Spatial Soil Research Articles

An Integrated Yield-Based Methodology for Improving Soil Nutrient Management at a Regional Scale

The relationships between crop yield and its selected related impact factors has often been explored using ordinary least squares regression (OLSR). However, this model is non-spatial and non-robust. This study first used stepwise regression to identify the main factors affecting winter wheat yield from twelve potential related factors in Yucheng County, China. Next, robust geographically weighted regression (RGWR) was used to explore the spatially non-stationary relationships between wheat yield and its main impact factors. Then, its modeling effect was compared with that of GWR and OLSR. Last, robust geostatistical analysis was conducted for spatial soil management measures in low-yield areas. Results showed that: (i) three main impact factors on wheat yield were identified by stepwise regression, namely soil organic matter, soil total phosphorus, and pH; (ii) the spatially non-stationary effects of the main impact factors on wheat yield were revealed by RGWR but were ignored by OLSR; (iii) RGWR obtained the best modeling effect (RI = 52.31%); (iv) robust geostatistics obtains a better spatial prediction effect and the low-yield areas are mainly located in the northeast and the middle east of the study area. Therefore, the integrated yield-based methodology effectively improves soil nutrient management at a regional scale.

S-map parameters for APSIM

Agroecosystem models have become an important tool for impact assessment studies, and their results are often used for management and policy decisions. Soil information is a key input for these models, yet site-specific soil property data are often not available, and soil databases are increasingly being used to provide input parameters. For New Zealand, the digital spatial soil information system S-map provides geospatial data on a range of soil characteristics, including estimates of soil water properties. We describe a protocol for how properties from S-map can be used as input parameters for the APSIM (Agricultural Production Systems sIMulator) framework. Finally, we investigate how changes in the physical description of soil layers, and soil organic matter pools, affect the various outputs of APSIM.•This paper presents a description of how information from S-map, a digital soil map of New Zealand, can be used for building a soil description for APSIM.•A sensitivity analysis shows the effect of soil layering and the set-up setup, size, and distribution of SOM pools on model outputs, including plant growth and N leaching

Socio-Economic Impact of the Rapid Response Erosion Database (RRED)

Rapid response is critical following natural disasters like wildfire. Fire, runoff, and erosion risks are highly heterogeneous in space, creating an urgent need for rapid, spatially-explicit assessment. In the past, data preparation has been time consuming and expensive, resulting in extensive losses in values-at-risk (VARs). The Rapid Response Erosion Database (RRED, http://rred.mtri.org/rred/) allows researchers and land managers to access properly-formatted spatial model inputs for the Water Erosion Prediction Project (WEPP) anywhere within the continental US and eventually beyond. Comprehensive support for post-fire hydrological modeling is provided by allowing users to upload spatial soil burn severity maps, and within moments download spatial model inputs. The database has been used to help assess and plan remediation on more than a dozen wildfires in the Western U.S. RRED has already saved $694,000 between May 2016-December 2018 in administrative costs. In the future, the potential to save time and money on data preparation can extend beyond wildfire to include tracking contaminated sediments, agricultural pollution, and construction site erosion. RRED may also be a useful tool to protect VARs as illustrated by our analysis of recreation, property values, and clean drinking water.

Assessment of Spatial Soil Erosion Using RUSLE Model Integration with GIS and RS Tools: The Case of Gilgel Gibe-I Catchment, South West Ethiopia

