Mapping Soil Properties Research Articles

Mapping soil organic matter and identifying potential controls in the farmland of Southern China: Integration of multi‐source data, machine learning and geostatistics

AbstractSoil organic matter (SOM) plays a critical role in terrestrial ecosystem functioning and is closely related to many global issues like soil fertility, soil health and climate regulation. Therefore, obtaining accurate information on the spatial distribution of SOM and its potential controlling factors is of global interest. However, this remains a great challenge since SOM is affected by numerous natural and anthropogenic factors and usually showed strong heterogeneity. In this study, we collected a total of 16,580 surface soil (0–20 cm) samples from the farmland throughout Jiangxi Province. And the Random Forest (RF), Cubist and gradient‐boosted models were compared and used to define the factor which is most associated with SOM. Then the ordinary kriging (OK) and machine learning‐ordinary co‐kriging (ML‐COK) were used to map SOM. We found that on average, 30.86 g kg−1 SOM was present in farmland soil of Jiangxi Province. Anthropogenic activities strongly affected SOM level, with five of the top 10 most important factors are anthropogenic related. The straw return amount was proved to have the largest importance (31.46%) for modelling SOM and a significant (p < 0.001) positive relationship between SOM content and the amount of straw returned to farmland was detected. Additionally, returning straw improved crop production. Soil derived from the Quaternary Subred Sand has the highest SOM content (37.82 g kg−1). Crop rotation also improved SOM content and the rice‐bean rotation system has the highest SOM content (34.27 g kg−1). With the best performance, the RF algorithm (R2 = 0.49, RMSE = 6.77 g kg−1) was selected to identify the primary control of SOM and integrated with COK, which we termed as ML‐COK, to map the SOM in the farmland of Jiangxi Province. ML‐COK outperformed OK method for mapping the SOM in farmland of Jiangxi Province with R2 of 0.351 and Lin's concordance correlation coefficient of 0.549. Farmland distributed in the central part of the province had high SOM content. In contrast, farmland in the north, south and east parts had relatively low SOM. Our study offers new insight for mapping soil properties, identifying potential factors driving variation in SOM, and also provides valuable information for making more reasonable and environmentally friendly farmland management measures.

DSOLMap, a novel high-resolution global digital soil property map for the SWAT + model: Development and hydrological evaluation

This research paper addresses the ongoing challenge of developing fine-resolution global digital soil property maps for hydrological modelling applications. Hydrological models are essential for understanding watershed dynamics and the impact of human activities on water resources. Soil data, which plays a crucial role in the hydrological cycle, is a requisite model input. Global digital soil property maps usually have coarse spatial resolutions, adding considerable uncertainty to hydrological models despite calibration efforts. To address this issue, a new global digital soil property map with 250 m spatial resolution, known as Digital Soil Open Land Map (DSOLMap), was developed and evaluated in this study. The DSOLMap has a finer spatial resolution than existing global soil maps and a more detailed soil profile divided into six soil horizons. This new high-resolution global digital soil property map was tailored to the SWAT + model format. SWAT + is the latest released version of the Soil and Water Assessment Tool (SWAT), one of the most comprehensive hydrological models, and is widely used worldwide. A hydrological evaluation was conducted with the DSOLMap and its results were compared to two other global soil databases using the SWAT + model in a basin located in the north of Spain. The findings showed that using more detailed, finer-resolution soil data, such as those that the DSOLMap offers, improved the hydrological performance of the SWAT + model on a daily scale before and after calibration and validation procedures. The DSOLMap represents a global step forward in hydrological modelling, notably for regions with scarce or unavailable soil information. This new digital soil property map can help decision-makers address global challenges related to water resources and environmental issues through hydrological modelling.

