Normalized difference water index
The Normalized Difference Water Index (NDWI) is a remote sensing spectral index developed to delineate and enhance open water features, such as lakes, rivers, and wetlands, in satellite imagery by contrasting the reflectance properties of water in the green and near-infrared (NIR) bands.[1] Introduced by Samuel K. McFeeters in 1996, it provides a normalized ratio that highlights water bodies against surrounding vegetation, soil, and built-up areas, with the index calculated using the formula NDWI = \frac{\text{Green} - \text{NIR}}{\text{Green} + \text{NIR}}, where Green represents reflectance in the green band (typically around 0.52–0.60 μm) and NIR in the near-infrared band (around 0.77–0.90 μm).[1][2] For Landsat Thematic Mapper imagery, this corresponds to Band 2 (Green) and Band 4 (NIR).[2] NDWI values range from -1 to +1, where positive values (typically > 0) indicate the presence of water due to its high absorption in the NIR and strong reflectance in the green, while negative values (≤ 0) correspond to non-water features like vegetation and soil, which reflect more in the NIR.[2] In practice, thresholds such as 0.3 may be applied in urban environments to refine water detection and reduce false positives from shadows or built structures.[2] The index's sensitivity to water turbidity also allows for qualitative assessments of water clarity, though it can overestimate water extent in areas with dense vegetation or confuse it with snow.[1] A variant, the Modified Normalized Difference Water Index (MNDWI), proposed by Hanqiu Xu in 2006, improves upon the original by substituting the mid-infrared (SWIR) band for NIR in the formula MNDWI = \frac{\text{Green} - \text{SWIR}}{\text{Green} + \text{SWIR}}, enhancing open water detection while better suppressing noise from built-up land, soil, and vegetation.[3] Note that a separate NDWI, developed by Bo-Cai Gao in 1996, uses NIR and SWIR bands to monitor vegetation liquid water content rather than surface water bodies, and the two should not be conflated despite the shared acronym.[4] NDWI and its variants are applied across environmental monitoring, including flood inundation mapping, where they delineate temporary water extent during events like the 2011 Mississippi River floods using Landsat data.[5] In urban planning and public health, NDWI aids in identifying standing water for mosquito breeding sites, achieving high accuracy (e.g., 78.4% in detecting swimming pools).[2] Agricultural and hydrological studies employ it for tracking water body changes, irrigation assessment, and drought monitoring via platforms like Landsat, Sentinel-2, and MODIS.[6] These applications leverage freely available satellite data, enabling cost-effective, large-scale analysis of water resources and ecosystem dynamics.[7]Introduction
Definition
The Normalized Difference Water Index (NDWI) is a remote sensing-derived spectral index that utilizes the difference between two specific bands in satellite or aerial imagery to enhance and detect signals associated with liquid water.[8][1] It operates on the principle of contrasting reflectance properties where water exhibits strong absorption in certain wavelengths and high reflectance in others, thereby isolating water-related features from surrounding land cover.[8] The primary purpose of NDWI is to quantify the presence and extent of liquid water, either as moisture content within vegetation canopies or as open surface water bodies such as lakes, rivers, and wetlands.[8][1] This makes it a valuable tool for environmental monitoring, including drought assessment, flood mapping, and ecosystem health evaluation in various geospatial applications.[8] Mathematically, NDWI follows the normalized difference structure, expressed as \frac{\text{Band A} - \text{Band B}}{\text{Band A} + \text{Band B}}, where Band A and Band B are spectral bands selected for their sensitivity to water characteristics.[8][1] This formulation normalizes the ratio to produce values ranging from -1 to +1, with the division by the sum of the bands ensuring scale invariance and improved contrast for water detection across diverse imaging conditions.[8][1] Variants of NDWI exist, tailored to emphasize either vegetation water content or open water surfaces, though both adhere to the core normalized difference framework.[8][1]Key Variants