Water-induced soil erosion is one of the serious environmental, agricultural, and socioeconomic problems in Ethiopian highlands. Accurate information on the rates of soil erosion helps environment protection and socio-economic development efforts of the nation. The objective of this research was to estimate annual soil loss, sediment yield, and map erosion risk areas of Gilgel Gibe-I (GG-I) catchment via integrating Revised Universal Soil Loss Equation (RULSE) model with Geographical Information System (GIS) and Remote Sensing (RS) technologies. The model inputs variables; rainfall erosivity (R), soil erodibility (K), topographic (LS), land cover (C) and land management (P) were derived from meteorological stations, Ethio-soil map, and satellite image of the catchment. The annual soil loss (t^-1ha^-1yr) was estimated using pixel-by-pixel ArcGIS map overlays to ensure the accuracy of RULSE output. The model output revealed on average 12.52 (t^-1ha^-1yr) soils was lost from GG-I catchment through sheet and rill erosion. The rates of soil loss were varying in the catchment, 59.8% of the catchment exposed to low rate (<5 t^-1ha^-1yr), 12.2% to moderate rate (5-12 t^-1ha^-1yr), 11.7% to high rate (12-30 t^-1ha^-1yr), and 6.6% to severe (>30 t^-1ha^-1yr). The annual sediment yield capacity of the catchment was 2.54 t^-1ha and delivery ration estimated 0.203% transported to outlet of the catchment-GGI hydropower dam. To combat the problems of GG-I hydropower dam siltation, land degradation, and low agricultural productivity an integrated natural resource management intervention is required throughout the catchment particularly in high and severe erosion risk areas.

Spatial Soil Interpolation from Sparse Measurements Based on Geostatistical Priors and L1-Regularized Total Variation Minimization

Spatial Soil Interpolation from Sparse Measurements Based on Geostatistical Priors and L1-Regularized Total Variation Minimization

Tier 4 maps of soil pH at 25 m resolution for the Netherlands

Accurate and high resolution spatial soil information is essential for efficient and sustainable land use, management and conservation. Since the establishment of digital soil mapping (DSM) and the goals set by the GlobalSoilMap (GSM) working group, great advances have been made to attain spatial soil information worldwide. Highly populated areas such as the Netherlands demand multi-functional land use, for which information of key soil properties such as pH is essential to make decisions. We a) provide soil pH prediction maps at six standard depth layers between 0 m to 2 m for the Netherlands at 25 m resolution, whereby the calibrated Quantile Regression Forest (QRF) model allows for prediction at any desired depth, and b) determine map accuracy using various statistical validation strategies and evaluation of prediction uncertainty. This study is unique among GSM products by including design-based inference of a probability sample as an external accuracy assessment and providing Tier 4 maps with spatially explicit accuracy thresholds for end-users based on GSM specifications.QRF models were tuned and calibrated using 15338 soil observations between 0 m and 2 m depth from 4230 locations and 195 covariates representing the soil-forming factors. The following statistical validation strategies were used for external accuracy assessment of map quality: out-of-bag, location-grouped 10-fold cross-validation, an independent validation set (5677 observations, 1367 locations) and a stratified random sample of the independent validation set separated by depth layer. Mean error (ME), root mean squared error (RMSE), model efficiency coefficient (MEC) and the prediction interval coverage probability (PICP) were calculated in all four strategies. In addition, the 90th prediction intervals were used to categorize each 25 m pixel into “none”, A, AA or AAA quality as a measure of the internal accuracy assessment.We obtained large differences depending on the four external accuracy assessment strategies and depth layer (ME = −0.08–0.20, RMSE = 0.41–0.83, MEC = 0.64–0.90, PICP of PI90 = 0.80–0.94). Design-based inference (LSK-SRS) was most indicative of map accuracy based on sampling theory (ME = 0.09–0.17, RMSE = 0.7–0.79, MEC = 0.73–0.82). QRF prediction uncertainty was slightly overestimated. Less than 10% of pixels were designated with AA and AAA and therefore we recommend future studies to also test the achievability of high quality thresholds for Tier 4 GSM maps. We believe these 3D soil pH maps at 25 m resolution are useful for a variety of end users and that our workflow can be applied elsewhere and for other soil properties to further diminish the gap of missing spatial soil information.

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.