Implementing Cloud Computing for the Digital Mapping of Agricultural Soil Properties from High Resolution UAV Multispectral Imagery

The spatial heterogeneity of soil properties has a significant impact on crop growth, making it difficult to adopt site-specific crop management practices. Traditional laboratory-based analyses are costly, and data extrapolation for mapping soil properties using high-resolution imagery becomes a computationally expensive procedure, taking days or weeks to obtain accurate results using a desktop workstation. To overcome these challenges, cloud-based solutions such as Google Earth Engine (GEE) have been used to analyze complex data with machine learning algorithms. In this study, we explored the feasibility of designing and implementing a digital soil mapping approach in the GEE platform using high-resolution reflectance imagery derived from a thermal infrared and multispectral camera Altum (MicaSense, Seattle, WA, USA). We compared a suite of multispectral-derived soil and vegetation indices with in situ measurements of physical-chemical soil properties in agricultural lands in the Peruvian Mantaro Valley. The prediction ability of several machine learning algorithms (CART, XGBoost, and Random Forest) was evaluated using R2, to select the best predicted maps (R2 > 0.80), for ten soil properties, including Lime, Clay, Sand, N, P, K, OM, Al, EC, and pH, using multispectral imagery and derived products such as spectral indices and a digital surface model (DSM). Our results indicate that the predictions based on spectral indices, most notably, SRI, GNDWI, NDWI, and ExG, in combination with CART and RF algorithms are superior to those based on individual spectral bands. Additionally, the DSM improves the model prediction accuracy, especially for K and Al. We demonstrate that high-resolution multispectral imagery processed in the GEE platform has the potential to develop soil properties prediction models essential in establishing adaptive soil monitoring programs for agricultural regions.

Mapping of Soil Properties Using Machine Learning Techniques

We aimed to estimate Soil Nutrients and relate the spectral signatures to that of the Laboratory reference Measurements utilizing CART analysis. Sustainable agriculture aims at controlled and/or precise soil fertility interventions based on spatial soil information. The profound advancements in remote sensing and geospatial techniques provide means for determining the spatial coverage and variability of the soil properties through the survey and image data incorporated in the mapping procedures (i.e.) Digital Soil Mapping. The soil moisture content at varying levels influences crop growth and decides the yield, as the crop requires water at critical crop growth stages. Machine learning techniques provide the means of optimized model calibration when compared to conventional geostatistical or statistical approaches.

Spatial Variability Mapping of Selected Soil Properties of District Tando Allahyar, Sindh, Pakistan

Spatial variability mapping of soil properties is essential to understand the differences among soils found in a geographical region for their better management. This study aimed at spatial variability mapping of selected soil properties of district Tando Allahyar, Sindh which is one of the most important districts of Sindh province. The results revealed that majority of soils (35.6%) had loamy slightly medium-textured type, including 30.8% silt loam, 3.8% loam and 1.0% silt, followed by clayey, fine-textured heavy type (27.9%), including 18.3% clay, 6.7% silty clay and 2.9% sandy clay. Soils with sandy, coarse-textured light type were 20.2%, including 16.3% sandy loam and 1.9% loamy sand and sand each. Soils with loamy, moderately medium-textured type were 16.3%, including 9.6% clay loam and 6.7% sandy clay loam, while silty clay loam soil texture was total missing. The EC ranged from 0.14 to 4.44 dS m-1 (mean: 0.69 dS m-1, mode: 0.42 dS m-1). Majority (91%) of soils had low (46%) to medium (45) level of salinity. Soil pH ranged from 6.2 to 8.6 (mean: 7.7, mode: 8.3). Majority of soils (84%) had either slightly (24%), medium (22%) or strongly alkaline (38%) soil pH. Interestingly, some soils (13%) had slightly acidic pH. Organic matter ranged from 0.10 to 1.6% (mean: 0.64%, mode: 0.17%). Majority (84%) of soils were deficient in organic matter. Soil ABDTPA-P ranged from 1.2 to 18.8 mg kg-1 (mean: 5.38 mg kg-1, mode: 2.4 mg kg-1). More than half (55%) of the soils had low P, while soils with marginal (14%) or adequate (31%) P were also present. Soil ABDTPA-K ranged from 31 to 376 mg kg-1 (mean: 154 mg kg-1, mode: 230 mg kg-1). Majority of soils (62%) had adequate K, while soils with marginal (28%) or low (11%) K were also found. Spatial variability mapping revealed the dominance of loam, sandy clay loam, sandy loam, silty clay and clay loam textures, medium > low and high > salinity, slightly to medium alkaline soils > strongly alkaline soils, low organic matter content, medium to adequate > low soil P, and adequate soil K. The spatial variation found in various soil properties of district Tando Allahyar, Sindh must be taken in mind while deciding about their site-specific management, using the recently developed maps.

