Digital soil mapping, downscaling and updating conventional soil maps using GIS, RS, statistics and auxiliary data

Spatial distribution of soil types and soil properties in the landscape are important in many environmental researches. Conventional soil surveys are not designed to provide the high-resolution soil information required in environmental modelling and site-specific farm management. The objectives of this study were to investigate the relationship between soil development, soil evolution in the landscape, updating legacy soil maps and pedodiversity in an arid and semi-arid region. The application of Digital Soil Mapping (DSM) techniques was investigated with a particular focus to predict soil taxonomic classes and spatial distribution of soil types by soil observations and covariate sets representative of s,c,o,r,p,a,n factors. In the first study, focus is on establishing relationships between pedodiversity and landform evolution in a 86,000 ha region in Borujen, Chaharmahal-Va-Bakhtiari Province, Central Iran. From an overview study, we could conclude that landform evolution was mainly affected by topography and its components. A second study compares various DSM-methods and a conventional soil mapping approach for soil class maps in terms of accuracy, information value and cost in central Iran. Also, the effects of different sample sizes were investigated. Our results demonstrated that in most predicted maps, in DSM approaches, the best results were obtained using the combination of terrain attributes and the geomorphology map. Furthermore, results showed that the conventional soil mapping approach was not as effective as DSM approach. In the third study, different models of the DSM approach were compared to predict the spatial distribution of some important soil properties such as clay content, soil organic carbon and calcium carbonate content. Among all studied models, the terrain attribute “elevation” is the most important variable to predict soil properties. Random forest had promising performance to predict soil organic carbon. But results revealed that all models could not predict the spatial distributions of clay content properly. The minimum area of land that can be legibly delineated in a traditional (printed) map is highly dependent upon mapping scale. For example, this area at a mapping scale of 1:24,000 is about 2.3 ha but at a mapping scale of 1:1,000,000 it is about 1000 ha. A mapping scale of 1:1,000,000 is just too coarse to show a fine-scale pattern or soil type with any degree of legibility, but finer-scale soil maps are more expensive and time-consuming to produce. Thus, spatial variation is often unavoidably obscured. The fourth study of this dissertation focuses on downscaling and updating soil map methods. Thus, the objectives were to apply supervised and unsupervised disaggregation approaches to disaggregate soil polygons of conventional soil map at a scale of 1: 1,000,000 in the selected area. Therefore, soil subgroups and great groups were selected because it is a basic taxonomic level in regional and national soil maps in Iran. In general, we conclude that DSM approach and also disaggregation approach are capable to predict soil types and properties, produce and update legacy soil maps. However, still a number of challenges need to be evaluated e.g. influence of expert knowledge on CSM approach, resolution of ancillary data, georeferenced legacy soil samples data to validate disaggregated soil maps

Developing global pedotransfer functions to estimate available soil phosphorus

There are a large number of investigations that estimate available soil phosphorous (P), but a paucity of global data on available soil P. One significant modern challenge is developing low cost, accurate approaches to predict available soil P that are useful to scientists around the world. We conducted a global meta-analysis using data on available soil P from 738 sites, 640 in the USA and 149 in 14 other countries. Four different methods of determining available soil P, New Zealand (NZ), acid oxalate, Bray and Mehlich 3 were represented in the dataset. Inputs evaluated for inclusion in the pedotransfer functions to predict available soil P were clay (C), fine silt, (FSi) coarse silt (CSi), very fine sand (VFS), fine sand (FS), medium sand (MS), coarse sand (CS), very coarse sand (VCS), organic carbon (OC), pH, calcium (Ca), magnesium (Mg), potassium (K), iron (Fe), aluminum (Al), and manganese (Mn). Available soil P was estimated for: 1) the entire dataset, 2) only the USA, and 3) the non-USA dataset. The best models to estimate available soil P were obtained for the NZ method (using the co-variates C, FSi, CSi, VFS, MS, CS, OC, Fe, Al, Mn, Ca, Mg, and pH) and for the acid oxalate method (using the co-variates C, FSi, Fe, Al, Mn, Ca, and Mg). Although estimation of available soil P determined with the acid oxalate method was poor for the entire dataset, good estimates were obtained for the USA and non-USA datasets separately. Models for the Bray and Mehlich 3 methods only predicted available soil P well for the non-USA dataset. Using pedotransfer function models to estimate available soil P could provide an efficient and cost effective way to estimate global distributions of a soil property that is important for a number of agricultural and environmental reasons

