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7

If you know R, you can use the package "gdalUtils" and run gdal_translate to do that. If you are on Linux make sure to install GDAL. If you are on Windows, you're good to go. These are the basic commands to handle the conversion to .tiff and the reprojection to WGS84. out.files <- list.files(getwd(), pattern="hdf$", full.names=FALSE) #create a list with ...


7

you can find the H and V index in all MODIS product file name. These indices refer to the grid below (from the MODIS Website). For instance you have H8V6 (MOD17A3.A2000001.h08v06.055.2011276103801.hdf).


6

All of the detailed information about MOD12 data can be found in the Algorithm Theoretical Basis Document (ATBD). On page 23, it says that the forest classes require >60% coverage of the pixel.


5

HDF4 support is not available in GDAL by default. If you're using the GDAL binaries and python libraries from GISInternals, these do not have HDF4 support compiled in. HDF4 support is compiled in the OSGeo4W GDAL binaries. You can test if you have HDF4 support with the gdalinfo command: gdalinfo --format hdf4 You can also batch the MRT from within your ...


5

You need the subdataset full name from the query on the file: gdalinfo MOD04_L2.A2003001.0005.051.2010313005421.hdf >2003.txt With the subdataset name, you get the GCP coordinates in pixel and lon/lat: gdalinfo HDF4_EOS:EOS_SWATH:"MOD04_L2.A2003001.0005.051.2010313005421.hdf":mod04:Image_Optical_Depth_Land_And_Ocean >>2003a.txt With the ...


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 ...


4

If you look at the product page at LPDAAC, under Layers there is a table that lists each of the bands in the dataset and their characteristics. For the NDVI layer, it is a 16-bit signed integer with a fill value of -3000, and a valid range from -2000 to 10000. However, there is also a scale factor of 0.0001, or 1/10,000. This means that a value of 10000 ...


4

You can use the "stack" or "brick" function(s) in the raster package to create a single raster object of your NDVI time-series. The names of each NDVI file will be retained in the object and the rasters will be held out-of-memory, making it memory safe. You can then retrieve the raster values, associated with a point feature, across the series using the ...


4

You cannot do this in any reliable fashion. The reason is that every MODIS product (like LST) is created from a number of observations (basically, the MOD01 radiance product) and you do not know how that is done. The MOD11A2/MYD11A2 products do not give you the number of observations that go into each product, nor the extremes. But if you really want to do ...


4

Landsat and Modis are optical sensors, which means that they provide digital numbers of reflected materials that are within the electromagnetic spectrum. These values correspond to the wave length of the corresponding satellite band. To get elevation from just the raw values would be impossible. The only potential means to collect elevation information would ...


3

Unfortunately, mapping burned areas--especially across large geographic areas--is not a simple task. However, there is a lot of good documentation on how burned area products are created in peer reviewed publications. For a regional perspective, I would refer you to the Monitoring trends in burn severity (MTBS) site. MTBS utilizes Landsat data to map all ...


3

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.


3

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)


3

for rainfall you can download the TRMM datasets: http://trmm.gsfc.nasa.gov/


3

You may take a look at "pyModis", a Free and Open Source Python library to work with MODIS data. It can bulk-download, mosaik and reproject: http://gis.cri.fmach.it/pymodis/


3

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 ...


3

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 ...


3

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 ...


3

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 ...


3

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 ...


3

So MODIS is a complex instrument and it has been a while since I worked with the data. So bear that in mind. If we look at band 1, the data is pretty good: But as you point out with band 7 the data does not line up. I had to check it myself because I was certain it had to do with the different channels per band (which is why the raw RSR file have from ...


3

You may also take a look at "pyModis", a Free and Open Source Python library to work with MODIS data. It can bulk-download, mosaik and reproject: http://gis.cri.fmach.it/pymodis/


3

You may check the "pyMODIS" software, a Free and Open Source Python library to work with MODIS data. It can bulk-download, mosaik and reproject: http://gis.cri.fmach.it/pymodis/


3

Use LDOPE-1.7 (https://lpdaac.usgs.gov/tools/ldope_tools), using "create_mask". this function takes MOD35_L2 HDF and creates a cloud mask in hdf. use MRTSwath tool for projection/re-sampling/clipping and convert new hdf to GeoTiff.


3

If you want the average value, then you could use a mean filter (focal statistics, ignoring the NoData value). This will be way faster than kriging, and it will work directly from a raster. Then you use the raster calculator to replace the nodata values with the mean values : Con(Isnull("input_raster"), "smoothed_raster", "input_raster") As a remark, ...


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

Sounds do-able. I would suggest looking at the docs, specifically the sections on listing data and the tools you need to use, such as the Extract Subdataset tool - there are lots of samples in both of those sections to get you going. If I understand the issue properly, you need to get a list of the rasters, then iterate through that list, processing as you ...


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

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

It depends on what you're trying to accomplish. What are you trying to accomplish in terms of processing? Both MODIS MRT Swath tool and MODIS MRT tool provide the capability for batch processing: mosaicking, resampling, etc. In the documentation for both tools there are some examples. MODIS MRT SWATH Documentation If you need to perform additional ...



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