Digital mapping of soil properties using multivariate statistical analysis and ASTER data in an Arid Region

Modeling and mapping of soil properties has been identified as key for effective land degradation management and mitigation. The ability to model and map soil properties at sufficient accuracy for a large agriculture area is demonstrated using Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) imagery. Soil samples were collected in the El-Tina Plain, Sinai, Egypt, concurrently with the acquisition of ASTER imagery, and measured for soil electrical conductivity (EC_e), clay content and soil organic matter (OM). An ASTER image covering the study area was preprocessed, and two predictive models, multivariate adaptive regression splines (MARS) and the partial least squares regression (PLSR), were constructed based on the ASTER spectra. For all three soil properties, the results of MARS models were better than those of the respective PLSR models, with cross-validation estimated R^2 of 0.85 and 0.80 for EC_e, 0.94 and 0.90 for clay content and 0.79 and 0.73 for OM. Independent validation of EC_e, clay content and OM maps with 32 soil samples showed the better performance of the MARS models, with R^2 = 0.81, 0.89 and 0.73, respectively, compared to R^2 = 0.78, 0.87 and 0.71 for the PLSR models. The results indicated that MARS is a more suitable and superior modeling technique than PLSR for the estimation and mapping of soil salinity (EC_e), clay content and OM. The method developed in this paper was found to be reliable and accurate for digital soil mapping in arid and semi-arid environments

Estimating the soil clay content and organic matter by means of different calibration methods of vis-NIR diffuse reflectance spectroscopy

The selection of calibration method is one of the main factors influencing measurement accuracy of soil properties estimation in visible and near infrared reflectance spectroscopy. In this study, the performance of three regression techniques, namely, partial least-squares regression (PLSR), support vector regression (SVR), and multivariate adaptive regression splines (MARS) were compared to identify the best method to assess organic matter (OM) and clay content in the salt-affected soils. One hundred and two soil samples collected from Northern Sinai, Egypt, were used as the data set for the calibration and validation procedures. The dry samples were scanned using a FieldSpec Pro FR Portable Spectroradiometer (Analytical Spectral Devices, ASD) with a measurement range of 350–2500 nm. The spectra were subjected to seven pre-processed techniques, e.g., Savitzky–Golay (SG) smoothing, first derivative with SG smoothing (FD-SG), second derivative with SG smoothing (SD-SG), continuum removed reflectance (CR), standard normal variate and detrending (SNV-DT), multiplicative scatter correction (MSC) and extended MSC. The results of cross-validation showed that in most cases MARS models performed better than PLSR and SVR models. The best predictions were obtained using MARS calibration methods with CR prep-processing, yielding R2, root mean squared error (RMSE), and ratio of performance to deviation (RPD) values of 0.85, 0.19%, and 2.63, respectively, for OM; and 0.90, 5.32%, and 3.15, respectively, for clay content

Remote sensing approaches and mapping methods for monitoring soil salinity under different climate regimes

Soil salinization is one of the severe land-degradation problems due to its adverse effects on land productivity. Each year several hectares of lands are degraded due to primary or secondary soil salinization, and as a result, it is becoming a major economic and environmental concern in different countries. Spatio-temporal mapping of soil salinity is therefore important to support decisionmaking procedures for lessening adverse effects of land degradation due to the salinization. In that sense, satellite-based technologies provide cost effective, fast, qualitative and quantitative spatial information on saline soils. The main objective of this work is to highlight the recent remote sensing (RS) data and methods to assess soil salinity that is a worldwide problem. In addition, this study indicates potential linkages between salt-affected land and the prevailing climatic conditions of the case study areas being examined. Web of science engine is used for selecting relevant articles. "Soil salinity" is used as the main keyword for finding "articles" that are published from January 1, 2007 up to April 30, 2018. Then, 3 keywords; "remote sensing", "satellite" and "aerial" were used to filter the articles. After that, 100 case studies from 27 different countries were selected. Remote sensing based researches were further overviewed regarding to their location, spatial extent, climate regime, remotely sensed data type, mapping methods, sensing approaches together with the reason of salinity for each case study. In addition, soil salinity mapping methods were examined to present the development of different RS based methods with time. Studies are shown on the Köppen-Geiger climate classification map. Analysis of the map illustrates that 63% of the selected case study areas belong to arid and semi-arid regions. This finding corresponds to soil characteristics of arid regions that are more susceptible to salinization due to extreme temperature, high evaporation rates and low precipitation

