i.modis.qc(1)

📦
version 8.4.2 · 8 versions
, in fedora 44

i.modis.qc 💅✨ — GRASS GIS User's Manual 🛰️💖

okay bestie, sit down, hydrate, because this one is a unit of a module. it lives in the GRASS GIS family and it's here to pull the quality-control receipts out of your MODIS QC layers. no cap. 🎀

🌸 NAME 🌸

i.modis.qc — extracts quality control parameters from MODIS QC layers. 🦋

🏷️ KEYWORDS 🏷️

imagery, imagery quality assessment, reflectance, land surface temperature, vegetation, satellite, MODIS

📜 SYNOPSIS 📜

i.modis.qc
i.modis.qc --help
i.modis.qc input=name output=name productname=string qcname=string
[band=string]   [--overwrite]  [--help]  [--verbose]  [--quiet]  [--ui]

🚩 Flags 🚩

  • --overwrite — let your output files bulldoze whatever's already sitting there with the same name. no survivors, periodt.
  • --help — prints the usage summary. your little "what does this do again" safety net. 🫶
  • --verbose — verbose module output. she talks more. we love a chatty queen. 💬
  • --quiet — quiet module output. shhh. she keeps it to herself. 🤫
  • --ui — force-launches the GUI dialog. main character energy, but with a window. 👑

🎛️ Parameters 🎛️

  • input=name [required] — name of the input surface reflectance QC layer [bit array]. yes, a bit array, bestie, buckle up. 🔢
  • output=name [required] — name for the output QC type classification layer. this is where the extracted tea gets written. 💾
  • productname=string [required] — name of the MODIS product type. you gotta tell her which product you're working with, she's not a mind reader. 🧚

Options:

mod09Q1, mod09A1, mod09A1s, mod09GA, mod09GAs, mod09CMG,
mod09CMGs, mod09CMGi, mod11A1, mod11A2, mod13A2, mcd43B2, mcd43B2q,
mod13Q1
  • mod09Q1: surf. refl. 250m 8-days
  • mod09A1: surf. refl. 500m 8-days
  • mod09A1s: surf. refl. 500m 8-days, State QA
  • mod09GA: surf. refl. 500m daily
  • mod09GAs: surf. refl. 500m daily, State QA
  • mod09CMG: surf. refl. 5000m daily
  • mod09CMGs: surf. refl. 5000m daily, State QA
  • mod09CMGi: surf. refl. 5000m daily, Internal Climatology
  • mod11A1: LST 1km daily (Day/Night)
  • mod11A2: LST 1km 8-days (Day/Night)
  • mod13A2: VI 1km 16-days
  • mcd43B2: Brdf-Albedo Quality (Ancillary SDS) 1km 8-days
  • mcd43B2q: Brdf-Albedo Quality (BRDF SDS) 1km 8-days
  • mod13Q1: VI 250m 16-days
  • qcname=string [required] — name of the QC type to extract. this is the flavor of quality info you want her to dig out for you. 🍓

Options:

adjcorr, atcorr, cloud, data_quality, diff_orbit_from_500m,
modland_qa, mandatory_qa_11A1, data_quality_flag_11A1,
emis_error_11A1, lst_error_11A1, data_quality_flag_11A2,
emis_error_11A2, mandatory_qa_11A2, lst_error_11A2,
aerosol_quantity, brdf_correction_performed, cirrus_detected,
cloud_shadow, cloud_state, internal_cloud_algorithm,
internal_fire_algorithm, internal_snow_mask, land_water,
mod35_snow_ice, pixel_adjacent_to_cloud, salt_pan, icm_cloudy,
icm_clear, icm_high_clouds, icm_low_clouds, icm_snow, icm_fire,
icm_sun_glint, icm_dust, icm_cloud_shadow,
icm_pixel_is_adjacent_to_cloud, icm_cirrus, icm_pan_flag,
icm_criteria_for_aerosol_retrieval, icm_aot_has_clim_val,
modland_qa, vi_usefulness, aerosol_quantity,
pixel_adjacent_to_cloud, brdf_correction_performed, mixed_clouds,
land_water, possible_snow_ice, possible_shadow, platform,
land_water, sun_z_angle_at_local_noon, brdf_correction_performed,
modland_qa, vi_usefulness, aerosol_quantity,
pixel_adjacent_to_cloud, brdf_correction_performed, mixed_clouds,
land_water, possible_snow_ice, possible_shadow

and here's what each one actually means, because the names alone are giving cryptic: 🌈

