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

Classification of North Africa for Use as an Extended Pseudo Invariant Calibration Sites (Epics) for Radiometric Calibration and Stability Monitoring of Optical Satellite Sensors

An increasing number of Earth-observing satellite sensors are being launched to meet the insatiable demand for timely and accurate data to help the understanding of the Earth’s complex systems and to monitor significant changes to them. The quality of data recorded by these sensors is a primary concern, as it critically depends on accurate radiometric calibration for each sensor. Pseudo Invariant Calibration Sites (PICS) have been extensively used for radiometric calibration and temporal stability monitoring of optical satellite sensors. Due to limited knowledge about the radiometric stability of North Africa, only a limited number of sites in the region are used for this purpose. This work presents an automated approach to classify North Africa for its potential use as an extended PICS (EPICS) covering vast portions of the continent. An unsupervised classification algorithm identified 19 “clusters” representing distinct land surface types; three clusters were identified with spatial uncertainties within approximately 5% in the shorter wavelength bands and 3% in the longer wavelength bands. A key advantage of the cluster approach is that large numbers of pixels are aggregated into contiguous homogeneous regions sufficiently distributed across the continent to allow multiple imaging opportunities per day, as opposed to imaging a typical PICS once during the sensor’s revisit period. In addition, this work proposes a technique to generate a representative hyperspectral profile for these clusters, as the hyperspectral profile of these identified clusters are mandatory in order to utilize them for performing cross-calibration of optical satellite sensors. The technique was used to generate the profile for the cluster containing the largest number of aggregated pixels. The resulting profile was found to have temporal uncertainties within 5% across all the spectral regions. Overall, this technique shows great potential for generation of representative hyperspectral profiles for any North African cluster, which could allow the use of the entire North Africa Saharan region as an extended PICS (EPICS) dataset for sensor cross-calibration. Furthermore, this work investigates the performance of extended pseudo-invariant calibration sites (EPICS) in cross-calibration for one of Shrestha’s clusters, Cluster 13, by comparing its results to those obtained from a traditional PICS-based cross-calibration. The use of EPICS clusters can significantly increase the number of cross-calibration opportunities within a much shorter time period. The cross-calibration gain ratio estimated using a cluster-based approach had a similar accuracy to the cross-calibration gain derived from region of interest (ROI)-based approaches. The cluster-based cross-calibration gain ratio is consistent within approximately 2% of the ROI-based cross-calibration gain ratio for all bands except for the coastal and shortwave-infrared (SWIR) 2 bands. These results show that image data from any region within Cluster 13 can be used for sensor crosscalibration. Eventually, North Africa can be used a continental scale PICS

EVALUATION OF A REMOTE SENSING BASED METHOD FOR THE ASSESSMENT OF AGRICULTURAL CROP RESIDUES ON THE SOIL SURFACE

Increased agricultural mechanization in the recent past and susceptibility of certain soils to degradation generate widespread concern among experts on the overall environmental sustainability of some of the current agricultural practices in Europe. A number of solutions could be adopted to better preserve soil resources, some of which are already supported by the Common Agricultural Policy (CAP). Researchers demonstrated that erosion and reduction in soil organic matter are among the most acute degradation issues in Europe and that the release of crop residues on the soil surface after harvesting can greatly reduce their incidence. The use of a permanent soil cover (e.g. by use of crop residues) is one of the three fundamental principles of Conservation Agriculture. Quantifying the amount of crop residues on the ground is important for soil and water protection, modelling of erosion processes and legislation enforcement purposes. However, common monitoring methods based on ground sampling are expensive and likely to be impracticable on vast surfaces. Remote sensing can offer a valid alternative for monitoring. The present research intends to contribute to the efforts towards the establishments of methods for the assessment and monitoring, through remote sensing, of the effects of conservation agriculture practices on the environment, with focus on soil resources. In this respect, the research specific objective is the evaluation of a remote sensing based method for the quantification of crop residue cover in a conservation agriculture farm in Northern Italy by use of hyperspectral satellite imagery. Results achieved show that not only crop residues percent cover is linearly related to certain remote sensing-based indices, therefore making possible to estimate how well soil is preserved from weathering, but also that spaceborne hyperspectral sensors such as Hyperion appear to have great potentiality towards monitoring of other environmental targets due to their very high spectral and spatial resolution. The research was deeply inspired by the outcomes of a European project (\u201cSustainable Agriculture and Soil Conservation through simplified cultivation techniques\u201d - SoCo) aimed at improving protection of soil resources in the European agriculture sector through a stock taking and promotion of soil-friendly agriculture practices and systems, in particular simplified cultivation techniques, within the current legislative framework

