Review of soil salinity assessment for agriculture across multiple scales using proximal and/or remote sensors

Mapping and monitoring soil spatial variability is particularly problematic for temporally and spatially dynamic properties such as soil salinity. The tools necessary to address this classic problem only reached maturity within the past 2 decades to enable field- to regional-scale salinity assessment of the root zone, including GPS, GIS, geophysical techniques involving proximal and remote sensors, and a greater understanding of apparent soil electrical conductivity (ECa) and multi- and hyperspectral imagery. The concurrent development and application of these tools have made it possible to map soil salinity across multiple scales, which back in the 1980s was prohibitively expensive and impractical even at field scale. The combination of ECa-directed soil sampling and remote imagery has played a key role in mapping and monitoring soil salinity at large spatial extents with accuracy sufficient for applications ranging from field-scale site-specific management to statewide water allocation management to control salinity within irrigation districts. The objective of this paper is: (i) to present a review of the geophysical and remote imagery techniques used to assess soil salinity variability within the root zone from field to regional scales; (ii) to elucidate gaps in our knowledge and understanding of mapping soil salinity; and (iii) to synthesize existing knowledge to give new insight into the direction soil salinity mapping is heading to benefit policy makers, land resource managers, producers, agriculture consultants, extension specialists, and resource conservation field staff. The review covers the need and justification for mapping and monitoring salinity, basic concepts of soil salinity and its measurement, past geophysical and remote imagery research critical to salinity assessment, current approaches for mapping salinity at different scales, milestones in multi-scale salinity assessment, and future direction of field- to regional-scale salinity assessment

Study of land degradation and desertification dynamics in North Africa areas using remote sensing techniques

In fragile-ecosystem arid and semi-arid land, climatic variations, water scarcity and human pressure accelerate ongoing degradation of natural resources. In order to implement sustainable management, the ecological state of the land must be known and diachronic studies to monitor and assess desertification processes are indispensable in this respect. The present study is developed in the frame of WADIS-MAR (www.wadismar.eu). This is one of the five Demonstration Projects implemented within the Regional Programme “Sustainable Water Integrated Management (SWIM)” (www.swim-sm.eu ), funded by the European Commission and which aims to contribute to the effective implementation and extensive dissemination of sustainable water management policies and practices in the Southern Mediterranean Region. The WADIS-MAR Project concerns the realization of an integrated water harvesting and artificial aquifer recharge techniques in two watersheds in Maghreb Region: Oued Biskra in Algeria and wadi Oum Zessar in Tunisia. The WADIS MAR Project is coordinated by the Desertification Research Center of the University of Sassari in partnership with the University of Barcelona (Spain), Institut des Régions Arides (Tunisia) and Agence Nationale des Ressources Hydrauliques (Algeria) and the international organization Observatorie du Sahara et du Sahel. The project is coordinated by Prof. Giorgio Ghiglieri. The project aims at the promotion of an integrated, sustainable water harvesting and agriculture management in two watersheds in Tunisia and Algeria. As agriculture and animal husbandry are the two main economic activities in these areas, demand and pressure on natural resources increase in order to cope with increasing population’s needs. In arid and semiarid study areas of Algeria and Tunisia, sustainable development of agriculture and resources management require the understanding of these dynamics as it withstands monitoring of desertification processes. Vegetation is the first indicator of decay in the ecosystem functions as it is sensitive to any disturbance, as well as soil characteristics and dynamics as it is edaphically related to the former. Satellite remote sensing of land affected by sand encroachment and salinity is a useful tool for decision support through