Shuttle Radar Topography Mission Digital Elevation Model Research Articles

A new digital soil mapping method with temporal-spatial-spectral information derived from multi-source satellite images

Detailed soil maps are essential for effective agricultural practices and environmental protection. Yet, despite the increasing accuracy of digital soil mapping (DSM) in recent years, generating regional-scale, high-accuracy soil maps remains a challenging task. Our study objective was to propose a new DSM method based on temporal-spatial-spectral (TSS) data and to compare the DSM result with a legacy soil map of China. In this study, 13 Landsat multispectral data from 2000 to 2019 were fused by discrete wavelet transform (DWT) to obtain temporal information, a shuttle radar topography mission digital elevation model (SRTM-DEM) was used as spatial information, and Gaofen-5 satellite hyperspectral data was used as spectral information. TSS information was obtained after the DWT and spectral band segmentation methods were used to fuse the temporal and spectral information, combined it with the spatial information. Then, the TSS information and random forest model were used for DSM. The results indicated that 1) The mapping result based on TSS information was highly correlated with the legacy soil map. In different soil classes, there were minute differences in the core area and large differences in adjacent soil classes between the two maps. The overall accuracy and kappa coefficient of DSM based on TSS information were 88.11% and 0.82, respectively. 2) With the same values of the soil moisture, the overall accuracy and kappa coefficient of DSM based on hyperspectral data were 6.80% and 0.02, respectively, higher than those based on multispectral data. 3) With increasing temporal information, the DSM accuracy continuously increased, and the mapping accuracy based on multi-temporal multispectral data was higher than that based on mono-temporal hyperspectral data when the number of multi-temporal multispectral images reached 6. 4) The DSM accuracy was effectively improved when terrain factors were considered, and the terrain factors featuring a strong separability for various soil classes differed. The DSM method proposed in this study based on TSS information from multi-sources remote sensing data greatly improved the DSM accuracy and provided new insight for future DSM research.

Integration of C-band SAR and high-resolution optical images for delineating palaeo-channels in Nagaur and Barmer districts, western Rajasthan, India.

Identifying hitherto unknown palaeo-channels, especially in the arid regions of the Thar Desert, is crucial since these channels may form excellent aquifers, and are also associated with valuable ore deposits of many precious minerals. This study employed integrated C-band Synthetic Aperture Radar (SAR) of Sentinel-1A and high-resolution multispectral Sentinel-2A data of pre- and post-monsoon seasons (June and November) to delineate playas and palaeo-channels. This approach is the first of its kind for this area. The palaeo-channels were delineated through a detailed visual inspection of colour composite (CC) images of Sentinel-2A data, SAR backscatter (VH) images and fused SAR and optical images. Then, we studied the topographic profiles generated from the Shuttle Radar Topography Mission -Digital Elevation Model (SRTM-DEM) across the identified palaeo-channels, Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI) to further confirm the existence of a palaeo-channel's course and playas. As a result, several playas and palaeo-channels in the area were successfully identified, some of which were previously unmapped and undetected. The results indicate that the post-monsoon datasets are more useful for the precise delineation of palaeo-channels due to the presence of relatively higher moisture along the palaeo-channels' courses.

A Method for SRTM DEM Elevation Error Correction in Forested Areas Using ICESat-2 Data and Vegetation Classification Data

