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| Once the at-sensor radiance values are obtained, the next step would account for solar radiation interference and earth-sun geometry, which is dependent on latitude and datum, to obtain values of top-of-atmosphere (TOA) reflectance. The effect of this source of distortions depend on solar power (which varies over time), the solar elevation angle (determining the amount of light reflected) and the Earth-Sun distance (which also varies over time). | Once the at-sensor radiance values are obtained, the next step would account for solar radiation interference and earth-sun geometry, which is dependent on latitude and datum, to obtain values of top-of-atmosphere (TOA) reflectance. The effect of this source of distortions depend on solar power (which varies over time), the solar elevation angle (determining the amount of light reflected) and the Earth-Sun distance (which also varies over time). | ||
| - | Thankfully, while solar flux can be a complicated process to model depending on the need for detail, the Earth and the Sun are reasonably well-behaved celestial bodies and the geometric relationships between them are easily calculated. It is worth noting that in some litterature, correcting for this is sometimes grouped together with the sensor correction step. | + | Thankfully, while solar flux can be a complicated process to model depending on the need for detail, the Earth and the Sun are reasonably well-behaved celestial bodies and the geometric relationships between them are easily calculated. It is worth noting that in some literature, correcting for this is sometimes grouped together with the sensor correction step. |
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| + | === The atmosphere === | ||
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| + | == Top of Atmosphere Radiance == | ||
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| + | Radiation, both from terrestrial and interstellar sources, has varying probability of scattering in parts of the volume segment from the surface of the earth to the sensor platform of the satellite. As such, the observed radiation is a superposition of all of those scattering processes distributed over space and wavelength. What that mix of scattered radiation consists of depends on a great variety of physical processes - Rayleigh scattering in the atmosphere, for instance, results in a large part of the blue region of the visible spectrum scattering in the atmosphere. | ||
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| + | Knowing what processes and types of radiation yield what type of scattering allows us to selectively correct for certain regions. Removing the scattered light in the atmosphere and only preserving the surface contribution provides a bottom of atmosphere (BOA) corrected product, and the inverse is true for the top of atmopshere (TOA) variation. However, simply knowing that a data product has been TOA or BOA corrected is no enough since there are many ways of caluculating those values. The user is adviced to read up on what kind of correction has been applied, before using the data in their analysis. | ||
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| + | Reflectance is dimensionless because radiance is divided by irradiance so the units cancel. However (and this confuses many people) reflectance is not constrained to fall between 0-1; sometimes it is stored in integer format (e.g. 12-bit or 16- bit) because floating point format (0-1) takes up too much disk space. If you have pixel values of 1000 then the data can certainly be in top-of-atmosphere or surface reflectance. However, you need to look at the metadata and/or read the data description to be certain what level of processing the data had. Do not look at the data and try to make guesses about the processing - the level of processing is explained in the data documentation.If you are using Landsat climate data record (CDR) data then it is mostly in surface reflectance but it needs to be slightly rescaled as well as have a sun angle correction applied. If the data was distributed as TOA reflectance then the radiance at the Landsat sensor was divided by the exo-atmospheric irradiance and this is better than nothing but there will be band-specific biases (according to Rayleigh scattering). | ||
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| + | The energy that is captured by Landsat sensors is influ- enced by the Earth’s atmosphere. These effects include scattering and absorption due to interactions of the elec- tromagnetic radiation with atmospheric particles (i.e., | ||
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| + | However, some atmospheric effects are highly variable over the Earth’s surface and can be difficult to correct in Landsat imagery. While it is not always necessary to atmospherically correct Landsat data to surface values, there are instances where this level of correction is needed. In general, absolute atmospheric corrections are needed when (1) an empirical model is being created for application beyond the data used to develop it, (2) there is a comparison being made to ground reflectance data such as a field-based spectroradi- ometer, or (3) as an alternative to relative correction when comparisons are being made across multiple images. All atmospheric correction methods have associated assump- tions about the target and the nature of the atmospheric particles or emissivity (for land surface temperature). There are numerous atmospheric correction methods available, ranging from simple approaches that use only within-image information such as dark object subtraction (Chavez 1988), to more complex and data-intensive approaches such as the method used for the Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS) products (Masek et al. 2006) | ||