Retraction Note: Evaluation of English translation accuracy of green plant surface irrigation and food words based on image processing

China’s agricultural planting area is large, irrigation water consumption is large, and water resources are relatively small. Agricultural irrigation water management can have a huge positive impact on water saving and improvement of water use efficiency. The content of water in the soil is a key focus of irrigation water management. Before irrigating crops, it can not only monitor the current climate conditions but also assess the water demand of crops. After the crops are irrigated with water, the change index of the water content in the soil can help people evaluate the effect of irrigation. Therefore, timely and accurate acquisition of temporal and spatial soil water content information plays an important role in the scientific management of agricultural irrigation water. According to one of the three major irrigation areas in China, as an irrigation area in the experimental area, the 2020 thermal infrared method is used to measure the soil moisture content, and at the same time, the crop water demand estimation model is built, and the remote irrigation area sensor monitoring model and remote sensing irrigation are established. The model estimates crop water demand, area of irrigated area, and irrigation water volume. In addition, in recent years, English has gradually improved its status as the most important language tool in the international communication environment. In this case, more and more attention will be paid to the correctness of English translation. The correctness of English translation is affected by various cultural factors. Therefore, in order to further improve the accuracy of English translation, this article comprehensively analyzes the accuracy of English translation from the cultural background, cognition and thinking habits, and the influence of different cultural factors and proposes corresponding improvement plans for it.

Spatial soil moisture estimation in agro-pastoral transitional zone based on synergistic use of SAR and optical-thermal satellite images

Synthetic Aperture Radar (SAR) Sentinel-1 (S1) surface soil moisture (SSM) retrieval is promising but challenging in canopy areas. Additional data from optical-thermal infrared (TIR) sensors (e.g., Moderate Resolution Imaging Spectroradiometer, MODIS) have potential to compensate for SSM monitoring from single S1 product, suggesting their synergistic use for SSM retrieval in vegetated terrains. A physical, straightforward and non-calibration synergy method of SAR S1 and optical-TIR MODIS was proposed in this study to enhance SSM estimation in an agro-pastoral transitional zone. Change detection for S1 backscatter (σ°) and simplified triangle method representing land surface temperature (LST) – normalized difference vegetation index (NDVI) space (Ts-VI) for MODIS, were combined under a synergistic framework regarding vegetation conditions. The synergy method performance was evaluated against in-situ soil moisture sites for different phenological stages. Correlation analysis results indicated that S1 σ° is highly sensitive to SSM in non-growing season, whereas MODIS LST is negatively linked to root-zone soil moisture during summer crop growth as the seasonal vegetation effect that can be explained via the observed trapezoidal Ts-VI shape. For the synergy, S1 is mainly involved in during non-growing period, while major contribution in growing period was from MODIS, which accounted for 90% improvements in correlation (R, 0.29 – 0.55) and 30% for Root-Mean-Square Error (RMSE, 0.093 – 0.065 m3m−3) during a four-month crop growth as compared to single S1 product. For entire period, the synergy method outperformed single S1 over all sites, where the average R and RMSE improved 30% (0.46 – 0.60) and 14% (0.070 – 0.060 m3m−3), respectively, with highest values (R = 0.73 and RMSE = 0.039 m3m−3) observed in a maize site. This synergy suggests a suitability of integrating optical-TIR data to improve SAR SSM estimation in agricultural lands, especially over the crop growth period when water is a major limiting factor.

Integrating GIS-based FAHP and WaTEM/SEDEM for identifying potential RWH areas in semi-arid areas

Rainwater harvesting (RWH) is an alternative technique to potentially overcome the increasing need for water. However, identifying suitable sites for RWH remains a complex task. The present study offers an approach that can enable water managers to use the Water and Tillage Erosion Model and Sediment Delivery Model (WaTEM/SEDEM) as a spatially soil erosion and sediment delivery model, Geographic Information System (GIS) and Fuzzy Analytical Hierarchy Process (FAHP) method to identify potential RWH sites. For this purpose, several data sources were employed to generate needful thematic layers. Five criteria: land use/land cover (LULC), slope, soil texture, drainage density and runoff were used. The Soil Conservation Service-Curve Number (SCS-CN) method was utilized for preparing a yearly runoff potential map. Then, the thematic layers were weighted for generating RWH suitability map. The results show that the average annual runoff is 248 mm. Moreover, approximatly 76.1% of the study area falls within slight soil erosion class. The classified RWH suitability map showed that 6.9% of the watershed falls within an extremely suitable category. Prediction accuracy of the developed map of RWH suitability showed an area under the curve value (AUC) of 59.6% for FAHP method in this study. It is concluded that involving a spatial soil erosion and sediment delivery model with GIS-based FAHP represents a valuable approach for locating suitable RWH sites. So, this method can be used in other parts of the world.