  • adjcorr: mod09: Adjacency Correction
  • atcorr: mod09: Atmospheric Correction
  • cloud: mod09: Cloud State
  • data_quality: mod09: Band-Wise Data Quality Flag
  • diff_orbit_from_500m: mod09: 250m Band is at Different Orbit than 500m
  • modland_qa: mod13Q1: MODIS Land General Quality Assessment
  • mandatory_qa_11A1: mod11A1: MODIS Land General Quality Assessment
  • data_quality_flag_11A1: mod11A1: Detailed Quality Indications
  • emis_error_11A1: mod11A1: Average Emissivity Error Classes
  • lst_error_11A1: mod11A1: Average LST Error Classes
  • data_quality_flag_11A2: mod11A2: Detailed Quality Indications
  • emis_error_11A2: mod11A2: Average Emissivity Error Classes
  • mandatory_qa_11A2: mod11A2: MODIS Land General Quality Assessment
  • lst_error_11A2: mod11A2: Average LST Error Classes
  • aerosol_quantity: mod13Q1: Quantity range of Aerosol
  • brdf_correction_performed: mod13Q1: if BRDF correction performed
  • cirrus_detected: mod09*s: StateQA Cirrus Detected
  • cloud_shadow: mod09*s: StateQA Cloud Shadow
  • cloud_state: mod09*s: StateQA Cloud State
  • internal_cloud_algorithm: mod09*s: StateQA Internal Cloud Algorithm
  • internal_fire_algorithm: mod09*s: StateQA Internal Fire Algorithm
  • internal_snow_mask: mod09*s: StateQA Internal Snow Mask
  • land_water: mod13Q1: separate land from various water objects
  • mod35_snow_ice: mod09*s: StateQA mod35 Snow Ice
  • pixel_adjacent_to_cloud: mod13Q1: if pixel is a cloud neighbour
  • salt_pan: mod09*s: StateQA Salt Pan (mod09GAs)
  • icm_cloudy: mod09*i: Internal CM: Cloudy
  • icm_clear: mod09*i: Internal CM: Clear
  • icm_high_clouds: mod09*i: Internal CM: High Clouds
  • icm_low_clouds: mod09*i: Internal CM: Low Clouds
  • icm_snow: mod09*i: Internal CM: Snow
  • icm_fire: mod09*i: Internal CM: Fire
  • icm_sun_glint: mod09*i: Internal CM: Sun Glint
  • icm_dust: mod09*i: Internal CM: Dust
  • icm_cloud_shadow: mod09*i: Internal CM: Cloud Shadow
  • icm_pixel_is_adjacent_to_cloud: mod09*i: Internal CM: Pixel is Adjacent to Cloud
  • icm_cirrus: mod09*i: Internal CM: Cirrus
  • icm_pan_flag: mod09*i: Internal CM: Pan Flag
  • icm_criteria_for_aerosol_retrieval: mod09*i: Internal CM: Criteria for Aerosol Retrieval
  • icm_aot_has_clim_val: mod09*i: Internal CM: AOT (aerosol optical depth) has clim. val.
  • vi_usefulness: mod13Q1: Quality estimation of the pixel
  • mixed_clouds: mod13Q1: if pixel mixed with clouds
  • possible_snow_ice: mod13Q1: if snow/ice present in pixel
  • possible_shadow: mod13Q1: if shadow is present in pixel
  • platform: mcd43B2: Quality of BRDF correction performed
  • sun_z_angle_at_local_noon: mcd43B2: Quality of BRDF correction performed
  • band=string — band number of MODIS product. 🎚️

(mod09Q1=[1,2],mod09A1=[1-7],m[o/y]d09GA=[1-7],m[o/y]d09CMG=[1-7], mcd43B2q=[1-7])

Options: 1, 2, 3, 4, 5, 6, 7

  • 1: Band 1: Red
  • 2: Band 2: NIR
  • 3: Band 3: Blue
  • 4: Band 4: Green
  • 5: Band 5: SWIR 1
  • 6: Band 6: SWIR 2
  • 7: Band 7: SWIR 3

