Procjena utjecaja atmosferske korekcije na podatke Landsat 8 i Sentinel-2 za određivanje saliniteta tla

Remote sensing technology effectively determines and evaluates salinity-affected areas\u27 spatial and temporal distribution. Soil salinity maps for large areas can be obtained with low cost and low effort using remote sensing methods and techniques. Remote sensing data are delivered raw as Level-1 data, and they can be further atmospherically corrected to surface reflectance values, Level-2 data. This study evaluates the atmospheric correction impact on Landsat 8 and Sentinel-2 data for soil salinity determination. The study has been supported with in-situ measurements in Alpu, Eskisehir, Turkey, where samples were collected from various agricultural fields simultaneously with the overpass of the satellites. Two different analysis cases have been used to determine the effect of atmospheric correction. The first is to examine the relationship between the measurements taken from the areas with mixed product groups and the salinity indices for both data types. The other is to investigate the relationship between the measurement values taken only from the wheat and beet groups and the salinity index values. The results show that atmospheric correction has a high effect on the relationship between spectral indices and in situ salinity measurement values. Especially in all cases examined in Landsat, it was observed that atmospheric correction led to an improvement of over 140%, while nearly 50% was observed in Sentinel on a product basis.Uz pomoć tehnologije daljinskih istraživanja učinkovito se određuje i procjenjuje prostorna i vremenska rasprostranjenost područja zahvaćenih salinitetom. Karte saliniteta tla za velika područja mogu se izraditi uz niske troškove i malo truda koristeći metode i tehnike daljinskih istraživanja. Podaci dobiveni daljinskim istraživanjima isporučuju se neobrađeni kao podaci Level-1 te se zatim mogu atmosferski korigirati na vrijednosti površinske refleksije, podaci Level-2. Ova studija procjenjuje utjecaje atmosferske korekcije na podatke Landsat 8 i Sentinel-2 za određivanje saliniteta tla. Studija je potkrijepljena mjerenjima in situ u Alpu, Eskisehir, Turska, gdje su uzorci bili prikupljeni na različitim poljoprivrednim poljima istovremeno s preletima satelita. Upotrijebljene su dvije različite analize kako bi se odredio učinak atmosferske korekcije. Prva je analiza primijenjena kako bi se ispitao odnos između mjerenja provedenih na područjima s miješanim skupinama proizvoda i indeksima saliniteta za obje vrste podataka. Druga je analiza primijenjena kako bi se istražio odnos između vrijednosti mjerenja dobivenih samo iz skupina pšenice i repe te vrijednosti indeksa saliniteta. Rezultati pokazuju da atmosferska korekcija ima visok učinak na odnos između spektralnih indeksa i vrijednosti mjerenja saliniteta in situ. Posebno se u svim slučajevima ispitivanja putem Landsata moglo primijetiti da je atmosferska korekcija dovela do poboljšanja za više od 140%, dok je gotovo 50% primijećeno za Sentinel na temelju proizvoda

Procjena utjecaja atmosferske korekcije na podatke Landsat 8 i Sentinel-2 za određivanje saliniteta tla

Remote sensing technology effectively determines and evaluates salinity-affected areas\u27 spatial and temporal distribution. Soil salinity maps for large areas can be obtained with low cost and low effort using remote sensing methods and techniques. Remote sensing data are delivered raw as Level-1 data, and they can be further atmospherically corrected to surface reflectance values, Level-2 data. This study evaluates the atmospheric correction impact on Landsat 8 and Sentinel-2 data for soil salinity determination. The study has been supported with in-situ measurements in Alpu, Eskisehir, Turkey, where samples were collected from various agricultural fields simultaneously with the overpass of the satellites. Two different analysis cases have been used to determine the effect of atmospheric correction. The first is to examine the relationship between the measurements taken from the areas with mixed product groups and the salinity indices for both data types. The other is to investigate the relationship between the measurement values taken only from the wheat and beet groups and the salinity index values. The results show that atmospheric correction has a high effect on the relationship between spectral indices and in situ salinity measurement values. Especially in all cases examined in Landsat, it was observed that atmospheric correction led to an improvement of over 140%, while nearly 50% was observed in Sentinel on a product basis.Uz pomoć tehnologije daljinskih istraživanja učinkovito se određuje i procjenjuje prostorna i vremenska rasprostranjenost područja zahvaćenih salinitetom. Karte saliniteta tla za velika područja mogu se izraditi uz niske troškove i malo truda koristeći metode i tehnike daljinskih istraživanja. Podaci dobiveni daljinskim istraživanjima isporučuju se neobrađeni kao podaci Level-1 te se zatim mogu atmosferski korigirati na vrijednosti površinske refleksije, podaci Level-2. Ova studija procjenjuje utjecaje atmosferske korekcije na podatke Landsat 8 i Sentinel-2 za određivanje saliniteta tla. Studija je potkrijepljena mjerenjima in situ u Alpu, Eskisehir, Turska, gdje su uzorci bili prikupljeni na različitim poljoprivrednim poljima istovremeno s preletima satelita. Upotrijebljene su dvije različite analize kako bi se odredio učinak atmosferske korekcije. Prva je analiza primijenjena kako bi se ispitao odnos između mjerenja provedenih na područjima s miješanim skupinama proizvoda i indeksima saliniteta za obje vrste podataka. Druga je analiza primijenjena kako bi se istražio odnos između vrijednosti mjerenja dobivenih samo iz skupina pšenice i repe te vrijednosti indeksa saliniteta. Rezultati pokazuju da atmosferska korekcija ima visok učinak na odnos između spektralnih indeksa i vrijednosti mjerenja saliniteta in situ. Posebno se u svim slučajevima ispitivanja putem Landsata moglo primijetiti da je atmosferska korekcija dovela do poboljšanja za više od 140%, dok je gotovo 50% primijećeno za Sentinel na temelju proizvoda