Assessing the Influence of Soil Quality on Rainfed Wheat Yield

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 with two soil orders on 53.20 km2 of agricultural land in western Iran. A total of 18 soil quality indicators were determined for 100 soil samples (0–20 cm depth) from two soil orders (Inceptisols and Entisols). The soil properties measured were: pH, soil texture, organic carbon (OC), cation exchange capacity (CEC), electrical conductivity (EC), soil microbial respiration (SMR), carbonate calcium equivalent (CCE), soil porosity (SP), bulk density (BD), exchangeable sodium percentage (ESP), mean weight diameter (MWD), available potassium (AK), total nitrogen (TN), available phosphorus (AP), available Fe (AFe), available Zn (AZn), available Mn (AMn), and available Cu (ACu). Wheat grain yield for all of the 100 sampling sites was also gathered. The SQIw was calculated using two weighting methods (FA and MLR) and maps were created using a digital soil mapping framework. The soil indicators determined for the minimum data set (MDS) were AK, clay, CEC, AP, SMR, and sand. The correlation between the MLR weighting technique (SQIw-M) and the rainfed wheat yield (r = 0.62) was slightly larger than that the correlation of yield with the FA weighted technique (SQIw-F) (r = 0.58). Results showed that the means of both SQIw-M and SQIw-F and rainfed wheat yield for Inceptisols were higher than for Entisols, although these differences were not statistically significant. Both SQIw-M and SQIw-F showed that areas with Entisols had lower proportions of good soil quality grades (Grades I and II), and higher proportions of poor soil quality grades (Grades IV and V) compared to Inceptisols. Based on these results, soil type must be considered for soil quality assessment in future studies to maintain and enhance soil quality and sustainable production. The overall soil quality of the study region was of poor and moderate grades. To improve soil quality, it is therefore recommended that effective practices such as the implementation of scientifically integrated nutrient management involving the combined use of organic and inorganic fertilizers in rainfed wheat fields should be promoted.publishedVersio

Assessing Variation of Soil Quality in Agroecosystem in an Arid Environment Using Digital Soil Mapping

Monitoring the soil quality (SQ) in agricultural ecosystems is necessary for using sustainable soil and land resources. Therefore, to evaluate the SQ variation in an arid environment in the Bajestan region, northeastern Iran, two soil quality indices (weighted additive soil quality index-SQIw and nemoro soil quality index-SQIn) were applied. SQIs were assessed in two datasets (total data set-TDS and minimum data set-MDS) by linear (L) and nonlinear (NL) scoring methods. Physicochemical properties of 223 surface soil samples (0–30 cm depth) were determined. The random forest (RF) model was used to predict the spatial variation of SQIs. The results showed the maximum values of the SQIs in areas with saffron land covers, while the minimum values were acquired in the north of the study area where pistachio orchards are located due to higher EC and SAR. The environmental variables such as topographic attributes and groundwater quality parameters were the main driving factors that control SQIs distribution. These findings are beneficial for identifying suitable locations sites to plan agricultural management and sustainable usage of groundwater resources strategy to avoid further increase of soil salinity

Ground Observations and Environmental Covariates Integration for Mapping of Soil Salinity: A Machine Learning-Based Approach

Soil salinization is a severe danger to agricultural activity in arid and semi-arid areas, reducing crop production and contributing to land destruction. This investigation aimed to utilize machine learning algorithms to predict spatial soil salinity (dS m−1) by combining environmental covariates derived from remotely sensed (RS) data, a digital elevation model (DEM), and proximal sensing (PS). The study is located in an arid region, southern Iran (52°51′–53°02′E; 28°16′–28°29′N), in which we collected 300 surface soil samples and acquired the spectral data with RS (Sentinel-2) and PS (electromagnetic induction instrument (EMI) and portable X-ray fluorescence (pXRF)). Afterward, we analyzed the data using five machine learning methods as follows: random forest—RF, k-nearest neighbors—kNN, support vector machines—SVM, partial least squares regression—PLSR, artificial neural networks—ANN, and the ensemble of individual models. To estimate the electrical conductivity of the saturated paste extract (ECe), we built three scenarios, including Scenario (1): Synthetic Soil Image (SySI) bands and salinity indices derived from it; Scenario (2): RS data, PS data, topographic attributes, and geology and geomorphology maps; and Scenario (3): the combination of Scenarios (1) and (2). The best prediction accuracy was obtained for the RF model in Scenario (3) (R2 = 0.48 and RMSE = 2.49), followed by Scenario (2) (RF model, R2 = 0.47 and RMSE = 2.50) and Scenario (1) for the SVM model (R2 = 0.26 and RMSE = 2.97). According to ensemble modeling, a combined strategy with the five models exceeded the performance of all the single ones and predicted soil salinity in all scenarios. The results revealed that the ensemble modeling method had higher reliability and more accurate predictive soil salinity than the individual approach. Relative improvement (RI%) showed that the R2 index in the ensemble model improved compared to the most precise prediction for the Scenarios (1), (2), and (3) with 120.95%, 56.82%, and 66.71%, respectively. We applied the best model in each scenario for mapping the soil salinity in the selected area, which indicated that ECe tended to increase from the northwestern to south and southeastern regions. The area with high ECe was located in the regions that mainly had low elevations and playa. The areas with low ECe were located in the higher elevations with steeper slopes and alluvial fans, and thus, relief had great importance. This study provides a precise, cost-effective, and scientific base prediction for decision-making purposes to map soil salinity in arid regions

Ground Observations and Environmental Covariates Integration for Mapping of Soil Salinity: A Machine Learning-Based Approach

