Effects of Moisture on Soil Reflectance Properties - Assignment Example

The answers to the questions 1. Compare and contrast the relative magnitude (eg lower, higher, the same, etc.) of the spectral r e flectance of a) dry silt loam and b) moist silt loam in the visible, near infrared and middle infrared wavelengths. Discuss the effect(s) of moisture / water on the soil's reflectance prope r ties. Soil reflectance according to JENSEN (2000) is determined form studies of soil properties that are firstly soil texture which is percentage of sand, silt, and clay in the soil, secondly soil moisture that is a measure of amount of moisture in the soil and characterizes soil into dry soil, moist soil and saturated soil, thirdly soil organic chemicals and inorganic chemicals content that varies from iron-oxide content of soil and other mineral salts and the fourth is soil surface roughness , a view that is equally shared by Consequently, soil spectra curves or signatures are unique for each soil and remote sensing can play an important role in identification, chemical composition, and mapping of soils according to BAUMGARDNER et al., (1985), a view that is shared by IRONS et al., (1989) and JENSEN (2000). Measurements of soil reflectance near infrared (NIR), and middle infrared (MIR) regions of the electromagnetic spectrum are important for remote sensing the land surface. Soil reflectance may also play an important role in identification, chemical composition, and mapping of soils. The silt loam has a low spectral reflectance due to low reflectivity arising from di-electric constant. This is because it has very little amount of moisture. Hence little radar energy is reflected. Moist silt loam has a higher spectral reflectance. This is because it has a higher di-electric constant, making it a higher reflector and therefore more radar energy is reflected. Effects of moisture on soil reflectance properties Moisture absorbs significant amounts of incident radiation in all wavelengths and especially in the water absorption band in the MIR centered at 1450 nm and 1940 nm (JENSEN, 2000). The result is a dampening of the entire spectral response of the wet soils compared to the dry soils, with two deeper and wider troughs at the water absorption bands. 2. Discuss the principles of remote sensing of vegetation, focusing on the spectral reflectance and the factors that may affect the spectral r e sponse(s). Which spectral region(s) or band(s) will be more useful to differentiate between pine trees and macadamia trees? Explain. The leaves of a plant contain different organic compounds that effectively scatter light. This ability to reflect light into different angles of reflection is due to high contrast in the index of refraction, angle of incidence, angle of refraction between the chemicals compounds that are present in the leaves some that are organic and others that are inorganic with a higher percentage of the compounds being organic and the space between the organic compounds especially in the endodermis of the leaves of the plant and occur between 400-700 nanometers. This is due to high absorption from the pigments present in the leaves like chlorophyll, proto-chlorophyll, xanthophylls and organic compounds that are biosynthesized by the plant. A remarkable difference occurs in the spectral range of 700-1300 nanometers. This spectral range is the electronic transitions. This spectral range provides absorption observed in the visible and molecular vibrations that have ability to absorb in longer wavelengths. In spectral range of 700-1300 nanometers, there is absence of strong absorption because scattering is very high. From 1300 nanometers to utmost 2500 nanometers vegetation appears to be relatively dark and this is due to spectral interference of water molecules. The higher the number of water molecules the higher the interference. Other complex organic substances like alpha and beta Cellulose depending on the building unit of the polymer for instance alpha glucose or beta glucose and lignin, an insoluble carbohydrate have spectral absorptions in the range of 1300 nanometers to 2500 nanometers. The theory of different wavelength and incidence angle can be used to study the characteristics of the recorded backscatter from the leaves that is used to differentiate plants. In this case, the interpreted data is used to differentiate pine tree from the macadamia. The assumption is based on observation of Foley, J. A. et al (2003) that different types of forest vegetations, depended on the prevailing climate like the desert vegetation, grasslands vegetation and the tropical vegetations have different backscatter signatures. at the same time soil reflectivity that is depended on the organic nutrients that result from decomposing organic matter from the present forests called dielectric constant varies depending on percentage of water and organic matter that the soil contains. Normally Dry soil has a very low dielectric constant because dry soil water content is very little. This leads into little radar energy that is reflected. Saturated soil on the other hand has a very high dielectric constant and is therefore a strong reflector. Moist and partially frozen soils will have intermediate values of dielectric constant. Short wavelength radar of three centimeters is reflected from the tops of trees. Pine trees being very tall will be observed in this range while macadamia that are not as tall as the pine will be observed from the short wavelength radar of three centimeters. Long wavelength radar data especially of twenty four centimeters