💖 DESCRIPTION 💖

so like, i.modis.qc extracts Requested Quality Assessment flags from the following MODIS products: MOD09A1, MOD09Q1, MOD11A1, MOD11A2, MOD13A2, MOD13Q1, MCD43B2. and yes, this does include the MOD09A1 QA_state_500m layer (peep the Notes, iykyk). 👀

Added MOD09GA support in 2016, it follows MOD09A1 and its StateQA, but does not have BRDF State QA, instead has Salt Pan State QA. okay so she evolved, we love growth. 🌱

🧴 MOD09A1 and MOD09Q1 🧴

  • MOD09A1/Q1: MODLAND QA Bits. bits=[0-1]
    • [00]= class 0: Corrected product produced at ideal quality -- all bands
    • [01]= class 1: Corrected product produced at less than ideal quality -- some or all bands
    • [10]= class 2: Corrected product NOT produced due to cloud effect -- all bands
    • [11]= class 3: Corrected product NOT produced due to other reasons -- some or all bands maybe be fill value (Note that a value of [11] overrides a value of [01])
not me flagging that [11] overrides [01] — that's the drama, bestie, don't forget it. 💀
  • MOD09Q1: Cloud State. bits=[2-3]
    • [00]= class 0: Clear -- No clouds
    • [01]= class 1: Cloudy
    • [10]= class 2: Mixed
    • [11]= class 3: Not Set ; Assumed Clear
  • MOD09Q1: Band-wise Data Quality 250m bits=[4-7][8-11]
  • MOD09A1: Band-wise Data Quality 500m bits=[2-5][6-9][10-13][14-17][18-21][22-25][26-29]
    • [0000]= class 0: highest quality
    • [0111]= class 1: noisy detector
    • [1000]= class 2: dead detector; data interpolated in L1B
    • [1001]= class 3: solar zenith ≥ 86 degrees
    • [1010]= class 4: solar zenith ≥ 85 and < 86 degrees
    • [1011]= class 5: missing input
    • [1100]= class 6: internal constant used in place of climatological data for at least one atmospheric constant
    • [1101]= class 7: correction out of bounds, pixel constrained to extreme allowable value
    • [1110]= class 8: L1B data faulty
    • [1111]= class 9: not processed due to deep ocean or cloud
    • Class 10-15: Combination of bits unused
  • MOD09A1/Q1: Atmospheric correction bit=[12]/[30]
    • [0]= class 0: Not Corrected product
    • [1]= class 1: Corrected product
  • MOD09A1/Q1: Adjacency correction bit=[13]/[31]
    • [0]= class 0: Not Corrected product
    • [1]= class 1: Corrected product
  • MOD09Q1: Different orbit from 500m product, bit=[14]
    • [0]= class 0: same orbit as 500m
    • [1]= class 1: different orbit from 500m
  • MOD09A1s: Cloud State bits=[0-1]
    • [00]= class 0: clear
    • [01]= class 1: cloudy
    • [10]= class 2: mixed
    • [11]= class 3: not set, assumed clear
  • MOD09A1s: Cloud shadow bits=[2]
    • [0]= class 0: no
    • [1]= class 1: yes
  • MOD09A1s: Land/Water Flag bits=[3-5]
    • [000]= class 0: Shallow ocean
    • [001]= class 1: Land
    • [010]= class 2: Ocean coastlines and lake shorelines
    • [011]= class 3: Shallow inland water
    • [100]= class 4: Ephemeral water
    • [101]= class 5: Deep inland water
    • [110]= class 6: Continental/moderate ocean
    • [111]= class 7: Deep ocean
  • MOD09A1s: Aerosol Quantity bits=[6-7]
    • [00]= class 0: Climatology
    • [01]= class 1: Low
    • [10]= class 2: Average
    • [11]= class 3: High
  • MOD09A1s: Cirrus detected bits=[8-9]
    • [00]= class 0: None
    • [01]= class 1: Small
    • [10]= class 2: Average
    • [11]= class 3: High
  • MOD09A1s: Internal Cloud Algorithm Flag bits=[10]
    • [0]= class 0: No cloud
    • [1]= class 1: Cloud
  • MOD09A1s: Internal Fire Algorithm Flag bits=[11]
    • [0]= class 0: No fire
    • [1]= class 1: Fire
  • MOD09A1s: MOD35 snow/ice flag bits=[12]
    • [0]= class 0: No
    • [1]= class 1: Yes
  • MOD09A1s: Pixel adjacent to cloud bits=[13]
    • [0]= class 0: No
    • [1]= class 1: Yes
  • MOD09A1s: BRDF correction performed bits=[14]
    • [0]= class 0: No
    • [1]= class 1: Yes
  • MOD09A1s: Internal Snow Mask bits=[15]
    • [0]= class 0: No snow
    • [1]= class 1: Snow