Modeling and Assessing Potential Soil Erosion Hazards Using USLE and Wind Erosion Models in Integration with GIS Techniques: Dakhla Oasis, Egypt

Soil erosion modeling is becoming more significant in the development and implementation of soil management and conservation policies. For a better understanding of the geographical distribution of soil erosion, spatial-based models of soil erosion are required. The current study proposed a spatial-based model that integrated geographic information systems (GIS) techniques with both the universal soil loss equation (USLE) model and the Index of Land Susceptibility to Wind Erosion (ILSWE). The proposed Spatial Soil Loss Model (SSLM) was designed to generate the potential soil erosion maps based on water erosion and wind erosion by integrating factors of the USLE and ILSWE models into the GIS environment. Hence, the main objective of this study is to predict, quantify, and assess the soil erosion hazards using the SSLM in the Dakhla Oasis as a case study. The water soil loss values were computed by overlaying the values of five factors: the rainfall factor (R-Factor), soil erodibility (K-Factor), topography (LS-Factor), crop types (C-Factor), and conservation practice (P-Factor). The severity of wind-driven soil loss was calculated by overlaying the values of five factors: climatic erosivity (CE-Factor), soil erodibility (E-Factor), soil crust (SC-Factor), vegetation cover (VC-Factor), and surface roughness (SR-Factor). The proposed model was statistically validated by comparing its outputs to the results of USLE and ILSWE models. Soil loss values based on USLE and SSLM varied from 0.26 to 3.51 t ha−1 yr−1 with an average of 1.30 t ha−1 yr−1 and from 0.26 to 3.09 t ha−1 yr−1 with a mean of 1.33 t ha−1 yr−1, respectively. As a result, and according to the assessment of both the USLE and the SSLM, one soil erosion class, the very low class (&lt;6.7 t ha−1 yr−1), has been reported to be the prevalent erosion class in the study area. These findings indicate that the Dakhla Oasis is slightly eroded and more tolerable against water erosion factors under current management conditions. Furthermore, the study area was classified into four classes of wind erosion severity: very slight, slight, moderate, and high, representing 1.0%, 25.2%, 41.5%, and 32.3% of the total study area, respectively, based on the ILSWE model and 0.9%, 25.4%, 43.9%, and 29.9%, respectively, according to the SSLM. Consequently, the Dakhla Oasis is qualified as a promising area for sustainable agriculture when appropriate management is applied. The USLE and ILSWE model rates had a strong positive correlation (r = 0.97 and 0.98, respectively), with the SSLM rates, as well as a strong relationship based on the average linear regression (R2 = 0.94 and 0.97, respectively). The present study is an attempt to adopt a spatial-based model to compute and map the potential soil erosion. It also pointed out that designing soil erosion spatial models using available data sources and the integration of USLE and ILSWE with GIS techniques is a viable option for calculating soil loss rates. Therefore, the proposed soil erosion spatial model is fit for calculating and assessing soil loss rates under this study and is valid for use in other studies under arid regions with the same conditions.

Construction of Evaluation Model of Surface Soil Erodibility Factors in Black Soil Area

The increasing soil erosion in black soil area has caused widespread concern from all walks of life. Based on this background, the evaluation model of surface soil erodibility factor in black soil area is studied and constructed. The data of erosion gully is from the general survey data of surface soil erosion gully in black soil area. After quantifying the morphological characteristics of the data, the vector data of erosion gully are gridded by fractal theory. The number of non-empty grids is calculated by the attribute query function of ArcGIS, and the pixel size is transformed in turn to obtain different coverage grids and corresponding fractal parameters. The surface soil runoff and soil erosion process in black soil area are simulated by PESERA model. To build the surface soil erosion model of black soil area, in the process of building the user-defined model, it needs to carefully select the parameters used for modeling, and it needs to consider all the factors that may play a role in the whole process of soil erosion. The factors of surface soil erodibility in black soil area are analyzed, including spatial distribution characteristics of soil erodibility’s K value, semivariance function analysis of soil erodibility’s K value, and spatial distribution characteristics analysis of soil erodibility’s K value. Finally, the evaluation model of surface soil erodibility factor in black soil area is constructed. By testing the quantitative performance and evaluation accuracy of erodibility factors, it is proved that this method has good quantitative performance and evaluation accuracy of erodibility factors, and has strong practicability.