detection and evaluation of desertification indicating features. Land cover, land use, soil salinization and sand encroachment are examples of such indicators that if integrated in a diachronic assessment, can provide quantitative and qualitative information on the ecological state of the land, particularly degradation tendencies. In recent literature, detecting and mapping features in saline and sandy environments with remotely sensed imagery has been reported successful through the use of both multispectral and hyperspectral imagery, yet the limitations to both image types maintain “no agreed-on best approach to this technology for monitoring and mapping soil salinity and sand encroachment”. Problems regarding the image classification of features in these particular areas have been reported by several researchers, either with statistical or neural/connectionist algorithms for both fuzzy and hard classifications methods. In this research, salt and sand features were assessed through both visual interpretation and automated classification approaches, employing historical and present Landsat imagery (from 1984 to 2015). The decision tree analysis was chosen because of its high flexibility of input data range and type, the easiness of class extraction through non-parametric, multi-stage classification. It makes no a priori assumption on class distribution, unlike traditional statistical classifiers. The visual interpretation mapping of land cover and land use was undergone according to acknowledged standard nomenclature and methodology, such as CORINE land cover or AFRICOVER 2000, Global Land Cove 2000 etc. The automated one implies a decision tree (DT) classifier and an unsupervised classification applied to the principal components (PC) extracted from Knepper ratios composite in order to assess their validity for the change detection analysis. In the Tunisian study area, it was possible to conduct a thorough ground truth survey resulting in a record of 400 ground truth points containing several information layers (ground survey sheet information on various land components, photographs, reports in various file formats) stored within the a shareable standalone geodatabase. Spectral data were also acquired in situ using the handheld ASD FieldSpec 3 Jr. Full Range (350 – 2500 nm) spectroradiometer and samples were taken for X-ray diffraction analysis. The sampling sites were chosen on the basis of a geomorphological analysis, ancillary data and the previously interpreted land cover/land use map, specifically generated for this study employing Landsat 7 and 8 imagery. The spectral campaign has enabled the acquisition of spectral reflectance measurements of 34 points, of which 14 points for saline surfaces (9 samples); 10 points for sand encroachment areas (10 samples); 3 points for typical vegetation (halophyte and psammophyte) and 7 points for mixed surfaces. Five of the eleven indices employed in the Decision Tree construction were constructed throughout the current study, among which we propose also a salinity index (SMI) for the extraction of highly saline areas. Their application have resulted in an accuracy of more than 80%. For the error estimation phase, the interpreted land cover/use map (both areas) and ground truth data (Oum Zessar area only) supported the results of the 1984 to 2014 salt – affected areas diachronic analysis obtained through both automatic methods. Although IsoDATA classification maps applied to Knepper ratios Principal Component Analysis has proven its good potential as an approach of fast automated, user-independent classifier, accuracy assessment has shown that decision tree outstood it and was proven to have a substantial advantage over the former. The employment of the Decision Tree classifier has proven to be more flexible and adequate for the extraction of highly and moderately saline areas and major land cover types, as it allows multi-source information and higher user control, with an accuracy of more than 80%. Integrating results with ancillary spatial data, we could argue driving forces, anthropic vs natural, as well as source areas, and understand and estimate the metrics of desertification processes. In the Biskra area (Algeria), results indicate that the expansion of irrigated farmland in the past three decades contributes to an ongoing secondary salinization of soils, with an increase of over 75%. In the Oum Zessar area (Tunisia), there was substantial change in several landscape components in the last decades, related to increased anthropic pressure and settlement, agricultural policies and national development strategies. One of the most concerning aspects is the expansion of sand encroached areas over the last three decades of around 27%

