مقایسه مدل شبکه عصبی مصنوعی و رگرسیون خطی چندمتغیره در پیش بینی کربن آلی خاک به کمک داده های آنالیز سطح زمین

Chapter 34 - Digital soil mapping of organic carbon at two depths in loess hilly region of Northern Iran

Much older soils data and soils information lies idle in libraries and archives and is largely unused, especially in developing countries like Kenya. We demonstrated the usefulness of a stepwise approach to bring legacy soils data ‘back to life’ using the 1980 Reconnaissance Soil Map of the Busia Area (quarter degree sheet No. 101) in western Kenya as an example. Three studies were conducted by using agronomic information, field observations, and laboratory data available in the published soil survey report as inputs to several digital soil mapping techniques. In the first study, the agronomic information in the survey report was interpreted to generate 10 land quality maps. The maps represented the ability of the land to perform specific agronomic functions. Nineteen crop suitability maps that were not previously available were also generated. In the second study, a dataset of 76 profile points mined from the survey report was used as input to three spatial prediction models for soil organic carbon (SOC) and texture. The three predictions models were (i) ordinary kriging, (ii) stepwise multiple linear regression, and (iii) the Soil Land Inference Model (SoLIM). Statistically, ordinary kriging performed better than SoLIM and stepwise multiple linear regression in predicting SOC (RMSE = 0.02), clay (RMSE = 0.32), and silt (RMSE = 0.10), whereas stepwise multiple linear regression performed better than SoLIM and ordinary kriging for predicting sand content (RSME = 0.11). Ordinary kriging had the narrowest 95% confidence interval while stepwise multiple linear regression had, the widest. From a pedological standpoint, SoLIM conformed better to the soil forming factors model than ordinary kriging and had a narrower confidence interval compared to stepwise multiple linear regression. In the third study, rules generated from the map legend and map unit descriptions were used to generate a soil class map. Information about soil distribution and parent material from the map unit polygon descriptions were combined with six terrain attributes, to generate a disaggregated fuzzy soil class map. The terrain attributes were multiresolution ridgetop flatness (MRRTF), multiresolution valley bottom flatness (MRVBF), topographic wetness index (TWI), topographic position index (TPI), planform curvature, and profile curvature. The final result was a soil class map with a spatial resolution of 30 m, an overall accuracy of 58% and a Kappa coefficient of 0.54. Motivated by the wealth of soil agronomic information generated by this study, we successfully tested the feasibility of delivering this information in rural western Kenya using the cell phone-based Soil Explorer app (https://soilexplorer.net/). This study demonstrates that legacy soil data can play a critical role in providing sustainable solutions to some of the most pressing agronomic challenges currently facing Kenya and most African countries.

Soil organic carbon (SOC) is an important indicator of soil quality and directly determines soil fertility. Hence, understanding its spatial distribution and controlling factors is necessary for efficient and sustainable soil nutrient management. In this study, machine learning algorithms including artificial neural network (ANN), support vector machine (SVM), cubist regression, random forests (RF), and multiple linear regression (MLR) were chosen for advancing the prediction of SOC. A total of sixty (n = 60) soil samples were collected within the research area at 30 cm soil depth and measured for SOC content using the Walkley–Black method. From these samples, 80% were used for model training and 21 auxiliary data were included as predictors. The predictors include effective cation exchange capacity (ECEC), base saturation (BS), calcium to magnesium ratio (Ca_Mg), potassium to magnesium ratio (K_Mg), potassium to calcium ratio (K_Ca), elevation, plan curvature, total catchment area, channel network base level, topographic wetness index, clay index, iron index, normalized difference build-up index (NDBI), ratio vegetation index (RVI), soil adjusted vegetation index (SAVI), normalized difference vegetation index (NDVI), normalized difference moisture index (NDMI) and land surface temperature (LST). Mean absolute error (MAE), root-mean-square error (RMSE) and R2 were used to determine the model performance. The result showed the mean SOC to be 1.62% with a coefficient of variation (CV) of 47%. The best performing model was RF (R2 = 0.68) followed by the cubist model (R2 = 0.51), SVM (R2 = 0.36), ANN (R2 = 0.36) and MLR (R2 = 0.17). The soil nutrient indicators, topographic wetness index and total catchment area were considered an indicator for spatial prediction of SOC in flat homogenous topography. Future studies should include other auxiliary predictors (e.g., soil physical and chemical properties, and lithological data) as well as cover a broader range of soil types to improve model performance.

