Application of Artificial Neural Network (ANN) to Predict Soil Organic Matter Using Remote Sensing Data in Two Ecosystems

Concern over global problems induced by rising CO2 has prompted attention on the role of forests and pastures as carbon ‘storage’ because forests and pastures store a large amount of carbon in vegetation biomass and soil. Soil organic matter (SOM) plays a critical role in soil quality and has the potential to cost-effectively mitigate the detrimental effects of rising atmospheric CO2 and other greenhouse gas emissions that cause global warming and climate change(Causarano-Medina, 2006). SOM, an important source of plant nutrients is itself influenced by land use, soil type, parent material, time, climate and vegetation (Loveland &Webb, 2003). Important climatic factors influencing SOM include rainfall and temperature. Within the same isotherm, the SOM content increases with increase in rainfall regime. For the same isohyet, the SOM content...............

The Brazilian Soil Spectral Service (BraSpecS): A User-Friendly System for Global Soil Spectra Communication

Although many Soil Spectral Libraries (SSLs) have been created globally, these libraries still have not been operationalized for end-users. To address this limitation, this study created an online Brazilian Soil Spectral Service (BraSpecS). The system was based on the Brazilian Soil Spectral Library (BSSL) with samples collected in the Visible–Near–Short-wave infrared (vis–NIR–SWIR) and Midinfrared (MIR) ranges. The interactive platform allows users to find spectra, act as custodians of the data, and estimate several soil properties and classification. The system was tested by 500 Brazilian and 65 international users. Users accessed the platform (besbbr.com.br), uploaded their spectra, and received soil organic carbon (SOC) and clay content prediction results via email. The BraSpecS prediction provided good results for Brazilian data, but performed variably for other countries. Prediction for countries outside of Brazil using local spectra (External Country Soil Spectral Libraries, ExCSSL) mostly showed greater performance than BraSpecS. Clay R2 ranged from 0.5 (BraSpecS) to 0.8 (ExCSSL) in vis–NIR–SWIR, but BraSpecS MIR models were more accurate in most situations. The development of external models based on the fusion of local samples with BSSL formed the Global Soil Spectral Library (GSSL). The GSSL models improved soil properties prediction for different countries. Nevertheless, the proposed system needs to be continually updated with new spectra so they can be applied broadly. Accordingly, the online system is dynamic, users can contribute their data and the models will adapt to local information. Our community-driven web platform allows users to predict soil attributes without learning soil spectral modeling, which will invite end-users to utilize this powerful technique

Using Cesium-137 to estimate soil particle redistribution by wind in an arid region of central Iran

This study was conducted to estimate soil erosion and deposition rates along a transect using 137Cs technique in an arid of Isfahan Province, central Iran. Sixteen sites along a northeast-southwest transect with 42 km length were used. Eighty soil samples collected from five depths (0-5, 5-10, 10-20, 20-30, 30-50 cm) were analyzed for 137Cs concentration. Additional 20 soil samples were collected from the reference site for computing soil loss and deposition using 137Cs measurement. The results showed that the northern part of the transect showed erosion rates ranging from12.90 to 46.86 t ha-1yr-1. The major factor affecting soil erosion process in northern part of the studied transect is associated dominantly with occurrence of improper gypsum mining operations and human activities. In the southern part of the transect deposition rates changed between 3.10 - 7.44 t ha-1yr-1, presumably influenced by increasing plant cover. Significant correlations between 137Cs and magnetic susceptibility, soil organic matter (SOM), total nitrogen (TN) and particle size distributions indicated that soil redistribution by wind erosion might have modified the soil properties along the studied transect. A multiple linear regression model was developed for estimating 137Cs by frequency dependence (χfd), TN, clay and sand contents which explained about 87% of the 137Cs variability. This study of using 137Cs to assess wind erosion is unique in the arid region of central Iran and had significant implications for further research

Slope and Land Use Changing Effects on Soil Properties and Magnetic Susceptibility in Hilly Lands, Yasouj Region