Soil Salinity Assessing and Mapping Using Several Statistical and Distribution Techniques in Arid and Semi-Arid Ecosystems, Egypt

Oasis lands in Egypt are commonly described as salty soils; therefore, waterlogging and higher soil salinity are major obstacles to sustainable agricultural development. This study aims to map and assess soil salinization at El-Farafra Oasis in the Egypt Western Desert based on salinity indices, Imaging Spectroscopy (IS), and statistical techniques. The regression model was developed to test the relationship between the electrical conductivity (ECe) of 70 surface soil samples and seven salinity indices (SI 1, SI 2, SI 5, SI 6, SI 7, SI 8, and SI 9) to produce soil salinity maps depending on Landsat-8 (OLI) images. The investigations of soil salinization and salinity indices were validated in a studied area based on 30 soil samples; the obtained results represented that all salinity indices have shown satisfactory correlations between ECe values for each soil sample site and salinity indices, except for the SI 5 index that present non-significant correlations with R2 value of 0.2688. The SI 8 index shows a higher negative significant correlation with ECe and an R2 value of 0.6356. There is a significant positive correlation at the (p < 0.01) level between SI 9 and ECe (r = 0.514), a non-significant correlation at the (p < 0.05) level between soil ECe and SI 1 index (r = 0.495), and the best-verified salinity index was for SI 7 that has a low estimated RMSE error of 8.58. Finally, the highest standard error (R2) was represented as ECe (dS m−1) with an R2 of 0.881, and the lowest one was SI 9 with an R2 of 0.428, according to Tukey’s test analysis. Therefore, observing and investigating soil salinity are essential requirements for appropriate natural resource management plans in the future

Quantitatively estimating main soil water-soluble salt ions content based on Visible-near infrared wavelength selected using GC, SR and VIP

Soil salinization is the primary obstacle to the sustainable development of agriculture and eco-environment in arid regions. The accurate inversion of the major water-soluble salt ions in the soil using visible-near infrared (VIS-NIR) spectroscopy technique can enhance the effectiveness of saline soil management. However, the accuracy of spectral models of soil salt ions turns out to be affected by high dimensionality and noise information of spectral data. This study aims to improve the model accuracy by optimizing the spectral models based on the exploration of the sensitive spectral intervals of different salt ions. To this end, 120 soil samples were collected from Shahaoqu Irrigation Area in Inner Mongolia, China. After determining the raw reflectance spectrum and content of salt ions in the lab, the spectral data were pre-treated by standard normal variable (SNV). Subsequently the sensitive spectral intervals of each ion were selected using methods of gray correlation (GC), stepwise regression (SR) and variable importance in projection (VIP). Finally, the performance of both models of partial least squares regression (PLSR) and support vector regression (SVR) was investigated on the basis of the sensitive spectral intervals. The results indicated that the model accuracy based on the sensitive spectral intervals selected using different analytical methods turned out to be different: VIP was the highest, SR came next and GC was the lowest. The optimal inversion models of different ions were different. In general, both PLSR and SVR had achieved satisfactory model accuracy, but PLSR outperformed SVR in the forecasting effects. Great difference existed among the optimal inversion accuracy of different ions: the predicative accuracy of Ca2+, Na+, Cl−, Mg2+ and SO42− was very high, that of CO32− was high and K+ was relatively lower, but HCO3− failed to have any predicative power. These findings provide a new approach for the optimization of the spectral model of water-soluble salt ions and improvement of its predicative precision

Remote Sensing in Applications of Geoinformation

Remote sensing, especially from satellites, is a source of invaluable data which can be used to generate synoptic information for virtually all parts of the Earth, including the atmosphere, land, and ocean. In the last few decades, such data have evolved as a basis for accurate information about the Earth, leading to a wealth of geoscientific analysis focusing on diverse applications. Geoinformation systems based on remote sensing are increasingly becoming an integral part of the current information and communication society. The integration of remote sensing and geoinformation essentially involves combining data provided from both, in a consistent and sensible manner. This process has been accelerated by technologically advanced tools and methods for remote sensing data access and integration, paving the way for scientific advances in a broadening range of remote sensing exploitations in applications of geoinformation. This volume hosts original research focusing on the exploitation of remote sensing in applications of geoinformation. The emphasis is on a wide range of applications, such as the mapping of soil nutrients, detection of plastic litter in oceans, urban microclimate, seafloor morphology, urban forest ecosystems, real estate appraisal, inundation mapping, and solar potential analysis