Assessment and mapping of soil water repellency using remote sensing and prediction of its effect on surface runoff and phosphorus losses : A thesis submitted in partial fulfilment of the requirements for the Degree of Doctor of Philosophy at Lincoln University

The soil water repellency spatial and temporal dynamics remain ambiguous. Water repellency is an inherent soil property that refers to the impedance in dry soil wetting. This phenomenon was ascribable to the hydrophobic compounds coating the soil particles and has emerged as a recalcitrant issue impacting multiple processes upon agroecosystems. The apprehensions around soil water repellency include its impact on surface runoff, plant growth, and nutrients losses (e.g. phosphorus). The soil hydrophobic compounds, which are intrinsic constituents of the soil carbon pool, have different sources including plant leaves and roots, soil microbial communities and fungi. Previous methods for water repellency measurements are laborious, time-consuming and costly. The raison d'être of this thesis was to i) explore and test novel approaches for estimation of soil water repellency in pastoral ecosystems, and ii) study the factors controlling soil water repellency and assess its impact on surface runoff volumes and phosphorus losses in surface runoff. In the present work, multiple remote sensing approaches were tested to assess and map soil water repellency at multiple scales. The liaison between water repellency and soil surface reflectance was exploited to access the water repellency using the satellite multispectral reflectance and hyperspectral satellite data. A novel approach implicating the use of time series of surface reflectance and water deficit data was used to study the impact of both surface biomass and soil moisture temporal dynamics on the occurrence of water repellency and carbon content in pastoral systems. Multispectral broadband data from both Landsat-7 and Sentinel-2 satellites showed big potential for assessing soil water repellency and carbon content in permanent pastures. Partial least square regression models were calibrated and cross-validated using topsoil measurement of water repellency and soil carbon from 41 and 35 pastoral sites that were matched with reflectance spectra from Landsat-7 and Sentinel-2, respectively. Soil carbon showed higher predictability compared to water repellency with R2v=0.50, RMSEv=2.58 when using Landsat-7 spectra. The higher predictability performance for water repellency persistence was reached using Sentinel-2 spectral (R2v=0.45; RMSEv=0.98). However, using hyperspectral narrowband data from the Hyperion satellite showed a higher prediction accuracy (R2v=0.78; RMSEv=0.58). Prediction performance was generally higher when using the calibration sets, indicating the possibility of improving these prediction models when using larger datasets. A novel approach was tested using multiple predictors for soil water repellency occurrence. The predictors included time series of surface biomass assessed through normalised difference vegetation index (NDVI) and soil moisture data estimated through water deficit and synthetic aperture radar satellite data. The results showed an attractive opportunity for water repellency and soil carbon mapping. Three machine learning algorithms including artificial neural networks, random forest, and support vector machine were trained and cross-validated using multiple configurations of satellite time-series data and topsoil measurement from 58 pastoral sites. Random forest and support vector machine (RMSEv=0.82 and 0.87, respectively) outperformed artificial neural networks (RMSEv=1.23). With increasingly available remote sensing data, the use of satellite time-series data will open unprecedented opportunities for soil carbon, water repellency mapping, and potentially other functional chemical and physical soil attributes. To understand water repellency dynamics and evaluate their impact on surface runoff and phosphorus losses in pastoral soils, two experiments were conducted. The first experiment aimed to understand the relationship between the actual water repellency persistence and water content in drying hydrophobic soils. The second experiment had the objective to evaluate the impact of soil water repellency on the surface runoff and phosphorus losses in runoff. Results from the first experiment showed that the actual water repellency increased dramatically when water content decreased, especially when moisture dropped below a critical value. Using lab measurements, the actual water repellency was modelled using a simple sigmoidal model, as a function of water content, the potential water repellency, and two characteristic parameters related to the response curve shape. Results from the runoff trial showed that the surface runoff was influenced by soil water repellency to some extent (R2=0.46). Although more than 90 % of phosphorus losses happened in incidental losses following fertiliser application, the data point to non-incidental phosphorus loads being related to soil water repellency (R2=0.56). These results bespoke the effect of soil water repellency on background phosphorus losses through surface runoff during post-summer runoff events in pastoral ecosystems

