NDMI - Vegetation Moisture

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NDMI - Vegetation Moisture

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NDMI - Vegetation Moisture

NDMI — Normalized Difference Moisture Index

Overview

The Normalized Difference Moisture Index (NDMI) is a spectral index derived from satellite imagery that measures the water content in the vegetation canopy. Values range from -1 to +1, where higher values indicate greater foliar moisture and favourable water conditions, while low or negative values signal water stress or absence of vegetation.

NDMI is an essential tool for:

Water stress monitoring — identifying vegetation areas subject to drought or water deficit
Wildfire risk assessment — vegetation dryness is the primary predisposing factor
Phenological condition analysis — tracking seasonal variations in water content
Forest and agricultural management — optimising irrigation and planning silvicultural interventions

The SWIR band (Short-Wave Infrared) used in NDMI is sensitive to water in leaf tissues: water strongly absorbs at 1610 nm, while NIR (842 nm) is primarily influenced by cellular structure. The ratio between the two bands isolates the water signal from the structural vegetation response (Gao, 1996).

Calculation Methodology

NDMI is calculated from ESA Sentinel-2 L2A imagery (surface reflectance) with native resolution of 10–20 m:

Formula: NDMI = (B8 - B11) / (B8 + B11) where B8 = NIR band (842 nm, 10 m) and B11 = SWIR band (1610 nm, 20 m)

The calculation process follows the same pipeline as NDVI:

Image selection — acquisitions within the time interval of interest
Cloud masking — using the Scene Classification Layer (SCL) band to exclude cloudy pixels, shadows, and snow
Water body masking — exclusion of water surfaces from the composite
Temporal composite — median of valid values to maximise spatial coverage and reduce noise
Index calculation — application of the NDMI formula pixel by pixel
Aggregation — statistics (mean, median, percentiles) for each polygon

Note: B11 has a native resolution of 20 m and is resampled to 10 m for alignment with B8.

Data Sources

Code	Name	Provider	Resolution	Availability
`WRD_S2XXX_99`	ESA Sentinel-2 L2A	ESA/Copernicus	10–20 m	2017 — present

Quality Range and Levels

Indicator	Unit	Range	Inverted
`ndmi`	—	[-1, -0.2, 0, 0.2, 0.4, 1]	No

Ecological interpretation by level:

Level	NDMI Range	Interpretation
E	< -0.2	Severe water stress, bare soil or extremely dry vegetation
D	-0.2 → 0	Moderate water stress, vegetation with water deficit
C	0 → 0.2	Moderate moisture, acceptable water conditions
B	0.2 → 0.4	Good foliar moisture, vegetation in favourable water conditions
A	> 0.4	High moisture, vigorous and well-hydrated vegetation

Inverted = No: higher values indicate better water conditions.

Technical Notes

NDMI maps must be computed before polygon-level aggregation
Band B11 (20 m) is resampled to 10 m for alignment with B8 (10 m): this introduces slight spatial interpolation
In areas with persistent snow or glaciers, the SCL mask may produce artefacts: verify the temporal coverage of composites
The index is sensitive to foliar water content but does not distinguish between free and tissue-bound water
For temporal comparisons, ensure equivalent seasonal windows are used

References

Gao, B.C. (1996). "NDWI — A normalized difference water index for remote sensing of vegetation liquid water from space". Remote Sensing of Environment, 58(3), 257–266.
Wilson, E.H. & Sader, S.A. (2002). "Detection of forest harvest type using multiple dates of Landsat TM imagery". Remote Sensing of Environment, 80(3), 385–396.
Delegido, J. et al. (2011). "Evaluation of Sentinel-2 Red-Edge Bands for Empirical Estimation of Green LAI and Chlorophyll Content". Sensors, 11(7), 7063–7081.

Quality Ranges and Levels

ndmi

ECritical

DLow

CModerate

BGood

AExcellent

-1 → -0.21

-0.2 → -0.01

0 → 0.19

0.2 → 0.39

0.4 → 1

Critical-1 – -0.21

Low-0.2 – -0.01

Moderate0 – 0.19

Good0.2 – 0.39

Excellent0.4 – 1

Data Sources & References

Sources

WRD_S2XXX_99

Methodology

NDMI = (B8 - B11) / (B8 + B11) calculated on ESA Sentinel-2 L2A imagery (10–20 m resolution). B8 = NIR (842 nm), B11 = SWIR (1610 nm). Same pipeline as NDVI: cloud masking via SCL, water body masking, temporal median composite. B11 (20 m) resampled to 10 m for alignment with B8.