goes down to the ground and is reflected back according to Kachhwaha, T.S. (1983). This provides information on macadamia. Normally, pine is coniferous and macadamia is deciduous. Pine tree is needle shaped and does not form a thick canopy. Macadamia being deciduous and umbrella shaped forms a thick canopy. Pine as a coniferous plant does not shed a lot of leaves therefore its ground base will be mostly bare. The ground base for macadamia has so many leaves because as a deciduous plant, the macadamia sheds many leaves. Intermediate wavelengths of six centimeters experience multiple scattering between the leaf canopy, the branches present and the ground base. By acquiring multiple wavelengths, multi directional radar imagery of a forested area, it is possible to discern information about the canopy structure. SAR is able to transmit pulses of microwaves and in turn is able to measure the strength and the time delay of the energy that is scattered back to the antenna. 3. What is microwave / radar remote sensing? Identify and discuss three advantages of Radar r e mote sensing over those systems operating in the visible and infrared bands. RADAR is abbreviation for RAdio Detection And Ranging. Radar is classified as an active sensor by virtue of sending out pulses of microwave electromagnetic radiation. It measures the time between pulses and their reflected components to determine distance. Different pulse intervals, different wavelengths, different geometry and polarizations can be combined to roughness characteristics of the earth surface. Radar uses a relative long wavelength which allows these systems to penetrate through clouds, smoke, and some vegetation. It's an active system and can therefore be operated day or night. Advantages of microwave /radar remote sensing The first advantage of microwave remote sensing is that Radar remote sensing is far better than optical sensors like the Landsat and SPOT 1, spot 2, spot 3 spot 4, or the recently launched spot 5 that produces 256 intensity levels. A Radar system easily differentiates intensity levels up to high levels of 100,000. Human eye can only analyze about 40 intensity levels at one time meaning the radar remote sensing levels of 100,000 is too much information for visual interpretation. Even a typical computer is likely to experience difficulty when analyzing such a wide range of information. Most radars therefore record and process the original data as 16 bits that translates into 65,536 levels of intensity which is then further scaled down to 8 bits around 256 levels for visual interpretation and for digital computer analysis. The second advantage of radar remote sensing is that Radar images have slant-range distortion properties. This application of slant range scale variability ensures that features that are in the near-range are compressed relative to features in the far range. The radar image is then presented in a format that corrects for presence of this distortion and this measure enables true distance measurements between features either in the near-range or far range. This application makes use slant range image in order to convert into ground range display. This process is performed by the radar processor before the image is created and sometimes after data acquisition and is performed by applying a transformation to the slant range image. In many instances this conversion is just an estimate of the geometry of the ground features because of the complications that are introduced by variations in terrain relief, terrain and topography. the third advantage of radar or microwave remote sensing is that a radar antenna transmits more power in the mid-range portion of the illuminated swath than at the near range and far range. This effect characteristic of remote sensing is termed as antenna pattern and produces more strong returns from the centre portion of the swath than at its edges. When this feature is combined with antenna pattern there is an observation to the effect that the energy returned to the radar decreases dramatically as the range distance increases. This means the strength of the returned signal becomes smaller and smaller as one moves across the swath. Combination of these effects produces an image which varies in intensity or tone in the range direction across the image. Application of a process called antenna pattern correction produces a uniform average brightness across the imaged swath and this improves and facilitates visual interpretation. The fourth advantage of microwave remote sensing is that Microwave sensing utilizes both active and passive forms of remote sensing. In this case the microwave spectrum covers a range of approximately 1centimetre to 1meter in wavelength. Because of microwave long wavelengths compared to the visible and infrared regions microwaves remote sensing has special associated properties that are essential in remote sensing. Longer wavelength microwave radiation makes it possible for the radar to penetrate through cloud cover and dust particles. At the same time, it is evident that longer microwave wavelengths are not susceptible to atmospheric scattering which affects shorter optical wavelengths. This property allows detection of microwave energy under almost all weather and environmental conditions so that data can be collected at any time an make microwave remote sensing an important tool. 4. Differentiate Landsat TM (Thematic Mapper) system from SPOT 4. Focus on several Aspects , such as number of bands, ground/spatial resolution, swath width, type of scanner, Etc. Assu m ing that you will map the