Gradients in urban material composition: A new concept to map cities with spaceborne imaging spectroscopy data

To understand processes in urban environments, such as urban energy fluxes or surface temperature patterns, it is important to map urban surface materials. Airborne imaging spectroscopy data have been successfully used to identify urban surface materials mainly based on unmixing algorithms. Upcoming spaceborne Imaging Spectrometers (IS), such as the Environmental Mapping and Analysis Program (EnMAP), will reduce the time and cost-critical limitations of airborne systems for Earth Observation (EO). However, the spatial resolution of all operated and planned IS in space will not be higher than 20 to 30 m and, thus, the detection of pure Endmember (EM) candidates in urban areas, a requirement for spectral unmixing, is very limited. Gradient analysis could be an alternative method for retrieving urban surface material compositions in pixels from spaceborne IS. The gradient concept is well known in ecology to identify plant species assemblages formed by similar environmental conditions but has never been tested for urban materials. However, urban areas also contain neighbourhoods with similar physical, compositional and structural characteristics. Based on this assumption, this study investigated (1) whether cover fractions of surface materials change gradually in urban areas and (2) whether these gradients can be adequately mapped and interpreted using imaging spectroscopy data (e.g. EnMAP) with 30 m spatial resolution. Similarities of material compositions were analysed on the basis of 153 systematically distributed samples on a detailed surface material map using Detrended Correspondence Analysis (DCA). Determined gradient scores for the first two gradients were regressed against the corresponding mean reflectance of simulated EnMAP spectra using Partial Least Square regression models. Results show strong correlations with R2 = 0.85 and R2 = 0.71 and an RMSE of 0.24 and 0.21 for the first and second axis, respectively. The subsequent mapping of the first gradient reveals patterns that correspond to the transition from predominantly vegetation classes to the dominance of artificial materials. Patterns resulting from the second gradient are associated with surface material compositions that are related to finer structural differences in urban structures. The composite gradient map shows patterns of common surface material compositions that can be related to urban land use classes such as Urban Structure Types (UST). By linking the knowledge of typical material compositions with urban structures, gradient analysis seems to be a powerful tool to map characteristic material compositions in 30 m imaging spectroscopy data of urban areas

Satellite remote sensing for hydrothermal alteration minerals mapping of subtle geothermal system in unexplored aseismic environment

Mapping prospective geothermal (GT) resources and monitoring associated surface manifestations can be challenging and prohibitively expensive in subtle systems especially when using conventional survey methods. Remote sensing offers a synoptic and costeffective capability for identification of GT systems. The objective of this research is to refine and develop methods of identifying unconventional GT systems by evaluating the applicability of the ASTER, Landsat 8 and Hyperion satellite data for mapping hydrothermal alteration indicator minerals as proxy for detecting subtle GT targets in unexplored aseismic settings. The study area is Yankari Park in North Eastern Nigeria, characterized by the thermal springs; Wikki, Mawulgo, Gwana and Dimmil. Spectral Angle Mapper (SAM), Linear spectral Unmixing (LSU) and Mixture Tuned Matched Filtering (MTMF) were comparatively evaluated by using image derived spectra and corresponding library spectra for mapping pixel abundance of GT indicator minerals in a novel and efficient manner. The results indicated that employing image derived spectra from field validated and laboratory verified regions of interest as reference, gives more accurate results than using library spectra around known alteration zones remotely detectable on the imagery. The MTMF provided high performance subpixel target detection with an accuracy of 50-100% and 70-100% subpixel abundance for argillicphyllic- silicic and propylitic alteration mineral assemblages respectively, as compared to less than 10% for the same endmembers when using library spectra. The MTMF is thus best suited for mapping alterations associated with subtle GT systems than the less selective LSU. The per-pixel SAM was unsuitable for target detection of alteration indicators of interest with poor overall accuracy of 33.81% and 0.24 Kappa coefficient at 0.02 radian angle. Results of mapping thermally anomalous pixels do not conform to known locations of the thermal springs signifying the limitations of the current thermal sensors in mapping low temperature GT systems even at 60m spatial resolution. However, examining the spatial correlation of the anomaly areas with the major geologic structure systems from geological map of the study area indicates a close affinity between them and with previously reported thermal gradients within heat insulating sedimentary formations. This study establishes the integrative applicability of Multispectral and Hyperspectral data for mapping subtle GT targets in unexplored regions using in-situ validated alteration mineral mapping and thermal anomaly detection. This has significant implication for the GT green energy industry as the developed methods and GT prospect map could aid the prefeasibility stage narrowing of targets for in-depth geophysical, geochemical, geothermometric and related surveys

