Soil separability through remote sensing for land evaluation

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Dathan.C.S

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IntroductionBeing a part of the Earth’s spheres, the

pedosphere is responding and contributing to the environmental changes

(Macías and Arbestain, 2010)

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‘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

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SOIL SEPARABILITY THROUGH REMOTE SENSING FOR LAND

EVALUATION

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“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)

Digital Soil Mapping (DSM)

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-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

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The Foundation of RSEnergy interactions with earth surface features

Basic interactions between electro magnetic energy and an Earth Surface feature

Energy balance equation EI() = ER() + EA() + ET()

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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

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• 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 EMREg - Landsat

Types of remote sensing-Passive

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• 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

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Region Wavelength RemarksGamma 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 RADARS

EMR-Spectrum characteristics

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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.

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.

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The Foundation of RSAtmospheric windows-Atmospheric window is that portion of the electromagnetic spectrum that can be transmitted through the atmosphere with least distortion or absorption.

Spatial resolution - Spatial resolution states about the clarity of an image.

Idava, Kollam. Toposheet (1 : 25 000)

Spectral signatures

16Idava, Kollam. Google Earth. True color composite

Spectral signatures

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Idava, Kollam. Satellite imagery (LISS II ). False color composite

Spectral signatures

18Idava, kollam. Satellite imagery (LISS II ). Grey color composite

Spectral signatures

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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.

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Optical sensors(b) Multi spectral imaging- Passive RS

Multi spectral imaging - Record data in fewer bands, resulting in a coarser spectral resolution compared to hyper spectral sensors.

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RADAR sensorsActive & Passive

Radar - Both systems are highly suitable to quantify soil moisture, whereas active systems are additionally used to derive terrain and soil attributes.

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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.

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RS assisted soil parameters

Mineralogy

Soil texture

Soil moisture

Soil organic carbon

Iron content

Soil salinity

Soil degradation

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Remote sensing- digital soil products1. Mineralogy

In order to discriminate between different minerals, subtle differences in the spectral signature throughout the VNIR to TIR may be used.

(Clark et al., 2003 )

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1. Mineralogy

USGS spectral library - spib06 ASTER spectral library version 2.0

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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

2. Soil texture

Typical spectra curves for various soils and nonsoil classes (Venkataratnam, 1980)

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2. Soil texture

Nominal clay content (%) for distinct soil units of Wales, based on Aster data (Mayr and Palmer, 2006 )

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3. Soil Moisture

Characteristic peaks of water and hydroxyl bands

(Baumgardner et al., 1985)

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2. Soil moisture

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4. Soil organic matter

Effect of organic matter on spectralreflectance of soil

(Sinha, 1987).

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Generally, the greater the amount of organic content in a soil, the greater the absorption of incident energy and the lower the spectral reflectance.

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Remote sensing- digital soil products4. 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

DEM of the study area(Stevens et al., 2006)

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Range of organic carbon (%) for the topsoil. Namoi Valley, Australia.


(Minasny, 2006 )

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Harmonized World Soil Database (HWSD)


FAO soils portal -

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5. Iron content

Effect of iron oxide on spectral reflectance of soil

(Sinha, 1987)

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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

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5. Iron content

Map of free iron oxides at the Ashdod sand dunes, Israel.

(Ben-Dor et al., 2008 )

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Raw reflectance spectra of five soil salinity classes.

6. soil salinity

The spectral response patterns of saline soils are a function of the quantity and mineralogy of the salts they contain

(Mougenot et al., 1993).

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Case studyAim - Monitoring of salinity in the area using multi-temporal satellite images.Study area - South west PunjabImagery 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.

Map showing preliminary interpreted units on FCC with base details

6. soil salinity

(Koshik, 2010)

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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

(Kemper and Sommer, 2003 )

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RS-techs for soil attribute retrievalSoil attributes RADAR LIDAR Optical Relevant

spectraregionActive 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-NIRNumbers (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].

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Ongoing and future missions1. 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].

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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

Ongoing and future missions

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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.


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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..


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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


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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


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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)

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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

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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

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Serial No. Satellite Year of

Launch Status

1 IRS P6 (Resourcesat-1) 2003 In service2 IRS P5 (Cartosat -1) 2005 In service3 Cartosat -2 (IRS P7) 2007 In service4 Cartosat -2A 2008 In service5 IMS 1 2008 In service6 Oceansat -2 2009 In service7 Cartosat -2B 2010 In service8 Resourcesat -2 2011 In service9 Megha -Tropiques 2011 In service10 RISAT-1 2012 In service11 SARAL 2013 In service

Indian RS satellites

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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.

54Thank you

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Education

Soil separability through remote sensing for land evaluation