general extent (i.e. not species type or detailed attributes) of native vegetation in a catchments' (300kmx 300km), which of the two above Systems will you use? Why? (You can make assumption(s), but please state them.) The Landsat Thematic Mapper is an advanced, multi-spectral scanning, earth resources sensor that has the following characteristics: first it has a to a higher image resolution, second thematic mapper has a sharper spectral separation, third, thematic mapper has improved geometric fidelity, fourth thematic Mapper has a greater radiometric accuracy, fifth thematic mapper has greater resolution than that of the MSS sensor. The MSS is short form of Multi Spectral Scanner. The reference of MSS is through the old system. The MSS bands are: first MSS4 that covers 500-600 nm, second is MSS5 that covers 600-700 nm, third is MSS6 that covers 700-800 nm and the fourth is MSS7 that covers 800-1100 nm. The Thematic Mapper sensor images a swath that is 185 km or 115 miles wide. Every pixel in a Thematic Mapper scene represents a 30 meters by 30 meters ground area, except for the far-infrared band 7, which uses a larger 120 meters by 120 meters pixel. The band covers 2080-2350 nanometers. The Thematic Mapper sensor has seven bands that simultaneously record reflected or emitted radiation from the Earth's surface in the blue-green region termed as band one. Band one covers 450-520 nanometers. The second is green region that is termed as band two and covers 520-600 nanometers. The third band is in the red region and is termed as band three and covers 630-690 nanometers. The fourth band is in the near-infrared region and is termed as band four that covers 760-900 nanometers. The fifth band is in the mid-infrared region and this falls on fifth and seventh band and covers 1550-1750 nanometers and the sixth is in the far-infrared region and is termed as band six of the electromagnetic spectrum. Thematic Mapper band two is build to detect green reflectance from healthy vegetation. Band three detects chlorophyll absorption in vegetation. Thematic Mapper band four is used for near-infrared reflectance peaks in healthy green vegetation. Thematic mapper four is also used for detecting water-land interfaces. Thematic Mapper band one on the other hand is designed to penetrate water bodies for bathymetric or water depth mapping along coastal areas. Therefore band one is useful for soil vegetation and vegetation cover differentiation. Thematic mapper one is also used for distinguishing different types of forests and vegetations. The two mid-infrared regions on Thematic Mapper are used for studying vegetation moisture properties as well as soil moisture properties. They are also used in discriminating between different types of rocks and minerals. The far-infrared band on Thematic Mapper is used in studies related with thermal mapping as well as studies on soil moisture and vegetation studies. SPOT is abbreviation for Systeme Pour l'Observation de la Terre (Essery, C.I. and D.N. Wilcock, 1986). SPOT4 has a band that covers 790-890 nm. SPOT 4 has geometric imaging characteristics of: a swath of 60 km per instrument and oblique viewing capability of 27 on each side of the local vertical and has a shortwave infrared spectral band (SWIR), that extends its nominal lifetime from 3 to 5 years and improving operational possibilities. If I am given a choice between thematic mapper and the spot 4, I would choose Thematic Mapper. This is because Thematic Mapper has Systematic Correction that includes first radiometric correction. The thematic mapper also includes geometric correction and is able to replace of all missing image pixels. The replacement of the missing image pixels is done within the estimated values that are based on histogram matched data from one or more user-defined fill scenes. The image is rotated and aligned to a user specified projection. A scan gap mask is included with the final product. It provides Spatial Resolution of thirty meters and Orbit 705 with a deviation of around +/- 5 km at the equator. It has sun synchronous Orbit Inclination of approximately 98.2 with a deviation of +/- 0.15 and has an Orbit Period of 98.9 minutes. It has a Grounding Track Repeat Cycle of 16 days that is exactly 233 orbits and that is coupled with a resolution of 15 to 90 meters. Thematic Mapper is reliable for many image requests because matching image could easily be located in the global archives of LANDSAT 7 imagery. If no image data is available in the LANDSAT 7 imagery a new Landsat 7 satellite image data is easily acquired via a satellite tasking process. 5. Identify a specific application area (eg agriculture, geology, urban st u dies, etc.) where high (spatial) resolution remote sensing products (eg i m agery from QuickBird,IKONOS, etc.) will be highly suitable. Justify your choice. What are the potentialproblems, issues, or constraints when using high spatial resolution imagery? (You can make assumption(s), but please state them.) The high resolution remote sensing products like QUICKBIRD and IKONOS are suitable in urban studies. IKONOS and QUICKBIRD both have high geometric accuracy potential for three Dimensional images, point positioning, and ortho-image and DSM generation. Quickbird according to Noguchi, M. et al (2004) is equipped with sufficient modeling and has a plan metric accuracy is 0.4 - 0.5 m. Ortho-images can be generated in both QUICKBIRD and IKONOS with an accuracy of between 0.5-0.8 m, using a laser DTM. The main problem affecting both Quickbird and IKONOS are snow, long shadows, and occlusions due to presence