The Normalized Difference Water Index (NDWI) encompasses two primary variants, each tailored to distinct remote sensing applications through specific spectral band selections. Gao's NDWI focuses on quantifying liquid water content within vegetation canopies, leveraging the near-infrared (NIR) and short-wave infrared (SWIR) bands to capture subtle absorption features associated with moisture in leaves.[9] This variant is particularly effective for detecting variations in plant water status, such as those indicative of drought stress.[9] In contrast, McFeeters' NDWI is optimized for extracting open water bodies from imagery, employing the green and NIR bands to exploit the reflectance contrast between water surfaces—which exhibit low NIR reflectance and higher green reflectance—and surrounding features like soil and vegetation. This approach enhances the delineation of standing water, such as lakes and rivers, by highlighting high-contrast boundaries. The core distinction between these variants lies in their targeted phenomena: Gao's addresses biophysical moisture within vegetation for agricultural and ecological monitoring, whereas McFeeters' prioritizes surface water mapping for hydrological applications.[10] Both are routinely implemented on multispectral satellite data from platforms like Landsat and Sentinel-2, where the choice of bands dictates the suitability for vegetation versus water body analysis.[11]History and Development
Gao's Contribution (1996)
In 1996, Bo-Cai Gao introduced the Normalized Difference Water Index (NDWI) in his seminal paper published in Remote Sensing of Environment, addressing a critical gap in remote sensing capabilities for monitoring vegetation liquid water content.[9] The motivation stemmed from the limitations of established indices like the Normalized Difference Vegetation Index (NDVI), which primarily reflects vegetation density but saturates at high leaf area indices (typically ≥3) and shows insensitivity to variations in liquid water content.[9] Gao aimed to develop a complementary index suitable for global-scale assessment of vegetation moisture, particularly in agricultural and forestry applications, by leveraging the near-infrared (NIR) and shortwave infrared (SWIR) spectral bands to detect liquid water absorption features.[9] Gao's initial validation involved hyperspectral data from the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS), testing the index across diverse ecosystems to demonstrate its sensitivity to vegetation water status.[9] Key datasets included AVIRIS imagery from Jasper Ridge, California (acquired on June 2, 1992), featuring mixed oak woodlands, grasslands, and chaparral, and from the High Plains region of northern Colorado (acquired on August 7, 1990), encompassing agricultural fields and rangelands.[9] Laboratory measurements of leaf water content from these sites showed strong linear correlations with NDWI values, with the index yielding positive values for green, hydrated vegetation and negative values for dry or senescent areas, effectively highlighting spatial patterns such as drainage influences in Jasper Ridge.[9] This contribution pioneered quantitative remote sensing of canopy-level vegetation water content, establishing NDWI as a foundational tool that has influenced subsequent models for drought monitoring and fire risk assessment.[12] For instance, it has been integrated into wildfire risk mapping frameworks, where higher NDWI values indicate reduced flammability due to elevated moisture levels.[13] The work's emphasis on hyperspectral data also spurred advancements in multi-band indices for ecosystem stress detection.[14]McFeeters' Contribution (1996)
In 1996, S.K. McFeeters published the seminal paper introducing the Normalized Difference Water Index (NDWI) specifically for delineating open water features in remotely sensed imagery.[1] The work appeared in the International Journal of Remote Sensing, volume 17, issue 7, pages 1425–1432, and proposed NDWI as an advancement over traditional methods for extracting water bodies from satellite data.[1] McFeeters developed NDWI to address limitations in simple band ratioing techniques applied to Landsat Thematic Mapper (TM) imagery, which often struggled to isolate water pixels amid mixed land cover.[1] The index was designed to maximize the contrast between water surfaces and surrounding non-water elements, such as vegetation and soil, by leveraging the green and near-infrared spectral bands where water exhibits distinct reflectance properties.[1] Initial testing of the NDWI involved applying it to multiple Landsat TM scenes from diverse environments, including urban and rural settings with varying water body sizes and turbidities.[1] Results demonstrated that NDWI values for water pixels clustered positively, enabling straightforward thresholding to accurately map open water features while minimizing false positives from vegetation.[1] This approach proved effective for both clear and turbid waters, highlighting its robustness for practical remote sensing applications.[1] McFeeters' NDWI has since become a foundational tool in remote sensing, widely adopted as a standard for flood mapping, wetland inventory, and surface water extraction in environmental monitoring.[1] The 1996 paper underscores its enduring influence on water-related geospatial analysis.[15]Formulation and Calculation
Gao's NDWI Formula