Mapping Soil Salinity and Its Impact on Agricultural Production in Al Hassa Oasis in Saudi Arabia

Soil salinity is considered as one of the major environmental issues globally that restricts agricultural growth and productivity, especially in arid and semi-arid regions. One such region is Al Hassa Oasis in the eastern province of Saudi Arabia, which is one of the most productive date palm (Phoenix dactylifera L.) farming regions in Saudi Arabia and is seriously threatened by soil salinity. Development of remote sensing techniques and modelling approaches that can assess and map soil salinity and the associated agricultural impacts accurately and its likely future distribution should be useful in formulating more effective, long-term management plans. The main objective of this study was to detect, assess and map soil salinity and and its impact on agricultural production in the Al Hassa Oasis. The presented research first started by reviewing the related literature that have utilized the use of remote sensing data and techniques to map and monitor soil salinity. This review started by discussing soil salinity indicators that are commonly used to detect soil salinity. Soil salinity can be detected either directly from the spectral reflectance patterns of salt features visible at the soil surface, or indirectly using the vegetation reflectance since it impacts vegetation. Also, it investigated the most commonly used remote sensors and techniques for monitoring and mapping soil salinity in previous studies. Both spectral vegetation and salinity indices that have been developed and proposed for soil salinity detection and mapping have been reviewed. Finally, issues limiting the use of remote sensing for soil salinity mapping, particularly in arid and semi-arid regions have been highlighted. In the second study, broadband vegetation and soil salinity indices derived from IKONOS images along with ground data in the form of soil samples from three sites across the Al Hassa Oasis were used to assess soil salinity in the Al-Hassa Oasis. The effectiveness of these indices to assess soil salinity over a dominant date palm region was examined statistically. The results showed that very strongly saline soils with different salinity level ranges are spread across the three sites in the study area. Among the investigated indices, the Soil Adjusted Vegetation Index (SAVI), Normalized Differential Salinity Index (NDSI) and Salinity Index (SI-T) yielded the best results for assessing the soil salinity in densely vegetated area, while NDSI and SI-T revealed the highest significant correlation with salinity for less densely vegetated lands and bare soils. In the third study, combined spectral-based statistical regression models were developed using IKONOS images to model and map the spatial variation of the soil salinity in the Al Hassa Oasis. Statistical correlation between Electrical Conductivity (EC), spectral indices and IKONOS original bands showed that the Salinity Index (SI) and red band (band 3) had the highest correlation with EC. Integrating SI and band 3 into one model produced the best fit with R2 = 0.65. The high performance of this combined model is attributed to: (i) the spatial resolution of the images; (ii) the great potential of SI in enhancing and delineating the spatial variation of soil salinity; and (iii) the superiority of band 3 in retrieving soil salinity features and patterns. Soil salinity maps generated using the selected model showed that strongly saline soils (>16 dS/m) with variable spatial distribution were the dominant class over the study area. The spatial variability of this class over the investigated areas was attributed to a variety factors, including soil factors, management related factors and climate factors.16 dS/m) with variable spatial distribution were the dominant class over the study area. The spatial variability of this class over the investigated areas was attributed to a variety factors, including soil factors, management related factors and climate factors. In the fourth study, Landsat time series data of years 1985, 2000 and 2013 were used to detect the temporal change in soil salinity and vegetation cover in the Al Hassa Oasis and investigate