Scale-Dependent Field Partition Based on Water Retention Functional Data

Functional data are being used increasingly in recent years and in many environmental sciences, such as hydrology applied to agriculture. This means that the output, instead of a scalar variable represented by a spatial map, is given by a function. Furthermore, in site-specific management, there is a need to delineate the field into management areas depending on the agricultural procedure and on the scale of application. In this paper, an approach based on multivariate geostatistics is illustrated that uses the parameters of Dexter’s water retention model and some soil properties to arrive at a multiscale delineation of an agricultural field in Iran. One hundred geo-referenced soil samples were taken and subjected to various measurements. The volumetric water contents at the different suctions were fitted to Dexter’s model. The estimated curve parameters plus the measurements of the soil variables were transformed into standardized Gaussian variables and the values transformed were subjected to geostatistical cokriging and factorial cokriging procedures. These results show that soil properties (organic carbon, bulk density, saturated hydraulic conductivity and tensile strength of soil aggregates) influence the parameters of Dexter’s model, although to different extents. The thematic maps of both soil properties and water retention curve parameters displayed a varying degree of spatial association that allowed the identification of homogeneous areas within the field. The first regionalized factors (F1) at the scales of 508 m and 3000 m made it possible to provide different delineations of the field into homogeneous areas as a function of scale, characterized by specific physical and hydraulic properties. F1 at a short and long distance could be interpreted as “porosity indicator” and “hydraulic indicator”, respectively. Such type of field delineation proves particularly useful in sustainable irrigation management. This paper emphasizes the importance of taking the spatial scale into account in precision agriculture.

Assessment of Soil Properties Using Geospatial Techniques at DDUCE-OF Farm

The purpose of this study was to identify the geographic variability and mapping of different soil properties using geospatial techniques at DDUCE-OF Farm. For this purpose, surface soil samples (0-15 cm depth) were collected with the help of GPS at definite locations of research farm area at DDUCE-OF, CCSHAU, Hisar during rabi, 2021. These samples were analyzed in the laboratory for various physico-chemical and biological properties. Soil properties of DDUCE-OF farm revealed that soils are sandy loam to loam in texture having pH ranged from 7.15 to 8.65 and electrical conductivity (EC) ranged from 0.30 to 2.75 dS/m. The soil organic carbon (SOC) content was observed medium to high (0.41 to 0.94%).The available N was low (126 to 196 kg/ha), P ranged from 8.50 to 23.5 kg/ha (from low to high in content) and available K was found medium to high and ranged from 128.5 to 554.0 kg/ha. The available S ranged between 60 to 725 mg/kg and was observed sufficient. The soil microbiological bacterial count status ranged between 4.6x103-9.8x109 CFU count per g of soil in various blocks of organic farm. The DTPA-extractable micronutrients Zn (1.00-4.47 mg/kg), Fe (7.26-19.92 mg/kg), Mn (3.88-17.77 mg/kg) and Cu (0.93-4.64 mg/kg) were sufficient in amount and heavy metal contents (Ni, Pb, Cd, Cr, As) were found below their permissible limit. The study concluded that the soil mapping and survey is significant because it aids in the evaluation of soil qualities and their application in organic farming.

Assessment and Spatial Modelling of Agrochernozem Properties for Reclamation Measurements

Traditional land-use systems can be modified under the conditions of climate change. Higher air temperatures and loss of productive soil moisture lead to reduced crop yields. Irrigation is a possible solution to these problems. However, intense irrigation may have contributed to land degradation. This research assessed the ameliorative potential of soil and produced large-scale digital maps of soil properties for arable plot planning for the construction and operation of irrigation systems. Our research was carried out in the southern forest–steppe zone (Southern Ural, Russia). The soil cover of the site is represented by agrochernozem soils (Luvic Chernozem). We examined the morphological, physicochemical and agrochemical properties of the soil, as well as its heavy metal contents. The random forest (RF) non-linear approach was used to estimate the spatial distribution of the properties and produce maps. We found that soils were characterized by high organic carbon content (SOC) and neutral acidity and were well supplied with nitrogen and potassium concentrations. The agrochernozem was characterized by favorable water–physical properties and showed good values for water infiltration and moisture categories. The contents of heavy metals (lead, cadmium, mercury, cobalt, zinc and copper) did not exceed permissible levels. The soil quality rating interpretation confirms that these soils have high potential fertility and are convenient for irrigation activities. The spatial distribution of soil properties according to the generated maps were not homogeneous. The results showed that remote sensing covariates were the most critical variables in explaining soil properties variability. Our findings may be useful for developing reclamation strategies for similar soils that can restore soil health and improve crop productivity.

