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Soil separability through remote sensing for land evaluation
- 1. 1 Dathan.C.S
- 2. Introduction Being a part of the Earth’s spheres, the pedosphere is responding and contributing to the environmental changes (Macías and Arbestain, 2010) 2
- 3. 3
- 4. ‘Assessment of land performance when used for a specified purpose, involving the execution and interpretation of surveys and studies of land forms, soils, vegetation, climate and other aspects of land in order to identify and make a comparison of promising kinds of land use in terms applicable to the objectives of the evaluation’. (FAO, 1976) Land evaluation 4
- 5. SOIL SEPARABILITY THROUGH REMOTE SENSING FOR LAND EVALUATION 5
- 6. “The creation of spatial soil information by the use of field and laboratory observation methods coupled with spatial and non spatial soil inference systems.” (Grunwald, 2010) 6 Digital Soil Mapping (DSM)
- 7. -Remote sensing deals with data collected by electromagnetic energy -Distribution of the continuum of radiant energy can be plotted as a function of wavelength (or frequency) and is known as the electromagnetic radiation (EMR) spectrum Remote sensing- A basic overview 7
- 8. The Foundation of RS Energy interactions with earth surface features Basic interactions between electro magnetic energy and an Earth Surface feature Energy balance equation EI() = ER() + EA() + ET() 8 1 1 1
- 9. Seven Elements of Remote Sensing A. Energy Source or Illumination B. Radiation and atmosphere C. Interaction with target D. Recording of Energy by the Sensor E . Transmission, Reception, and Processing F. Interpretation and analysis G. Application 9
- 10. • Source of energy is Sun or earth • Remote sensing systems which measure energy that is naturally available are called passive sensors • Operate in the VIS, IR and micro wave portions EMR Eg - Landsat Types of remote sensing-Passive 10
- 11. • Source of energy is part of the remote sensor system • Active sensors provide their own energy source for illumination • Operate in the microwave portion of EMR Eg. Radar, Lidar Types of remote sensing- Active 11
- 12. Region Wavelength Remarks Gamma rays 3x10-5 Not available for RS. Absorbed by moistureX rays 3x10-5 - 3x10-3 UV rays 0.03-0.4 Between 0.03 and 0.4 is called photographic UV band. Not used for RS. Visible 0.4 – 0.7 Used for RSInfrared 0.7 - 100 Micro wave 103 - 106 Radio 106 Longest wave length. Used for remote sensing by some RADARS12 EMR-Spectrum characteristics
- 13. The Foundation of RS Spectral signatures – Each object on earth has a particular reflectance or emittance when exposed to a particular region of the EMR. This would be unique to that object which is called its spectral signature. 13 Bands - Each sensor in a satellite contain wavelength ranges in which information in the form of spectral signatures are stored. This is band. A sensor can have many bands of the EMS.
- 14. The Foundation of RS Atmospheric windows-Atmospheric window is that portion of the electromagnetic spectrum that can be transmitted through the atmosphere with least distortion or absorption. 14 Spatial resolution - Spatial resolution states about the clarity of an image.
- 15. Idava, Kollam. Toposheet (1 : 25 000) Spectral signatures
- 16. 16 Idava, Kollam. Google Earth. True color composite Spectral signatures
- 17. 17 Idava, Kollam. Satellite imagery (LISS II ). False color composite Spectral signatures
- 18. 18 Idava, kollam. Satellite imagery (LISS II ). Grey color composite Spectral signatures
- 19. RS technologies - Optical sensors (a) Hyperspectral imaging Hyperspectral imaging: Passive RS technology, acquiring simultaneous images in many spectrally contiguous, registered bands such that for each pixel a reflectance spectrum can be derived. 19
- 20. Multi spectral imaging - Record data in fewer bands, resulting in a coarser spectral resolution compared to hyper spectral sensors. 20
- 21. 21 Radar - Both systems are highly suitable to quantify soil moisture, whereas active systems are additionally used to derive terrain and soil attributes.