The past decade has witnessed the rapid development of the SRTM (Shuttle Radar Topography Mission) DEM (digital elevation model) in engineering applications and scientific research. The near-global SRTM DEM was generated based on radar interference theory. The latest version of the SRTM DEM with a resolution of 1 arc-second has been widely used in various applications. However, many studies have shown the poor elevation accuracy of the SRTM DEM in forested areas. Recent developments in the field of spaceborne lidar have provided an additional chance to correct the elevation error of the SRTM DEM in forested areas. We developed an easy-to-use method to correct the elevation error of the SRTM DEM based on the spatial interpolation method using the recent Ice, Cloud and land Elevation Satellite-2 data. First, an ICESat-2 terrain control point selection criterion was proposed to reject some erroneous ICESat-2 terrains caused by many factors. Second, we derived the elevation correction surface based on the interpolation method using the refined ICESat-2 terrain. Finally, a corrected SRTM DEM of forested areas was generated through the obtained elevation correction surface. The proposed method was tested in the typical forested area located in Massachusetts, USA. The results show that the RMSE of the selected terrain control points in vegetation areas and non-vegetation areas are 1.03 and 0.68 m, respectively. The corrected SRTM DEM have an RMSE of 4.2 m which is significantly less than that of the original SRTM DEM with an RMSE of 9.8 m, which demonstrates the proposed method is feasible to correct the elevation error in forested areas. It can be concluded that the proposed method obviously decreases the elevation error of the original SRTM DEM.

Evaluation of Morphometric and Hypsometric Analysis of the Bagh River Basin using Remote Sensing and Geographic Information System Techniques

Water availability and scarcity are impacted by geomorphological changes that occur within a catchment. As a result, determining the influence of geomorphological processes on the catchment's hydrology requires a quantitative study of the catchment geometry. Approaches based on remote sensing (RS) and geographic information systems (GIS) have grown in popularity in recent years because they assist strategists and decision-makers in making accurate and effective choices and plans. For this research, the Bagh River basin was chosen. The study shows that GIS and RS data can be used to analyse and approximate the period and erosional operations' speed in a Bagh river basin for better design and maintenance. The method utilises a 30-metre shuttle radar topography mission digital elevation model (SRTM-DEM) for morphometric parameters and hypsometric analysis extraction that is both operative and time-saving. The thirteen morphometric parameters were applied to the Bagh catchment's linear, shape, and relief aspects. An elongated basin shape is suggested by the Re, Rc, and Ff. Statistical analysis shows that there is a good relationship between stream order and stream length, as well as stream order and stream number. The hypsometric curves' structure as well as estimated hypsometric integral results reflects the Bagh river basin's erosional stages. As a result, the study concludes that morphometric and hypsometric analysis findings may be useful to stakeholders participating in catchment development and management projects.

Morphometric Analysis of Saz-Çayırova Drainage Basin using Geographic Information Systems and Different Digital Elevation Models

Drainage basin/watershed analysis based on morphometric parameters has an essential role in watershed management and planning. Reliable delineation of watersheds and drainage networks is critical for hydrological and geomorphological studies. Since access to high-resolution digital elevation models (DEMs) and digital surface models (DSMs) is costly, many researchers need to evaluate low-resolution open-source products. Several data sources produced from different surveying techniques are used in the morphometric analysis. In this study, five different datasets such as Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) GDEM, Shuttle Radar Topography Mission (SRTM) DEM, Advanced Land Observing Satellite (ALOS) DSM, National Aeronautics and Space Administration (NASA) DEM, and a DEM from topographic maps (TOPO DEM), were investigated based on morphometric parameters. The tests was carried out in the Saz-Çayırova Basin, which is one of the critical urbanization and industrialization regions of Kocaeli, Turkey. In this study, the TOPO DEM, whose horizontal resolution is 30 m, was produced from 1:25K scaled digitized topographical maps. It was used for comparative analysis, as in all DEMs sources. The morphometric parameters' result of the TOPO DEM was used as the reference data for comparing the results of the other DEMs sources. In addition, the mean absolute percentage error (MAPE) was used to compute the accuracy between the freely available DEMs and the TOPO DEM for each morphometric parameter. The outcomes of this study reveal that the most consistent results with the TOPO DEM are provided by SRTM DEM, following the NASA DEM.