Soil salinization is a severe danger to agricultural activity in arid and semi-arid areas, reducing crop production and contributing to land destruction. This investigation aimed to utilize machine learning algorithms to predict spatial soil salinity (dS m−1) by combining environmental covariates derived from remotely sensed (RS) data, a digital elevation model (DEM), and proximal sensing (PS). The study is located in an arid region, southern Iran (52°51′–53°02′E; 28°16′–28°29′N), in which we collected 300 surface soil samples and acquired the spectral data with RS (Sentinel-2) and PS (electromagnetic induction instrument (EMI) and portable X-ray fluorescence (pXRF)). Afterward, we analyzed the data using five machine learning methods as follows: random forest—RF, k-nearest neighbors—kNN, support vector machines—SVM, partial least squares regression—PLSR, artificial neural networks—ANN, and the ensemble of individual models. To estimate the electrical conductivity of the saturated paste extract (ECe), we built three scenarios, including Scenario (1): Synthetic Soil Image (SySI) bands and salinity indices derived from it; Scenario (2): RS data, PS data, topographic attributes, and geology and geomorphology maps; and Scenario (3): the combination of Scenarios (1) and (2). The best prediction accuracy was obtained for the RF model in Scenario (3) (R2 = 0.48 and RMSE = 2.49), followed by Scenario (2) (RF model, R2 = 0.47 and RMSE = 2.50) and Scenario (1) for the SVM model (R2 = 0.26 and RMSE = 2.97). According to ensemble modeling, a combined strategy with the five models exceeded the performance of all the single ones and predicted soil salinity in all scenarios. The results revealed that the ensemble modeling method had higher reliability and more accurate predictive soil salinity than the individual approach. Relative improvement (RI%) showed that the R2 index in the ensemble model improved compared to the most precise prediction for the Scenarios (1), (2), and (3) with 120.95%, 56.82%, and 66.71%, respectively. We applied the best model in each scenario for mapping the soil salinity in the selected area, which indicated that ECe tended to increase from the northwestern to south and southeastern regions. The area with high ECe was located in the regions that mainly had low elevations and playa. The areas with low ECe were located in the higher elevations with steeper slopes and alluvial fans, and thus, relief had great importance. This study provides a precise, cost-effective, and scientific base prediction for decision-making purposes to map soil salinity in arid regions

Disaggregating and updating a legacy soil map using DSMART, fuzzy c-means and k-means clustering algorithms in Central Iran

Increasing demand for food production, global change, and growing population are the enormous challenges in recent decades. Accurate soil maps and adequate models are indispensable tools to assist managers, scientists, and decision-makers in addressing these challenges. Legacy soil polygon maps at national and regional scales are available widely, but lack detail, and therefore effective methods such as digital soil mapping (DSM) are needed to disaggregate these maps. The objective of this study was to disaggregate a legacy 1:1,000,000 soil map by three methods of disaggregation: a supervised classification method (DSMART algorithm) and two unsupervised classification methods including fuzzy c-means (FCM) and k-means (KM) clustering in Borujen region, Chaharmahal-Va-Bakhtiari Province, Central Iran for both great group and subgroup Taxonomic levels. Although field validation indicated that the accuracy of the disaggregated soil maps was lower than that of the conventional soil map at both levels of Soil Taxonomy, disaggregated approaches produced more detailed soil maps when compared with the first, second, and third most probable soil classes of the conventional soil map. The higher overall accuracy of the conventional soil map was due to soil association units which consist of more than one soil taxonomic class. FCM and DSMART methods produced more accurate and detailed disaggregated soil maps than KM clustering algorithm at the great group and subgroup levels, respectively. We concluded that the decision on what method to use depends on the map, the level of available information (map detail), available expert knowledge, and the availability of the soil unit composition percentages in the soil map legend

Integration of PCA and Fuzzy Clustering for Delineation of Soil Management Zones and Cost-Efficiency Analysis in a Citrus Plantation

Citrus spp. are one of the most important commercial crops with global marketing potential in the world, as in Iran. A soil management zone (MZ) as an appropriate approach is necessary to achieve sustainable production, along with improving soil management and increasing economic benefits in the commercial citrus plantations of northern Iran. As the first report, the biological and terrain attributes along with the physicochemical properties (57 soil samples, 0–30 cm) were used for MZ delineation using the integration of principal component analysis (PCA) and the fuzzy c-means clustering methods. An economic analysis based on the MZ results was also performed to determine the changes in each MZ using a relative cost (RC) value. The high correlation between soil properties and terrain attributes and the considerable spatial variation of these factors in the study area call for site-specific nutrient management. The optimal number of MZs was six and there was a significant heterogeneity variation among different MZs. The ranking of the MZs were MZ5 > MZ2 > MZ6 > MZ1 > MZ3 > MZ4 based on higher soil quality and lower costs per tree. The MZ4, MZ3, MZ1, MZ6, and MZ2 required 34.4, 30.6, 29.4, 9.77, and 9.44% more costs than MZ5 (as reference MZ) for achieving similar productivity, respectively. Therefore, this simple and cost-effective approach could be an initial step to utilize fertilizers site-specifically for data-scarce areas and reduce the soil property variability within the delineated MZs, which is fundamental for precision agriculture management