- 22. LIDAR sensors - Active (Light Detection And Ranging) LIDAR-is a surveying technology that measures distance by illuminating a target with a laser light. LiDAR (Light Detection and Ranging) is a widely used data source to generate DEM. 22
- 23. RS assisted soil parameters Mineralogy Soil texture Soil moisture Soil organic carbon Iron content Soil salinity Soil degradation 23
- 24. Remote sensing- digital soil products 1. Mineralogy In order to discriminate between different minerals, subtle differences in the spectral signature throughout the VNIR to TIR may be used. 24(Clark et al., 2003 )
- 25. 1. Mineralogy USGS spectral library - spib06 25 ASTER spectral library version 2.0
- 26. 1. Mineralogy Examples of ASTER mineral library reflectance spectra of several classes demonstrating the variety in spectral shapes across both the visible to shortwave infra- red (2.0–15.4 µm) WL ranges 26
- 27. 2. Soil texture Typical spectra curves for various soils and non soil classes (Venkataratnam, 1980)
- 28. 2. Soil texture Nominal clay content (%) for distinct soil units of Wales, based on Aster data 28(Mayr and Palmer, 2006 )
- 29. 3. Soil Moisture Characteristic peaks of water and hydroxyl bands 29(Baumgardner et al., 1985)
- 31. 4. Soil organic matter 31 Effect of organic matter on spectral reflectance of soil (Sinha, 1987).
- 32. 4. Soil organic matter 32 Generally, the greater the amount of organic content in a soil, the greater the absorption of incident energy and the lower the spectral reflectance.
- 33. Remote sensing- digital soil products 4. Soil organic matter Map of SOC content in a freshly ploughed field using airborne imaging spectroscopy. Dashed lines denote boarders of the original, separated fields 33DEM of the study area (Stevens et al., 2006)
- 34. Range of organic carbon (%) for the topsoil. Namoi Valley, Australia. 4. Soil organic matter 34 (Minasny, 2006 )
- 35. Harmonized World Soil Database (HWSD) 4. Soil organic matter FAO soils portal - 35
- 36. 5. Iron content Effect of iron oxide on spectral reflectance of soil 36 (Sinha, 1987)
- 37. 5. Iron content Iron oxide in a sandy loam soil causes an increase in reflectance in the red portion of the spectrum (0.6 - 0.7 mm) and a decrease in in near-infrared (0.85 - 0.90 mm) reflectance 37
- 38. 5. Iron content Map of free iron oxides at the Ashdod sand dunes, Israel. 38(Ben-Dor et al., 2008 )
- 39. Raw reflectance spectra of five soil salinity classes. 6. soil salinity 39 The spectral response patterns of saline soils are a function of the quantity and mineralogy of the salts they contain (Mougenot et al., 1993).
- 40. Case study Aim - Monitoring of salinity in the area using multi-temporal satellite images. Study area - South west Punjab Imagery used – IRS 1C satellite, LISS III Results During ground verification salt accumulation was also found to be associated with salt grass and salt tolerant wild vegetation. The area mapped in the classes of moderate and severe salt affected soil was 1.72 % and 7.90% of the total area. 40 Map showing preliminary interpreted units on FCC with base details 6. soil salinity (Koshik, 2010)
- 41. 7. Soil degradation Sludge abundance map based on HyMap data from 1999 in Aznalcollar, Spain. The sludge affected area (black background) is super imposed on the HyMap false color image 41(Kemper and Sommer, 2003 )
- 42. RS-techs for soil attribute retrieval Soil attributes RADAR LIDAR Optical Relevant spectra regionActive Passive MS HS Minerology -- -- -- 3 4 VNIR-TIR Soil texture -- 3 -- 3 3 SWIR-TIR Iron content -- -- -- 1 3 VNIR SOC -- -- -- 1 5 VIS-SWIR Soil moisture 4 4 -- 3 2 MW Salinity -- -- -- 2 3 VIS-FIR Carbonates -- -- -- 2 2 VIS Vegetation patterns -- -- -- 5 5 NIR Land cover -- 2 -- 4 5 VIS-NIR Numbers (1–5) indicate the feasibility to determine attributes with RS. The feasibility represents the weighted average of scores based on studies reported, dataset quality, obtained result and applicability to field surveys. 1=low, 2=low-medium, 3=medium, 4=medium-high and 5=high [Mulder et al., 2011]. 42