🌡️ MOD11A1 🌡️

  • MOD11A1: Mandatory QA Flags bits=[0-1]
    • [00]= class 0: LST produced, good quality, not necessary to examine more detailed QA
    • [01]= class 1: LST produced, other quality, recommend examination of more detailed QA
    • [10]= class 2: LST not produced due to cloud effects
    • [11]= class 3: LST not produced primarily due to reasons other than cloud
  • MOD11A1: Data Quality Flag bits=[2-3]
  • [00] — class 0: Good data quality of L1B in bands 31 and 32. ✨
  • [01] — class 1: Other quality data. 🙈
  • [10] — class 2: TBD. 🤷‍♀️
  • [11] — class 3: TBD. 🤷‍♀️

MOD11A1: Emis Error Flag bits=[4-5]

  • [00] — class 0: Average emissivity error ≤ 0.01. 🎀
  • [01] — class 1: Average emissivity error ≤ 0.02. 🎀
  • [10] — class 2: Average emissivity error ≤ 0.04. 🎀
  • [11] — class 3: Average emissivity error > 0.04. 🚨

MOD11A1: LST Error Flag bits=[6-7]

  • [00] — class 0: Average LST error ≤ 1. 🔥
  • [01] — class 1: Average LST error ≤ 2. 🔥
  • [10] — class 2: Average LST error ≤ 3. 🔥
  • [11] — class 3: Average LST error > 3. 💀

MOD11A2

MOD11A2: Mandatory QA Flags bits=[0-1]

  • [00] — class 0: LST produced, good quality, not necessary to examine more detailed QA. 👑
  • [01] — class 1: LST produced, other quality, recommend examination of more detailed QA. 🔍
  • [10] — class 2: LST not produced due to cloud effects. ☁️
  • [11] — class 3: LST not produced primarily due to reasons other than cloud. 🌧️

MOD11A2: Data Quality Flag bits=[2-3]

  • [00] — class 0: Good data quality of L1B in 7 TIR bands. ✨
  • [01] — class 1: Other quality data. 🙈
  • [10] — class 2: TBD. 🤷‍♀️
  • [11] — class 3: TBD. 🤷‍♀️

MOD11A2: Emis Error Flag bits=[4-5]

  • [00] — class 0: Average emissivity error ≤ 0.01. 🎀
  • [01] — class 1: Average emissivity error ≤ 0.02. 🎀
  • [10] — class 2: Average emissivity error ≤ 0.04. 🎀
  • [11] — class 3: Average emissivity error > 0.04. 🚨

MOD11A2: LST Error Flag bits=[6-7]

  • [00] — class 0: Average LST error ≤ 1. 🔥
  • [01] — class 1: Average LST error ≤ 2. 🔥
  • [10] — class 2: Average LST error ≤ 3. 🔥
  • [11] — class 3: Average LST error > 3. 💀

MOD13A2

MOD13A2: Mandatory QA Flags 1km bits[0-1]

  • [00] — class 0: VI produced, good quality. 👑
  • [01] — class 1: VI produced, but check other QA. 🔍
  • [10] — class 2: Pixel produced, but most probably cloud. ☁️
  • [11] — class 3: Pixel not produced due to other reasons than clouds. 🌧️

MOD13A2: VI Usefulness Flag bits[2-5]

  • [0000] — class 0: Highest quality. 💎
  • [0001] — class 1: Lower quality. 🥈
  • [0010] — class 2: Decreasing quality. 📉
  • [0100] — class 3: Decreasing quality. 📉
  • [1000] — class 4: Decreasing quality. 📉
  • [1001] — class 5: Decreasing quality. 📉
  • [1010] — class 6: Decreasing quality. 📉
  • [1100] — class 7: Lowest quality. 🗑️
  • [1101] — class 8: Quality so low that it is not useful. 💀
  • [1110] — class 9: L1B data faulty. ⚠️
  • [1111] — class 10: Not useful for any other reason/not processed. 🚫

