New answers tagged

1

As you mention yourself, different wavelengths behave differently through the atmosphere. In theory, this can be counteracted through atmospheric correction, however, modelling the atmosphere accurately is a complicated matter, especially for medium and high resolution satellites where the required information is not acquired along with the images (due to ...


0

I believe downloading data from the Sentinel platform provided by ESA is very cumbersome. If you are willing to learn javascript, I recommend using the platform provided by Google (Google Earth Engine).


2

Well this is not exactly a GIS (definitely not arcgis) question, but remote sensing is somewhat related. Anyway, EVI (enhanced vegetation index) is similar to NDVI and to SAVI (Soil-adjusted vegetation index). To wit it corrects for atmospheric and soil distortions. As stated in the NASA website "While the EVI is calculated similarly to NDVI, it corrects ...


1

The ESA/EU make radar products from the Sentinel satellites available for free through the Copernicus Data Portal.


0

No way to calculate NDVI from SAR imagery (Interferometric Wide swath SLC VV polarisation), because this image consist of only 1 band. You only can get some value about biomass and after that you can try to search some formulas to indexing vegetation for specific fields, agricultural, forests or something else but anyway you need to use both polarisations. ...


0

Although it requires signing up for an account, you can do this through Google Earth Engine's API quite easily. They give comprehensive tutorials and scripts for how to do so, as well as the data products you are asking for. You can remove the cloudy/bad pixels from the NDVI product by adjusting the script of this tutorial for the data layers you'd like to ...


0

From my quick search on available articles, there appears to be no published article covering this topic, as such, it looks like you have stumbled upon a topic that could be a reasonable article - the derivation of constants for calculating EVI from Sentinel-2 imagery. If that is too big a task for you at this stage, I'd suggest relying on other vegetation ...


1

In step 4 of the tutorial it states "If desired, choose spatial and/or temporal search criteria". In essence, what you should do is draw a box on the map covering the area that you wish to search for data in (while leaving the temporal options alone). This will give you only the tiles within your search box, rather than each and every tile available in the '...


0

You can try using QA band. The leftmost 4 bits of the 16 bits indicate cloud and cirrus cloud presence. 00 -> Not determined 01 -> No (0 to 33% probability) 10 -> Maybe (34 to 66% probability) 11 -> Yes (67 - 100% probability)


0

I don't know of any other way to do this unless you could access the image's EXIF data. Test "datetime" or "datetimeoriginal" and check the month the image was taken. The Python image library (PIL) module can do this like below. import arcpy, os, glob, re from arcpy import env from arcpy.sa import * from PIL import Image arcpy.env.overwriteOutput = True ...


0

I'm not familiar with the MODIS file naming conventions, but maybe you could try the regular expression module (re) to add rasters matching a pattern in your root folder, add them to "dekad" based on the pattern, then perform CellStatistics. Below, I've assumed the month is the 3 digits before the "Fpar_1km" (??) You could adjust it to suit your needs. ...


1

It sounds like you just need to iterate over each month's folder(?), add each month's tif files to the list "dekad", perform the CellStatistics command and save result as the monthly average raster, correct? Instead of the "for i in range... dekad.append[]..." method, you could do something like below using "glob"... import arcpy, os, glob from arcpy ...


0

ENVI does not provide an option for fixed size ROI. One possible way to do that is by creating polygon shapefile and draw polygons with fixed length and width using ArcGIS, then import the polygon shapefile from ENVI as ROI.


0

I advise you to do more reading of academic journals. Another good paper is by Calderon et al (2014) : Detection of downy mildew of opiumpoppy using high-resolution multi-spectral and thermal imagery acquired with an unmanned aerial vehicle. What you ask now is a tricky question that many Agro-consulting companies are still researching. This is cutting edge ...


2

What camera are you using- and what bands? (Sounds like the micasense) You may want something that reaches towards the SWIR to see water characteristics. Anyways, from what I know- it is best to pair thermal imagery (measuring canopy temperature) with atmospheric temperature, and use something like the crop water stress index (CWSI) - see paper by Bellvert ...



Top 50 recent answers are included