The spatial variability of soil organic carbon (SOC) is one of the reasons leading to uncertainty in the estimation of carbon stocks. Simulation and analysis research of SOC spatial distribution, especially the three-dimensional (3D) spatial distribution characteristics, is of great significance for revealing the soil nutrient and pollutant migration, the precise management of farmland and agricultural sustainable development. The 3D spatial distribution pattern of SOC was estimated using 3D ordinary Kriging with regard to the isotropy (3DOKI), 3D ordinary Kriging and 3D CoKriging coupling Markov with regard to anisotropy (3DOKA and 3DCKMA) in the region. Based on estimation results, the paper revealed 3D distribution pattern of regional SOC content. The mean root mean square error (RMSE), relative improvement (RI), the scatter diagram of SOC simulated and measured value and specific value coverage ratio were used to estimate the accuracy of different approaches, and the mean squared deviation ratio (MSDR) and the 3D variogram were used to evaluate model fitting effect and spatial local uncertainty. The results showed that 3D spatial distribution patterns of SOC content for the 3 kinds of estimation methods were basically the same, and the overall spatial distribution pattern was SOC content of the west and the north was higher, and that of the east and the south was relatively low. SOC contents of 5 layers (0-20, >20-40, >40-60, >60-80 and >80-100 cm) were 11.88±5.76, 10.08±4.89, 8.40±5.49, 7.83±5.89 and 7.17±5.22 g/kg, respectively. With the increase of soil depth, the SOC content of the patch gradually reduced. No matter which kind of method, the spatial distribution of SOC in different soil depth was similar, that was, the distribution characteristics of surface layer of the patch were also the embodiment of the deep soil layer. Compared to 3DOKI, the spatial search strategy and its parameters with regard to anisotropy were able to reduce defect (Bovine and central tendency effect) of Kriging method in a certain extent. The RMSE values for 3DOKI, 3DOKA and 3DCKMA were 3.1645, 2.0523 and 1.6215, respectively, so the RMSE value was the minimum for 3DCKAM, whose RI reached nearly 50%. Specific value coverage ratio for 3DOKI, 3DOKA and 3DCKMA were 33.12%, 57.83% and 76.15%, respectively, which was the largest for 3DCKMA method, whose MSDR value (1.4409) was the most close to 1, and variance was the least. The same position CoKriging method coupling Markov was more accurate, and its model fitting effect was the best, and uncertainty was the smallest, which could better highlight the volatility and reflect the specific value. The related research results of this paper will provide method reference for the regional soil properties, and provide technical guidance for the reasonable and scientific research of the spatial distribution pattern of regional SOC. © 2016, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.