Integrating additional spectroscopically inferred soil data improves the accuracy of digital soil mapping

Digital soil mapping has been increasingly advocated as an efficient approach to deliver fine-resolution and up-to-date soil information in evaluating soil ecosystem services. Considering the great spatial heterogeneity of soils, it is widely recognized that more representative soil observations are needed for better capturing the soil spatial variation and thus to increase the accuracy of digital soil maps. In reality, the budget for the field work and soil laboratory analysis is commonly limited due to its high cost and low efficiency. In the last two decades, being an alternative to wet chemistry, soil spectroscopy, such as visible-near infrared (Vis-NIR), mid-infrared (MIR) spectroscopy has been developed in measuring soil information in a rapid and cost-effective manner and thus enable to collect more soil information for digital soil mapping (DSM). However, spectroscopically inferred (SI) data are subject to higher uncertainties than reference laboratory analysis. Many DSM practices integrated SI data with soil observations into spatial modelling while few studies addressed the key question that whether these non-errorless soil data improve map accuracy in DSM. In this study, French Soil Monitoring Network (RMQS) and Land Use and Coverage Area frame Survey Soil (LUCAS Soil) datasets were used to evaluate the potential of SI data from Vis-NIR and MIR in digital mapping of soil properties (i.e. soil organic carbon, clay, and pH) at a national scale. Cubist and quantile regression forests were used for spectral predictive modelling and DSM modelling, respectively. For both RMQS and LUCAS Soil dataset, different scenarios regarding varying proportions of SI data and laboratory observations were tested for spectral predictive models and DSM models. Repeated (50 times) external validation suggested that adding additional SI data can improve the performance of DSM models regardless of soil properties (gain of R2 proportion at 3–19%) when the laboratory observations are limited (≤50%). Lower proportion of SI data used in DSM model and higher accuracy of spectral predictive models led to greater improvement of DSM. Our results also showed that a greater proportion of SI data lowered the prediction intervals which may result in an underestimation of prediction uncertainty. The determination of accuracy threshold on SI data for the use in DSM needs to be explored in future studies.

Assessing the capability of Sentinel-2 time-series to estimate soil organic carbon and clay content at local scale in croplands

The use of remote sensing data methods is affordable for the mapping of soil properties of the plowed layer over croplands. Carried out in the framework of the ongoing STEROPES project of the European Joint H2020 Program SOIL, this work is focused on the feasibility of Sentinel-2 based approaches for the high resolution mapping of topsoil clay and organic carbon (SOC) contents at the within-farm or within-field scales, for cropland sites of contrasted climates and soil types across the Northern hemisphere. Four pixelwise temporal mosaicking methods, using a two years-Sentinel-2 time series and several spectral indices (NDVI, NBR2, BSI, S2WI), were developed and compared for i) pure bare soil condition (maxBSI), ii) driest soil condition (minS2WI), iii) average bare soil condition (Median) and iv) dry soil conditions excluding extreme reflectance values (R90). Three spectral modeling approaches, using the Sentinel-2 bands of the output temporal mosaics as covariates, were tested and compared: (i) Quantile Regression Forest (QRF) algorithm; (ii) QRF adding longitude and latitude as covariates (QRFxy); (iii) a hybrid approach, Linear Mixed Effect Model (LMEM), that includes spatial autocorrelation of the soil properties. We tested pairs of mosaic and spectral approaches on ten sites in Türkiye, Italy, Lithuania, and USA where soil samples were collected and SOC and clay content were measured in the lab. The average RPIQ of the best performances among the test sites was 2.50 both for SOC (RMSE = 0.15%) and clay (RMSE = 3.3%). Both accuracy level and uncertainty were mainly influenced by site characteristics of cloud frequency, soil types and management. Generally, the models including a spatial component (QRFxy and LMEM) were the best performing, while the best spatial mosaicking approaches mostly were Median and R90. The most frequent optimal combination of mosaicking and model type was Median or R90 and QRFxy for SOC, and R90 and LMEM for clay estimation.