- 43. Ongoing and future missions 1. Global Soil Map ( A consortium) Soil depth, water storage, texture, fertility and carbon at fine spatial resolution (~100 m). Interpretation and functionality options for a range of global issues such as food production, climate change, and environmental. Freely available, web-accessible [Arrouays et al., 2014]. 43
- 44. 2. TanDEM-x mission (public and private) -Two satellites in a closely controlled formation with typical distances between 250 and 500 m. -This unique twin satellite constellation will allow the generation of the World DEM 44 Ongoing and future missions
- 45. 3. SMAP and EnMAP (NASA ) -SMAP is designed to measure soil moisture in the top 5 cm (2 inches) of soil everywhere on Earth’s surface over a three-year period, every 2-3 days -Weather and climate studies will use these data. Ongoing and future missions 45
- 46. 4.Coppernicus programme (EC and ESA ) -Provides geographical information on land cover/land use and on variables related to vegetation state and the water cycle. -The Copernicus programme comprises satellite-borne earth observation and in-situ data and a services component that combines these in order to provide information essential for monitoring the terrestrial environment.. 46 Ongoing and future missions
- 47. 5. THEIA Land Data Centre (French initiative) -Designed to promote the use of satellite data for (1) environmental research on land surfaces, (2) public policy monitoring and (3) management of environmental resources. North morombe, Madagascar. Imagery from Theia LDC North morombe, Madagascar. Imagery from Google earth 47 Ongoing and future missions
- 48. 6.LP DAAC (NASA and USGS ) -Land Process Data Distributed Active Archive Center - Land data Products and services from NASA -The LP DAAC is a component of NASA’s Earth Observing System (EOS). A large crack found through land data in Arizona Ongoing and future missions 48
- 49. 49 Natural resources monitoring and management in India, using RS NRSC is responsible for acquisition, processing, supply of aerial and satellite remote sensing data. NRSC has images from Indian and foreign RS satellites in its archives and also has the capability to acquire data pertaining to any part of the globe on demand. National Remote Sensing Agency, Hyderabad (ISRO)
- 50. 50 IRS -Applications IMSD programme Biodiversity Characterizations at landscape level Crop area estimation Drought monitoring and assessment based on vegetation condition Coastal studies Mineral mapping Environmental impact analysis Forest survey IRSLand use/cover mapping Wetland mapping
- 51. 51 Natural resources monitoring and management in India, using RS National Natural Resources Management System (NNRMS) • Nodal agency – Dept of Space (DOS) • Data from the IRS satellites is received and disseminated by several countries all over the world. • The IRS system is the largest constellation of remote sensing satellites in operation today in the world, with 11 operational satellites
- 52. Serial No. Satellite Year of Launch Status 1 IRS P6 (Resourcesat-1) 2003 In service 2 IRS P5 (Cartosat -1) 2005 In service 3 Cartosat -2 (IRS P7) 2007 In service 4 Cartosat -2A 2008 In service 5 IMS 1 2008 In service 6 Oceansat -2 2009 In service 7 Cartosat -2B 2010 In service 8 Resourcesat -2 2011 In service 9 Megha -Tropiques 2011 In service 10 RISAT-1 2012 In service 11 SARAL 2013 In service 52 Indian RS satellites
- 53. 53 Conclusions Conventional mapping can greatly benefit from RS, especially nowadays where many different satellite images and aerial photographs are available with different spatial, spectral, and temporal resolutions. Different unexplored fields of RS are under research and definitely promising tools for mapping soils in the near future. RS tools are very useful for soil mapping in conventional and DSM. RS and Pedometrics are directly linked in generating ancillary data layers for mapping soils. Thus, they have huge potential in soil mapping.
