17
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 in ...
13
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 ...
12
var sst = ee.ImageCollection('NASA/OCEANDATA/MODIS-Aqua/L3SMI')
.select('sst')
.filterDate(ee.Date('2013-01-01'), ee.Date('2017-12-31'))
var months = ee.List.sequence(1, 12);
var years = ee.List.sequence(2013, 2017);
var byMonthYear = ee.ImageCollection.fromImages(
years.map(function(y) {
return months.map(function (m) {
return sst
...
11
The project website hosts the MOD16 dataset on an FTP server.
As FTPs allow directory listings you can easily download complete folders without having to click individual links. This can be done with most FTP clients - a popular one would be FileZilla. Just right click the folder you want and select download.
edit:
The question now specifies that only one ...
11
// UPDATE: The easiest way is now toBands().
var merged = MOD1.toBands();
// For completeness, this is the iterate() way:
var mergeBands = function(image, previous) {
return ee.Image(previous).addBands(image);
};
var merged = MOD1.iterate(mergeBands, ee.Image([]));
10
Information on how to convert the digital values ("digital numbers" or DN) in satellite remote sensing data that is operationally produced and distributed into physical quantities of interest is generally found in the data user guide. In this case, you need the user guide for the MODIS 11 Level 2 product. Under "scientific data sets (SDS" there is a table ...
9
This is a good question, naturally isn't obvious how to handle this kind of data. For example, the product State QA has 16 bits and there are single, double and triple bit flags combinations:
For example, a bit flag combination as 0-0-0-0-0-0-00-01-001-0-00 means:
00 cloud state: clear
0 cloud shadow: no
001 land/water flag: land
01 aerosol quality: low
00 ...
8
Here is a modification of the first example from this presentation about tables and vectors. Note that you can "transpose" the table if there are other properties in the points that are of interest:
var rectangle = ee.Geometry.Rectangle(96.01669, 18.52621, 96.04819, 18.49634);
Map.centerObject(rectangle);
Map.addLayer(rectangle, {}, 'rectangle')
var ...
7
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 (at the time this answer was originally posted in July 2013, however HDF4 support was added to the GISInternal GDAL build in Nov. 2013). HDF4 support is compiled in the OSGeo4W GDAL binaries.
...
7
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
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).
7
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 ...
7
The Readme.docx inside the zip file gives some hints on the projection, but maybe misleading.
The raster is in an interrupted Goode Homolosine projection, based on the MODIS sphere.
So I created the following custom CRS:
+proj=igh +R=6371007 +no_defs
reprojected a Natural Earth shapefile into it, and it fits to the raster data with the same CRS assigned (...
7
Here is my code I use to convert all HDFs in a folder to GeoTIFF via GDAL (OSGEO4w) in Windows when working with SST data. Remember to use the OSGEO4W version to get hdf support.
for %A in ("C:\temp\*.hdf") do gdal_translate -of GTiff -a_srs "+init=epsg:4326" -a_ullr -180 90 180 -90 -co "COMPRESS=PACKBITS" -sds "%A" "%A.tiff
for %A in ("C:\temp\*.hdf") do ...
7
Got rid of the downvote, somebody is just trolling.
I have done this with MODIS rasters without problem before, in sinusoidal, you are right, should retain the area over other projections. That being said that is barely and as long as you are chopping out the same area on both, and they are in the same modern style projection i.e. wgs84 or sinusoidal, it ...
7
You just need the function 'toBands()' and apply that on the image collection. Unfortunately, the function cannot deal with similar band names (at least, for your MODIS collection: it works fine for landsat and Sentinel), so you will need to change the band names. For simplicity, I changed the band names to the date the image was acquired.
Update: bandnames ...
6
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 20-...
6
I think you're misunderstanding the bit packing in the QA flags as your example with 32 is incorrect. 32 is 00100000 in binary so can only mean bad data in the 6th input (bits are numbered from right to left). As another example, 11 is 0001011 in binary, so means there was bad data in the 1st, 2nd and 4th inputs.
The quality flags for MOD44B are:
Bit Input ...
6
If you are working with a GDAL version that supports HDF5 datasets this is how you can extract a single subdataset from it and convert it to Geotiff in Python. This example was used for MOD09 and MOD13 band extraction (hence the no_data value conversion).
from osgeo import gdal
import numpy as np
def hdf_subdataset_extraction(hdf_file, dst_dir, subdataset):...
6
Projection of this kind of files is sinusoidal. For this one:
ftp://ladsweb.nascom.nasa.gov/allData/6/MOD13Q1/2016/129/MOD13Q1.A2016129.h07v06.006.2016147112419.hdf
the next code can access to coordinates for 256 values for subDatasets[0][0] (NDVI values).
from osgeo import gdal
import struct
nameraster = "/home/zeito/Desktop/MOD13Q1.A2016129.h07v06....
6
I have used a robust regression approach, for downscaling of climate data, with consistent success. The idea is that you treat the lower-resolution data as the dependent variable, as a sample or population, and sample the higher-resolution data, treating it as the independent variable, to build a bivariate (or even multivariate) regression model. You then ...
6
My guess is you are trying to combine the two 16-day composites into a synthethic 8-day composite containing data from both Aqua and Terra (as has been proposed in many research papers).
You can do this by simply merging both collections and then sorting according to the central acquisition date.
https://code.earthengine.google.com/...
6
Per https://lpdaac.usgs.gov/dataset_discovery/modis, the viewing swath width of MODIS is 2,330 km, thus a large portion of the image is off-nadir in some way. https://modis.gsfc.nasa.gov/about/specifications.php
The following forum post gives an explanation of how to calculate pixel size based on viewing position. (Note: still an estimate due to factors ...
5
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 ...
5
Just a tiny error, as far as I can see. Your substrings are incorrect. This can be seen by comparing the result from a 'which(df$bits="0000100001000100")' with a number of observed unique values, which can be seen in ArcGIS when colouring the tif-file by unique values. 00001000 01000100 = 2116, and there are 3891233 of that number in both ArcGIS and R. This ...
5
In Windows (run OSGeo4W shell):
Scaling:
for %i in (*.tif) DO gdal_translate -scale -2000 10000 -0.2 1 %i outputs\%i
You might find recalculating instead better:
for %i in (*.tif) DO gdal_calc.bat -A %i --outfile=outputs\%i --calc="A*0.0001" --NoDataValue=0
In Ubuntu looping through files is slightly different:
for i in *.tif; do
gdal_calc -A $i ......
5
As mentioned in the files metadata, the modis source data has a scale factor of 0.02. QGIS applies it on-the-fly.
But the reprojection with gdalwarp destroys the scale factor information, hence the result is unscaled. You have to apply it manually to get the correct results.
5
It represent from January 1st to January 16th. Here you got some information:
From Agricultural and Forest Meteorology 161 (2012) 15–25
The day of year (DOY) for each MODIS image represents the first day of the period of the 8- or 16-day composite. The day during the composite period when the best observation is recorded is called the day of pixel ...
5
If you click the "Take me to the download..." button in the page you linked, it will eventually lead you to the download link for the full StarFM distribution.
Upon extracting that file, you will find StarFM's source under StarFM/source.
Also, you can find a CUDA implementation of the algorithm on GitHub at https://github.com/HPSCIL/cuSTARFM.
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