MOD13A2: Aerosol quantity Flags 1km bits[6-7]

  • [00] — class 0: Climatology. 🌍
  • [01] — class 1: Low. 🌤️
  • [10] — class 2: Average. ⛅
  • [11] — class 3: High. 🌫️

MOD13A2: Adjacent cloud detected 1km bit[8]

  • [00] — class 0: No. 🚫
  • [01] — class 1: Yes. ☁️

MOD13A2: Atmosphere BRDF correction performed 1km bit[9]

  • [00] — class 0: No. 🚫
  • [01] — class 1: Yes. ✅

MOD13A2: Mixed clouds 1km bit[10]

  • [00] — class 0: No. 🚫
  • [01] — class 1: Yes. ☁️

MOD13A2: Land/Water Flags 1km bits[11-13]

  • [000] — class 0: Shallow Ocean. 🌊
  • [001] — class 1: Land (Nothing else but land). 🏞️
  • [010] — class 2: Ocean Coastlines and lake shorelines. 🏖️
  • [011] — class 3: Shallow inland water. 💧
  • [100] — class 4: Ephemeral water. 🌧️
  • [101] — class 5: Deep inland water. 🌊
  • [110] — class 6: Moderate or continental ocean. 🌊
  • [111] — class 7: Deep ocean. 🌊

MOD13A2: Possible Snow/Ice 1km bits[14]

  • [0] — class 0: No. 🚫
  • [1] — class 1: Yes. ❄️

MOD13A2: Possible Shadow 1km bits[15]

  • [0] — class 0: No. 🚫
  • [1] — class 1: Yes. 🌑

MOD13Q1

MOD13Q1: Mandatory QA Flags 250m bits[0-1]

  • [00] — class 0: VI produced, good quality. 👑
  • [01] — class 1: VI produced, but check other QA. 🔍
  • [10] — class 2: Pixel produced, but most probably cloud. ☁️
  • [11] — class 3: Pixel not produced due to other reasons than clouds. 🌧️

MOD13Q1: VI Usefulness Flag 250m bits[2-5]

  • [0000] — class 0: Highest quality. 💎
  • [0001] — class 1: Lower quality. 🥈
  • [0010] — class 2: Decreasing quality. 📉
  • [0100] — class 3: Decreasing quality. 📉
  • [1000] — class 4: Decreasing quality. 📉
  • [1001] — class 5: Decreasing quality. 📉
  • [1010] — class 6: Decreasing quality. 📉
  • [1100] — class 7: Lowest quality. 🗑️
  • [1101] — class 8: Quality so low that it is not useful. 💀
  • [1110] — class 9: L1B data faulty. ⚠️
  • [1111] — class 10: Not useful for any other reason/not processed. 🚫

MOD13Q1: Aerosol quantity Flags 250m bits[6-7]

  • [00] — class 0: Climatology. 🌍
  • [01] — class 1: Low. 🌤️
  • [10] — class 2: Average. ⛅
  • [11] — class 3: High. 🌫️

MOD13Q1: Adjacent cloud detected 250m bit[8]

  • [00] — class 0: No. 🚫
  • [01] — class 1: Yes. ☁️

MOD13Q1: Atmosphere BRDF correction performed 250m bit[9]

  • [00] — class 0: No. 🚫
  • [01] — class 1: Yes. ✅

MOD13Q1: Mixed clouds 250m bit[10]

  • [00] — class 0: No. 🚫
  • [01] — class 1: Yes. ☁️

MOD13Q1: Land/Water Flags 250m bits[11-13]

  • [000] — class 0: Shallow Ocean. 🌊
  • [001] — class 1: Land (Nothing else but land). 🏞️
  • [010] — class 2: Ocean Coastlines and lake shorelines. 🏖️
  • [011] — class 3: Shallow inland water. 💧
  • [100] — class 4: Ephemeral water. 🌧️
  • [101] — class 5: Deep inland water. 🌊
  • [110] — class 6: Moderate or continental ocean. 🌊
  • [111] — class 7: Deep ocean. 🌊

