GIS and Remote Sensing for Agriculture: Spectral Properties of Ground Materials PDF

Module M.SIA.I14M GIS and Remote Sensing for Agriculture L6: Spectral properties of ground materials Dr Jayan Wijesingha [email protected] +49 561 804-1245 Spectral signature When EM energy interact with the object, the proce...

Module M.SIA.I14M GIS and Remote Sensing for Agriculture L6: Spectral properties of ground materials Dr Jayan Wijesingha [email protected] +49 561 804-1245 Spectral signature When EM energy interact with the object, the process of absorption, reflectance, transmittance or emittance happen due to the object’s chemical and structural properties Each object has its own spectral property Reflectance curves are using to explain spectral property of the object Reflectance as a function of wavelength Because reflected energy can be measured The unique spectral curve of the known object is called as spectral signature 2 Bare soil (a) organic dominated (b) minimally altered (c) iron altered (d) organic affected (e) iron dominated Tempfi et al., 2001 – Principles of Remote Sensing What changes the spectral property of soil? Soil colour, soil moisture content, presence of carbonates and 3 iron dioxide content Water (a) ocean water (b) turbid water (c) water with chlorophyll Tempfi et al., 2001 – Principles of Remote Sensing Water has a lower reflectance 10 % in visible range and very little amount in NIR Beyond 1.2 µm all the energy is absorbed 4 Snow and ice Strong reflection in visible and NIR String absorption in longer wavelength Grain size, age of ice/snow effect Tedesco, M. (Ed.). (2015) 5 Vegetation 6 Spectral property of vegetation Depends on Leaf orientation Structure of leaf canopy Leaf pigmentation Leaf thickness Cell structure Water content in the leaves 7 Vegetation spectral signature Roman, Anamaria & Ursu, Tudor. (2016). Multispectral satellite imagery and airborne laser scanning techniques for the detection of archaeological vegetation marks. 8 Vegetation in Visible spectrum (400 – 750 nm) Chlorophyll (a and b) absorb the energy from visible spectrum Specially blue and red Other leaf pigments also have different absorption capacities Xanthophyll – blue Carotenoids – absorb blue (0.44 and 0.46 µm) Anthocyanins – blue and http://www.eumetrain.org/data/3/36/navmenu.php?page=3.0.0 green 9 Vegetation in NIR spectrum (780– 1300 nm) The optical properties in NIR range are mostly Two minor water related absorption bands at 0.97 influenced by leaf and 1.2 µm structure The spongy mesophyll cells located in the interior leaves strongly reflects NIR Due to high reflectance in NIR, vegetation appears http://www.eumetrain.org/data/3/36/navmenu.php?page=3.0.0 bright in NIR wavelengths 10 Vegetation in Red-Edge (750– 780 nm) The red-edge is a region in the red, NIR transition zone of the spectrum In the vegetation spectral signature, red region has lower reflectance and very high reflectance in NIR So the transition zone between red-NIR show very high gradient of http://www.eumetrain.org/data/3/36/navmenu.php?page=3.0.0 reflectance 11 Vegetation in SWIR (1300 – 2500 nm) The absorption due to water, cellulose and lignin and several other biochemical constituents Prominent water absorption wavelengths are 1.40 and 1.94 µm So the transition zone between red-NIR show very high gradient of reflectance http://www.eumetrain.org/data/3/36/navmenu.php?page=3.0.0 12 Absorption features summary Wavelength (µm) Chemical Electronic transition or bon vibration 0.43, 0.46, 0.64, 0.66 Chlorophyll Electronic transitions 0.97, 1.20, 1.40, 1.94 Water O-H bond stretching N-H stretching & 1.51, 2.18 Protein, nitrogen bending, C-H stretching C-H stretching & 2.31 Oil bending 1.69 Lignin C-H stretching 1.78 Cellulose and sugar -- Jones, H.G., Vaughan, R.A., 2010. Remote Sensing of Vegetation - Principles, Techniques, and Applications. Oxford University Press, New York. 13 Effect of leaf structure 14 Jones, H.G., Vaughan, R.A., 2010. Remote Sensing of Vegetation - Principles, Techniques, and Applications. Oxford University Press, New York.  Effect of leaf canopy Kattenborn, Teja. (2018). Linking Canopy Reflectance and Plant Functioning through Radiative Transfer Models. 10.5445/IR/1000089168. 15  Leaf area index (LAI) LAI (m²/m²) is geometrically defined as the total one-sided area of photosynthetic tissue per unit of ground surface area LAI is helpful to derive canopy reflectance 16 Xie et al. (2018). Vegetation Indices Combining the Red and Red-Edge Spectral Information for Leaf Area Index Retrieval. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 11. 10.1109/JSTARS.2018.2813281.