The ability to remotely monitor crop water status, water stress, and water availability is a key component of remote-sensing-based crop management.
These tools are widely used in precision agriculture, forestry, and natural disaster risk management.
Their importance continues to grow as the risk of extreme weather events increases and climatic conditions for crop production deteriorate in many regions of the world.
Below, I will discuss these indexes using their implementation in the OneSoil platform.
Three widely used remote sensing indexes — the Normalized Difference Moisture Index (NDMI), Soil Moisture Index (SMI), and Normalized Difference Water Index (NDWI) — are often mistakenly treated as interchangeable. In fact, they are based on different physical mechanisms, reflect different components of the water cycle within the soil–plant system, and therefore require different interpretation.💧 Moisture and Water Content Indexes
Index | Description | Best Use Case |
NDMI (Normalized Difference Moisture Index) | Estimates plant moisture content. | Detecting drought or irrigation needs. |
SMI (Soil Moisture Index) | Measures moisture directly in the soil. | Early-stage water availability. |
NDWI (Normalized Difference Water Index) | Highlights surface water and high moisture zones. | Flooding or waterlogging analysis. |
🌿 NDMI - the one you will use most
NDMI displays water content inside the plants. It works because cellular water interacts differently with the near infrared and the shortwave infrared, and the contrast between those two bands is a direct proxy for how well or how poor the crop is supplied with water.
Why it matters: it registers water stress before you can see it. A crop can hold its colour - and its NDVI - while turgor is already dropping. By the time leaves visibly change, you have lost the window in which the information was still actionable.
The catch: it needs leaves. On a thin stand or early in the season, with bare soil signal dominating in the satellite image - therefore NDMI readings are less connected to your crop.
NDMI helps farmers monitor crop water status and see how it changes during the season.
Detecting water stress
A drop in NDMI can show areas where crops are starting to suffer from water shortage. This may happen before stress becomes clearly visible in the field.Improving irrigation
NDMI helps identify drier parts of the field and check how crops respond to irrigation or rainfall. It can also support variable-rate irrigation.Detecting excess moisture
After heavy rainfall, unusually high NDMI values may highlight areas with too much moisture or possible waterlogging.Better field scouting and harvest planning
NDMI can show which parts of the field are drying faster or staying wetter. These areas can then be checked first or considered when planning field operations and harvest.
The most useful information usually comes from comparing NDMI maps over time, not from a single image.
1. May — wheat grown on a field with heterogeneous soil conditions. Areas with lower crop water availability are clearly visible.
2. June 30 — cereal crop approaching maturity. A section of the field still shows very high crop moisture levels. This difference is difficult to distinguish in true-color imagery.
3. Increase in NDMI values across the entire field following rainfall on April 18.
When is NDMI less useful?
NDMI becomes less reliable when crop cover is sparse and a substantial proportion of bare soil is visible to the satellite sensor.
Because NDMI is primarily sensitive to water content in vegetation, it becomes less useful when the satellite signal is strongly influenced by exposed soil.
In such conditions, SMI may provide more relevant information.
NDMI FAQ
What is NDMI?
What is NDMI?
NDMI (Normalized Difference Moisture Index) is a satellite-based index used to monitor water content in vegetation.
What is the NDMI formula?
What is the NDMI formula?
It relies on the specific optical properties of plant leaves in two regions of the electromagnetic spectrum: near-infrared (NIR, approximately 780–860 nm) and short-wave infrared (SWIR, approximately 1,550–1,750 nm).
Differences in the absorption and scattering of radiation at these wavelengths, particularly those associated with water contained in plant tissues, make it possible to assess crop water status.
NDMI = (NIR − SWIR) / (NIR + SWIR)
How should NDMI be interpreted?
How should NDMI be interpreted?
In general, higher NDMI values indicate higher vegetation moisture, while lower values may indicate drier crops or developing water stress.
💧 SMI - the early-season specialist
The Soil Moisture Index (SMI) is used to assess differences in soil moisture and water availability for crops. Unlike NDMI, which mainly reflects moisture in vegetation, SMI combines information about land surface temperature and vegetation cover.