Evaluating Vertical Accuracies of Open-Source Digital Elevation Models over Multiple Sites in China Using GPS Control Points

Digital elevation models (DEMs) are widely used across a range of fields. Several open-source global DEMs have been released, including the advanced land observing satellite world 3D 30 m DEM (AW3D30DEM), advanced spaceborne thermal emission and reflection radiometer global DEM (ASTER GDEM), shuttle radar topography mission DEM (SRTMDEM), and TerraSAR-X for digital elevation measurement (TanDEM-X). ASTER and SRTM are the most widely used DEMs, while the newer models AW3D30DEM and TanDEM-X are becoming increasingly popular. Many studies have evaluated the qualities of these DEMs; however, few multi-regional studies have been conducted in China. To comprehensively and systematically evaluate the qualities of these DEMs in China, the vertical accuracies of AW3D, ASTER, STRM (all 30 m), and TanDEM-X (90 m) were tested across 16 regions in China. Using high-precision global positioning system control points for reference, error values were determined by subtracting these reference values from corresponding global DEM elevation values. As the study only covered flat areas (slope < 5°), slope was treated as a controlled variable. After assessing the impacts of the slope aspect and land cover type, variations in vertical accuracy were examined with respect to longitude and latitude. Overall, TanDEM-X exhibited the highest stability and accuracy, AW3D30 and SRTM also performed well, while ASTER exhibited the worst accuracy. The DEMs showed relationships with the slope aspect and land cover type, assuming that slope had no influence on vertical accuracy. In general, vertical accuracy in high latitudes was slightly better than that in low latitudes, and no evident variations were observed with respect to longitude. This study is the first to conduct DEM analysis across many regions in China from open sources. Since most of the users rely on public domain DEM datasets, this work contributes to their analysis in academic and engineering fields.

Glacier Mass Balance in the Manas River Using Ascending and Descending Pass of Sentinel 1A/1B Data and SRTM DEM

Mountain glaciers monitoring is important for water resource management and climate changes but is limited by the lack of a high-quality Digital Elevation Model (DEM) and field measurements. Sentinel 1A/1B satellites provide alternative data for glacier mass balance. In this study, we tried to generate DEMs from C-band Sentinel 1A/1B ascending and descending pass SLC images and evaluate the overall accuracy of INSAR DEMs based on Shuttle Radar Topography Mission (SRTM) DEM and ICESat/GLAS. The low Standard Deviation (STD)and Root Means Square Error (RMSE) displayed the feasibility of Sentinel 1A/1B satellites for DEM generation. Glacier elevation changes and glacier mass balance were estimated based on INSAR DEM and SRTM DEM. The results showed that the most glaciers have exhibited obvious thinning, and the mean annual glacier mass balance between 2000 and 2020 was −0.18 ± 0.1 m w.e.a−1. The south-facing and-east facing aspects, slope and elevation play an important role on glacier melt. This study demonstrates that ascending and descending orbit data of Sentinel-1A/1B satellites are promising for the detailed retrieval of surface elevation changes and mass balance in mountain glaciers.

A Multi-Data Geospatial Approach for Understanding Flood Risk in the Coastal Plains of Tamil Nadu, India

The coastal plains of Tamil Nadu, India, are prone to floods, the most common disaster experienced in this region almost every year. This research aims to identify flood risks in the coastal plain region of Tamil Nadu, delineated through a watershed approach with 5020 micro-administrative units covering an area of about 26,000 sq. km. A comprehensive flood risk assessment covering hazard, vulnerability, and exposure parameters was carried out using multiple datasets derived from field surveys, satellite data, and secondary data sources. The flood hazard layer was prepared on a probability scale (0–1) with the help of Sentinel-1 Synthetic Aperture Radar data coupled with GIS-based water rise modelling using Shuttle Radar Topography Mission Digital Elevation Model (SRTM-DEM) and reports of the District Disaster Management Plans of 13 coastal districts. In addition, the National Resources Conservation Service-Curve Number (NRCS-CN) method was adopted to estimate surface runoff potential for identifying low probability flood-prone regions. The vulnerability and exposure of the population to flood hazards were determined using census and household data-based indicators. The different categories of built-up areas were delineated and intersected with the flood hazard layer to estimate elements at flood risk. An exhaustive field survey was conducted at 514 locations of the study area, targeting deprived communities of all major settlements to validate the flood hazard layer and understand the public perceptions. The amalgamation of results shows that very high flood risk prevails in the northern parts of coastal Tamil Nadu, especially the stretch between Chennai and Cuddalore. In addition, to provide baseline datasets for the first time at micro-administrative units for the entire coastal plains of Tamil Nadu, the study offers a pragmatic methodology for determining location-specific flood risks for policy interventions.