of mountains. The accuracy lies within a range of 1m to 5 m. This is dependent on the type of land cover. In open areas accuracy is one meter. This accuracy sometimes is lowered to about 0.5 m. This makes IKONOS and QUICKBIRD the best options for urban studies. High-resolution satellite (HRS) imagery can be used for updating urban maps. In order to improve the radiometric quality and optimize the images for subsequent processing, a series of filters are applied. The performed preprocessing encompasses noise reduction, contrast and edge enhancement and reduction to 8-bit by non-linear methods. All filters are applied to the 11 bit data. Noise reduction filters aim at reducing noise, while sharpening edges and preserving corners and one pixel wide lines. The two local filters employed have similar effects although they use different parameters. Apart from the visual verification, reduction of noise is quantified by noise estimation in inhomogeneous areas. Following noise reduction, local contrast enhancement is applied using the Wallis filter. Moreover, 11-bit data are reduced to 8-bit by an iterative non-linear method in order to preserve the grey values that are more frequently occurring. The roundabouts in the urban centre and straight line intersections -nearly orthogonal with at least 10 pixels length- are measured semi-automatically in the satellite images and the aerial ortho-images. Measurement of GCPs by least squares template matching is not convenient or possible due to highly varying image content and scale. The height is interpolated from the DTM used in the ortho-image generation. Quickbird is much less linear than IKONOS. This is expected partly due to its less stable orbit and pointing and continuous rotation during imaging. Only RPC2 performs with submetre accuracy and only with this model can quickbird achieve similar accuracy as IKONOS. Reduction of the GCPs does not have any significant influence with RPC2. Hence, using simple RPCs will not lead to very accurate results with quickbird. Quickbird image is Basic that is it is not rectified. A rectified image show a more linear behaviour, and the respective RPCs is more stable The high resolution space images from IKONOS and QuickBird can be used for the ortho-image generation up to a scale 1:8000 / 1:4800 (Wang, J et al: 2005). The image geometry does not cause any problems. For the orientation, no special orientation information like rational polynomial coefficients is required from SpaceImaging or DigitalGlobe. Also the generation of ortho-images can be done by the user with appropriate software. Stereo high-resolution satellite imagery, such as Space Imaging's IKONOS (1 m resolution) and DigitalGlobe's QuickBird (sub-meter resolution), provide accurate three-dimensional (3D) mapping products. The QuickBird stereo pair happens to have the greatest convergent angle, and it also has the best accuracies in all directions. The IKONOS and QuickBird high resolution images are sufficient for the generation of orthoimages at a scale of approximately 1 : 8000 up to 1 : 5000. Also the geometric potential is satisfactory, but it is necessary to use a strict geometric model. Rational functions for the relation of the image to the ground coordinates should not be determined directly based on control points, because this will hide discrepancies at the control points and do not guarantee the correct geometry for areas with poor control point distribution. The main problems are color distortion, another issue of IHS/BT image fusion for IKONOS/QuickBird imagery arises from panchromatic changesand Spilling is probably the gravest radiometric problem. References BAUMGARDNER, M.F. et al. Reflectance properties of soils. Advances in Agronomy, New York, v. 38, n. 1,p. 1-44, 1985 Choudhury, B. J., Ahmed, N. U., Idso, S. B., Reginato, R. J., and Daughtry, C. S. T.: Relations between evaporation coefficients and vegetation indices studied by model simulations,Remote Sens. Environ., 50, 1-17, 1994 Essery, C.I. and D.N. Wilcock, 1986. SPOT-Simulation Campaign: A Preliminary Land Use Classification for 200 sq. km River Catchment. International Journal of Remote Sensing 7(6):801-814 Foley, J. A., Costa, M. H., Delire, C., Ramankutty, N., and Snyder, P.: Green surprise? How terrestrial ecosystems could affect earth's climate, Frontiers Ecol. Environ., 1, 38-44, 2003 IRONS, J.R.. WEISMILLER, R.A.. PETERSEN, G.W. Soil reflectance. In: ASRAR, G. Theory and application of optical remote sensing. New York: John Wiley & Sons, 1989, Cap. 3, p. 66-106. JENSEN, J.R. Remote sensing of the environment: An Earth resource perspective. Upper Saddle River: Prentice-Hall, 2000. Cap. 13: Remote sensing of soils, minerals and geomorphology. p. 471-530. JENSEN, J.R. Remote sensing of the environment: An Earth resource perspective. Upper Saddle River: Prentice-Hall, 2000. Cap. 13: Remote sensing of soils, minerals and geomorphology. p. 471-530. Kachhwaha, T.S., 1983. Spectral Signature obtained from Landsat Digital Data for Forest Vegetation and Land Use Mapping in India, Photogrammetric Engineering 49(5):685-689 Li, R., 1998. Potential of high-resolution satellite imagery for national mapping products Photogramm. Eng. Remote Sens., 64(2), pp. 1165-1169. Noguchi, M., C. S. Fraser, T. Nakamura, T. Shimono, and S. Oki, 2004. Accuracy assessment of QuickBird stereo imagery. The Photogrammetric Record, 19(106), pp. 128-137. Wang, J., K. Di, and R. Li, 2005. Evaluation and improvement of geopositioning accuracy of IKONOS stereo imagery. ASCE Journal of Surveying Engineering, 131(2), pp. 35-42 Read More