Use of 137Cs and 210Pbex fallout radionuclides for spatial soil erosion and redistribution assessment on steeply sloping agricultural highlands

Use of 137Cs and 210Pbex fallout radionuclides for spatial soil erosion and redistribution assessment on steeply sloping agricultural highlands

Near‐surface water fluxes and their controls in a sloping heterogeneously layered volcanic soil beneath a supra‐wet tropical montane cloud forest (NW Costa Rica)

AbstractTropical montane cloud forests (TMCF) receive additional (‘occult’) inputs of water from fog and wind‐driven rain. Together with the concomitant reduction in evaporative losses, this typically leads to high soil moisture levels (often approaching saturation) that are likely to promote rapid subsurface flow via macropores. Although TMCF make up an estimated 6.6% of all remaining montane tropical forest and occur mostly in steep headwater areas that are protected in the expectation of reduced downstream flooding, TMCF hillslope hydrological functioning has rarely been studied. To better understand the hydrological response of a supra‐wet TMCF (net precipitation up to 6535 mm y−1) on heterogeneously layered volcanic ash soils (Andosols), we examined temporal and spatial soil moisture dynamics and their contribution to shallow subsurface runoff and stormflow for a year (1 July 2003–30 June 2004) in a small headwater catchment on the Atlantic (windward) slope near Monteverde, NW Costa Rica. Particular attention was paid to the partitioning of water fluxes into lateral subsurface flow and vertical percolation. The presence of a gravelly layer (C‐horizon) at ~25 cm depth of very high hydraulic conductivity (geometric mean: 502 mm h−1) intercalated between two layers of much lower conductivity (7.5 and 15.7 mm h−1 above and below, respectively), controlled both surface infiltration and delayed vertical water movement deeper into the soil profile. Soil water fluxes during rainfall were dominated by rapid lateral flow in the gravelly layer, particularly at high soil moisture levels. In turn, this lateral subsurface flow controlled the magnitude and timing of stormflow from the catchment. Stormflow amount increased rapidly once topsoil moisture content exceeded a threshold value of ~0.58 cm3 cm−3. Responses were not affected appreciably by rainfall intensity because soil hydraulic conductivities across the profile largely exceeded prevailing rainfall intensities.

Spatial patterns of aboveground phytogenic Si stocks in a grass-dominated catchment – results from UAS-based high-resolution remote sensing

Abstract. Various studies have been performed to quantify silicon (Si) stocks in plant biomass and related Si fluxes in terrestrial biogeosystems. Most studies are deliberately designed on the plot scale to ensure low heterogeneity in soils and plant composition, hence similar environmental conditions. Due to the immanent spatial soil variability, the transferability of results to larger areas, such as catchments, is therefore limited. However, the emergence of new technical features and increasing knowledge on details in Si cycling lead to a more complex picture at landscape and catchment scales. Dynamic and static soil properties change along the soil continuum and might influence not only the species composition of natural vegetation but also its biomass distribution and related Si stocks. Maximum likelihood (ML) classification was applied to multispectral imagery captured by an unmanned aerial system (UAS) aiming at the identification of land cover classes (LCCs). Subsequently, the normalized difference vegetation index (NDVI) and ground-based measurements of biomass were used to quantify aboveground Si stocks in two Si-accumulating plants (Calamagrostis epigejos and Phragmites australis) in a heterogeneous catchment and related corresponding spatial patterns of these stocks to soil properties. We found aboveground Si stocks of C. epigejos and P. australis to be surprisingly high (maxima of Si stocks reach values up to 98 g Si m−2), i.e. comparable to or markedly exceeding reported values for the Si storage in aboveground vegetation of various terrestrial ecosystems. We further found spatial patterns of plant aboveground Si stocks to reflect spatial heterogeneities in soil properties. From our results, we concluded that (i) aboveground biomass of plants seems to be the main factor of corresponding phytogenic Si stock quantities, and (ii) a detection of biomass heterogeneities via UAS-based remote sensing represents a promising tool for the quantification of lifelike phytogenic Si pools at landscape scales.