whether there is any linkage of vegetation cover change to the change in soil salinity over a 28-year period. Normalized Difference Vegetation Index (NDVI) and Soil Salinity Index (SI) differencing images were used to identify vegetation and salinity change/no-change for the two periods. The results revealed that soil salinity during 2000-2013 exhibited much higher increase compared to 1985-2000, while the vegetation cover declined for the same period. Highly significant (p In the fifth study, the effects of physical and proximity factors, including elevation, slope, soil salinity, distance to water, distance to built-up areas, distance to roads, distance to drainage and distance to irrigation factors on agricultural expansion in the Al Hassa Oasis were investigated. A logistic regression model was used for two time periods of agricultural change in 1985 and 2015. The probable agricultural expansion maps based on agricultural changes in 1985 was used to test the performance of the model to predict the probable agricultural expansion after 2015. This was achieved by comparing the probable maps of 1985 and the actual agricultural land of 2015 model. The Relative Operating Characteristic (ROC) method was also used and together these two methods were used to validate the developed model. The results showed that the prediction model of 2015 provides a reliable and consistent prediction based on the performance of 1985. The logistic regression results revealed that among the investigated factors, distance to water, distance to built-up areas and soil salinity were the major factors having a significant influence on agricultural expansion. In the last study, the potential distribution of date palm was assessed under current and future climate scenarios of 2050 and 2100. Here, CLIMEX (an ecological niche model) and two different Global Climate Models (GCMs), CSIRO-Mk3.0 (CS) and MIROC-H (MR), were employed with the A2 emission scenario to model the potential date palm distribution under current and future climates in Saudi Arabia. A sensitivity analysis was conducted to identify the CLIMEX model parameters that had the most influence on date palm distribution. The model was also run with the incorporation of six non-climatic parameters, which are soil taxonomy, soil texture, soil salinity, land use, landform and slopes, to further refine the distributions. The results from both GCMs showed a significant reduction in climatic suitability for date palm cultivation in Saudi Arabia by 2100 due to increment of heat stress. The lower optimal soil moisture, cold stress temperature threshold and wet stress threshold parameters had the greatest impact on sensitivity, while other parameters were moderately sensitive or insensitive to change. A more restricted distribution was projected with the inclusion of non-climatic parameters. Overall, the research demonstrated the potential of remote sensing and modeling techniques for assessing and mapping soil salinity and providing the essential information of its impacts on date palm plantation. The findings provide useful information for land managers, environmental decision makers and governments, which may help them in implementing more suitable adaptation measures, such as the use of new technologies, management practices and new varieties, to overcome the issue of soil salinity and its impact on this important economic crop so that long-term sustainable production of date palm in this region can be achieved. Additionally, the information derived from this research could be considered as a useful starting point for public policy to promote the resilience of agricultural systems, especially for smallholder farmers who might face more challenges, if not total loss, not only due to soil salinity but also due to climate change

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

Efficacy of machine learning, earth observation and geomorphometry for mapping salt-affected soils in irrigation fields