The Benefits of Using State‐Of‐The‐Art Digital Soil Properties Maps to Improve the Modeling of Soil Moisture in Land Surface Models

AbstractThis study assesses the added value of using emerging maps of soil properties to improve surface soil moisture simulations using the HydroBlocks land surface model with different soil hydraulic parameterization schemes. Simulations were run at an hourly 30‐m resolution between 2012 and 2019 and evaluated against U.S. Climate Reference Network measurements. The results show that state‐of‐the‐art soil properties maps (POLARIS and SoilGrids250m V2.0) improve the accuracy of simulated surface soil moisture when compared to the STATSGO‐derived CONUS‐SOIL map. Contemporary pedotransfer functions (multi‐linear regression and Artificial Neural Networks‐based) also improve model performance in comparison to the lookup table‐derived soil parameterization schemes. The addition of vertical heterogeneity to the soil properties further improves the mean Kling‐Gupta efficiency by 0.04 and lowers the mean Root mean square error by 0.003 over the CONUS. This study demonstrates that land surface modeling can be improved by using state‐of‐the‐art maps of soil properties, accounting for the vertical heterogeneity of soils, and advancing the use of contemporary pedotransfer functions.

Soil information on a regional scale: Two machine learning based approaches for predicting saturated hydraulic conductivity

Saturated hydraulic conductivity (Ksat) and other soil (hydraulic) properties are fundamental for applications that depend on modeling hydrological processes, such as the quantification of future groundwater recharge rates. Yet, for most areas in the world, local soil information is lacking. Additionally, access to local soil surveys is often restricted or costly. Available global and regional digital soil mapping (DSM) products differ in scale and degree of data aggregation, as well as in spatial coverage. Ksat – and soil properties in general – are also characterized by a high spatial variability at all scales. Most often, there is no single data product available that covers the whole study area and still displays the variability of local soil observations. Thus, it is often a challenge to combine and predict soil data from different sources and resolutions while preserving the characteristically high spatial variability of soil properties.This study develops and compares two approaches for producing spatially distributed Ksat maps. First, an indirect approach based on two machine learning (ML) models – eXtreme Gradient Boosting (XGBoost) and feed-forward neural network (FNN) – that are trained with available local soil data sources and environmental raster datasets to predict the soil parameters sand, silt, clay, and organic matter content. Ksat is then determined by applying existing pedotransfer-functions (PTFs) on these regionalized soil parameters. Second, a direct approach in which ML models are directly trained with available soil hydraulic datasets to predict Ksat.Both approaches are applied to predict Ksat for Austria. While the resulting soil property maps of the indirect approach are able to largely reproduce the original data variability, the prediction of Ksat includes high levels of uncertainties and the predicted vertical distribution of Ksat is not plausible. The spatial distribution of Ksat in the direct approach resembles available global Ksat maps. In the existing global Ksat maps as well as in the results of the direct approach the small-scale variability of Ksat is reduced. In both approaches XGBoost outperforms FNN. The derived soil property maps help to reduce current gaps in soil data availability for Austria, but also highlight the need for additional Ksat field data acquisition.

Variability modeling and mapping of soil properties for improved management in Ethiopia

AbstractManaging soils for improved agricultural production requires information on soil fertility status. Our objective was to map for better soil management in Ethiopia and determine their spatial correlation at a separation distance of 29 m. We collected 82 soil samples (0–20 cm depth) at 560 ha of land and determined pH, Olsen extractable phosphorus (Olsen‐P), and organic carbon (OC). We then interpolated between sample points (ordinary kriging‐OK and distance weighting‐IDW [inverse distance weighting]) to evaluate spatial dependence. Olsen‐P ranged from 2.68–42 mg/kg and exhibited high variability with a coefficient of variation (CV) ≥35%. Conversely, soil pH showed low variability (CV ≤ 15%) and ranging from 4.84 to 6.81. Soil OC content varied from 0.81% to 3.17%. The IDW (R2 = 0.86; RMSE = 0.019) outperformed the OK. The semivariogram results indicate a strong dependence for pH and OC for spherical, exponential, and Gaussian models, while moderately spatially auto correlated for Olsen‐P for all models. The IDW and OK predict the spatial variability of the pH (moderately acidic), Olsen‐P (low), and OC (very low) contents. The soil maps may help to improve soil management alternatives, increase crop productivity, and secure environmental quality.