Evaluation of Spatio-Temporal Soil Moisture Variability in Semi-Arid Rangeland Ecosystem, Maasai Mara National Reserve, Kenya

Aim: To evaluate the spatio-temporal soil moisture storage and retention capacities in semi-arid rangeland ecosystem, Maasai Mara National Reserve (MMNR), Kenya&#x0D; Study Design: Randomized complete block design (RCBD) of reference Cosmic Ray Neutron Sensor (CRNS) station, ten-(10) spatially distributed (soil moisture and temperature capacitance) probes (5TM-ECH20) sites.&#x0D; Place and Duration of Study: Kenya, MMNR, the oldest natural semi-arid rangeland ecosystem and globally unique for the great wildebeest migration, between May 2017 and April 2019.&#x0D; Methodology: Soil moisture (SM) variation data was collected using (CRNS) at spatial and point-scale 5TM-ECH2O probes, and gravimetric water content from (10) spatially distributed stations. Both CRNS and 5TM-ECH2O probes were used to monitor near-real time moisture levels at different soil layers ranging between 0-5cm, 5-10cm, 15-20cm, 35-40cm, and 75-80cm. Soil physical and chemical properties were laboratory analyzed. Calibration and validation datasets were obtained from 5TM-ECH2O probe and gravimetric soil samples extracted from respective layers and sites.&#x0D; Results: The pedological characteristics of the investigated ecosystem soil profile indicate decreased bulk density by 2.1% to 11.12% from upper layers (0-5cm) to deeper layers at (75–80 cm). Across the rangeland, 70% of soil textural classes were sandy clay loam (SCL) with higher clay percent and 30% sandy clay (SC) and soil porosity varied between 30.1% and 51% in the ecosystem. Moreover, volumetric&#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; 2&#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; water content (VWC) of spatially distributed 5TM-ECH2O probes ranged between 0.11m3m-3 and 0.32m3m-3 during wet season with mean VWC of 0.16m3m-3, however, the VWC ranged between 0.04 m3m-3 and 0.17m3m-3 during the dry season with a mean volume of 0.11m3m-3 across the rangeland ecosystem.&#x0D; Conclusion: In this study, SM exhibited an annual periodicity of seasonal variation of spatial and temporal moisture partitioned as moisture gaining, losing, and a moisture stable period. This probably could be a consequence of increased movement of water to deeper layers caused by high precipitation and less evaporative demand caused by lower temperatures. The calibrated CRNS probe provided good estimates of spatial soil moisture variation when calibrated with 5TM-ECH20 and gravimetric sampling in relation to precipitation events and that deeper soil layers showed higher amount of soil moisture than shallow layers. The findings of the study will provide better formulation of the ecosystem vegetation management policies, conservation and planning for sustainable wildlife tourism industry.

Machine learning based soil maps for a wide range of soil properties for the forested area of Switzerland

Spatial soil information in forests is crucial to assess ecosystem services such as carbon storage, water purification or biodiversity. However, spatially continuous information on soil properties at adequate resolution is rare in forested areas, especially in mountain regions. Therefore, we aimed to build high-resolution soil property maps for pH, soil organic carbon, clay, sand, gravel and soil density for six depth intervals as well as for soil thickness for the entire forested area of Switzerland. We used legacy data from 2071 soil profiles and evaluated six different modelling approaches of digital soil mapping, namely lasso, robust external-drift kriging, geoadditive modelling, quantile regression forest (QRF), cubist and support vector machines. Moreover, we combined the predictions of the individual models by applying a weighted model averaging approach. All models were built from a large set of potential covariates which included e.g. multi-scale terrain attributes and remote sensing data characterizing vegetation cover.Model performances, evaluated against an independent dataset were similar for all methods. However, QRF achieved the best prediction performance in most cases (18 out of 37 models), while model averaging outperformed the individual models in five cases. For the final soil property maps we therefore used the QRF predictions. Prediction performance showed large differences for the individual soil properties. While for fine earth density the R2 of QRF varied between 0.51 and 0.64 across all depth intervals, soil organic carbon content was more difficult to predict (R2 = 0.19–0.32). Since QRF was used for map prediction, we assessed the 90% prediction intervals from which we derived uncertainty maps. The latter are valuable to better interpret the predictions and provide guidance for future mapping campaigns to improve the soil maps.