- 54. 54Thank you
- 55. 55
Notes de l'éditeur
- We all are changing. I mean the climate is changing . so are we. Climatic catastrophes, natural calamities, unseen sudden u and down shifts in temperatures, manmade disasters, everything are on the rise. This is likely to continue. The food that we had, the clothes we used, the money we spend for comforts ( Cars with ACs are a must now. A luxury modified to a necessity, influenced by climate]. We are in the brinks of a natural resource deletion. We think we have plenty. But in fact not. Humans are like viruses. They invade a place. Utilize all the resource there, multiply, grow, and when overcrowded and resources become scarce, Move to new places. We also do the same thing. We never really replenish much. Soils are the complex dynamic, delicate, sensitive systems that supports every life form on earth. Soils being a vital natural resource that provide multiple ecosystem services. Being a part of the Earth’s spheres, the pedosphere is responding and to these environmental changes
- Observed changes in the functioning of the pedosphere alarmed the recognition amoung scientists that land evaluations are necessary for assuring food security (Global Soil Partnership, 2011; Grunwald, 2011; Mulder, 2013). In this context, monitoring tools are needed for maintaining a sustainable soil health and resource planning for future needs.
- Land evaluation is the key for any sustainable agricultural development activity which assesses the suitability of land for specified land uses. For land evaluations generally we use soil surveys. But Conventional soil surveys are time consuming, costly, and limited in retrieving the temporal and spatial variability. In this context, remote sensing (RS) is now in a strong position to provide meaningful spatial data for studying soil properties on various spatial scales using different parts of the electromagnetic spectrum. Though conventional soil surveys were providing information on soils they are subjective, time consuming and laborious. Remote sensing techniques have significantly contributed speeding up conventional soil survey programmes. In conventional approach approximately 80% of total work requires extensive field traverses in identification of soil types and mapping their boundaries and 20% in studying soil profiles, topographical features and for other works. In the case of soil surveys with aerial photographs or satellite data considerable field work with respect to locating soil types and boundaries is reduced owing to synoptic view. Remote sensing techniques have reduced field work to a considerable extent and soil boundaries are more precisely delineated than in conventional methods.
- The increasing power of tools such as Geographic Information Systems (GIS), Global Positioning System (GPS), remote and proximal sensors (RS and PS) and data sources such as those provided by digital elevation models (DEM) increased the potential of mapping soils over vast areas. Consequently, worldwide, organizations are investigating the possibility of applying the new information technology and science to assess soil properties. This recent approach of soil surveying combines limited field and laboratory observations with the vast amount of RS data using GIS have aved way for a new concet called DSM. RS can be used as a data source supporting DSM. Using a broad range of data sources and methods, DSM aims to provide up-to-date and accurate soil maps to meet the current and future need for soil information. DSM is flexible and more suitable in providing soil information for specific applications compared to conventional soil mapping.
- The electromagnetic spectrum is the collective term for all known frequencies and their linked wavelengths of the known photons ( electromagnetic radiation ). The "electromagnetic spectrum" of an object has a different meaning, and is instead the characteristic distribution of electromagnetic radiation emitted or absorbed by that particular object. The electromagnetic spectrum extends from high WL used for modern radio communication to gamma radiation at the short-wavelength (high-frequency) end, thereby covering wavelengths from thousands of kilometers down to a fraction of the size of an atom. Visible light lies toward the shorter end, with wavelengths from 400 to 700 nanometres.
- When electromagnetic energy is incident on any given earth surface feature, three fundamental energy interactions with the feature are, reflected, absorbed, and or transmitted with all energy components being a function of wavelength. The above equation is an energy balance equation expressing the interrelationship between the mechanisms of reflection, absorption, and transmission.