Thesis (MSc)--Stellenbosch University, 2018.ENGLISH ABSTRACT: There is a need to monitor salt accumulation throughout agricultural irrigation schemes as it can have a major negative impact on crop yields and subsequently result in a lower food production. Salt accumulation can result from natural processes, human interference or prolonged waterlogging. Most irrigation schemes are large and therefore difficult to monitor via conventional methods (e.g. regular field visits). More cost-effective, less time-consuming approaches in identifying salt-affected and salt-prone areas in large irrigation schemes are therefore needed. Remote sensing has been proposed as an alternative approach due to its ability to cover a large region on a timely basis. The approach is also more cost-effective because less field visits are required. A literature review on salt accumulation and remote sensing identified several direct and indirect methods for identifying salt-affected or salt-prone areas. Direct methods focus on the delineation of salt crusts visible on the bare soil in multispectral satellite imagery, whereas indirect methods, which include vegetation stress monitoring and geomorphometry (terrain analysis), attempt to take subsurface conditions into account. A disadvantage of the direct approach is that it does not take subsurface conditions into account, while vegetation stress monitoring (an indirect method) can produce inaccurate results because the vegetation stress can be a result of other factors (e.g. poor farming practices). Geomorphometry offers an alternative (modelling) approach that can either replace or augment direct and other indirect methods. Two experiments were carried out in this study, both of which focussed on machine learning (ML) algorithms (namely k-nearest neighbour (kNN), support vector machine (SVM), decision tree (DT) and random forest (RF)) and statistical analyses (regression or geostatistics) to identify salt-affected soils. The first experiment made use of very high resolution WorldView-2 (WV2) imagery. A number of texture measures and salinity indices were derived from the WV2 bands and considered as predictor variables. In addition to the ML and statistical analyses, a classification and regression tree (CART) model and Jeffries-Matusita (JM) distance thresholds were also produced from the predictors. The CART model was the most accurate in differentiating salt-affected and unaffected soils, but the accuracy of kNN and RF classifications were only marginally lower. The normalized difference salinity index showed the most promise among the predictors as it featured in the best JM, regression and CART models. The second experiment applied geomorphometry approaches to two South African irrigation schemes. Elevation sources include the Shuttle Radar Topographic Mission (SRTM) digital elevation model (DEM) and a digital surface model (DSM) produced from stereoscopic aerial photography. A number of morphological (e.g. slope gradient) and hydrological (e.g. flow direction) terrain parameters were derived from the SRTM DEM and the DSM and used as predictors. In addition to the algorithms used for the first experiment, the geostatistical method Kriging with external drift (KED) was also evaluated in this experiment. The source of elevation had an insignificant impact on the accuracies, although the DSM did show promise when combined with ML. KED outperformed regression modelling and ML in most cases, but ML produced similar results for one of the study areas. The experiments showed that direct and geomorphometry approaches hold much potential for mapping salt-affected soil. ML also proved to be a viable option for identifying salt-affected or salt-prone soil. It is recommended that a combination of direct and indirect (e.g. vegetation stress monitoring) approaches are considered in future research. Making use of alternative data sources such as hyperspectral imagery or higher spatial resolution DEMs may also prove useful. Clearly, more research is needed before such approaches can be operationalized for detecting, monitoring and mapping salt accumulation in irrigated areas.AFRIKAANSE OPSOMMING: Daar is 'n behoefte om soutophoping deur middel van landboubesproeiingskemas te monitor aangesien dit 'n beduidende negatiewe uitwerking op oesopbrengste kan hê en gevolglik tot laer voedselproduksie kan lei. Soutophoping kan voortspruit uit natuurlike prosesse, menslike inmenging of langdurige deurdrenking. Die meeste besproeiingskemas is groot en daarom moeilik om te monitor via konvensionele metodes (bv. gereelde veldbesoeke). Meer koste-effektiewe, minder tydrowende benaderings is dus nodig om soutgeaffekteerde areas en areas wat geneig is tot soutophoping in groot besproeiingskemas te identifiseer. Afstandswaarneming is voorgestel as 'n alternatiewe benadering weens sy vermoë om 'n groot streek op 'n tydige basis te dek. Die benadering is ook meer koste-effektief omdat minder veldbesoeke vereis word. 