Spatial soil loss risk assessment for priority intervention using GIS and remote sensing techniques: A case of Neri Watershed Southwestern Ethiopia

Soil loss by runoff is a severe and continuous ecological problem in Neri watershed. It affects agricultural production, drinking water quality, ecosystem health, and landscape aesthetics. This study was conducted to assess spatial risk of soil by estimating the average annual soil loss, and creating a soil erosion hazard map for this watershed. Revised Universal Soil Loss Equation (RUSLE) model supported by a GIS framework was used. To this end, data for the model parameters were derived from, a digital elevation model (DEM) of (30*30 m), thirty years (1988-2017) rainfall data at rain gauge stations, soil erodibility data from field soil samples, Landsat-8 satellite image for cover management and conservation support practices. Once raster layer of the input parameters created, overlay analysis was carried to assess the spatial distribution of soil loss. The estimated annual soil loss varies from 0 to 465.16 t ha−1 yr−1 with a mean annual soil loss of 9.95 t ha−1 yr−1. The empirical analysis also confirmed that the watershed losses a total of about 463365.46 t of soil annually. Out of the total area; 54.9% of the watershed was categorized below moderate classes and the remaining 45.1% of land area was classified under high to very high classes, which is about several times the maximum tolerable soil loss rate. Based on the classification of soil loss hazard map, six out of eleven Neri sub-watersheds need prior intervention in terms of integrated cover-management and mechanical conservation measures. Furthermore, model results can be refined by analyzing along with sub-watersheds level real time monitoring for conservation practices.

Assessment of Soil Erosion and Its Impact on Agricultural Productivity by Using the RMMF Model and Local Perception: A Case Study of Rangun Watershed of Mid-Hills, Nepal

Soil erosion is a major concern for the environment and natural resources leading to a serious threat to agricultural productivity and one of the major causes of land degradation in the mid-hills region of Nepal. An accurate assessment of soil erosion is needed to reduce the problem of soil loss in highly fragile mountainous areas. The present study aimed to assess spatial soil loss rate and identified risk areas and their perceived impact on agricultural productivity by using the Revised Morgan–Morgan–Finney (RMMF) model and social survey in the Rangun watershed of Dadeldhura district, Nepal. Soil erosion was assessed by using data on soil, digital elevation model, rainfall, land use, and land cover visually interpreted from multitemporal satellite images, and ILWIS 3.3 academic software was used to perform the model. A household questionnaire survey (n = 120) and focus group discussion (n = 2) in identified risk areas were carried out to understand the people’s perception towards soil erosion and its impact on agricultural productivity. The predicted average soil erosions from the forest, agriculture, and barren land were 2.7 t ha−1 yr−1, 53.73 t ha−1 yr−1, and 462.59 t ha−1 yr−1, respectively. The erosion risk area under very low to low, moderate to moderately high, and high to very high covers 92.32%, 4.96%, and 2.73%, respectively. It indicates that the rate of soil erosion was lower in forest areas, whereas it was higher in the barren land. The cropped area of the watershed has been reduced by 2.96 ha−1 yr−1, and productivity has been decreased by 0.238 t ha−1 yr−1. The impacts such as removal of topsoil (weighted mean = 4.19) and gully formation (weighted mean = 3.56) were the highest perceived factors causing productivity decline due to erosion. People perceived the impact of erosion in agricultural productivity differently ( ∗ significant at P ≤ 0.05 ). The study concluded that, comparatively, barren and agricultural lands seem more susceptible to erosion, so the long-term conservation and management investment in susceptible areas for restoration, protection, and socioeconomic support contribute significantly to land rehabilitation in the Rangun watershed.

Spatial soil organic matter at OAL-UK

spatial soil organic matter at OAL-UK