Ruhollah Taghizadeh

• PhD
• Researcher at University of Tübingen

An understanding of the key factors and processes influencing the variability of soil organic carbon (SOC) is essential for the development of effective policies aimed at enhancing carbon storage in soils to mitigate climate change. In recent years, complex computational approaches from the field of machine learning (ML) have been developed for mod...

Artificial intelligence and machine learning applications in archaeology have increased significantly in recent years, and these now span all subfields, geographical regions, and time periods. The prevalence and success of these applications have remained largely unexamined, as recent reviews on the use of machine learning in archaeology have only...

Monitoring the soil organic carbon (SOC) dynamics through temporal environmental controlling covariates could indicate the soil and environment quality status. In this study, we address the main challenge of SOC changes at the landscape scale in dry and semi-arid regions, particularly in West Azarbaijan, Kermanshah, and Hamadan provinces of northwe...

Soil salinization stands as a prominent global environmental challenge, necessitating enhanced assessment methodologies. This study is dedicated to refining soil salinity assessment in the Lake Urmia region of Iran, utilizing multi-year data spanning from 2015 to 2018. To achieve this objective, soil salinity was measured at 915 sampling points dur...

Soil characteristics can be used to calculate the soil quality index (SQI). However, measuring these qualities is expensive and time-consuming, one option is to employ a digital soil mapping technique. The present study aims to digitally map SQI in the Urmia Plain (Northwest of Iran) using covariate data, random forest (RF), and RF-ordinary Kriging...

Abstract (1) Background: The use of multiscale prediction or the optimal scaling of predictors can enhance soil maps by applying pixel size in digital soil mapping (DSM). (2) Methods: A total of 200, 50, and 129 surface soil samples (0–30 cm) were collected by the CLHS method in three different areas, namely, the Marvdasht, Bandamir, and Lapuee pla...

Soil organic matter (SOM) and the ratio of soil organic carbon to total nitrogen (C/N ratio) are fundamental to the ecosystem services provided by soils. Therefore, understanding the spatial distribution and relationships between the SOM components mineral-associated organic matter (MAOM), particulate organic matter (POM), and C/N ratio is crucial....

Land suitability assessment, as an important process in modern agriculture, involves the evaluation of numerous aspects such as soil properties, climate, relief, hydrology and socio-economic aspects. The aim of this study was to evaluate the suitability of soils for wheat cultivation in the Gavshan region, Iran, as the country is facing the task of...

Soil organic carbon (SOC) is a crucial factor for soil fertility, directly impacting agricultural yields and ensuring food security. In recent years, remote sensing (RS) technology has been highly recommended as an efficient tool for producing SOC maps. The PRISMA hyperspectral satellite was used in this research to predict the SOC map in Fars prov...

Accurate spatiotemporal monitoring and modeling of soil moisture (SM) is of paramount importance for various applications ranging from food production to climate change adaptation. This study deals with modeling SM with the random forest (RF) algorithm using datasets comprising multispectral data from Sentinel-2, Landsat-8/9, and hyperspectral data...

Climate change affects soil salinization and is responsible for food production threats and unsustainable development. However, global salinization trend and its temporal response to climate remains unclear. Here we show global soil salinization changes from 2003 to 2022 at 500 m resolution estimated by a machine learning approach, remote sensing,...

Land suitability assessment is an important process in modern agricultural management, involving the evaluation of various factors such as soil properties, climate, relief, hydrology, crop varieties and socioeconomic considerations. Various methods have been used to assess land suitability, such as the parametric method developed by Sys et al. (199...

Scoping large amounts of data for literature review is a time-consuming task, and automated solutions emerged as promising tools to sorting and analysing the vast array of results. These classification methods can take the form of query systems akin to research portals, employing criteria like publication date, authorship, and keywords, or automate...

Salinization is a threat to global agricultural and soil resource allocation. Current investigations of global soil salinity are limited to coarse spatial resolution of the available datasets (>250 m) and semi-qualitative classification rules (five ranks). Based on these two limitations, we proposed a framework to quantitatively estimated global so...