Riparian Zone DEM Generation From Time-Series Sentinel-1 and Corresponding Water Level: A Novel Waterline Method

Topography data are essential for land management, hydrology, and earth science applications. The topography in a riparian zone varies with time. Available global digital elevation models (GDEMs) do not satisfy the hydrological and environmental modeling requirements for riparian zones. In this study, a novel waterline method was proposed to acquire the digital elevation model (DEM) of riparian zones. Synthetic aperture radar (SAR) images are used to extract waterlines and corresponding water level is applied to acquire the elevation. The uneven water surfaces are considered to acquire the elevation of waterlines and Shuttle Radar Topography Mission (SRTM) DEM is used to interpolate the elevation of pixels without waterlines. The proposed method is applied in the riparian zone of Three Gorges Reservoir and Dongting Lake to test its feasibility. More than 80 Sentinel-1 SAR images at different water levels within three years are used to detect waterlines. Based on corresponding water level of hydrological stations, the elevation of waterlines are acquired via inverse distance weighting method. The distance layer and slope derived from the original SRTM DEM are used to interpolate the elevation of pixels between waterlines. The DEM generated by the proposed method is compared against the DEM derived in 2017, SRTM DEM, the DEM which does not include the uneven effects, and Ice, Cloud and land Elevation Satellite -2 (ICESat-2) ATL08. The good performance of the proposed method implies its high efficiency in revealing riparian zone topography.

Evaluation of Various DEMs for Quantifying Soil Erosion Under Changing Land Use and Land Cover in the Himalaya

Soil erosion is one of the serious environmental threats in the Himalayas, primarily exacerbated by the steep slopes, active tectonics, deforestation, and land system changes. The Revised Universal Soil Loss Equation was employed to quantify soil erosion from the Vishav watershed in the Kashmir Himalaya, India. Topography and land use/land cover (LULC) are important driving factors for soil erosion. Most often, a Digital Elevation Model (DEM) is used in erosion models without any evaluation and testing which sometimes leads to erroneous estimates of soil erosion. For the best topographic characterization of the watershed, four publicly available DEMs with almost identical resolution (∼30 m), were evaluated. The DEMs were compared with GPS measurements to determine the most reliable among the tested DEMs for soil erosion estimation. Statistical evaluation of the DEMs with GPS data indicated that the CARTO DEM is better with root mean square error (RMSE) of 18.2 m than the other three tested DEMs viz., Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), Shuttle Radar Topography Mission (SRTM), and Advanced Land Observing Satellite (ALOS). Slope length and slope steepness factors were computed from the DEMs. Crop cover and management factors were generated from the satellite-derived LULC. Moreover, rainfall data of the nearest stations were used to compute rainfall erosivity and soil erodibility factor was derived from the soil texture data generated from 375 soil samples. The simulated erosion estimates from SRTM, ALOS, and CARTO DEMs showed similar spatial patterns contrary to the ASTER estimates which showed somewhat different patterns and magnitude. The mean erosion in the study area has almost doubled from 2.3 × 106 tons in 1981 to 4.6 × 106 tons in 2019 mainly driven by the anthropogenic LULC changes. The increased soil erosion is due to the degradation of forest cover, urbanization, steep slopes, and land system changes observed during the period. In absence of the observations, the simulated soil erosion was validated with the land degradation map of the watershed which showed a good correspondence. It is hoped that the results from this work would inform policymaking on soil and water conservation measures in the data-scarce mountainous Kashmir Himalaya.