Remote Sensing as a Precision Farming Tool in the Nile Valley, Egypt

Detecting stress in plants resulting from different stressors including nitrogen deficiency, salinity, moisture, contamination and diseases, is crucial in crop production. In the Nile Valley, crop production is hindered perhaps more fundamentally by issues of water supply and salinity. Predicting stress in crops by conventional methods is tedious, laborious and costly and is perhaps unreliable in providing a spatial context of stress patterns. Accurate and quick monitoring techniques for crop status to detect stress in crops at early growth stages are needed to maximize crop productivity. In this context, remotely sensed data may provide a useful tool in precision farming. This research aims to evaluate the role of in situ hyperspectral and high spatial resolution satellite remote sensing data to detect stress in wheat and maize crops and assess whether moisture induced stress can be distinguished from salinity induced stress spectrally. A series of five greenhouse based experiments on wheat and maize were undertaken subjecting both crops to a range of salinity and moisture stress levels. Spectroradiometry measurements were collected at different growth stages of each crop to assess the relationship between crop biophysical and biochemical properties and reflectance measurements from plant canopies. Additionally, high spatial resolution satellite images including two QuickBird, one ASTER and two SPOT HRV were acquired in south-west Alexandria, Egypt to assess the potential of high spectral and spatial resolution satellite imagery to detect stress in wheat and maize at local and regional scales. Two field work visits were conducted in Egypt to collect ground reference data and coupled with Hyperion imagery acquisition, during winter and summer seasons of 2007 in March (8-30: wheat) and July (12-17: maize). Despite efforts, Hyperion imagery was not acquired due to factors out with the control of this research. Strong significant correlations between crop properties and different vegetation indices derived from both ground based and satellite platforms were observed. RDVI showed a sensitive index to different wheat properties (r > 0.90 with different biophysical properties). In maize, GNDVIbr and Cgreen had strong significant correlations with maize biophysical properties (r > 0.80). PCA showed the possibility to distinguish between moisture and salinity induced stress at the grain filling stages. The results further showed that a combined approach of high (2-5 m) and moderate (15-20) spatial resolution satellite imagery can provide a better mechanistic interpretation of the distribution and sources of stress, despite the typical small size of fields (20-50 m scale). QuickBird imagery successfully detects stress within field and local scales, whereas SPOT HRV imagery is useful in detecting stress at a regional scale, and therefore, can be a robust tool in identifying issues of crop management at a regional scale. Due to the limited spectral capabilities of high spatial resolution images, distinguishing different sources of stress is not directly possible, and therefore, hyperspectral satellite imagery (e.g. Hyperion or HyspIRI) is required to distinguish between moisture and salinity induced stress. It is evident from the results that remotely sensed data acquired by both in situ hyperspectral and high spatial resolution satellite remote sensing can be used as a useful tool in precision farming in the Nile Valley, Egypt. A combined approach of using reliable high spatial and spectral satellite remote sensing data could provide better insight about stress at local and regional scales. Using this technique as a precision farming and management tool will lead to improved crop productivity by limiting stress and consequently provide a valuable tool in combating issues of food supply at a time of rapid population growth

Examining the Capability of Supervised Machine Learning Classifiers in Extracting Flooded Areas from Landsat TM Imagery: A Case Study from a Mediterranean Flood

