LSWI: The Surface-Water Index Behind Rice and Flood Mapping
LSWI uses the very same NIR and SWIR bands as NDMI, but it has grown its own identity in the flood- and paddy-mapping literature, where a sharp rise in surface water is the signal that matters.
How it works
Like NDMI, LSWI contrasts NIR against SWIR1, which is acutely sensitive to liquid water on and within the surface. When a field floods or a paddy is transplanted, LSWI jumps — and the timing of that jump relative to NDVI or EVI is what algorithms use to detect inundation and rice phenology.
How it is calculated
where NIR is near-infrared reflectance, SWIR1 is shortwave-infrared 1 reflectance (~1.6 µm).
What the numbers mean
Higher = wetter surface / leaf water. Sharp rises are used to flag flooding and rice-paddy transplanting.
Band configuration
| Sentinel-2 | NIR B8 · SWIR1 B11 |
| Landsat 8/9 | NIR B5 · SWIR1 B6 |
Where it is used
It underpins large-area paddy-rice mapping, flood-inundation detection and wetland dynamics, often combined with EVI in decision rules (for example, LSWI ≥ EVI flags flooding beneath a canopy).
Limitations to know
Numerically LSWI is identical to NDMI — the distinction is purely conventional. Do not report both as independent evidence; they are the same measurement wearing two names.
Compute LSWI on your own study area
Skip the code. Draw or upload a boundary and Spatial Research Suite runs LSWI on live Sentinel-2 or Landsat imagery — with cloud masking, exports and citations built in.
Run this analysis in GISforus →Frequently asked
Is LSWI different from NDMI?
Mathematically no — both are (NIR − SWIR1)/(NIR + SWIR1). The name reflects the flood/paddy application rather than a different formula.
How does LSWI detect rice paddies?
Transplanting floods the field, spiking LSWI relative to EVI; that temporal signature is used to map paddy rice.