National soil organic carbon (SOC) maps are essential to improve greenhouse gas accounting and support climate-smart agriculture. Large-scale SOC models based on wall-to-wall soil information from remote sensing remain a challenge due to the high diversity of natural soil conditions and the difficulty of accounting for the spatial location of the s...

Soil acidification is an ongoing problem in intensively cultivated croplands due to inefficient nitrogen (N) fertilization. We collected high-resolution data comprising 19969 topsoil (0-20 cm) samples from the Land Use and Coverage Area frame Survey (LUCAS) of the European commission in 2009 to calculate the impact of N fertilization on buffering s...

Using machine learning and earth observation data to capture real-world variability in spatial predictive mapping depends on sample size, design, and spatial extent. Nonetheless, there is still ambiguity in answering some basic questions: a) How many samples are necessary for fitting the model? b) Which sampling techniques are suitable for modeling...

Soils are incorporating many different chemical and physical properties and this numerous information is a useful tool for geoarchaeologists and archaeologists. Indeed, soils can give information on climate, human activities, vegetation, and geological or geomorphological features in the present and past times. The different components of soil can...

Biological soil crusts (biocrusts) are a key factor in the protection of arid and semiarid ecosystems and, therefore, playing a major role to combat against desertification. Biocrusts are also of profound importance in sand dune areas, as they are recognized as the first colonizers after environmental disturbances and can help to prevent sediment r...

This paper offers a brief review of digital soil mapping (DSM) in Iran, which utilizes machine learning and environmental data to create soil maps for better soil management. The review examines the history of DSM in Iran, the latest advances in machine learning methods, and the environmental covariates commonly used in DSM. Despite a short history...

Soils are an important environmental factor (Duchaufour et al., 2020) and influenced human occupation through time (Fritzsch et al., 2022). Soil variables (e.g., texture, acidity, and soil organic carbon) gather information on water capacity, geomorphological landscape, geological substratum, vegetation, precipitation, and temperature. Many regions...

t Understanding the spatial variation of soil properties is essential for monitoring land capabilities as well as the sustainable management of soil resources. The aim of this study was to predict digital soil properties mapping using 23 environmental variables, i.e., terrain attributes and remote sensing (RS) indices, across 1500 km2 of Mashhad pl...

Inorganic carbon is the largest source of carbon in terrestrial surface, particularly in arid and semiarid regions, including the Chahardowli Plain in western Iran. Inorganic carbon plays an equal or greater role than organic soil carbon in these areas, although less attention has been paid in quantifying their variability. The objective of this st...

We study the effects of spatial extent on the interactions between estimated NPK stoichiometry across Africa and potential drivers that may be affecting its balance or shifts.

Poster on Computer Applications and Quantitative Methods in Archaeology conference (CAA), Amsterdam 03 - 06 April 2023: "50 Years of Synergy" Session 34: "Computational Approaches and Remote Sensing Applications in Desertic Areas" Archaeological predictive modelling (APM) is a powerful tool for collecting quantitative data used in cultural heritag...

Precise knowledge about the soil organic carbon (SOC) content in cropland soils is one requirement to design and execute effective climate and food policies. In digital soil mapping (DSM), machine learning algorithms are used to predict soil properties from covariates derived from traditional soil mapping, digital elevation models, land use, and Ea...

Applying fertilizers to soil in a site-specific way that maximizes yields and minimizes environmental damage is an important goal. Developing soil management zones (MZs) is a suitable method for achieving sustainable agricultural production. Thus, this work aims to investigate MZs delineated based on the different soil properties using machine lear...

Biological soil crusts (biocrusts) are a key factor in the protection of arid and semiarid ecosystems and, therefore, playing a major role to combat against desertification. Biocrusts are also of profound importance in sand dune areas, as they are recognized as the first colonizers after environmental disturbances and can help to prevent sediment r...

Farming on hillslopes often affects the accumulation and loss of soil organic matter (SOM) depending on slope position and cropping patterns. Most hillslope studies focus on soil movement to characterize SOM turnover under erosive conditions. In this study, we trace erosion and characterize agronomic practices erosive impacts on SOM translocation a...