Digital Mapping of Soil Properties Using Ensemble Machine Learning Approaches in an Agricultural Lowland Area of Lombardy, Italy

Sustainable agricultural landscape management needs reliable and accurate soil maps and updated geospatial soil information. Recently, machine learning (ML) models have commonly been used in digital soil mapping, together with limited data, for various types of landscapes. In this study, we tested linear and nonlinear ML models in predicting and mapping soil properties in an agricultural lowland landscape of Lombardy region, Italy. We further evaluated the ability of an ensemble learning model, based on a stacking approach, to predict the spatial variation of soil properties, such as sand, silt, and clay contents, soil organic carbon content, pH, and topsoil depth. Therefore, we combined the predictions of the base learners (ML models) with two meta-learners. Prediction accuracies were assessed using a nested cross-validation procedure. Nonetheless, the nonlinear single models generally performed well, with RF having the best results; the stacking models did not outperform all the individual base learners. The most important topographic predictors of the soil properties were vertical distance to channel network and channel network base level. The results yield valuable information for sustainable land use in an area with a particular soil water cycle, as well as for future climate and socioeconomic changes influencing water content, soil pollution dynamics, and food security.

Delineation of Soil Management Zone Maps at the Regional Scale Using Machine Learning

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 learning methods. To achieve these, 202 soil samples were collected at the agricultural land of pomegranate, pistachio, and saffron. A “random forest” model was applied to map soil properties based on environmental covariates. The predicted “Lin’s concordance correlation coefficient” values in validation soil properties varied from 0.65 to 0.79. The maps indicated low amounts of soil organic carbon, available potassium, available phosphate, and total nitrogen in most of the region. Furthermore, the study identified four different MZs according to relationships between soil properties and environmental covariates. Generally, the ranking of zones in terms of soil fertility was MZ4 > MZ1 > MZ3 > MZ2 based on the investigated soil properties and the soil quality (SQ) map. The five grades of SQ (i.e., very high, high, moderate, low, and very low) indicated that there was heterogeneous SQ in each MZ in the study area. There were 1.65 ha identified in MZ4 with very low SQ. This result is important in determining the amount of fertilizer to add to the soil in the different areas. It confirms the need for more specific regional management of agriculture lands in this region.

Spatial relationship between peach tree productivity and soil properties in a newly reclaimed orchard

ABSTRACT The construction of banks and surface soil cutting involved in newly reclaiming orchards may cause spatial variability in soil properties, which could cause poor fruit tree productivity. This study examined spatial relationships between variations in peach tree properties and soil properties around each tree and assessed the possibility of site-specific field management for individual trees in a newly reclaimed orchard. Three and four years after reclamation in 2016, the 0.1-ha field located in Kyoto, Japan was divided into 32 grid cells at 5-m intervals for all individual peach trees and 128 grid cells at 2.5-m intervals for measuring soil properties around each tree (field scale). The 128 grid cells were each further divided into five positions at 0.71-m intervals for assessing selected soil properties (grid-cell scale). The soil data were geostatistically analyzed by calculating the nugget/sill ratios and anisotropy ratios (degree of variation in relation to direction) and drawing kriged maps. Coefficients of variation of soil properties at the grid-cell scale were much higher than those at the field scale. Pearson’s correlation analysis at the field scale indicated that the tree productivity was most strongly correlated with relative altitude, followed by the degree of soil reduction, time-domain reflectometry electrical conductivity (TDR-EC), subsoil pH (H2O), and TDR-volumetric water content. Tree productivity tended to be lower at lower elevations, where the degree of soil reduction, TDR-EC, subsoil pH (H2O), and soil water content tended to be high. Poor productivity appeared to be related to wet injury. Semivariograms of the soil properties related to soil water showed strong spatial dependence and had nugget/sill ratios of ≤24.0%. Kriged maps and anisotropy ratios further indicated that most tree property values decreased from southeast to northwest and showed variations similar to those of the soil properties related to soil water. Fine-scale mapping of soil properties in the orchard could reveal spatial soil-property variations around individual peach trees, and the result would imply the importance of soil management practices on a per-tree basis. Our findings could help to improve tree productivity in the northwestern area by increasing soil drainage for individual trees.