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

A PLSR model to predict soil salinity using Sentinel-2 MSI data

Salinization is one of the most widespread environmental threats in arid and semi-arid regions that occur either naturally or artificially within the soil. When exceeding the thresholds, salinity becomes a severe danger, damaging agricultural production, water and soil quality, biodiversity, and infrastructures. This study used spectral indices, including salinity and vegetation indices, Sentinel-2 MSI original bands, and DEM, to model soil salinity in the Great Hungarian Plain. Eighty-one soil samples in the upper 30 cm of the soil surface were collected from vegetated and nonvegetated areas by the Research Institute for Soil Sciences and Agricultural Chemistry (RISSAC). The sampling campaign of salinity monitoring was performed in the dry season to enhance salt spectral characteristics during its accumulation in the subsoil. Hence, applying a partial least squares regression (PLSR) between salt content (g/kg) and remotely sensed data manifested a highly moderate correlation with a coefficient of determination R2 of 0.68, a p-value of 0.000017, and a root mean square error of 0.22. The final model can be deployed to highlight soil salinity levels in the study area and assist in understanding the efficacy of land management strategies

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

Hyperspectral Imaging for Fine to Medium Scale Applications in Environmental Sciences

The aim of the Special Issue “Hyperspectral Imaging for Fine to Medium Scale Applications in Environmental Sciences” was to present a selection of innovative studies using hyperspectral imaging (HSI) in different thematic fields. This intention reflects the technical developments in the last three decades, which have brought the capacity of HSI to provide spectrally, spatially and temporally detailed data, favoured by e.g., hyperspectral snapshot technologies, miniaturized hyperspectral sensors and hyperspectral microscopy imaging. The present book comprises a suite of papers in various fields of environmental sciences—geology/mineral exploration, digital soil mapping, mapping and characterization of vegetation, and sensing of water bodies (including under-ice and underwater applications). In addition, there are two rather methodically/technically-oriented contributions dealing with the optimized processing of UAV data and on the design and test of a multi-channel optical receiver for ground-based applications. All in all, this compilation documents that HSI is a multi-faceted research topic and will remain so in the future

Mapping Soil Alkalinity and Salinity in Northern Songnen Plain, China with the HJ-1 Hyperspectral Imager Data and Partial Least Squares Regression

In arid and semi-arid regions, identifying and monitoring of soil alkalinity and salinity are in urgently need for preventing land degradation and maintaining ecological balances. In this study, physicochemical, statistical, and spectral analysis revealed that potential of hydrogen (pH) and electrical conductivity (EC) characterized the saline-alkali soils and were sensitive to the visible and near infrared (VIS-NIR) wavelengths. On the basis of soil pH, EC, and spectral data, the partial least squares regression (PLSR) models for estimating soil alkalinity and salinity were constructed. The R2 values for soil pH and EC models were 0.77 and 0.48, and the root mean square errors (RMSEs) were 0.95 and 17.92 dS/m, respectively. The ratios of performance to inter-quartile distance (RPIQ) for the soil pH and EC models were 3.84 and 0.14, respectively, indicating that the soil pH model performed well but the soil EC model was not considerably reliable. With the validation dataset, the RMSEs of the two models were 1.06 and 18.92 dS/m. With the PLSR models applied to hyperspectral data acquired from the hyperspectral imager (HSI) onboard the HJ-1A satellite (launched in 2008 by China), the soil alkalinity and salinity distributions were mapped in the study area, and were validated with RMSEs of 1.09 and 17.30 dS/m, respectively. These findings revealed that the hyperspectral images in the VIS-NIR wavelengths had the potential to map soil alkalinity and salinity in the Songnen Plain, China

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