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NDMI - Vegetation Moisture

Land Usevv0

Responsible:Niccolò Calandri

Layer NDMI - Vegetation Moisture on real sites

Real Estate UK

NDMI - Vegetation Moisture

Catania

NDMI - Vegetation Moisture

Berlino Quarter

NDMI - Vegetation Moisture

Mina el Cerrejon

NDMI - Vegetation Moisture

NDMI — Normalized Difference Moisture Index

Overview

NDMI is an essential tool for:

Water stress monitoring — identifying vegetation areas subject to drought or water deficit
Wildfire risk assessment — vegetation dryness is the primary predisposing factor
Phenological condition analysis — tracking seasonal variations in water content
Forest and agricultural management — optimising irrigation and planning silvicultural interventions

Calculation Methodology

NDMI is calculated from ESA Sentinel-2 L2A imagery (surface reflectance) with native resolution of 10–20 m:

Formula: NDMI = (B8 - B11) / (B8 + B11) where B8 = NIR band (842 nm, 10 m) and B11 = SWIR band (1610 nm, 20 m)

The calculation process follows the same pipeline as NDVI:

Image selection — acquisitions within the time interval of interest
Cloud masking — using the Scene Classification Layer (SCL) band to exclude cloudy pixels, shadows, and snow
Water body masking — exclusion of water surfaces from the composite
Temporal composite — median of valid values to maximise spatial coverage and reduce noise
Index calculation — application of the NDMI formula pixel by pixel
Aggregation — statistics (mean, median, percentiles) for each polygon

Note: B11 has a native resolution of 20 m and is resampled to 10 m for alignment with B8.

Data Sources

Code	Name	Provider	Resolution	Availability
`WRD_S2XXX_99`	ESA Sentinel-2 L2A	ESA/Copernicus	10–20 m	2017 — present

Quality Range and Levels

Indicator	Unit	Range	Inverted
`ndmi`	—	[-1, -0.2, 0, 0.2, 0.4, 1]	No

Ecological interpretation by level:

Level	NDMI Range	Interpretation
E	< -0.2	Severe water stress, bare soil or extremely dry vegetation
D	-0.2 → 0	Moderate water stress, vegetation with water deficit
C	0 → 0.2	Moderate moisture, acceptable water conditions
B	0.2 → 0.4	Good foliar moisture, vegetation in favourable water conditions
A	> 0.4	High moisture, vigorous and well-hydrated vegetation

Inverted = No: higher values indicate better water conditions.

Technical Notes

NDMI maps must be computed before polygon-level aggregation
Band B11 (20 m) is resampled to 10 m for alignment with B8 (10 m): this introduces slight spatial interpolation
In areas with persistent snow or glaciers, the SCL mask may produce artefacts: verify the temporal coverage of composites
The index is sensitive to foliar water content but does not distinguish between free and tissue-bound water
For temporal comparisons, ensure equivalent seasonal windows are used

References

Gao, B.C. (1996). "NDWI — A normalized difference water index for remote sensing of vegetation liquid water from space". Remote Sensing of Environment, 58(3), 257–266.
Wilson, E.H. & Sader, S.A. (2002). "Detection of forest harvest type using multiple dates of Landsat TM imagery". Remote Sensing of Environment, 80(3), 385–396.
Delegido, J. et al. (2011). "Evaluation of Sentinel-2 Red-Edge Bands for Empirical Estimation of Green LAI and Chlorophyll Content". Sensors, 11(7), 7063–7081.

Quality Ranges and Levels

ndmi

ECritical

DLow

CModerate

BGood

AExcellent

-1 → -0.21

-0.2 → -0.01

0 → 0.19

0.2 → 0.39

0.4 → 1

Critical-1 – -0.21

Low-0.2 – -0.01

Moderate0 – 0.19

Good0.2 – 0.39

Excellent0.4 – 1

Data Sources & References

Sources

WRD_S2XXX_99