Geohazard characterization using remote sensing to model flash floods of the southeast Sinai, Egypt

Flash floods have led to disruptions of human activities and the destruction of properties particularly in the Nuweiba region of southeast Sinai. Despite the arid nature of Sinai, flash floods still pose a great hazard to the region. Using remote sensing characterization, geohazard models were developed to identify flash flood areas; delineate and discriminate morphological features, active channels areas, and soil physiography with a view to categorize risk areas exposed to flash floods hazards in a hazard map; and proffer measures for mitigation. The characterization was achieved using a workflow model developed from spatial data sets of the Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM) and the red-green-blue (RGB) composites from Landsat bands 7 Enhanced Thematic Mapper (ETM+). The data were used to investigate and assess flash flood areas for morphological attributes, watershed basin characterization, and generation of false color composite from the Landsat 7 ETM+ 7, 4, and 2 RGB bands, and the principal component analysis band from a composite of 742 RGB. Multivariate analysis of generated raster layer attributes provided enhancements and attribute discrimination for delineating areas of active flood channels, upslope zones, and soil physiography discrimination from their spectral reflectance. The results are presented in an integrated approach of remote sensing with geographic information systems (GIS) and indicate that the soil types and geologic units contribute greatly toward activation of these flash floods, which is triggered by intense rainstorms. Components of the generated map attributes of SRTM DEM, gradient of DEM, and Landsat 7 ETM+ composite of 742 bands in GIS were used to generate a hazard map using spatial analysis to depict the nature and scale of the issues identified. Due to the flash flood vulnerabilities, the study area was classified into stable/low-, moderate-, and high-risk areas. Mitigation measures to control flash floods were proposed to enable adequate preparations to mitigate impending flash flood disasters.

Generating and analyzing Terrain characteristics from Shuttle Radar Topographic Mission (SRTM), DEM

Terrain analysis is the quantitative analysis of topographic surfaces. The purpose of a digital terrain system is to provide the digital representation of terrain so that environmental problem like soil erosion may be approached accurately and efficiently through automated means. Traditionally this was (and still is!) being done manually by using topographic/contour maps. With the availability of Digital Elevation Models (DEM) and GIS tools, watershed properties can be extracted by using automated procedures. Remote Sensing and Digital elevation models (DEMs) are known to be very useful data sources for the automated delineation of flow paths, sub watersheds and flow networks for hydrologic modelling and watershed characterization. The digital terrain model was extracted from a 90m resolution Shuttle Radar Topographic Mission (SRTM) of the study area. The SRTM data was corrected by removing voids, striping, tree offsets and random noise. The SRTM DEM data was projected from geographic coordinate WGS 84 to UTM zone 32 of the study area. The 3-D analysis tool of the ArcGIS 10.1 was used for this process. The DEM was processed to obtain the Slope, Contour, Flow direction, Flow accumulation, Flow length, Stream power Index of the study area. The study proved that SRTM elevation dataset has the ability to obviate the lack of terrain data for hydrologic modelling using ArcGIS where appropriate data for terrain modelling and simulation of hydrological processes is unavailable.

Stream Network Modeling Using Remote Sensing Data in an Alpine Cold Catchment

The hydrological information derived from a digital elevation model is very important in distributed hydrological modeling. As part of alpine hydrological research on stream network modeling using remote sensing data in the northeast of the Tibetan Plateau, three digital elevation model (DEM) datasets were obtained for the purpose of hydrological features, mainly including channel network, watershed extent and terrain character. The data sources include the airborne light detection and ranging (LiDAR) with point spacing of 1 m, the High Mountain Asia (HMA) DEM and the Shuttle Radar Topography Mission (SRTM) DEM. Mapping of the watershed and stream network was conducted using each of the three DEM datasets. The modeled stream networks using the different DEMs were verified against the actual network mapped in the field. The results show that the stream network derived from the LiDAR DEM was the most accurate representation of the network mapped in the field. The SRTM DEM overestimated the basin hypsometry relative to the LiDAR watershed at the lowest elevation, while the HMA DEM underestimated the basin hypsometry relative to the LiDAR watershed at the highest elevation. This may be because, compared with the SRTM DEM and the HMA DEM, the LiDAR DEM has higher initial point density, accuracy and resolution. It can be seen that the LiDAR data have great potential for the application in hydrologic modeling and water resource management in small alpine catchments.

