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1

you can replace the list raster with glob.glob in order to get the list of your raster : import glob, arcpy list_composites = [] list_images = glob.glob("path_to_your_image.SAFE\GRANULE\*\IMG_DATA\*B01.jp2") #first band in each folder for image in list_images: rasters = glob.glob(image[:-6]+"*") #all bands in one folder ...


2

One of the problems with thresholding is that there really is no good way to automate the optimization of a threshold value. There are several reasons for this. When it comes to extracting features of interest, such as urban areas, there will be error no matter which threshold value you choose. If you select a value too high, classes start merging--to low ...


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SNAP contains tools to help you automate tasks: in the Tools menu you'll find entries for GraphBuilder and Batch Processing.


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See my answer here for a description on how to calculate land surface temperature, using the simplest approach. However, the Landsat 8 TIRS sensor is currently not fully functional, and it has been in that state since 1st of November 2015. The current plan is that the data acquired in the 4th quarter of 2015 is to be re-released with the issue fixed during ...


0

The BYTSCL rescales your output from -1 to 1 -> 0 to 255. Remove that last bit, and you don't have your problem. BYTSCL is usually used for minimizing storage and bandwidth requirements associated with displaying data online. If you want to make sure that you do not have values below -1 and above 1, you can use this function: (float(b1) lt ...


3

Sun synchronous satellites like Landsat always capture an image at the same time of the day. Furthermore, Landsat "only" repeats its acquisitions every 16 days. If you want an image in the afternoon, MODIS AQUA has a daily coverage at 1.30 PM, which is a better timing but at a lower spatial resolution (500m). Finally, you can get the highest temporal ...


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After unzipping, you should find an xml file inside the root directory with the same name as the zipped file. Select the xml file and be patient because opening the file takes a little while. You can then view the data per band, or right click on the scene name to open an RGB composite. Normally this should also work directly from the zip file.


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Are you sure you didn't double click one too many times to get to the bands list? if you go back one up, you should be able to see a composite band layer, that contains all the 13 sub-datasets, even if it is clickable. Do not click it and add the composite image as it is. I am assuming your 13 subdatasets relate to the number of bands you have on the MODIS ...


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take a look at this page http://gistack.rozblog.com/post/32/qa_cloudmask_ports.html at the bottom of this page you can download an erdas imagine model it takes landsat8 QA band and replace cloud pixel values with 0


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This article might be of interest even if it utilizes Landsat-8 data. As mentioned in the comments above, the authors suggest that a combination of Tasseled Cap transformation (TCB) and Normalized Difference Bareness Index (NDBaI) can improve the detection of bare soil.


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It must indeed be difficult to separate the classes with the spectral information available on one date. But as Landsat data are usually available for several dates within a year, you might try to combine these dates. Except in very dry areas, there is often a time when the soils are covered by vegetation and easy to differentiate from buildings and roads.


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Conversion to At-Satellite Brightness Temperature http://landsat.usgs.gov/Landsat8_Using_Product.php TIRS band data can be converted from spectral radiance to brightness temperature using the thermal constants provided in the metadata file: T = K2 ln( K1 +1) Lλ where: T = At-satellite brightness temperature (K) Lλ = ...


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The SRTM data is a digital surface model as it includes canopy (and buildings and other infrastructure etc), there's further information in this question. To get a bare earth DEM from SRTM data requires some processing, for example see Gallant et al (2012). You don't specify what part of the world you want data for, but there is 30m SRTM DSM and DEM data ...


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InSAR on Sentinel-1 imagery would require the following steps in SNAP: Data input and baseline evaluation Coregistration and interferogram generation Computation of coherence Removal of topographic phase Phase filtering (multi-looking) Phase unwrapping (this is a critical task and strongly depends on the software) Conversion into vertical ...



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