Many MODIS data products are organized in a tile grid based on a sinusoidal projection. You can find an online calculator here, that will convert from tiles to latitude and longitude coordinates. This tutorial will demonstrate how to perform this conversion in Python.
- Read in MODIS tile bounding coordinates
- Define point with given latitude and longitude coordinates
- Find corresponding tile
import numpy as np import urllib2
MODIS provides a text file containing the range of latitude and longitude coordinates for each tile. We will load this data into a numpy two dimensional array. Next, we will define the coordinates of the point we would like to convert.
--2016-12-14 16:28:08-- https://modis-land.gsfc.nasa.gov/pdf/sn_bound_10deg.txt Resolving modis-land.gsfc.nasa.gov... 18.104.22.168, 2001:4d0:2310:150::244 Connecting to modis-land.gsfc.nasa.gov|22.214.171.124|:443... connected. HTTP request sent, awaiting response... 200 OK Length: 32585 (32K) [text/plain] Saving to: ‘sn_bound_10deg.txt’ sn_bound_10deg.txt 100%[===================>] 31.82K --.-KB/s in 0.05s 2016-12-14 16:28:08 (610 KB/s) - ‘sn_bound_10deg.txt’ saved [32585/32585]
# first seven rows contain header information # bottom 3 rows are not data data = np.genfromtxt('sn_bound_10deg.txt', skip_header = 7, skip_footer = 3) lat = 40.015 lon = -105.2705
Now we can loop through each row (corresponds to one tile) in the array and check to see if our point is contained in the range of coordinates for that tile.
in_tile = False i = 0 while(not in_tile): in_tile = lat >= data[i, 4] and lat <= data[i, 5] and lon >= data[i, 2] and lon <= data[i, 3] i += 1
Once you reach a tile including the point you are searching for the code will exit the loop, and you can print the vertical and horizontal position of the tile.
vert = data[i-1, 0] horiz = data[i-1, 1] print('Vertical Tile:', vert, 'Horizontal Tile:', horiz)
('Vertical Tile:', 4.0, 'Horizontal Tile:', 9.0)