Introduction: Saffron is one of the most important economic plants in the Khorasan province. Awareness of soil quality in agricultural lands is essential for the best management of lands and for obtaining maximum economic benefit. In general, plant growth is a function of environmental factors especially chemical and physical properties of soil (20). It has been demonstrated that there was a positive and high correlation between soil organic matter and saffron yield. Increasing the yield of saffron due to organic matter is probably due to soil nutrient, especially phosphorous and nitrogen and also improvement of soil physical quality (6, 28, 29). The yield of saffron in soils with high nitrogen as a result of vegetative growth is high (8). Shahandeh (6) found that most of the variation of saffron yield depends on soil properties. Due to the economic importance of saffron and the role of soil properties on saffron yield, this research was conducted to find the relationship between saffron yield and some soil physical and chemical properties, and to determine the contribution of soil properties that have the greatest impact on saffron yield in the Ghayenat area. Materials and Methods: This research was performed in 30 saffron fields (30 soil samples) of the Ghayenat area (longitude 59° 10΄ 10.37˝ - 59° 11΄ 38.41˝ and latitude 33° 43΄ 35.08˝ - 33΄ 44΄ 02.78˝), which is located in the Khrasan province of Iran. In this research, 21 soil properties were regarded as the total data set (TDS). Then the principal component analysis (PCA) was used to determine the most important soil properties affecting saffron yield as a minimum data set (MDS) and the stepwise regression to estimate saffron yield. To estimate the yield of saffron in stepwise regression method, saffron yield was considered as a dependent variable and soil physical and chemical properties were considered to be independent variables. Results and Discussion: According to the PCA method, among the 21 studied properties, 7 out of them including calcium, iron, zinc contents, sand, calcium carbonate equivalent percent, mean weight diameter of aggregates (MWD) and manganese (Mn) had the higher Eigenvalues. Therefore, the above properties were introduced as the most important soil properties in saffron fields. Calcium carbonate had the negative effect on the availability of micro-nutrients (26). Christensen et al. (15) found that by increasing the calcium carbonate in soil due to high pH and formation of insoluble components, the uptake of micro-nutrients is especially limited. The results of stepwise regression method (equation 1) showed that soil acidity (pH), zinc content, bulk density, MWD, iron content, salinity (EC), organic carbon and available potassium in soil were the most important properties that affect the yield of saffron, so that the determination coefficient (R2) of the regression model was high (Table 2) and it can explain 74% of the variation of saffron yield. Y = 6924.51 – 1187.31 pH – 89.65 EC + 71.6 Fe – 826.02 Zn + 471.55 OC, + 5490.96 K + 1353.56 BD + 752.82 MWD (1) where Y: saffron yield (kgha-1), pH: soil acidity, EC: electoral conductivity (dSm-1), Fe: iron concentration (mgkg-1), Zn: zinc concentration (mgkg-1), OC: organic carbon (%), K: soil potassium (%), BD: soil bulk density (Mgm-3), and MWD: mean weight diameter of aggregates (MM). Based on the absolute values of standard s in the regression model (Table 3), pH value and then after Zn concentration had the most effect on saffron yield. In general, responses of different plants to soil pH is varied, and saffron grows satisfactory in pH = 7.8 (5). Soil pH influences the uptake of soil nutrients by plants (15), so that this parameter had the most effect on saffron yield and by increasing the soil pH, the yield of saffron decreases. According to the regression model, Zn concentration was the second parameter in saffron yield. Zn has the important role in structure of plant enzymes (30). After these 2 parameters, Bd, MWD, Fe concentration, EC, Organic carbon and K concentration in soil had more effect on saffron yield (Table 3). Conclusion: According to both PCA and regression methods, the concentration of iron and zinc and MWD were determined as the important and effective soil properties on saffron yield in the Ghayenat area. In addition, soil pH in stepwise regression method and calcium carbonate in PCA method were determined as the effective properties on saffron yield. Therefore, it is suggested that the parameters of Zn, Fe, and MWD along with soil pH and calcium carbonate which were regarded individually in two methods, were considered as the most soil properties in saffron yield.

Investigations on temporal and spatial variation of slope-scale SOC erosion and deposition

Estimating soil organic carbon of sown biodiverse permanent pastures in Portugal using near infrared spectral data and artificial neural networks

Evaluation of using digital photography as a cost-effective tool for the rapid assessment of soil organic carbon at a regional scale

Spatial information on soil organic carbon (SOC) in the Indian Himalayan region is vital for sustainable land management and conservation efforts, as it helps to identify areas vulnerable to erosion and degradation. However, the difficult Himalayan terrain is a major roadblock to the very high spatial resolution mapping of SOC. Previous studies mapped SOC in Himalayan region with resolutions varying from 250 to 90 m. The increase in spatial resolution definitely enhances the data quality and supports towards decision making for sustainable soil management. The present study tried to overcome this challenge and mapping of SOC was done at a resolution of 30 m by integrating various machine learning (ML) techniques i.e. random forest regression (RF), support vector regression (SVR) and extreme gradient boosting (XGB).Surface soil samples were strategically collected from 421 georeferenced locations representing the dominant elevation zones, geology and land use land cover (LULC) types to develop spatial models for predicting SOC. Environmental covariates representing various pedogenic factors, namely climatic variables, terrain attributes, spectral indices, LULC as well as lithological information were generated using diverse data sources employing geospatial data analysis and google earth engine (GEE) platform. A feature ranking and variable selection protocol was used for the selection of optimal set of covariates prior to model development. The three validated models were used for mapping the spatial distribution of SOC in the study area. The pixel wise uncertainty in SOC prediction by different models were also spatially mapped by generating the lower (LL) and upper limits (UL) of the 90% prediction interval.Results revealed that the RF model (R2train: 0.92, R2test: 0.72) performed better compared to XGB (R2train: 0.59, R2test: 0.37) and SVR (R2train: 0.53, R2test: 0.35) models during both training and testing phases indicated by various evaluation metrics. Covariates representing vegetation, climate as well as topography were found to equally dominate (03 nos. each) among the first 10 important predictors governing the better prediction performance of RF model. The SOC maps indicated soils in the north, north-east and north-western parts of the study area were exhibited comparatively higher SOC contents than rest of the study area. Among the predominant land use types, evergreen forest was found to exhibit the highest SOC values (2.88%) compared to others.The study demonstrated the potential of digital soil mapping (DSM) techniques, enhanced by RS and ML, for mapping SOC in the hilly tracts of Himalayas with a 30 m spatial resolution. This detailed database can prove beneficial in devising effective land management and resource conservation strategies in the fragile mountain ecosystems of Indian Himalayas.