NASA's surface biology and geology designated observable: A perspective on surface imaging algorithms

The 2017–2027 National Academies' Decadal Survey, Thriving on Our Changing Planet, recommended Surface Biology and Geology (SBG) as a “Designated Targeted Observable” (DO). The SBG DO is based on the need for capabilities to acquire global, high spatial resolution, visible to shortwave infrared (VSWIR; 380–2500 nm; ~30 m pixel resolution) hyperspectral (imaging spectroscopy) and multispectral midwave and thermal infrared (MWIR: 3–5 μm; TIR: 8–12 μm; ~60 m pixel resolution) measurements with sub-monthly temporal revisits over terrestrial, freshwater, and coastal marine habitats. To address the various mission design needs, an SBG Algorithms Working Group of multidisciplinary researchers has been formed to review and evaluate the algorithms applicable to the SBG DO across a wide range of Earth science disciplines, including terrestrial and aquatic ecology, atmospheric science, geology, and hydrology. Here, we summarize current state-of-the-practice VSWIR and TIR algorithms that use airborne or orbital spectral imaging observations to address the SBG DO priorities identified by the Decadal Survey: (i) terrestrial vegetation physiology, functional traits, and health; (ii) inland and coastal aquatic ecosystems physiology, functional traits, and health; (iii) snow and ice accumulation, melting, and albedo; (iv) active surface composition (eruptions, landslides, evolving landscapes, hazard risks); (v) effects of changing land use on surface energy, water, momentum, and carbon fluxes; and (vi) managing agriculture, natural habitats, water use/quality, and urban development. We review existing algorithms in the following categories: snow/ice, aquatic environments, geology, and terrestrial vegetation, and summarize the community-state-of-practice in each category. This effort synthesizes the findings of more than 130 scientists