Accounting for DEM Error in Sea Level Rise Assessment within Riverine Regions; Case Study from the Shatt Al-Arab River Region

Global elevation datasets such as the Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM) are the best available terrain data in many parts of the world. Consequently, SRTM is widely used for understanding the risk of coastal inundation due to climate change-induced sea level rise. However, SRTM elevations are prone to error, giving rise to uncertainty in the quality of the inundation projections. This study investigated the error propagation model for the Shatt al-Arab River region (SARR) to understand the impact of DEM error on an inundation model in this sensitive, low-lying coastal region. The analysis involved three stages. First, a multiple regression model, parameterized from the Mississippi River delta region, was used to generate an expected DEM error surface for the SARR. This surface was subtracted from the SRTM DEM for the SARR to adjust it. Second, residuals from this model were simulated for the SARR. Modelled residuals were subtracted from the adjusted SRTM to produce 50 DEM realizations capturing potential elevation variation. Third, the DEM realizations were each used in a geospatial “bathtub” inundation model to estimate flooding area in the region given 1 m of sea level rise. Across all realizations, the area predicted to flood covered about 50% of the entire region, while predicted flooding using the raw SRTM covered only about 28%, indicating substantial underprediction of the affected area when error was not accounted for. This study can be an applicable approach within such environments worldwide.

Quality assessment of open sourced digital elevation models in southeast coast of India

Digital elevation model (DEM) provides elevation values of the terrain. At present several open source DEMs are available and its vertical accuracy varies from place to place. The present study assessed the vertical accuracy of 1 arc-sec data of Advanced Land Observing Satellite digital surface model (AW3D30) Version-3, Shuttle Radar Topographic Mission (SRTM) DEM (V3), Carto DEM (V3R1) and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global DEM (V2) using Ice, Cloud, and land Elevation Satellite/Geoscience Laser Altimeter System (ICESat/GLAS) and Real Time Kinematic Global Positioning System (RTK GPS) data in Southeast coast of India. Root mean square error (RMSE) was calculated for DEMs, showed the elevation accuracy as 2.48 m (AW3D30), 3.02 m (SRTM DEM), 3.88 m (Carto DEM) and 8.02 m (ASTER GDEM) in the Tamil Nadu state while it was 2.29 m (AW3D30), 3.10 m (SRTM DEM), 3.93 m (Carto DEM) and 8.98 m (ASTER GDEM) in the 10 km buffer zone of coastal region and the results are satisfactory than the official product’s specifications. Landform analysis was carried out at selected RTK GPS surveyed sites where the elevation of different coastal landforms is validated with DEMs and it was found the AW3D30 showed least RMSE than others. This paper suggests that AW3D30 is best for representing terrain elevation values followed by SRTM and Carto DEMs which are apt choices for application studies.

Remote Sensing Investigation of the Offset Effect between Reservoir Impoundment and Glacier Meltwater Supply in Tibetan Highland Catchment