Synergetic use of DEM derivatives, Sentinel-1 and Sentinel-2 data for mapping soil properties of a sloped cropland based on a two-step ensemble learning method

Understanding the spatial variability of soil organic matter (SOM), soil total nitrogen (STN), soil total phosphorus (STP), and soil total potassium (STK) is important to support site-specific agronomic management, food production, and climate change adaptation. High-resolution remote sensing imageries have emerged as an innovative solution to investigate the spatial variation in agricultural soils with machine learning (ML) algorithms. However, the predictive power of the individual and combined effects of Sentinel-1 (S1) synthetic aperture radar (SAR) and Sentinel-2 (S2) multispectral images for mapping soil properties, especially STN, STP, and STK, have rarely been investigated. Moreover, single ML model may achieve unstable performance for predicting multiple soil properties due to strong spatial heterogeneity. This study explored the combine use of S1, S2, and DEM derivatives to map SOM, STN, STP, and STK content of a sloped cropland of northeastern China. A two-step method with a weighted sum of four ML models was proposed to improve the accuracy and robustness in predicting multiple soil properties. Our results showed that single ML model has various performance in predicting the four soil properties. The optimal ML models could explain approximately 56 %, 53 %, 56 % and 37 % of the variability of SOM, STN, STP, and STK, respectively. Using the weights estimated through a 10-fold cross-validation procedure, the two-step ensemble learning model was retrained and showed more robust performance than the four ML models, in which the prediction accuracy was improved by 2.38 %, 1.40 %, 3.52 %, and 3.29 % for SOM, STN, STP, and STK, respectively. Our results also showed that the optical S2 derived features, especially the two S2 short-wave infrared bands, enhanced vegetation index, and soil adjusted vegetation index, were more important for soil property prediction than S1 data and DEM derivatives. Compared with individual sensor, a combination of S1 and S2 data yielded more accurate predictions of STN and STP but not for SOM and STK. The results of this study highlight the potential of high-resolution S1 and S2 data and the two-step method for soil property prediction at farmland scale.

Mapping of soil properties using geographic information systems (on the example of Tashkent region)

The article analyzes the options for mapping soil properties using GIS systems. The perspectives on the use of GIS in the efficient mapping of soil mechanical content, agrochemical, and salinization characteristics are given. The study is conducted for irrigated sierozem soils of the Parkent district of Tashkent region. The basis of the research is the analysis of soil map data of the study areas, and the generalization of the results of soil-cartographic, laboratory, and cameral-analytical studies, which were conducted by commonly use methodological instructions. As a result of the research, digital thematic maps were created for the selected areas, such as various thematic maps, such as soil mechanical composition, soil nutrient status, and salinity maps.

Mapping soil properties using geostatistical methods for mid to high altitude temperate zone of Kashmir Himalayas

Mapping soil properties using geostatistical methods for mid to high altitude temperate zone of Kashmir Himalayas

Field-Scale Apparent Electrical Conductivity Mapping of Soil Properties in Precision Agriculture

The physical examination of a functioning cacao farm revealed varying pod production rates in its area. Agricultural soil nutrients assessment is usually through soil geochemical/chemical analyses which are laborious and expensive, necessitating faster/cheaper alternatives. This investigation assessed the physical properties that can substitute for geochemical analysis of soil nutrient. The study was executed at 0.3 m depth. The Volumetric Water Content-VWC and Apparent Electrical Conductivity-ECa of the soils were determined using VG-meter-200 moisture-meter and resistivity earth-meter. 912 ECa/VWC points were measured. Soil textural classes (51-sample) were established using Bouyoucos method. Falling head permeability test was conducted on nine cored soil samples for water infiltration assessment. The soils ECa (10-344 µS/cm) and VWC distributions (2-69%) showed similar variation, increase in VWC corresponds to rise in ECa value; soil moisture aids the mobility of ions in solution and a rise in ECa connotes presence of more dissolved ions. The soils were classified as sandy loam, loamy sand and sandy clayey loam. Soils’ permeability ranged from 4.11x10-5-3.97x10-3 cm/sec; infiltration rate varied inversely with the ECa accounting for the moisture variation. Low permeable soil has high nutrient retention and water-holding capacities. Soil physical properties were effective in evaluating the nutrient inconsistency.