- Here, each pixel acquires many bands of light intensity data from the spectrum, instead of just the three bands of the RGB color model. Spectral imaging can allow extraction of additional information the human eye fails to capture . These data allow mapping of key iron mineralogy such as hematite, goethite and jarosite as well as alteration minerals such as kaolinite, dickite, alunite and sericite (Clark et al., 1990). Hyperspectral sensors represent one of the most important technological trends in remote sensing, The system includes datasets composed of large (about 100 to 200) spectral bands of relatively narrow bandwidths (5-20 nm). The system includes datasets composed of large (about 100 to 200) spectral bands of relatively narrow bandwidths (5-20 nm). Soils are complex dynamic systems, which are formed and developed as a result of the combined effects of climate, biotic activities, and topography. Soil genesis modifies the chemical, physical, and mineralogical properties of soil surfaces. This process results in distinct spectral absorption features, which can be detected using high-resolution reflectance spectra [Leone and Sommer, 2000]. Some of the most significant absorption features occur in the VNIR and SWIR range (0.4 nm to 2.5 !m) (Fig. 7 A,B). These absorption characteristics can vary in their spectral depth, width, and location and therefore serve as diagnostic indicators, which enable us to characterize soil properties. In particular, the amount of organic matter and iron content, particle size istribution, clay mineralogy, water content, soil contamination, CEC and calcium carbonate content, can be determined with imaging spectroscopy [Ben-Dor et al., 2009].
- A multispectral image is one that captures image data at specific frequencies across the electromagnetic spectrum. Multispectral remote sensing is defined as the collection of reflected, .mitted, or backs.attered energy from an object or area of interest in multiple bands of electromagnetic spectrum. Typically, multispectral data has been used to derive information on land cover and land use, vegetation indices, land degradation and terrain attributes, crop residues and soil, distinguishing iron oxides, soil texture, mineralogy etc. [Abrams and Hook, 1995; Hubbard and Crowley, 2005; Hubbard et al., 2003]. Some multispectral sensors also include spectral bands in the TIR, which measure the thermally emitted radiance from the soil surface. The emissivity is conditioned by temperature, the chemical composition, surface roughness, and physical parameters of the surface, e.g. moisture content. TIR data have been used in combination with other spectral data to discriminate dark clay soils and bright sandy soils from non-photosynthetic vegetation [Breunig et al., 2008; Salisbury and D'Aria, 1992]. Further applications include determining soil salinity and soil moisture as well as establishing soil–vegetation–atmosphere transfer models to estimate root zone soil moisture.
- The main advantage of radar sensors in comparison to optical and LiDAR sensors is their ability to make ground observations independent of most weather conditions (e.g., clouds, fog). Radar sensors can penetrate through soil to a depth that is equal to 10–25% of their wavelength which equals few millimetres to centimetres depending of the wavelength range. Radar active Active microwave sensors can achieve high spatial resolutions on a local to regional scale using Synthetic Aperture RADAR (SAR) systems. SAR is the most common imaging active microwave configuration, where microwave pulses are processed together to simulate a very long aperture capable of high spatial resolution. In addition to soil moisture, active SAR data are widely used to generate DEMs and other soil attributes, such as soil texture and salinity [Paulik et al., 2014]. Radar Passive Passive microwave systems Passive microwaves sensors measure the intensity of a soil’s microwave emission in a low spatial resolution (~10-50 km) due to the low signal-strength at these wavelengths.
- Uses blending this topographical information with the farmland yield crop mapping in orchards and vineyards. A main limitation for LiDAR based approach is vegetation cover density. For LiDAR, too small gap fractions in the canopy prevent the laser pulse to reach the ground.
- Spectral response pattern of soil is generally governed by a number of factors. Chemical compositions of the soil influences spectral signature of soils through the absorption processes. In near infrared (NIR) and middle infrared (MIR) domain, absorption feature of soil components in solid phase originate primarily from the vibrations of bounded nuclei. In MIR region, in addition to vibrations, molecular rotation and transition may occur in the pores where gas and water molecules reside, which also results in higher absorption.
- Airborne imaging spectroscopy data are highly suitable for this task (e.g. AVIRIS, HyMAP), given its high spatial and spectral resolution [Green et al., 1998]. For example, AVIRIS data has been used to analyse the variation in type and their mineralogical and chemical compositions, by mapping SiO2 and Al2O3 in order to estimate the degree of soil weathering [Bedini et al., 2009; Galvão, 2008; Green et al., 2003; Kruse et al., 2003; Launeau et al., 2004; Martini et al., 2004]. The combination of Landsat TM data and ASTER data revealed promising results to differentiate the general lithological variability The spectral features of typical rocks on Earth are mostly found in the TIR region, where quartzite, carbonate, silicate and mafic minerals can be discerned. In local studies, advanced methods for deriving minerals from ASTER data have resulted, in classification accuracies up to 86%.
