Spectral Signatures

The spectral signatures produced by wavelength-dependent absorption and transmittance provide the key to discriminating different materials in images of reflected solar energy. The property used to quantify these spectral signatures is called spectral reflectance: the ratio of reflected energy to incident energy as a function of wavelength. The spectral reflectance of different materials can be measured in the laboratory or in the field, providing reference data that can be used to interpret images. As an example, Figure 7.5 shows contrasting spectral reflectance curves for three common natural materials: dry soil, green vegetation, and water. The reflectance of dry soil rises uniformly through the visible and near-infrared wavelength ranges, peaking in the middle-infrared range. It shows only minor dips in the middle-infrared range due to absorption by clay minerals. Green vegetation has a very different spectrum. Reflectance is relatively low in the visible range but is higher for green light than for red or blue, producing the green color we see. The reflectance pattern of green vegetation in the visible wavelengths is due to selective absorption by chlorophyll, the primary photosynthetic pigment in green plants. The most noticeable feature of the vegetation spectrum is the dramatic rise in reflectance across the visible near-infrared boundary, and the high near-infrared reflectance. Infrared radiation pene trates plant leaves and is intensely scattered by the leaves' complex internal structure, resulting in high reflectance. The dips in the middle-infrared portion of the plant spectrum are due to absorption by water. Deep, clear water bodies effectively absorb all wavelengths longer than the visible range, which results in very low reflectivity for infrared radiation.

Spatial Resolution

The spatial, spectral, and temporal components of an image or set of images all provide information that we can use to form interpretations about surface materials and conditions. For each of these properties we can define the resolution of the images produced by the sensor system. These image resolution factors place limits on what information we can derive from remotely sensed images.

Spatial resolution is a measure of the spatial detail in an image, which is a function of the design of the sensor and its operating altitude above the surface. Each of the detectors in a remote sensor measures energy received from a finite patch of the ground surface. The smaller these individual patches are, the more detailed will be the spatial information that we can interpret from the image. For digital images, spatial resolution is most commonly expressed as the ground dimensions of a picture element (pixel).

Spectral Resolution

The spectral resolution of a remote sensing system can be described as its ability to distinguish different parts of the range of measured wavelengths. In essence, this amounts to the number of wavelength intervals ("bands") that are measured and how narrow each interval is. An "image" produced by a sensor system can consist of one very broad wavelength band, a few broad bands, or many narrow wavelength bands. The names normally used for these three image categories are panchromatic, multi-spectral, and hyperspectral, respectively. Panchromatic images record an average response over the entire visible wavelength range (blue, green, and red). Because this film is sensitive to all visible colors, it is called panchromatic film. A panchromatic image reveals spatial variations in the gross visual properties of surface materials but does not allow for spectral discrimination. Some satellite remote-sensing systems record a single very broad band to provide a synoptic overview of the scene, commonly at a higher spatial resolution than other sensors onboard. Despite varying wavelength ranges, such bands are also commonly referred to as panchromatic bands.

Multispectral Images

To provide increased spectral discrimination, remote-sensing systems designed to monitor the surface environment employ a multispectral design: parallel sensor arrays detecting radiation in a small number of broad wavelength bands. The commonly used satellite systems use from three to six spectral bands in the visible to middle-infrared wavelength region. Some systems also employ one or more thermal infrared bands. Bands in the infrared range are limited in width to avoid atmospheric water vapor absorption effects that significantly degrade the signal in certain wavelength intervals. These broadband multispectral systems allow discrimination of different types of vegetation, rocks and soils, clear and turbid water, and some man-made materials. A three-band sensor with green, red, and near-infrared bands is effective at discriminating vegetated and nonvegetated areas. The high resolution visible (HRV) sensor aboard the French SPOT (Système Probatoire d'Observation de la Terre) 1, 2, and 3 satellites (20 meter spatial resolution) has this design. Color-infrared film used in some aerial photography provides similar spectral coverage, with the red emulsion recording near infrared, the green emulsion recording red light, and the blue emulsion recording green light. The IKONOS satellite from Space Imaging (4-meter resolution) and the LISS-II sensor on the Indian Remote Sensing satellites IRS-1A and 1B (36-meter resolution) add a blue band to provide complete coverage of the visible light range and allow natural-color band composite images to be created. The Landsat Thematic Mapper (TM) (Landsat 4 and 5) and Enhanced Thematic Mapper Plus (ETM+) (Landsat 7) sensors add two bands in the middle infrared (MIR). Landsat TM band 5 (1.55 to 1.75 pm) and band 7 (2.08 to 2.35 |^m) are sensitive to variations in the moisture content of vegetation and soils. Band 7 also covers a range that includes spectral absorption features found in several important types of minerals. An additional TM band (band 6) records part of the thermal infrared wavelength range (10.4 to 12.5 (am). Current multispectral satellite sensor systems with spatial resolution better than 200 meters are compared in Tables 7.1 and 7.2. Note that DigitalGlobe successfully launched and deployed the QuickBird high-resolution satellite that began commercial operations in early 2002 with the offer of imagery at 0.61 m panchromatic and 2.44 m multispectral resolutions. See <http://www.digitalglobe.com>.

Hyperspectral Images

Multispectral remote sensors such as the Landsat Enhanced Thematic Mapper and SPOT XS produce images with a few relatively broad wave-

Remote-Sensing Applications in Agrometeorology TABLE 7.1. Remote-sensing satellites in space

Platform/ Image size sensor/ Picture (cross x along- Panchromatic Nominal revisit launch year element size track) Spectral bands cell size interval*


Solar Panel Basics

Solar Panel Basics

Global warming is a huge problem which will significantly affect every country in the world. Many people all over the world are trying to do whatever they can to help combat the effects of global warming. One of the ways that people can fight global warming is to reduce their dependence on non-renewable energy sources like oil and petroleum based products.

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