This article presents multi-source remote sensing measurements to quantify the water impoundment and regulation of the Zhikong Reservoir (ZKR) and Pangduo Reservoir (PDR), together with the estimation of the glacier mass balance to explore whether the increased glacier meltwater supply can buffer the influences of the reservoir impoundment to some degree in the Tibetan highland catchment. The ZKR and PDR are two reservoirs constructed on the upper Lhasa River that originate from the Nyainqentanglha glaciers in the remote headwater in the Tibetan Plateau (TP) and lacks historical in situ hydrological observations in the long term. Therefore, the Joint Research Center (JRC) Global Surface Water dataset (GSW), and the Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM) data were used for estimating the total amount of water storage of the two reservoirs, and the SRTM and TanDEM-X DEMs were used for estimating the glacier mass balance. The result shows that the total amount of water impounded by reservoirs is 0.76 Gt, roughly 54% of their design capacities. The mass balance of the glaciers is estimated by comparing the elevation changes between the SRTM and TanDEM-X DEMs. The glaciers in this region melt at an average rate of 0.09 ± 0.02 Gt·year−1 from 2000 to circa 2013, and the impounded water of these reservoirs is comparable to the amount of glacier-fed meltwater in eight years.

SRTM DEM correction over dense urban areas using inverse probability weighted interpolation and Sentinel-2 multispectral imagery

The objective of this research is to develop an approach to correct nonlinear errors in the SRTM (Shuttle Radar Topography Mission) elevations, which cannot be handled by most traditional methods. First, a set of uncorrelated feature attributes has been generated from the SRTM digital elevation model (DEM) together with the new freely available Sentinel-2 multispectral imagery, over a dense urban area in Egypt. Second, the SRTM DEM, Sentinel-2 image, and the generated attributes have been applied as input data in an artificial neural network (ANN) classification model to assign each pixel to each of 12 reference elevations. Finally, the posterior probabilities obtained for ANN have been combined based on an inverse probability weighted interpolation (IPWI) approach to estimate revised SRTM elevations. The results were compared with a reference DEM with 1-m vertical accuracy derived through image matching of the Worldview-1 stereo satellite imagery. The process of performance evaluation is based on various statistics such as scatter plots, correlation coefficient (R), standard deviation (SD), and root mean square error (RMSE). The results show that, using the SRTM DEM as a single data source, the RMSE of estimated elevations has improved to 3.04 m. On the other hand, including the Sentinel-2 image has improved the RMSE of elevations to 2.93 m. Including the generated attributes as well has improved the estimated RMSE of the elevations to 2.07 m. Compared with the results from the commonly used multiple linear regression (MLR) method, the improvement in RMSE of the estimated elevations can reach 45%.

The Soil Nutrient Digital Mapping for Precision Agriculture Cases in the Trans-Ural Steppe Zone of Russia Using Topographic Attributes

Topographic features of territory have a significant impact on the spatial distribution of soil properties. This research is focused on digital soil mapping (DSM) of main agrochemical soil properties—values of soil organic carbon (SOC), nitrogen, potassium, calcium, magnesium, sodium, phosphorus, pH, and thickness of the humus-accumulative (AB) horizon of arable lands in the Trans-Ural steppe zone (Republic of Bashkortostan, Russia). The methods of multiple linear regression (MLR) and support vector machine (SVM) were used for the prediction of soil nutrients spatial distribution and variation. We used 17 topographic indices calculated using the SRTM (Shuttle Radar Topography Mission) digital elevation model. Results showed that SVM is the best method in predicting the spatial variation of all soil agrochemical properties with comparison to MLR. According to the coefficient of determination R2, the best predictive models were obtained for content of nitrogen (R2 = 0.74), SOC (R2 = 0.66), and potassium (R2 = 0.62). In our study, elevation, slope, and MMRTF (multiresolution ridge top flatness) index are the most important variables. The developed methodology can be used to study the spatial distribution of soil nutrients and large-scale mapping in similar landscapes.