'n Literatuuroorsig oor soutophoping en afstandswaarneming het verskeie direkte en indirekte metodes geïdentifiseer om soutgeaffekteerde areas of areas geneig tot soutophoping te identifiseer. Direkte metodes fokus op die afbakening van soutkorste wat in multispektrale satellietbeelde op die kaal grond sigbaar is. Indirekte metodes, insluitende plantstresmonitering en geomorfometrie (terreinanalise), aan die ander kant, poog om die ondergrondse toestande in ag te neem. 'n Nadeel van die direkte benadering is dat dit nie ondergrondse toestande in ag neem nie, terwyl plantstresmonitering ('n indirekte metode) onakkurate resultate kan veroorsaak, aangesien die plantstres die gevolg kan wees van ander faktore (bv. swak boerderypraktyke). Geomorfometrie bied 'n alternatiewe (modellering) benadering wat direkte of ander indirekte metodes kan vervang of uitbrei. In hierdie studie is twee eksperimente uitgevoer. Albei het gefokus op masjienleer (ML) algoritmes, naamlik k-nearest neighour (kNN), ondersteunende vektormasjien, besluitboom en ewekansige woud (EW), en statistiese ontledings (regressie of geostatistiek) om soutgeaffekteerde gronde te identifiseer. Die eerste eksperiment het gebruik gemaak van baie hoë resolusie WorldView-2 (WV2) beelde. 'n Aantal tekstuurmaatreëls en soutindekse is afgelei van die WV2-bande en is beskou as voorspeller-veranderlikes. Benewens die ML en statistiese ontledings, is 'n klassifikasie- en regressieboom (KARB) model en Jeffries-Matusita (JM) afstandsdrempels ook van die voorspellers vervaardig. Die KARB-model het die mees akkuraatste differensiasie tussen sout-geaffekteerde en ongeaffekteerde grond gemaak, maar die akkuraatheid van kNN- en EW-klassifikasies was slegs marginaal laer. Van al die voorspellers het die genormaliseerde-verskil-saliniteit-indeks die meeste belofte getoon aangesien dit in die beste JM-, regressie- en KARB-modelle presteer het. Stellenbosch University https://scholar.sun.ac.za vi Die tweede eksperiment het geomorfometriese benaderings toegepas op twee Suid-Afrikaanse besproeiingskemas. Elevasiebronne sluit in die Shuttle Radar Topographic Mission (SRTM) digitale elevasie-model (DEM) en 'n digitale oppervlakmodel (DOM) wat uit stereoskopiese lugfotografie vervaardig word. 'n Aantal morfologiese (bv. hellingsgradiënt) en hidrologiese (bv. vloeirigting) terreinparameters is afgelei van die SRTM DEM en die DOM en is gebruik as voorspellers. Benewens die algoritmes wat vir die eerste eksperiment gebruik is, is die geostatistiese metode Kriging met eksterne dryf (KED) ook in hierdie eksperiment geëvalueer. Die bron van elevasie het 'n onbeduidende impak op die akkuraatheid gehad, hoewel die DOM belofte getoon het wanneer dit met ML gekombineer is. KED het in meeste gevalle beter presteer as regressie modellering en ML, maar ML het soortgelyke resultate vir een van die studiegebiede opgelewer. Die eksperimente het getoon dat direkte en geomorfometriese benaderings baie potensiaal het vir die kartering van soutgeaffekteerde grond. ML het ook bewys dat dit 'n lewensvatbare opsie is om soutgeaffekteerde grond of grond wat geneig is tot soutophoping, te identifiseer. Daar word aanbeveel dat 'n kombinasie van direkte en indirekte (bv. plantegroei-stresmonitering) benaderings in toekomstige navorsing oorweeg word. Die gebruik van alternatiewe databronne soos hiperspektrale beelde of hoër ruimtelike resolusie-DOM's kan ook nuttig wees. Dit is duidelik dat meer navorsing nodig is voordat sulke benaderings geoperasionaliseer kan word vir die opsporing, monitering en kartering van soutophoping in besproeide gebiede

Quantitative Mapping of Soil Property Based on Laboratory and Airborne Hyperspectral Data Using Machine Learning

Soil visible and near-infrared spectroscopy provides a non-destructive, rapid and low-cost approach to quantify various soil physical and chemical properties based on their reflectance in the spectral range of 400–2500 nm. With an increasing number of large-scale soil spectral libraries established across the world and new space-borne hyperspectral sensors, there is a need to explore methods to extract informative features from reflectance spectra and produce accurate soil spectroscopic models using machine learning. Features generated from regional or large-scale soil spectral data play a key role in the quantitative spectroscopic model for soil properties. The Land Use/Land Cover Area Frame Survey (LUCAS) soil library was used to explore PLS-derived components and fractal features