This study explored the capability of Support Vector Machines (SVMs) and regularised kernel Fisher’s discriminant analysis (rkFDA) machine learning supervised classifiers in extracting flooded area from optical Landsat TM imagery. The ability of both techniques was evaluated using a case study of a riverine flood event in 2010 in a heterogeneous Mediterranean region, for which TM imagery acquired shortly after the flood event was available. For the two classifiers, both linear and non-linear (kernel) versions were utilised in their implementation. The ability of the different classifiers to map the flooded area extent was assessed on the basis of classification accuracy assessment metrics. Results showed that rkFDA outperformed SVMs in terms of accurate flooded pixels detection, also producing fewer missed detections of the flooded area. Yet, SVMs showed less false flooded area detections. Overall, the non-linear rkFDA classification method was the more accurate of the two techniques (OA = 96.23%, K = 0.877). Both methods outperformed the standard Normalized Difference Water Index (NDWI) thresholding (OA = 94.63, K = 0.818) by roughly 0.06 K points. Although overall accuracy results for the rkFDA and SVMs classifications only showed a somewhat minor improvement on the overall accuracy exhibited by the NDWI thresholding, notably both classifiers considerably outperformed the thresholding algorithm in other specific accuracy measures (e.g. producer accuracy for the “not flooded” class was ~10.5% less accurate for the NDWI thresholding algorithm in comparison to the classifiers, and average per-class accuracy was ~5% less accurate than the machine learning models). This study provides evidence of the successful application of supervised machine learning for classifying flooded areas in Landsat imagery, where few studies so far exist in this direction. Considering that Landsat data is open access and has global coverage, the results of this study offers important information towards exploring the possibilities of the use of such data to map other significant flood events from space in an economically viable way

Mapping And Monitoring Wetland Environment By Analysis Different Satellite Images And Field Spectroscopy

Tez (Doktora) -- İstanbul Teknik Üniversitesi, Fen Bilimleri Enstitüsü, 2010Thesis (PhD) -- İstanbul Technical University, Institute of Science and Technology, 2010Bu çalışmada farklı spektral ve mekansal çözünürlükte uydu görüntülerinin “Terkos Havzası Sulak Alanı” örneğinde; arazi örtüsünde meydana gelen değişimleri ve sulak alan bitki türlerinin belirlenmesinde kullanılabilirlikleri için uygulanabilecek uzaktan algılama yöntemleri ele alınmıştır. Kullanılan yöntemler ile elde edilen yeni işlenmiş görüntülerin performanslarının yersel yansıtım değerleri kullanılarak desteklenmesi ile doğal alanların sürdürülebilir korunma ve yönetimi için uzaktan algılama verilerine dayalı bir altlık rehberin oluşturulması imkanı araştırılmıştır. Elde edilen sonuçlara göre heterojen arazi örtüsü yapısına sahip olan çalışma bölgesinde değişim tespiti için Ana Bileşen Dönüşümüne dayalı değişim tespit yöntemi en iyi sonucu vermiştir. Ayrıca bu çalışmada, hiperspektral Hyperion EO-1 görüntüsü ile sulak alan bitki örtüsünün diğer bitki türlerinden doğru olarak ayırt edilebildiği ortaya konmuştur. Sulak alan bitki türlerinin kendi içinde ayırt edilebilmesi ancak yersel spektroskopi ile mümkün olduğu sonucuna ulaşılmıştır.In this study, different satellite data that has different spectral and spatial resolution and in-situ spectroradiometer measurements were used to analyze hydrophytic vegetation and surrounded land cover for sustainable development and conservation of Terkos wetlands. By supporting performances of processed images with field collected reflectance values, the feasibility of structuring a basic guide based on remote sensing data for sustainable preservation and management of natural lands was searched. According to result, land cover changes in the complex natural area were determined more accurately by using PCA based change detection method Therefore, the performance of spaceborne Hyperion EO-1 hyperspectral data was analyzed to determine the capability of the data for wetland vegetation discrimination than the other vegetated areas. At the last stage of the study, field collected reflectance values that have different wetland flora types were compared by statistical ANOVA method and reflectance differences between vegetation types were put forward through calculations.DoktoraPh