Publisher: University of West of England (UWE) Publication year: 2022 Abstract The present study was conducted for the spatial distribution and concentration evaluation of heavy metals, including Cu, Cd, Mn, Fe, Zn, and Pb, within 102 soil samples collected from Kushk Mine in Bafgh, Iran. This work employed hierarchical clustering analysis (HCA), p...

Spatial variability of soil properties is a critical factor for the planning, management, and exploitation of soil resources. Thus, the use of different digital soil mapping models to provide accuracy plays a crucial role in providing soil physicochemical properties maps. Soil spatial variability in forest stands is not well-known in Iran. Meanwhil...

Soil texture is an important property that controls the mobility of the water and nutrients in soil. This study examined the capability of machine learning (ML) models in estimating soil texture fractions using different combinations of remotely sensed data from Sentinel-1 (S1), Sentinel-2 (S2), and terrain-derived covariates (TDC) across two contr...

Spatial information on land and soil resources are critical towards addressing land degradation for ensuring sustainable soil and crop management. To address these needs, digital soil mapping techniques have emerged as an efficient and low-cost solution. Although digital soil mapping has typically leveraged geospatial environmental variables (e.g.,...

The present study was conducted for the spatial distribution and concentration evaluation of heavy metals, including Cu, Cd, Mn, Fe, Zn, and Pb, within 102 soil samples collected from Kushk Mine in Bafgh, Iran. This work employed hierarchical clustering analysis (HCA), principal component analysis (PCA), and spatial distribution patterns, to perfor...

Digital soil mapping (DSM) can be used to predict soils at unvisited sites, but problems arise when predictions are needed in areas without any soil observations. In such situations, DSM can still extend the results from reference areas with soil data to target areas that are alike in terms of soil-forming factors and obey the same rules. Such DSM...

Soil erosion is a major cause of damage to agricultural lands in many parts of the world and is of particular concern in semiarid parts of Iran. We use five machine learning techniques—Random Forest (RF), M5P, Reduced Error Pruning Tree (REPTree), Gaussian Processes (GP), and Pace Regression (PR)—under two scenarios to predict soil erodibility in t...

Soil provides a key interface between the atmosphere and the lithosphere and plays an important role in food production, ecosystem services, and biodiversity. Recently, demands for applying machine learning (ML) methods to improve the knowledge and understanding of soil behavior have increased. While real-world datasets are inherently imbalanced, M...

The basis of digital soil mapping (DSM) techniques is the relationship between the geospatial environmental covariates with any soil properties. Based on such relationships, DSM can produce and quantify spatial soil functions by implementing different machine learning (ML) algorithms. Although the supervised ML algorithms are routinely applied thro...

Digital soil mapping approaches predict soil properties based on the relationship between soil observations and related environmental covariates using machine learning models. In this research, we applied deep neural networks to predict the spatial distribution of soil properties in Germany using 1976 soil observations and 170 environmental covaria...

Soil Particle Size Distribution (PSD) is a fundamental physical property that can affect soil hydraulic properties, soil structure characterization, and available water. Many models have been applied to define the PSD curve, but predicting the spatial distribution information of PSD has been rarely investigated. Therefore, the main objective of the...

Soil quality, defined as the capacity of a soil to function, is one of the most important characteristics of soil. Methods for modelling and monitoring soil quality are needed for sustainable soil management and evaluating soil degradation. In Iran, resource demands have led to the deforestation of the semiarid oak forests. The impacts of these act...

Soil organic carbon (SOC) is an essential property of soil, and understanding its spatial patterns is critical to understanding vegetation management, soil degradation, and environmental issues. This study applies a framework using remote sensing data and digital soil mapping techniques to examine the spatiotemporal dynamics of SOC for the Yazd-Ard...

Fields are the original management zones used in agricultural ecosystems. Uniformity of soil within management zones (MZ) is crucial for sustainable soil management, long-term productivity, and avoiding environmental problems. When considering a new area for agricultural expansion or for improving the efficiency of existing agricultural practices,...

This study tested and evaluated a suite of nine individual base learners and seven model averaging techniques for predicting the spatial distribution of soil properties in central Iran. Based on the nested-cross validation approach, the results showed that the artificial neural network and Random Forest base learners were the most effective in pred...