It is especially useful at early growth stages, when crops are still small and much of the soil surface is visible. SMI can help identify dry areas, monitor rainfall or irrigation response, and detect parts of the field where water availability may become a limiting factor.
Because SMI relies on thermal satellite imagery, it usually has a lower spatial resolution than indexes calculated only from optical bands. As a result, SMI maps may look less detailed and are better suited for identifying larger moisture patterns rather than small differences within a field.
SMI FAQ
What does SMI show?
What does SMI show?
NDMI (Normalized Difference Moisture Index) is a satellite-based index used to monitor water content in vegetation.
What is the SMI formula?
What is the SMI formula?
SMI uses two Sentinel-2 spectral bands: B8A (NIR) and B11 (SWIR):
SMI = (SWIR − NIR) / (SWIR + NIR)
The contrast between near-infrared and short-wave infrared reflectance helps identify differences related to moisture conditions.
How should NDMI be interpreted?
How should NDMI be interpreted?
SMI is best used for comparing different areas within the same field and tracking changes over time, rather than relying on a single universal threshold.
What is the spatial resolution of SMI?
What is the spatial resolution of SMI?
Both Sentinel-2 B8A and B11 have a native spatial resolution of 20 m. This means SMI provides less spatial detail than indexes based on Sentinel-2 10 m bands, but it is still suitable for identifying larger moisture patterns within agricultural fields.
🌧️ NDWI - a different question entirely
Run NDWI over a healthy wheat field and it returns something that looks broken: flat, negative, near-uniform across a wide range of crop conditions. It isn't broken. In the formulation OneSoil uses - McFeeters' - it is built to find open water, not to describe a crop.
Why it matters: If you struggle with field flooding - after a prolonged rain or after the winter - the NDMI will pinpoint water lodged areas.. That is the difference between estimating losses and measuring them, and it scales from one field to a whole region.
The catch: it is not a crop condition layer, and reading it as one is one of the more expensive mistakes on this list.
In agriculture, this index is particularly useful for the rapid and unambiguous detection of surface water, flooded areas, waterlogging, and ponding within fields.
5. NDWI for a wheat-covered field.
6. Comparison of NDWI and NDMI in a scene containing surface water.
NDWI clearly delineates areas covered by water. For the same satellite scene, the NDMI moisture index shows much greater variability across the land surface, making it more difficult to distinguish open water or flooded areas.
NDWI FAQ
What does NDWI show?
What does NDWI show?
NDWI is mainly used to detect surface water, including flooded areas, standing water, and waterlogged zones.
How is NDWI calculated?
How is NDWI calculated?
The McFeeters NDWI uses the green and near-infrared (NIR) bands:
NDWI = (GREEN − NIR) / (GREEN + NIR)
How should NDWI be interpreted?
How should NDWI be interpreted?
In general, higher positive values are associated with surface water, while vegetation and dry land usually have lower or negative values.
What is NDWI used for in agriculture?
What is NDWI used for in agriculture?
Its main uses are detecting flooding, waterlogging, ponding, and wet areas within fields, especially after heavy rainfall.
Why is NDWI often negative on crop fields?
Why is NDWI often negative on crop fields?
Because the index is designed mainly to separate open water from land and vegetation. A healthy crop therefore does not need to have a positive NDWI value.
🧑🌾 Why the distinction is not academic
After rain, the moisture index rises across the entire field. That is the weather.
A uniform shift tells you what fell from the sky. Only a difference that persists between dates tells you something about the ground.
Read a single date without that context and the map appears to show improvement where nothing in the field has changed. Choose the wrong index for the situation, and you don't get a less precise answer - you get a wrong one, delivered with the same confidence as a right one.
🛰️ Seeing them side by side
Three indices that each answer a different question are only useful when you can put them next to each other. All three sit on the same field, the same date, one tap apart in OneSoil - and we've recently improved how NDMI, SMI, and NDWI are rendered, so the differences between them are much easier to read.
Our R&D team tested the update against fields of every kind: irrigated ground in the Middle East, European farms, rice paddies in Asia.