- 2. USGS library - Researchers at the Lab have measured the spectral reflectance of hundreds of materials in the lab, and have compiled a spectral library. The library called splib06 is used as a reference for material identification in remote sensing images. library includes many more minerals, organic and volatile compounds, vegetation, and man-made. The database is over 6,000 web pages. Researchers at the USGS Spectroscopy Lab are studying and applying methods for identifying and mapping materials through spectroscopic remote sensing on the earth and throughout the solar system using laboratory, field, airborne and spacecraft spectrometers. 1.Aster sPectral library - was released on December 3rd, 2008. The ASTER spectral library is available on CD-ROM at no charge by completing a simple order form. Please note it takes 6 to 8 weeks to ship your order. The ASTER spectral library includes a comprehensive search tool which allows you to search the library database for your material. The search returns a list of materials that match your search criteria.
- Soil texture refers to relative proportion of sand, silt & clay and affects the spectral reflectance of the soils due to its influence on water holding capacity and the size of soil particles. Finer the particles size, the soil surface becomes smoother and reflection more. An increase in particle size causes a decrease in reflectance. Silt content found to have major influence. The reflectance becomes lower as the silt content decreases (Hoffer 1978). However, it is commonly observed that sandy soil exhibits higher reflectance than that of clayey soil, which is due to abundance of macro pores and air-soil interface that cause multiple reflection/ scattering. In standard soil analysis, soil texture classes, such as silt, sand or clay are determined by their particle size distribution or physical texture. In RS, soil texture is typically determined using specific absorption features to differentiate between clay-rich and quartz-rich soils. Clay rich materials-captured with bands 5 and 6 of ASTER, referred to as the SWIR Clay Index. absorption at 2200 nm. quartz-rich soils-correspond with bands 10 to 14 of ASTER. detected using thermal bands between 8000 nm and 9500 nm in thermal bands. The combination of ASTER SWIR bands 5 and 6 and TIR bands 10 and 14 can then be used to discriminate both dark clayey soils and bright sandy soils.
- Mayr, T., and B. Palmer (2006), Chapter 26 Digital Soil Mapping: An England and Wales Perspective, in Developments in Soil Science, edited by A. B. M. P. Lagacherie and M. Voltz, pp. 365-618, Elsevier.
- Soil water exhibits absorption peaks (Fig. 1) at about 1450 nm, 1880 nm and 2660 nm Microwave RS of soil moisture content is based on the contrast in dielectric properties between dry soil and water derived from the backscatter data. n physics, backscatter (or backscattering) is the reflection of waves, particles, or signals back to the direction from which they came. It is a diffuse reflection due to scattering, as opposed to specular reflection like a mirror. Backscattering has important applications in astronomy, photography and medical ultrasonography.
- Soil colour is a first order indicator to estimate soil organic carbon (SOC); typically, dark soils contain more soil organic matter than pale soils Mapping SOC over vast areas, without extensive calibration by soil samples, can be achieved using spectrally-based indices. The SOC content is then determined based on the constituents of SOC: cellulose, starch and lignin; good relations have been found for indices based on the visible part of the spectrum (R2=0.80) and for the absorption features related to cellulose (around 2100 nm) (R2=0.81).
- Soil colour is a first order indicator to estimate soil organic carbon (SOC); typically, dark soils contain more soil organic matter than pale soils Mapping SOC over vast areas, without extensive calibration by soil samples, can be achieved using spectrally-based indices. The SOC content is then determined based on the constituents of SOC: cellulose, starch and lignin; good relations have been found for indices based on the visible part of the spectrum (R2=0.80) and for the absorption features related to cellulose (around 2100 nm) (R2=0.81).
- Soil colour is a first order indicator to estimate soil organic carbon (SOC); typically, dark soils contain more soil organic matter than pale soils Mapping SOC over vast areas, without extensive calibration by soil samples, can be achieved using spectrally-based indices. The SOC content is then determined based on the constituents of SOC: cellulose, starch and lignin; good relations have been found for indices based on the visible part of the spectrum (R2=0.80) and for the absorption features related to cellulose (around 2100 nm) (R2=0.81).