Uncertainty Assessment in Soil Erosion Modelling Using RUSLE, Multisource and Multiresolution DEMs

Soil erosion is a key concern for the environment and natural resources since it leads to a decline in-field productivity and soil quality, resulting in land degradation. In this study, assessment of uncertainty in soil erosion modelling of the Karso watershed, India, was carried out by employing the revised universal soil loss equation (RUSLE) and geospatial technologies to evaluate the effect of multi-source digital elevation models (DEMs) [Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), Cartosat and Shuttle Radar Topography Mission (SRTM)] with resampled multi-resolution grids. The rainfall erosivity factor (R) was computed using the mean monthly Tropical Rainfall Measuring Mission rainfall estimates for 1998 to 2012. The slope length factor was derived using the ASTER and Cartosat DEMs at grid sizes of 30 m, 50 m, 100 m, 150 m, 200 m, and 250 m, and for the SRTM DEM at 100 m, 150 m, 200 m and 250 m resolutions for the Karso watershed, Jharkhand, India. Significant differences were obtained in the soil loss estimates across the different DEM sources and resampled grid sizes. The Cartosat DEM with a 200 m grid was found to estimate the soil loss the best out of all the DEM combinations considered. The Cartosat DEM proved to be more reliable than the ASTER and SRTM DEMs. The results indicated that the RUSLE is a scale-dependent model since the model estimates were affected not only by the DEM source but also by its resolution. The prediction of erosion potential by employing the multisource, multiresolution DEMs and the RUSLE helped to identify the soil erosion's spatial pattern within the watershed. The study provided an impact analysis of the uncertainties when selecting the multisource, multiresolution DEMs for soil erosion modelling.

Water resources exploration and management based on remote sensing and geophysical data analysis: Al-Naqab Watershed, East Central Sinai, Egypt

This study integrates remote sensing and geophysical data to identify the hydrological conditions of the Naqab reservoir watershed. The main objectives of this study are identification and the interpretation of subsurface structures and their impact on groundwater flow, the relationships between structures and groundwater and the optimum way for watershed management in this sub-basin. Geophysical data including, ground magnetic survey and 1D electrical resistivity sounding helped in identifying the lithology and delineating zones of groundwater occurrence. The magnetic data delineated the basement rock, aided in characterizing the geometry of the subsurface structures that control the land surface features and constrain groundwater flow system. The interpreted subsurface structure elements include six sets of faults trending NE-SW, NW-SE, NWW-SEE, NEE-SWW, N-S and E-W. The basement depth was estimated at zero at the southern part and about 5187 m at the northern part. Two aquifer systems were characterized, the shallow aquifer of the Wata Formation (Upper Cretaceous) and the deep aquifer of the Malha Formation (Lower Cretaceous). The probability of groundwater occurrence increases towards the central part of the study area to north direction, where the thickness of the sedimentary basin reaches its maximum at the center of the study area. Different trends of faults were interpreted from are the geo-electrical cross-sections along two transects. Three patterns of faults were characterized including step faults, graben faults and horst faults. These faults could be indicated on the geo-electrical section by a marked difference in the layers` thicknesses. The magnetic data confirmed the locations of the faults delineated by the electrical resistivity profiles. The outlined faults are trending mainly in NE-SW, NW-SE, NWW-SEE, NEE-SWW and E-W directions. Lineament structures delineation and drainage pattern analysis were evaluated and interpreted from the analysis of remote sensing (RS) data and geographic information system (GIS) technique. The SRTM-DEM (Shuttle Radar Topographic Mission-Digital Elevation Model) was also utilized to automatically identify and extract drainage network. Interpretation and analysis of the inferred lineament structures indicate the presence of a number of main lineament populations that trends: NE-SW, NW-SE, NNW-SSE, NWW-SEE, E-W and N-S. Meanwhile, the interpretation and analysis of drainage pattern network indicate the presence of three main lineament structures that trends: NW-SE, NE-SW and NWW-SEE. Azimuth distribution analysis of both the measured structures and drainage channels shows similar trends, except for very few differences in the prevailing trends. Similarity in orientation of lineament structures, drainage system, and subsurface structural trends were recognized in the area under study. In conclusion, the integration between remote sensing and geophysical data revealed a close matching between the surface structural lineaments and subsurface structural trends could be verified. The preferred conduits of surface run-off and groundwater flow, as well as, the proper locations for groundwater exploitation could be determined.