MOD13Q1: Possible Snow/Ice 250m bits[14]

  • [0] — class 0: No. 🚫
  • [1] — class 1: Yes. ❄️

MOD13Q1: Possible Shadow 250m bits[15]

  • [0] — class 0: No. 🚫
  • [1] — class 1: Yes. 🌑

MCD43B2

MCD43B2: Albedo Quality Ancillary Platform Data 1km bits[0-3]

SDS: BRDF_Albedo_Ancillary

  • [0000] — class 0: Satellite Platform: Terra. 🌍
  • [0001] — class 1: Satellite Platform: Terrra/Aqua. 🌎🌍
  • [0010] — class 2: Satellite Platform: Aqua. 🌊
  • [1111] — class 15: Fill Value. 🕳️
  • Classes 3-14: Not used. 🚫

MCD43B2: Albedo Quality Ancillary Land/Water Data 1km bits[4-7]

SDS: BRDF_Albedo_Ancillary

  • [0000] class 0: Shallow Ocean. 🌊
  • [0001] class 1: Land (Nothing else but land). 🏞️
  • [0010] class 2: Ocean and lake shorelines. 🏖️
  • [0011] class 3: Shallow inland water. 💧
  • [0100] class 4: Ephemeral water. 🌧️
  • [0101] class 5: Deep inland water. 🌊
  • [0110] class 6: Moderate or continental ocean. 🌊
  • [0111] class 7: Deep ocean. 🌊
  • [1111] class 15: Fill Value. 🕳️
  • Classes 8-14: Not used. 🚫

MCD43B2: Albedo Quality Ancillary Sun Zenith Angle at Local Solar Noon Data 1km bits[8-14]

SDS: BRDF_Albedo_Ancillary

Returns integer value [0-90], 127 is Fill Value. ☀️

MCD43B2: Band-wise Albedo Quality Data 1km

SDS: BRDF_Albedo_Band_Quality bits[0-3][4-7][8-11][12-15][16-19][20-23][24-27]

  • [0000] — class 0: best quality, 75% or more with best full inversions. 💎
  • [0001] — class 1: good quality, 75% or more with full inversions. 👑
  • [0010] — class 2: Mixed, 50% or less full inversions and 25% or less fill values. 🔄
  • [0011] — class 3: All magnitude inversions or 50% or less fill values. 🔀
  • [0100] — class 4: 75% or more fill values. 🕳️
  • Classes 5-14: Not Used. 🚫
  • [1111] — class 15: Fill Value. 🕳️

📝 NOTES 📝

In MOD09A1: It seems that cloud related info is not filled properly in the standard QC (MOD09A1 in this module) since version 3, State-QA 500m images (MOD09A1s in this module) should be used (see Vermote et al., 2008). ☁️🚨

MOD11A2 quality control (QC) bands do not have a FillValue (No-data) according to MODIS Land Products site. However, the metadata of the QC bands (i.e.: gdalinfo QC_band) shows No-data=0. This value is then transformed into GRASS NULLs when data is imported through r.in.gdal. Applying i.modis.qc on those QC bands will not give the expected range of values in the different QC bits. Therefore, before using i.modis.qc, the user needs to set the NULL value in QC bands back to zero (i.e.: r.null map=QC_band null=0) or just edit the metadata with GDAL utilities before importing into GRASS GIS. This is a known issue for MOD11A2 (8-day LST product), but other MODIS products might be affected as well. 😭

bestie, this is important! don't skip this. 💅

TODO

Add more daily products. 📅

REFERENCES

  • MODIS Products
  • Vermote E.F., Kotchenova S.Y., Ray J.P. MODIS Surface Reflectance User’s Guide. Version 1.2. June 2008. MODIS Land Surface Reflectance Science Computing Facility. Homepage

SEE ALSO

i.vi

AUTHOR

Yann Chemin

SOURCE CODE

Available at: i.modis.qc source code (history)

Accessed: Saturday Jan 17 05:41:43 2026

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© 2003-2025 GRASS Development Team, GRASS GIS 8.4.2 Reference Manual

And that's the tea, bestie! This was i.modis.qc(1) from GRASS 8.4.2, accessed Saturday Jan 17 05:41:43 2026. Stay slay! 💖✨

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