Soil organic carbon (SOC) measurements are an indicator of soil health and an important parameter for the study of land-atmosphere carbon fluxes. Field sampling provides precise measurements at the sample location but entails high costs and cannot provide detailed maps unless the sampling density is very high. Remote sensing offers the possibility to quantify SOC over large areas in a cost-effective way. As a result, numerous studies have sought to quantify SOC using Earth observation data with a focus on inter-field or regional distributions. This study took a different angle and aimed to map the spatial distribution of SOC at the intra-field scale, since this distribution provides important insights into the biophysiochemical processes involved in the retention of SOC. Instead of solely using spectral measurements to quantify SOC, topographic and spectral features act as predictor variables. The necessary data on study fields in South-East England was acquired through a detailed SOC sampling campaign, including a LiDAR survey flight. Multi-spectral Sentinel-2 data of the study fields were acquired for the exact day of the sampling campaign, and for an interval of 18 months before and after this date. Random Forest (RF) and Support Vector Regression (SVR) models were trained and tested on the spectral and topographical data of the fields to predict the observed SOC values. Five different sets of model predictors were assessed, by using independently and in combination, single and multidate spectral data, and topographical features for the SOC sampling points. Both, RF and SVR models performed best when trained on multi-temporal Sentinel-2 data together with topographic features, achieving validation root-mean-square errors (RMSEs) of 0.29% and 0.23% SOC, respectively. These RMSEs are competitive when compared with those found in the literature for similar models. The topographic wetness index (TWI) exhibited the highest permutation importance for virtually all models. Given that farming practices within each field are the same, this result suggests an important role of soil moisture in SOC retention. Contrary to findings in dryer climates or in studies encompassing larger areas, TWI was negatively related to SOC levels in the study fields, suggesting a different role of soil wetness in the SOC storage in climates characterized by excess rainfall and poorly drained soils.

Mapping soil organic carbon and total nitrogen in croplands of the Corn Belt of Northeast China based on geographically weighted regression kriging model

Large topsoil organic carbon variability is controlled by Andisol properties and effectively assessed by VNIR spectroscopy in a coffee agroforestry system of Costa Rica

In this paper, the El-Hames empirical method was used to determine the peak runoff flow in Iranshahr catchment. The El-Hames method is based on morphological parameters and SCS Curve Number of the studied area. The implementing was processed by collecting the required data including soil type and vegetation cover, land use maps, digital elevation model and satellite images from the associated institutions such as Iran Water Resources Management Company (IWRM) and Iran Weather Organization. Then the all data was integrated by using GIS in order to create different layers of required information. The generated layers were applied to determine the morphological parameters through SCS method and to develop the CN map of the study area. Afterward, daily rainfall statistics over the study catchment obtained from synoptic and rain-gauge stations were estimated for 2 to 200 year return periods. Finally, the developed curve number map and the rainfall statistics were imported in the El-Hames formula to estimate the maximum flood peak flow for return periods corresponding to flood events and the hydrographic graphs were plotted. This paper also assessed the validity of El-Hames formula to determine its accuracy. The obtained results of CE and RMSE with value of respectively, 0.97 and 55.95cms prove the accuracy of applied model. Results of this paper can be used as a basis for design and implementation of plans for sustainable development of water resources over the study area and to provide viable strategies for controlling floods and dealing with water crises in this region.

High-resolution digital mapping of soil organic carbon and soil total nitrogen using DEM derivatives, Sentinel-1 and Sentinel-2 data based on machine learning algorithms

...¶ This thesis describes a range of laboratory and field investigations on the effects of salinity and sodicity on SOC dynamics.¶ In this research, the effects of a range of salinity and sodicity levels on C dynamics were determined by subjecting a vegetated soil from Bevendale, New South Wales (NSW) to one of six treatments. A low, mid or high salinity solution (EC 0.5, 10 or 30 dS/m) combined with a low or high sodicity solution (SAR 1 or 30) in a factorial design was leached through a non-degraded soil in a controlled environment. Soil respiration and the SMB were measured over a 12-week experimental period. The greatest increases in SMB occurred in treatments of high-salinity high-sodicity, and high-salinity low-sodicity. This was attributed to solubilisation of SOM which provided additional substrate for decomposition for the microbial population. Thus, as salinity and sodicity increase in the field, soil C is likely to be rapidly lost as a result of increased mineralisation.¶...