generated from soil spectra in this study. The gradient-boosting method performed well when coupled with extracted features on the estimation of several soil properties. Transfer learning based on convolutional neural networks (CNNs) was proposed to make the model developed from laboratory data transferable for airborne hyperspectral data. The soil clay map was successfully derived using HyMap imagery and the fine-tuned CNN model developed from LUCAS mineral soils, as deep learning has the potential to learn transferable features that generalise from the source domain to target domain. The external environmental factors like the presence of vegetation restrain the application of imaging spectroscopy. The reflectance data can be transformed into a vegetation suppressed domain with a force invariance approach, the performance of which was evaluated in an agricultural area using CASI airborne hyperspectral data. However, the relationship between vegetation and acquired spectra is complicated, and more efforts should put on removing the effects of external factors to make the model transferable from one sensor to another.:Abstract I Kurzfassung III Table of Contents V List of Figures IX List of Tables XIII List of Abbreviations XV 1 Introduction 1 1.1 Motivation 1 1.2 Soil spectra from different platforms 2 1.3 Soil property quantification using spectral data 4 1.4 Feature representation of soil spectra 5 1.5 Objectives 6 1.6 Thesis structure 7 2 Combining Partial Least Squares and the Gradient-Boosting Method for Soil Property Retrieval Using Visible Near-Infrared Shortwave Infrared Spectra 9 2.1 Abstract 10 2.2 Introduction 10 2.3 Materials and methods 13 2.3.1 The LUCAS soil spectral library 13 2.3.2 Partial least squares algorithm 15 2.3.3 Gradient-Boosted Decision Trees 15 2.3.4 Calculation of relative variable importance 16 2.3.5 Assessment 17 2.4 Results 17 2.4.1 Overview of the spectral measurement 17 2.4.2 Results of PLS regression for the estimation of soil properties 19 2.4.3 Results of PLS-GBDT for the estimation of soil properties 21 2.4.4 Relative important variables derived from PLS regression and the gradient-boosting method 24 2.5 Discussion 28 2.5.1 Dimension reduction for high-dimensional soil spectra 28 2.5.2 GBDT for quantitative soil spectroscopic modelling 29 2.6 Conclusions 30 3 Quantitative Retrieval of Organic Soil Properties from Visible Near-Infrared Shortwave Infrared Spectroscopy Using Fractal-Based Feature Extraction 31 3.1 Abstract 32 3.2 Introduction 32 3.3 Materials and Methods 35 3.3.1 The LUCAS topsoil dataset 35 3.3.2 Fractal feature extraction method 37 3.3.3 Gradient-boosting regression model 37 3.3.4 Evaluation 41 3.4 Results 42 3.4.1 Fractal features for soil spectroscopy 42 3.4.2 Effects of different step and window size on extracted fractal features 45 3.4.3 Modelling soil properties with fractal features 47 3.4.3 Comparison with PLS regression 49 3.5 Discussion 51 3.5.1 The importance of fractal dimension for soil spectra 51 3.5.2 Modelling soil properties with fractal features 52 3.6 Conclusions 53 4 Transfer Learning for Soil Spectroscopy Based on Convolutional Neural Networks and Its Application in Soil Clay Content Mapping Using Hyperspectral Imagery 55 4.1 Abstract 55 4.2 Introduction 56 4.3 Materials and Methods 59 4.3.1 Datasets 59 4.3.2 Methods 62 4.3.3 Assessment 67 4.4 Results and Discussion 67 4.4.1 Interpretation of mineral and organic soils from LUCAS dataset 67 4.4.2 1D-CNN and spectral index for LUCAS soil clay content estimation 69 4.4.3 Application of transfer learning for soil clay content mapping using the pre-trained 1D-CNN model 72 4.4.4 Comparison between spectral index and transfer learning 74 4.4.5 Large-scale soil spectral library for digital soil mapping at the local scale using hyperspectral imagery 75 4.5 Conclusions 75 5 A Case Study of Forced Invariance Approach for Soil Salinity Estimation in Vegetation-Covered Terrain Using Airborne Hyperspectral Imagery 77 5.1 Abstract 78 5.2 Introduction 78 5.3 Materials and Methods 81 5.3.1 Study area of Zhangye Oasis 81 5.3.2 Data description 82 5.3.3 Methods 83 5.3.3 Model performance assessment 85 5.4 Results and Discussion 86 5.4.1 The correlation between NDVI and soil salinity 86 5.4.2 Vegetation suppression performance using the Forced Invariance Approach 86 5.4.3 Estimation of soil properties using airborne hyperspectral data 88 5.5 Conclusions 90 6 Conclusions and Outlook 93 Bibliography 97 Acknowledgements 11