- For global spatial layers on soil parameters, the most recent and complete dataset available is Harmonized World Soil Database (HWSD). This is the result of a collaboration between the FAO with IIASA, ISRIC-World Soil Information, Institute of Soil Science, Chinese Academy of Sciences (ISSCAS), and the Joint Research Centre of the European Commission (JRC). The Harmonized World Soil Database is a database with over 15 000 different soil mapping units that combines existing regional and national updates of soil information worldwide and the characterization of selected soil parameters (organic Carbon, pH, water storage capacity, soil depth, CEC, clay fraction, total exchangeable nutrients, lime and gypsum contents, sodium exchange percentage, salinity, textural class etc)
- Soil iron can be seen as an indicator of soil fertility. Both soil colour and absorption features have been used to derive iron content. Iron oxide and iron hydroxides have specific absorption features that are located in the VNIR and can be measured from multispectral or imaging spectrometer images. However, these absorption features are less distinct in the presence of vegetation, which hampers retrieving of soil iron.
- The spectral response patterns of saline soils are a function of the quantity and mineralogy of the salts they contain [Mougenot et al., 1993]. Salinized soils have distinctive spectral features in the VNIR parts of the spectrum, related to water in hydrated evaporite minerals. Salt scalds and highly salinized soil show additional absorption features at 680, 1180 and 1780 nm. These features enable the recognition of minerals, such as gypsum, bassanite, and polyhalite, which can be used as salinity indicators. Yet another potentially usable characteristic, is as samples become more saline= the overall decrease in slope of the reflection curve between 800 and 1300 nm. Broad salinity classes can be mapped with ASTER, HyMAP and Landsat TM. Alternative methods for mapping saline areas are based on detecting the presence of salt scalds and halophytic vegetation. However, the spectral resolution must be high in order to detect the different vegetation types
- Soil erosion, an important soil degradation process can influence soil spectra. Soil erosion influences indirectly by influencing soil surface roughness and iron content in top soils. So the more is the erosion the more will be soil reflectance(Latz et al. 1984) in the longer wave length of visible and NIR region. Imaging spectroscopy enables the assessment of important soil erosion variables, such as water content and surface roughness In the event of a collapsed dam for mine tailings in southern Spain in 1999 the heavy metal contamination of soils was explored using HyMAP imaging spectroscopy data (Fig. 18). Based on chemical and spectroscopy analysis of soil samples, prediction of heavy metals COULD BE DONE USING THE SECTRAL LIBRARY. It was possible to predict six out of nine elements with high accuracy, using this approach.
- In 2008, a global consortium (GlobalSoilMap) has been formed that aims to make a new digital soil map of the world using state-of-the-art and emerging technologies for soil mapping and predicting soil properties at fine resolution. This new global soil map aims to predict primary functional soil properties that define soil depth, water storage, texture, fertility and carbon at fine spatial resolution (~100 m). These maps will be supplemented by interpretation and functionality options to support improved decisions for a range of global issues such as food production, climate change, and environmental degradation [Arrouays et al., 2014]. GlobalSoilMap will be freely available, web-accessible, and widely distributed.
- Germany's national space programme
- SMAP mission will enable advanced analysis of soil moisture. Furthermore, the planned imaging spectroscopy mission EnMAP (planned launch in 2017) aims to provide high quality data for global environmental monitoring, including soil status and properties
- The Copernicus programme comprises satellite-borne earth observation and in-situ data, and a services component that combines these in order to provide information essential for monitoring the terrestrial environment. The Copernicus land monitoring service provides geographical information on land cover/land use and on variables related to vegetation state and the water cycle.
- Designed to promote the use of satellite data for (1) environmental research on land surfaces, (2) public policy monitoring and (3) management of environmental resources. -THEIA aims fostering the use of remote sensing data to measure the impact of human pressure and climate on various scales, focusing on both natural and anthropological research [Hagolle, 2014].