfenstein@wur.nl LA - English DB - MTMT ER - TY - JOUR AU - Heuvelink, Gerard B.M. AU - Webster, Richard TI - Spatial statistics and soil mapping: A blossoming partnership under pressure JF - SPATIAL STATISTICS J2 - SPAT STAT VL - 50 PY - 2022 SN - 2211-6753 DO - 10.1016/j.spasta.2022.100639 UR - https://m2.mtmt.hu/api/publication/32692390 ID - 32692390 N1 - Cited By :1 Export Date: 28 July 2022 Correspondence Address: Heuvelink, G.B.M.; ISRIC – World Soil Information and Wageningen University, Netherlands; email: gerard.heuvelink@wur.nl LA - English DB - MTMT ER - TY - CHAP AU - Lizana, A.R. AU - Pereira, M.J. AU - Ramos, A. AU - Garcia, M.M. AU - Ribeiro, M. ED - Trofymchuk, O. ED - Rivza, B. TI - STUDY OF THE UNCERTAINTY OF THE AMOUNT OF PRUNING IN THE OLIVE GROVE USING GEOSTATISTICAL ALGORITHMS VL - 22 PB - International Multidisciplinary Scientific Geoconference T3 - SGEM Conference Proceedings, ISSN 1314-2704 ; 22. PY - 2022 IS - 3.2 SP - 431 EP - 437 PG - 7 DO - 10.5593/sgem2022V/3.2/s14.50 UR - https://m2.mtmt.hu/api/publication/34694092 ID - 34694092 N1 - University of Seville, Spain CERENA-Centro de Recursos Naturais e Ambiente, Technical University of Lisbon, Portugal Export Date: 29 February 2024 LA - English DB - MTMT ER - TY - JOUR AU - Rajkai, Kálmán László TI - Talajökológiai egyensúly érdekében csökkenő élelmiszer-termelés: dilemma vagy szükségszerűség? • Decreasing Food Production in Quest to Restore Soil Ecological Balance: Dilemma or Necessity? JF - MAGYAR TUDOMÁNY J2 - MAGYAR TUDOMÁNY VL - 183 PY - 2022 IS - 10 SP - 1246 EP - 1254 PG - 9 SN - 0025-0325 DO - 10.1556/2065.183.2022.10.2 UR - https://m2.mtmt.hu/api/publication/34088995 ID - 34088995 LA - Hungarian DB - MTMT ER - TY - CHAP AU - Rodriguez Lizana, Antonio AU - Pereira, Maria Joao AU - Ramos, Alzira AU - Moreno Garcia, Manuel AU - Ribeiro, Manuel ED - Trofymchuk, Oleksandr ED - Rivza, Baiba TI - STUDY OF THE UNCERTAINTY OF THE AMOUNT OF PRUNING IN THE OLIVE GROVE USING GEOSTATISTICAL ALGORITHMS T2 - Proceedings of 22nd International Multidisciplinary Scientific GeoConference SGEM 2022 PB - STEF92 Technology Ltd. CY - Sofia SN - 9786197603545 T3 - International Multidisciplinary Scientific GeoConference-SGEM, ISSN 1314-2704 ; 22, Issue 3.2. PY - 2022 SP - 431 EP - 438 PG - 8 DO - 10.5593/sgem2022V/3.2/s14.50 UR - https://m2.mtmt.hu/api/publication/33728124 ID - 33728124 AB - 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. LA - English DB - MTMT ER - TY - JOUR AU - Szatmári, Gábor AU - Kocsis, Mihály AU - Makó, András AU - Pásztor, László AU - Bakacsi, Zsófia TI - Joint Spatial Modeling of Nutrients and Their Ratio in the Sediments of Lake Balaton (Hungary): A Multivariate Geostatistical Approach. A Multivariate Geostatistical Approach TS - A Multivariate Geostatistical Approach JF - WATER J2 - WATER-SUI VL - 14 PY - 2022 IS - 3 SN - 2073-4441 DO - 10.3390/w14030361 UR - https://m2.mtmt.hu/api/publication/32641881 ID - 32641881 N1 - Institute for Soil Sciences, Centre for Agricultural Research, Herman Ottó út 15, Budapest, H-1022, Hungary Department of Physical Geography and Geoinformatics, Faculty of Science and Technology, University of Debrecen, Egyetem tér 1, Debrecen, H-4032, Hungary Export Date: 7 February 2022 Correspondence Address: Kocsis, M.; Institute for Soil Sciences, Herman