If you just want three ranges - 1, 0.5 and 0, I'd use MOD13Q1 Pixel Reliability instead of VI Quality. From the MOD13Q1 page:
TABLE 1: MOD13Q1 Pixel Reliability
Rank Key Summary QA Description
-1 Fill/No Data Not Processed
0 Good Data Use with confidence
1 Marginal data Useful, but look at other QA information
2 Snow/Ice Target covered with snow/ice
3 Cloudy Target not visible, covered with cloud
Read the 250m 16 days pixel reliability summary QA layer into an array and assign 1 to your relative importance where QA is 0, 0.5 where QA is 1 and 0 where QA is any other value.
I don't know r but in python it would be something like:
# Assuming the 250m 16 days pixel reliability summary QA layer has been read into "qa" array
importance = numpy.zeros(qa.shape)
importance[qa == 0] = 1
importance[qa == 1] = 0.5
If you want to use the usefulness flags, they are stored as 4 bits in a two dimensional unsigned 16 bit integer array:
TABLE 2: MOD13Q1 VI Quality
Bit 0 is the least significant (read bit words right to left)
bit Long Name Value Key
0–1 MODLAND_QA 00 VI produced, good quality
01 VI produced, but check other QA
10 Pixel produced, but most probably cloudy
11 Pixel not produced due to other reasons than clouds
2–5 VI usefulness 0000 Highest quality
0001 Lower quality
0010 Decreasing quality
0100 Decreasing quality
1000 Decreasing quality
1001 Decreasing quality
1010 Decreasing quality
1100 Lowest quality
1101 Quality so low that it is not useful
1110 L1B data faulty
1111 Not useful for any other reason/not processed
<SNIP>...
In python you can use the bitwise right shift (>>) and 'and' (&) operators to extract VI usefulness:
# Assuming the 250m 16 days VI Quality detailed QA layer has been read into a UInt16 array "qa"
usefulness = (qa >> 2) & 15 # Right shift 2 bits and extract 1st 4 bits (values of 0-15)
importance = numpy.zeros(qa.shape)
importance[usefulness <= 7] = 0.5
importance[usefulness == 0] = 1
Note the TimeSAT documentation states:
In previous work weights w = 0; 0.5 and 1. have been used for values in the time-series associated with, respectively, cloudy, mixed and clear conditions. There are, of course, no general rules for converting ancillary data to weights associated with the values in the time-series, and the user of the Timesat program is encouraged to take an experimental approach and test different settings.
So you don't have to confine yourself to three weights and can use the VI Quality to define a range of weights. Again a python example:
# Assuming the 250m 16 days VI Quality detailed QA layer has been read into a UInt16 array "qa"
usefulness = (qa >> 2) & 15 # Right shift 2 bits and extract 1st 4 bits (values of 0-15)
importance = numpy.zeros(qa.shape)
importance[usefulness == 0] = 1
importance[usefulness == 1] = 0.8
importance[usefulness == 2] = 0.6
importance[usefulness == 3] = 0.5
importance[usefulness == 4] = 0.4
importance[usefulness == 5] = 0.3
importance[usefulness == 6] = 0.2
importance[usefulness == 7] = 0.1
Best Answer
Below you'll find the documentation of Landsat 8 SR Code:
https://landsat.usgs.gov/sites/default/files/documents/lasrc_product_guide.pdf
On the page 19 there is a general table with values already calculated to human-friendly format. Note the detailed table at the page 20 - Landsat's QA is not just about Land/Cloud/Water etc., but it has a way more detailed distinction (that you can obviously group to fit your own needs). Hope it helps you.