Hot answers tagged modis
4
A quick Google has turned up MODIS Snow and Ice Project, which appears to give resolutions of 500m.
You could also look at Landsat data. Due to the high albedo of snow, it should be a fairly easy process to threshold highly reflective values, then average them out by collecting a time-series of images and applying map algebra.
Alternatively, you could ...
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The PRISM Climate Group's data is exceptional. Their raster products include precipitation, max temp, min temp, dewpoint and historic data.
NASA's MODIS site has a wealth of data as does this USGS site. You will find a wide range of products there from vegetation indices to emissivity and burn data.
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The WorldClim dataset has a lot of the data you want. It is free for non-commercial use and has interpolated 1 km resolution data on:
average monthly mean temperature (°C * 10)
average monthly minimum temperature (°C * 10)
average monthly maximum temperature (°C * 10)
average monthly precipitation (mm)
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I routinely do this with GDAL and a simple bash script. You can also do ith with python, but I think this is quite straightforward and easier to understand
#!/bin/bash
# This script reprojects and subsets a bunch of HDF files stored in
# a given dir (WORKDIR). The output is a GeoTIFF formatted file.
#
WORKDIR="./" # Where all HDF files are stored
...
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R is an excellent programming language designed for data manipulation and statistical computation. There is a very nice package called sp that adds good support for handling spatial data. The spsample function can pull a statistical sample from spatial data using 7 different methods. The sampling area may be controlled by specifying a bounding box and/or ...
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For the United States, National Snow Analyses. For global try Rutger's Global Snow Lab
These were the top results for a Google search for "snow cover" and "global snow cover", respectively. You'll have to come up with a quantitative definition of what you're looking for (e.g. has snow cover of depth x over period y) and apply it to the data. Map algebra ...
2
Most MODIS QA data (including the Cloud Mask data) are not stored as separate raster bands, where each band is a grid where each cell is one value of one QA data field. Instead, the QA data are concatenated into strings of bits. So instead of having Band 1 be 00 and Band 2 be 11, they just concatenated them (right-to-left) as 1100 which is a completely ...
2
After some more digging, I found that NOAA and NCDC keep a respectable number of freely available datasets detailing just the kind of information I'm looking for. I was able to find shapefiles for:
1. Average Mean Temperature
2. Mean Number of Days with Temperature 32 degrees Farenheit or below
3. Mean Number of Days with Snow Depth >= 1, 5, or 10 inches.
I ...
2
Hard to say without looking at the data, but there are some issues that you need to take into account. First, there's angular variation not corrected by the MxD13 data, and its effect can be different for grasses (continuous canopies if you want) and forests (discontinuous). You can use the MODIS NBAR product (MCD43?) to use nadir-reflectance. This product ...
2
i think you should check out grass plugin for your study. it has lots of raster analysis as Hydrologic modeling toolbox. you can check out GRASS GIS 6.4.3svn Reference Manual here
Watershed modeling related modules are r.basins.fill, r.water.outlet,
r.watershed, and r.terraflow. Water flow related modules are r.carve,
r.drain, r.fill.dir, ...
2
I got your email. Sadly I don't have experience creating MODIS cloud masks. I've used the MOD35 cloud mask data, but I've never done the calculations myself.
That being said, I believe that MOD09 reflectance data are already corrected for cloudcover. There is a 2-bit word in the MOD09 QA layer for "Cloud state" that can be either Clear, Cloudy, Mixed, or ...
2
You can use the Snap Raster environment setting to get the corners of the pixels of your two data sets to align properly.
Based on the screenshot, it looks like you are exactly 1/2 pixel off in both the X and Y directions. This could easily cause the Zonal Histogram tool to incorrectly count the number of pixels. This is because all of the Zonal tools in ...
2
You're talking about temporal compositing. This is a major part of MODIS data because the data is collected so frequently (every 36 hours at most). What's tricky is how to choose one observation to use to represent a week or a month of input data. The MODIS data processing algorithms take into account all sorts of other data in order to choose the highest ...
2
I think you almost have it. Be sure to add single quotes around the entire input name with double quotes around the hdf file name.
gdal_translate -of GTiff 'HDF4_EOS:EOS_GRID:"MOD13C2.A2001001.005.2007078152825.hdf":MOD_Grid_monthly_CMG_VI:CMG 0.05 Deg Monthly NDVI'
Hope that helps
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If you use -dstnodata 0 you will not be able to distinguish output areas for which there was no source pixel from those for which the input pixel was zero. Ideally you would first review the source data and pick a nodata value that is outside the range of valid input data. You can use "gdainfo -mm" on the source image to get the min max.
Of course the ...
1
Also, an image is an optical view. If you are looking at doing watershed modeling, which it seems you are, then you will want to have elevation data (DEM) and an idea of where water sources are. Depending on where in the world you are looking to study, there are a number of DEM data sources available, at ranging resolutions, for free.
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You may want to check this page:
https://lpdaac.usgs.gov/products/modis_products_table/mcd43a3
32767 is actually the fill value, so that's NO DATA (to be removed prior to rescaling). Then rescale the pixel values with 0.0010.
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For solar zenith, you can use the Solar Geom Calc. Assuming you have access to the metadata for your imagery, or know the lat/long and date and time of image acquisition, you should be good to go. As far as aerosol type, You should check out the Modis site for more information.
Can you provide the link to the site where you got your formula?
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I solved the problem. Strangely, it seems the Zonal_Histogram function works with the symbology of a file, and not only with the attribute table. I found it out by comparing my results by assigning both a stretched symbology, and a classified one to the DEM. Arcgis everytime considered the representation of the file, giving a field called "LABEL" when there ...
1
I think you are looking for the MODIS Reprojection Tool - Swath (MRTSwath):
MRTSwath provides the capability to transform MODIS level-2 land products from HDF-EOS swath format to a uniformly gridded image that is geographically referenced according to user-specified projection and resampling parameters. Functionality includes spectral subsetting, spatial ...
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I've worked with a few local government GIS departments in Europe, and many of them have data on when roads are de-iced, and the routes the gritting lorries take (as attributes of a GIS road network). All local government agencies should have some sort of figures on the budgets for maintaining roads in winter (salt, grit, gritters etc.)
After doing your ...
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