Ottó út 15, Hungary; email: kocsis.mihaly@atk.hu LA - English DB - MTMT ER - TY - CONF AU - Szatmári, Gábor AU - Laborczi, Annamária AU - Heuvelink, Gerard AU - Pirkó, Béla AU - Koós, Sándor AU - Bakacsi, Zsófia AU - Mészáros, János AU - Pásztor, László TI - Estimating soil organic carbon stock change at regional scales: Challenges and possible solutions. poster TS - poster T2 - 22nd World Congress of Soil Science : poster book of abstracts PB - International Union of Soil Sciences (IUSS) C1 - Glasgow PY - 2022 UR - https://m2.mtmt.hu/api/publication/33071860 ID - 33071860 LA - English DB - MTMT ER - TY - JOUR AU - Takoutsing, Bertin AU - Heuvelink, Gerard B.M. TI - Comparing the prediction performance, uncertainty quantification and extrapolation potential of regression kriging and random forest while accounting for soil measurement errors JF - GEODERMA J2 - GEODERMA VL - 428 PY - 2022 SN - 0016-7061 DO - 10.1016/j.geoderma.2022.116192 UR - https://m2.mtmt.hu/api/publication/33232227 ID - 33232227 N1 - Soil Geography and Landscape Group, Wageningen University, PO Box 47, AA Wageningen, 6700, Netherlands Soil and Land Health, World Agroforestry (ICRAF), Yaoundé, Cameroon ISRIC-World Soil Information, PO Box 353, AJ Wageningen, 6700, Netherlands Export Date: 16 January 2023 CODEN: GEDMA Correspondence Address: Takoutsing, B.; Soil Geography and Landscape Group, PO Box 47, Netherlands; email: b.takoutsing@cgiar.org LA - English DB - MTMT ER - TY - JOUR AU - Xie, Enze AU - Zhang, Xiu AU - Lu, Fangyi AU - Peng, Yuxuan AU - Chen, Jian AU - Zhao, Yongcun TI - Integration of a process-based model into the digital soil mapping improves the space-time soil organic carbon modelling in intensively human-impacted area JF - GEODERMA J2 - GEODERMA VL - 409 PY - 2022 SN - 0016-7061 DO - 10.1016/j.geoderma.2021.115599 UR - https://m2.mtmt.hu/api/publication/32505596 ID - 32505596 N1 - State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing, 210008, China University of Chinese Academy of Sciences, Beijing, 100049, China Cited By :3 Export Date: 20 January 2023 CODEN: GEDMA Correspondence Address: Zhao, Y.; State Key Laboratory of Soil and Sustainable Agriculture, China; email: yczhao@issas.ac.cn LA - English DB - MTMT ER - TY - JOUR AU - Zhang, Yue AU - Jiang, Yanyan AU - Jia, Zenghui AU - Qiang, Ruowen AU - Gao, Qiang TI - Identifying the scale-controlling factors of soil organic carbon in the cropland of Jilin Province, China JF - ECOLOGICAL INDICATORS J2 - ECOL INDIC VL - 139 PY - 2022 SN - 1470-160X DO - 10.1016/j.ecolind.2022.108921 UR - https://m2.mtmt.hu/api/publication/32801420 ID - 32801420 N1 - College of Resources and Environment, Jilin Agricultural University, Changchun, 130118, China Key Laboratory of Soil Resource Sustainable Utilization for Jilin Province Commodity Grain Bases, Jilin Agricultural University, Changchun, 130118, China Songliao Basin Soil and Water Conservation Monitoring Center of Songliao Water Resources Commission of the Ministry of Water Resources, Changchun, 130021, China Key Laboratory of Straw Comprehensive Utilization and Black Soil Conservation, Ministry of Education, China Cited By :3 Export Date: 20 January 2023 Correspondence Address: Gao, Q.; College of Resources and Environment, China; email: gyt9962@126.com LA - English DB - MTMT ER -