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ARCSI Ouput Commands
RAD
- Conversion from DN (Digital number) to Radiance (the amount of energy received by the sensor per second (
SATURATE
- At saturation, pixels lose their ability to accommodate additional charge. This additional charge will then spread into neighbouring pixels, causing them to either report erroneous values or also saturate. This spread of charge to adjacent pixels is known as blooming and appears as a white streak or blob in the image.
- In ARCSI, the saturated pixels are masked and exported as an output with the SATURATE command.
TOPOSHADOW
- Topographic shadow – masks the topographic shadow within the image and produces it as an output
TOA
- When spectral radiance values are compared within and between sensors, variations frequently occur because of different sun-target-sensor configurations. Hence, these data are often converted to Top of Atmosphere (TOA) or planetary reflectance or at sensor reflectance as this eliminates the effect of different solar zenith angles at the time of image acquisition, minimises the solar irradiance band differences occurring between different sensors accounts for differences in the Earth-Sun distance.
- TOA reflectance does not take into account any atmospheric effects and is simply a ratio of the at-sensor radiance with the incoming radiance from the sun, where the distance and angle of the sensor to the target are considered.
CLOUDS
- Undertakes a cloud mask – some outputs may have clouds removed from subsequent images.
DDVAOT
- Dark Dense Vegetation Aerosol Optical Thickness – used for atmospheric correction.
- “The dark dense vegetation approach (DDV) considers vegetation reflectance properties for aerosol detection. It uses the known correlation between the short-wave infrared (SWIR) or the NIR band to the red band in vegetation to get an estimate of the expected red reflectance. The aerosol amount is then found as the observed offset in the red band.” (Schläpfer et al., 2018)
DOSAOT/DOSAOTSGL
- Estimation of Aerial optical thickness (AOT) via a dark object subtraction.
- I am not so sure on what the difference between these two output commands are.
SREF
- Surface reflectance, also called ‘bottom of atmosphere reflectance’ is the ratio of incoming radiance (i.e., from the sun) with the radiance that is measured by the sensor without any atmospheric effect and should be equivalent to the signal measured if the sensor was at ground level or there was no atmosphere.
- Within ARCSI the 6S radiative transfer model is used.
STDSREF
- Standardised reflectance refers to a product which is normalised for the solar and sensor view angles and in this case topography. In terms of providing a topographic correction, this method only works for images where the solar elevation is between 50 and 70 degrees (i.e., from late spring to early autumn) but does not produce artefacts outside of this range.
SHARP
- Bands of lower resolution are sharpened to high resolution bands (e.g. from 20m to 10m).
- The sharpening product aims to enhance the 20 m image bands using the 10 m bands to produce a sharper stacked 10 m product and works through the application of local linear regression models. The method was first proposed by Dymond and Shepherd (2004) for pan-sharpening Landsat-7 imagery and has subsequently been extended for Sentinel-2. Within ARCSI it is applied to the radiance image where the 20 m bands have been oversampled to 10 m resolution image using a nearest neighbour interpolation. A 7×7 pixel filter is applied to the 10 m image stack where within the 7×7 window a linear regression is performed independently for each lower resolution band to each higher resolution band. The linear model for each lower resolution image band with the best fit, above 0.5, is then used to predict the image pixel value for the band. If not fit above 0.5 is identified, then the image pixel value is not altered.
FOOTPRINT
- Produces a shapefile format of the satellite footprint (where the imagery is taken from).
METADATA
- Produces a metadata output containing information on output commands used and bands of output products.
References:
1.Bunting, P. Introduction to ARCSI for generating Analysis Ready Data (ARD).
2. Schläpfer, D., Hueni, A., & Richter, R. (2018). Cast shadow detection to quantify the aerosol optical thickness for atmospheric correction of high spatial resolution optical imagery. Remote Sensing, 10(2), 200.
A good reading list:
1. Bunting, P., Clewley, D., Lucas, R. M., Gillingham, S., Jan. 2014. The Remote Sensing and GIS Software Library (RSGISLib). Computers and Geosciences 62, 216–226.
2. Bunting, P., Gillingham, S., 2013. The KEA image file format. Computers and Geo- sciences 57, 54–58.
3. Chavez, P. S., 1996. Image-based atmospheric corrections-revisited and improved. Pho- togrammetric Engineering And Remote Sensing.
4. Dymond, J. R., Shepherd, J. D., Mar. 2004. The spatial distribution of indigenous forest and its composition in the Wellington region, New Zealand, from ETM+ satellite imagery. Remote Sensing Of Environment 90 (1), 116–125.
5. Gillingham, S., Flood, N., 2013. Raster I/O Simplification Python Library. URL https://bitbucket.org/chchrsc/rios/overview
6. Gillingham, S., Flood, N., Gill, T. K., 2012. On determining appropriate aerosol optical depth values for atmospheric correction of satellite imagery for biophysical parame- ter retrieval: requirements and limitations under Australian conditions. International Journal Of Remote Sensing 34 (6), 2089–2100.
7. Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., Duchesnay, E., 2011. Scikit-learn: Machine learning in Python. Journal of Machine Learning Research 12, 2825–2830.
8. Shepherd, J. D., Dymond, J. R., 2003. Correcting satellite imagery for the variance of reflectance and illumination with topography. International Journal Of Remote Sensing 24 (17), 3503–3514.
9. Smith, G. M., Milton, E. J., Nov. 2010. The use of the empirical line method to calibrate remotely sensed data to reflectance. International Journal Of Remote Sensing 20 (13), 2653–2662.
10. Vermote, E., Tanre, D., Deuze, J., Herman, M., Morcrette, J., Jan. 1997. Second Simula- tion of the Satellite Signal in the Solar Spectrum, 6S: An overview. IEEE Transactions of Geoscience and Remote Sensing 35 (3), 675–686.
11. Wilson, R. T., Feb. 2013. Py6S: A Python interface to the 6S radiative transfer model.
12. Computers and Geosciences 51, 166–171.
13. Wilson, R. T., Milton, E. J., Nield, J. M., 2014. Spatial variability of the atmosphere over southern England, and its effect on scene-based atmospheric corrections. International Journal of Remote Sensing.
14. Zhu, Z., Wang, S., Woodcock, C. E., Mar. 2015. Improvement and expansion of the Fmask algorithm: cloud, cloud shadow, and snow detection for Landsats 4–7, 8, and Sentinel 2 images. Remote Sensing Of Environment 159, 269–277.
15. Zhu, Z., Woodcock, C. E., Sep. 2014. Automated cloud, cloud shadow, and snow detec- tion in multitemporal Landsat data: An algorithm designed specifically for monitoring land cover change. Remote Sensing Of Environment 152, 217–234.
16. Schläpfer, D., Hueni, A., & Richter, R. (2018). Cast shadow detection to quantify the aerosol optical thickness for atmospheric correction of high spatial resolution optical imagery. Remote Sensing, 10(2), 200.