With intense human activity causing constant environmental change, there is a greater need than ever to have accurate and frequently updated soil information. Traditional soil maps are large scale polygon maps of soil type and implicitly assume no variation in soil properties within polygons. DSM approaches can increase the accuracy of modeling. Th...

The purpose of this study was to investigate the potential of rhizosphere and endophytic fluorescent pseudomonads strains for reducing the application rate of phosphorus (P) fertilizer and improving the morphological and physiological traits of the wheat plant under field conditions during years 2016–2017 (Y1) and 2017–2018 (Y2). Experimental treat...

In the digital soil mapping (DSM) framework, machine learning models quantify the relationship between soil observations and environmental covariates. Generally, the most commonly used covariates (MCC; e.g., topographic attributes and single-time remote sensing data, and legacy maps) were employed in DSM studies. Additionally, remote sensing time-s...

Soil salinity and alkalinity are major soil limitations of agriculture and land degradation in arid and semiarid regions. Salinity is a dynamic phenomenon that varies continuously over time and space. Therefore, this chapter investigates the processes of Spatio-temporal changes in soil salinity and alkalinity in eastern (site 1, during the period 2...

Assessing the role of machine learning (ML) models concerning environmental predictors on spatial variation of soil organic carbon stocks (SOCS) in arid rangelands is very necessary. This study was conducted to explore the variability of surface SOCS in rangeland in the west of Iran using ML approaches. A number of 33 environmental predictors deriv...

Water depletion is a growing problem in the world’s arid and semi-arid areas, where groundwater is the primary source of fresh water. Accurate climatic data must be obtained to protect municipal water budgets. Unfortunately, the majority of these arid regions have a sparsely distributed number of rain gauges, which reduces the reliability of the sp...

Dust pollution is one of the major environmental crises in the arid regions of Iran and there is a need to predict dust pollution and identify its controlling factors to help reduce its adverse effects on the livelihood of residents of these areas. Although deep neural networks (DNN) are powerful tools in the modelling of environmental phenomena, t...

Destructive mining operations are affecting large areas of natural ecosystems, especially in arid lands. The present study aims at investigating the impact of iron mine exploitation on vegetation and soil in Nodoushan (Yazd province, central Iran). Based on the dominant wind, topography, slope, vegetation and soil of the area, soil and vegetation p...

Estimating sediment load of rivers is one of the major problems in river engineering that has been using various data mining algorithms and variables. It is desirable to obtain accurate estimates of sediment load while using techniques that limit computational intensity when datasets are large. This study investigates the usefulness of geo-morphome...

Drainage is a profitable water management operation in waterlogged soils, particularly in hot, arid environments where waterlogging is caused by irrigation and salts may build up when water evaporates from the soil surface. While drainage can reduce the buildup of salts, it may cause unwanted depletion of plant nutrients (nitrogen and phosphorus) f...

Abstract: The most critical aspect of application of digital soil mapping is its limited transferability. Modelling soil properties for regions where no or only sparse soil information is available is highly uncertain, when using the low-cost geo-spatial environmental covariates alone. To overcome this drawback, transfer learning has been introduce...

Weathering indices based on the relative proportions of different chemical elements are a useful tool to investigate the degree of weathering of soils. This characterization is missing in West Azerbaijan, northern Iran, and thus the main goals of this work were to assess the suitability of different indices to determine soil weathering, and to pred...

A correction to this paper has been published: https://doi.org/10.1007/s10661-021-08984-5

Predicting the spatio-temporal distribution of absorbable heavy metals in soil is needed to identify the potential contaminant sources and develop appropriate management plans to control these hazardous pollutants. Therefore, our aim was to develop a model to predict soil adsorbable heavy metals in arid regions of Iran from 1986 to 2016. Soil adsor...

This study was conducted to evaluate the performance of the support vector regression (SVR) model with and without applying wavelet transformation for predicting the PM10, PM2.5, SO2, NO2, CO, and O3 in Isfahan metropolis, central Iran. Ground-based data, TerraClimate, and MODIS products were used to predict air pollution parameters. These factors...