Spectroscopy-supported digital soil mapping

Global environmental changes have resulted in changes in key ecosystem services that soils provide. It is necessary to have up to date soil information on regional and global scales to ensure that these services continue to be provided. As a result, Digital Soil Mapping (DSM) research priorities are among others, advancing methods for data collection and analyses tailored towards large-scale mapping of soil properties. Scientifically, this thesis contributed to the development of methodologies, which aim to optimally use remote and proximal sensing (RS and PS) for DSM to facilitate regional soil mapping. The main contributions of this work with respect to the latter are (I) the critical evaluation of recent research achievements and identification of knowledge gaps for large-scale DSM using RS and PS data, (II) the development of a sparse RS-based sampling approach to represent major soil variability at regional scale, (III) the evaluation and development of different state-of-the-art methods to retrieve soil mineral information from PS, (IV) the improvement of spatially explicit soil prediction models and (V) the integration of RS and PS methods with geostatistical and DSM methods. A review on existing literature about the use of RS and PS for soil and terrain mapping was presented in Chapter 2. Recent work indicated the large potential of using RS and PS methods for DSM. However, for large-scale mapping, current methods will need to be extended beyond the plot. Improvements may be expected in the fields of developing more quantitative methods, enhanced geostatistical analysis and improved transferability to other areas. From these findings, three major research interests were selected: (I) soil sampling strategies, (II) retrieval of soil information from PS and (III) spatially continuous mapping of soil properties at larger scales using RS. Budgetary constraints, limited time and available soil legacy data restricted the soil data acquisition, presented in Chapter 3. A 15.000 km2 area located in Northern Morocco served as test case. Here, a sample was collected using constrained Latin Hypercube Sampling (cLHS) of RS and elevation data. The RS data served as proxy for soil variability, as alternative for the required soil legacy data supporting the sampling strategy. The sampling aim was to optimally sample the variability in the RS data while minimizing the acquisition efforts. This sample resulted in a dataset representing major soil variability. The cLHS sample failed to express spatial correlation; constraining the LHS by a distance criterion favoured large spatial variability over short distances. The absence of spatial correlation in the sampled soil variability precludes the use of additional geostatistical analyses to spatially predict soil properties. Predicting soil properties using the cLHS sample is thus restricted to a modelled statistical relation between the sample and exhaustive predictor variables. For this, the RS data provided the necessary spatial information because of the strong spatial correlation while the spectral information provided the variability of the environment (Chapter 3 and 6). Concluding, the RS-based cLHS approach is considered a time and cost efficient method for acquiring information on soil resources over extended areas. This sample was further used for developing methods to derive soil mineral information from PS, and to characterize regional soil mineralogy using RS. In Chapter 4, the influences of complex scattering within the mixture and overlapping absorption features were investigated. This was done by comparing the success of PRISM’s MICA in determining mineralogy of natural samples and modelled spectra. The modelled spectra were developed by a linearly forward model of reflectance spectra, using the fraction of known constituents within the sample. The modelled spectra accounted for the co-occurrence of absorption features but eluded the complex interaction between the components. It was found that more minerals could be determined with higher accuracy using modelled reflectance. The absorption features in the natural samples were less distinct or even absent, which hampered the classification routine. Nevertheless, grouping the individual minerals into mineral categories significantly improved the classification accuracy. These mineral categories are particularly useful for regional scale studies, as key soil property for parent material characterization and soil formation. Characterizing regional soil mineralogy by mineral categories was further described in Chapter 6. Retrieval of refined information from natural samples, such as mineral abundances, is more complex; estimating abundances requires a method that accounts for the interaction between minerals within the intimate mixture. This can be done by addressing the interaction with a non-linear model (Chapter 5). Chapter 5 showed that mineral abundances in complex mixtures could be estimated using absorption features in the 2.1–2.4 µm wavelength region. First, the absorption behaviour of mineral mixtures was parameterized by exponential Gaussian optimization (EGO). Next, mineral abundances were successfully predicted by regression tree analysis, using these parameters as inputs. Estimating mineral abundances using prepared mixes of calcite, kaolinite, montmorillonite and dioctahedral mica or field samples proved the validity of the proposed method. Estimating mineral abundances of field samples showed the necessity to deconvolve spectra by EGO. Due to the nature of the field samples, the simple representation of the complex scattering behaviour by a few Gaussian bands required the parameters asymmetry and saturation to accurately deconvolve the spectra. Also, asymmetry of the EGO profiles showed to be an important parameter for estimating the abundances of the field samples. The robustness of the method in handling the omission of minerals during the training phase was tested by replacing part of the quartz with chlorite. It was found that the accuracy of the predicted mineral content was hardly affected. Concluding, the proposed method allowed for estimating more than two minerals within a mixture. This approach advances existing PS methods and has the potential to quantify a wider set of soil properties. With this method the soil science community was provided an improved inference method to derive and quantify soil properties The final challenge of this thesis was to spatially explicit model regional soil mineralogy using the sparse sample from Chapter 3. Prediction models have especially difficulties relating predictor variables to sampled properties having high spatial correlation. Chapter 6 presented a methodology that improved prediction models by using scale-dependent spatial variability observed in RS data. Mineral predictions were made using the abundances from X-ray diffraction analysis and mineral categories determined by PRISM. The models indicated that using the original RS data resulted in lower model performance than those models using scaled RS data. Key to the improved predictions was representing the variability of the RS data at the same scale as the sampled soil variability. This was realized by considering the medium and long-range spatial variability in the RS data. Using Fixed Rank Kriging allowed smoothing the massive RS datasets to these ranges. The resulting images resembled more closely the regional spatial variability of soil and environmental properties. Further improvements resulted from using multi-scale soil-landscape relationships to predict mineralogy. The maps of predicted mineralogy showed agreement between the mineral categories and abundances. Using a geostatistical approach in combination with a small sample, substantially improves the feasibility to quantitatively map regional mineralogy. Moreover, the spectroscopic method appeared sufficiently detailed to map major mineral variability. Finally, this approach has the potential for modelling various natural resources and thereby enhances the perspective of a global system for inventorying and monitoring the earth’s soil resources. With this thesis it is demonstrated that RS and PS methods are an important but also an essential source for regional-scale DSM. Following the main findings from this thesis, it can be concluded that: Improvements in regional-scale DSM result from the integrated use of RS and PS with geostatistical methods. In every step of the soil mapping process, spectroscopy can play a key role and can deliver data in a time and cost efficient manner. Nevertheless, there are issues that need to be resolved in the near future. Research priorities involve the development of operational tools to quantify soil properties, sensor integration, spatiotemporal modelling and the use of geostatistical methods that allow working with massive RS datasets. This will allow us in the near future to deliver more accurate and comprehensive information about soils, soil resources and ecosystem services provided by soils at regional and, ultimately, global scale.</p