Introduction: Land use changes are the most reasons which affect natural ecosystem protection. Forest soils have high organic matter and suitable structure, but their land use management change usually affects soil properties and decreases soil quality. There are several outcomes of such land use changes and intensification: accelerated soil erosion and decline of soil nutrient conditions, change of hydrological regimes and sedimentation and loss of primary forests and their biodiversity. Establishing effects of land use and land cover changes on soil properties have implications for devising management strategies for sustainable use. Forest land use change in Yasouj caused soil losses and decreased soil quality. The objectives of this study were to assess some soil physical and chemical properties and soil magnetic susceptibility changes in different land uses and slope position. Materials and Methods: Soil samples were taken from natural forest, degraded forest and dryland farm from different slops (0-10, 10-20 and 20-30 percent) in sout east of Yasouj. They were from 0–10 cm depth in a completely randomized design with five replications. Soil moisture and temperature regimes in the study area are xeric and mesic, respectively. Particle size distribution was determined by the hydrometer method and soil organic matter, CaCO3 equivalent and bulk density was determined using standard procedures described in Methods of Soil Analysis book. Magnetic susceptibility was measured at low and high frequency of 0.46 kHz (χlf) and 4.6 kHz (χHf) respectively with a Bartington MS2D meter using approximately 20 g of soil held in a four-dram clear plastic vial. Frequency dependent susceptibility (χfd) is expressed as the difference between the high and the low frequency measurements as a percentage of χ at low frequency. Results and Discussion: Soil texture was affected by land use change from silty clay loam in forest to silty loam in dry land farm. Declining of organic matter and aggregate stability caused soil surface loss by erosion. The bulk density increased from 1.12 to 1.54 gcm-3 when forest changed to dry land farms. Soil compaction by tillage and lower amount of organic matter in farm lands are some of the reasons for increasing bulk density. Another possible reason could be decreasing of biological activity and parent material with greater calcite mixed with soil surface layer during land use change. Thus, the maximum and minimum amount of calcite was observed in dry land farm in 20-30 % slopes (57.46 %) and forest in 0-10 % slopes (13.37 %), respectively. In addition during soil formation calcite was translocated to lower horizons in natural forest. The greatest organic matter was 7.45 % and related to natural forest in 0-10 % slopes. Overall, the organic matter content was greater in all forest slopes than all other land use. In mineral soil, total organic carbon is not a proper factor in soil physical behavior. Complex and noncomplex organic carbon influence the soil physical behavior. Organic carbon in degraded forest and dry land farming was in complex form but in forest land use it was observed in two complex and noncomplex forms. Noncomplex organic matter was 53% and complex organic matter was 47%. It means that forest soil have better quality than degraded forest and dry land farm, respectively. Sedimentary rocks have rather low concentration of magnetic minerals with magnetic susceptibility from 0.1 (10-8 m3 kg-1) in the limestone to approximately 20 (10-8 m3 kg-1) in the siltstone. Low magnetite susceptibility in natural forest was more than degraded forest and dry land farm. Mean magnetite susceptibility values were 61.8, 48.6 and 42.4 10-8 m-3 kg-1, respectively which probably related to magnetic minerals formation during pedogenesis. Frequency magnetite susceptibility (χfd) was more than 3% in the most soils, significantly in forest soil (from 4.63-5 percent). Greater frequency magnetite susceptibility (χfd) values are suggested to be indicative of the dominance of super-paramagnetic grains and fiug single domain particles. χfd in soils reflects significant pedogenic magnetic minerals which formed during soil formation from calcitic parent materials

Effects of different sources and spatial resolutions of environmental covariates on predicting soil organic carbon using machine learning in a semi-arid region of Iran

The main goal of this study was to consider and compare the effects of different spatial resolutions of covariates from different sources on predicting SOC in a semi-arid region located in the west of Iran. For this purpose, 67 topsoil samples (0–30 cm) with the measured SOC contents were used as the dependent variable. The covariates controlling the SOC content from different sources were provided in two scenarios. For the first scenario (scenario I), six covariate sets with spatial resolution ranging from 2 to 30 m, and original and aggregated pixel sizes were prepared using the digital elevation models (DEMs) and remote sensing data to predict SOC. In the second scenario (scenario II), the available legacy data, including geology, land use and soil texture maps, were prepared with compatible spatial resolution and added to each covariate set provided for scenario I. After feature selection analysis, the modelling processes were performed using two machine learning models, namely, Random Forest (RF) and Support Vector Machine (SVM). The results of performance analysis, as obtained by leave one out cross validation (LOOCV), showed that the RF and covariate set B (with 10 m spatial resolution) in scenario I, with R2 = 0.21, CCC = 0.41, MAE = 0.26 and RMSE = 0.34%, and also, in scenario II, with R2 = 0.32, CCC = 0.51, MAE = 0.24, and RMSE = 0.32%, had a better performance in predicting SOC. In addition, the remote sensing data were identified as the most important variables controlling the spatial distribution of SOC. Finally, by using the RF model as the superior model, the SOC map provided by the covariate set B in scenario II, which was the combination of the three types of covariates (DEM, remote sensing data and legacy data), was shown to have the lowest uncertainty in comparison to the SOC provided by the covariate set B in scenario I. In general, our results showed that the model type, source, resolution and the combination of these variables could greatly influence the prediction outputs. In fact, the SOC map provided with the combination of parsimonious variables at the optimal pixel size could help decision-making in environmental resources management

Development and magnetic properties of loess-derived forest soils along a precipitation gradient in northern Iran

In order to investigate the development of forest soils formed on loess, six representative modern soil pedons were selected along a precipitation gradient extending from eastern Golestan (mean annual precipitation, MAP = 500 mm) to eastern Mazandaran Provinces (MAP = 800 mm). Physiochemical, micromorphological and magnetic properties, as well as clay mineralogy of soils were studied using standard methods. Soils are mainly classified as Alfisols and Mollisols. Downward decalcification and the subsequent clay illuviation were the main criteria of soil development in all study areas. Pedogenic magnetic susceptibility of pedons studied varied systematically across the precipitation gradient in Northern Iran, increasing from 14.66 x 10(-8) m(3) kg(-1) at the eastern part to 83.75 x 10(-8) m(3) kg(-1) at the western margin of this transect. The frequencydependent magnetic susceptibility showed an increasing trend with rainfall as well. The micromorphological study of soils indicated that there is a positive relationship between climate gradient (increasing rainfall) and the micromorphological index of soil development (MISECA). The area and thickness of clay coatings showed an increasing trend with rainfall. Grain size analysis indicates that pedogenic processes are responsible for changing original grain size distribution of loess in our soils. The correlation achieved among modern soil properties and precipitation could be applied to the buried paleosols in the whole study area to refer degree of paleosol development and to reconstruct the paleoclimate

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