Digital soil mapping approaches predict soil properties based on the relationships between soil observations and related environmental covariates using techniques such as machine learning (ML) models. In this research, a wide range of ML models (12 base learners) were tested in predicting and mapping soil properties. Furthermore, a super learner ap...

Land use change and soil organic carbon stock (SOCS) depletion over time is one of the predominant worldwide environmental problems related to global warming and the need to secure food production for an increasing world population. In our research, satellite images from 1988 and 2018 were analyzed for a 177.48 km2 region in Kurdistan Province, Ira...

Soil texture and particle size fractions (PSFs) are a critical characteristic of soil that influences most physical, chemical, and biological properties of soil; furthermore, reliable spatial predictions of PSFs are crucial for agro-ecological modeling. Here, series of hybridized artificial neural network (ANN) models with bio-inspired metaheuristi...

Soil presents a high vulnerability to the environmental degradation processes especially in arid and semiarid regions, requiring research that leads to its understanding. To date, there are no detailed soil maps covering a large extension of the Middle East region, especially for calcium carbonate content. Thus, we used topsoil data (0–20 cm) from...

A Correction to this paper has been published: https://doi.org/10.1007/s11356-020-11967-7

The low potential of agricultural productivity in the majority of central Iran is mainly attributed to high levels of soil salinity. To increase agricultural productivity, while preventing any further salinization, and implement effective soil reclamation programs, precise information about the spatial patterns and magnitude of soil salinity is ess...

Soil Organic Carbon (SOC) content is a key element for soil fertility and productivity, nutrient availability and potentially represents a measurement of the sink for greenhouse gas abatement. Improving our knowledge on the spatial distribution of SOC is hence essential for sustainable nutrient management and carbon storage capacity. The objective...

Salinization and alkalization are predominant environmental problem world-wide which their accurate assessment is essential for determining appropriate ways to deal with land degradation, for better soil and crop management. In the current research, a combination of random forests and covariate data were used to assess spatial variability of soil s...

Accurate prediction of the dust concentration (DC) is necessary to reduce its undesirable environmental effects in different geographical areas. Although the adaptive neuro-fuzzy inference system (ANFIS) is a powerful model for predicting dust events, no attempt has been made to investigate its uncertainty and interpretability. In this study, there...

It is necessary to predict wind erosion events and specify the related effective factors to prioritize management and executive measures to combat desertification caused by wind erosion in arid areas. Therefore, this work aimed to evaluate the applicability of nine machine learning (ML) models (including multivariate adaptive regression splines, le...

In order to manage soil salinity effectively, it is necessary to understand the origin and the spatial distribution of salinity. There are about 120 salt dome outcrops in southern Iran and little is known about their contribution as the potential sources of salts and the spatial pattern of salts around them. Six machine learning algorithms were app...

The soil science community needs to communicate about soils and the use of soil information to various audiences, especially to the general public and public authorities. In this global review article, we synthesis information pertaining to museums solely dedicated to soils or which contain a permanent exhibition on soils. We identified 38 soil mus...

The daily association between mortality and air pollution is alarming and there is consistent evidence that air pollution has short-term effects on mortality and respiratory morbidity. Accurate predictions of these health effects of air pollution are essential for efficient planning of various sectors related to economic performances as well as str...

The soil science community needs to communicate about soils and the use of soil information to various audiences, especially to the general public and public authorities. In this global review article, we synthesis information pertaining to museums solely dedicated to soils or which contain a permanent exhibition on soils. We identified 38 soil mus...

Soil quality assessment based on crop yields and identification of key indicators of it can be used for better management of agricultural production. In the current research, the weighted additive soil quality index (SQIw), factor analysis (FA), and multiple linear regression (MLR) are used to assess the soil quality of rainfed winter wheat fields...

Accurate prediction of the dust concentration (DC) is necessary to reduce its undesirable environmental effects in different geographical areas. Although the adaptive neuro-fuzzy inference system (ANFIS) is a powerful model for predicting dust events, no attempt has been made to investigate its uncertainty and interpretability. In this study, there...