Gao's Normalized Difference Water Index (NDWI) is formulated as the normalized difference between near-infrared (NIR) and short-wave infrared (SWIR) reflectance values, specifically designed to quantify liquid water content in vegetation canopies from satellite imagery. The index leverages the contrasting spectral responses of these bands to water: NIR reflectance (typically in the 0.7–1.3 μm range) is strongly reflected by healthy vegetation but absorbed by water bodies, while SWIR reflectance (1.3–2.5 μm range) is highly absorbed by liquid water within leaves and soil, amplifying sensitivity to moisture variations. The precise formula, as proposed by Gao, is: \text{NDWI} = \frac{\rho_{\text{NIR}} - \rho_{\text{SWIR}}}{\rho_{\text{NIR}} + \rho_{\text{SWIR}}} where \rho_{\text{NIR}} represents the reflectance in the NIR band (e.g., centered at 0.86 μm) and \rho_{\text{SWIR}} the reflectance in the SWIR band (e.g., centered at 1.24 μm). This structure normalizes the difference to a range of -1 to +1, where values approaching +1 indicate high vegetation water content (due to elevated NIR and reduced SWIR absorption) and values near -1 suggest low water content or non-vegetated surfaces. The choice of these wavelengths ensures both bands penetrate to similar depths within the canopy, minimizing confounding effects from soil background or atmospheric interference. To compute Gao's NDWI, surface reflectance values are first extracted from calibrated satellite imagery for the selected NIR and SWIR bands, ensuring atmospheric correction to derive accurate \rho values. These reflectances are then substituted directly into the formula, yielding a per-pixel index map suitable for quantitative analysis of water stress or drought monitoring. For modern sensors like Sentinel-2, Band 8 (NIR at 0.842 μm) and Band 11 (SWIR at 1.61 μm) are commonly used as proxies for the original MODIS channels specified by Gao, maintaining the index's sensitivity to vegetation liquid water.McFeeters' NDWI Formula
The McFeeters' Normalized Difference Water Index (NDWI) is computed using the formula: \text{NDWI} = \frac{X_{\text{[green](/page/Green)}} - X_{\text{[nir](/page/NIR)}}}{X_{\text{[green](/page/Green)}} + X_{\text{[nir](/page/NIR)}}} where X_{\text{[green](/page/Green)}} represents the reflectance in the green band (typically 0.5–0.6 μm) and X_{\text{[nir](/page/NIR)}} is the reflectance in the near-infrared band (0.7–1.3 μm).[16] This formulation leverages the spectral properties of water, which exhibits high reflectance in the green wavelengths due to low absorption and very low reflectance in the near-infrared due to strong absorption, resulting in positive NDWI values for open water features. In contrast, terrestrial vegetation and soil reflect highly in the near-infrared while showing lower green reflectance, leading to negative or near-zero NDWI values that help suppress these non-water elements. Computation involves pixel-wise application of the formula to calibrated remote sensing imagery, where reflectance values are derived from digital numbers after atmospheric correction to ensure comparability across scenes. The normalization in the denominator mitigates effects from topographic variations, illumination differences, and sensor noise, providing robustness for mixed pixels containing partial water coverage. For example, in Landsat 8 imagery, McFeeters' NDWI utilizes Band 3 (green, centered at 0.533 μm) for X_{\text{green}} and Band 5 (near-infrared, centered at 0.865 μm) for X_{\text{nir}}.[6]Required Spectral Bands
The computation of the Normalized Difference Water Index (NDWI) requires multispectral remote sensing data from sensors capable of capturing reflectance in specific wavelength regions, depending on the variant: the green band (approximately 0.52–0.59 μm) and near-infrared (NIR) band (centered around 0.77–0.86 μm) for McFeeters' formulation, or the NIR band and shortwave infrared (SWIR) band (typically 1.57–1.65 μm or 2.11–2.29 μm) for Gao's formulation. Atmospheric correction is essential to convert raw radiance data to top-of-atmosphere (TOA) or, preferably, surface reflectance values, as uncorrected data can introduce errors from scattering and absorption by atmospheric constituents.[17] Commonly used satellite sensors include the Landsat series, Sentinel-2, and MODIS, each providing the requisite bands with varying spatial resolutions and revisit frequencies. For the Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+), McFeeters' NDWI utilizes Band 2 (green: 0.52–0.60 μm) and Band 4 (NIR: 0.76–0.90 μm), while Gao's NDWI employs Band 4 (NIR) and Band 5 (SWIR: 1.55–1.75 μm).[18] Similarly, for Landsat 8 Operational Land Imager (OLI), McFeeters' variant uses Band 3 (green: 0.533–0.590 μm) and Band 5 (NIR: 0.851–0.879 μm), with Gao's using Band 5 (NIR) and Band 6 (SWIR: 1.566–1.651 μm).[19]| Satellite/Sensor | Variant | Bands Used | Wavelengths (μm) | Spatial Resolution |
|---|---|---|---|---|
| Landsat 5 TM/ETM+ | McFeeters | 2 (Green), 4 (NIR) | 0.52–0.60, 0.76–0.90 | 30 m |
| Landsat 5 TM/ETM+ | Gao | 4 (NIR), 5 (SWIR) | 0.76–0.90, 1.55–1.75 | 30 m |
| Landsat 8 OLI | McFeeters | 3 (Green), 5 (NIR) | 0.533–0.590, 0.851–0.879 | 30 m |
| Landsat 8 OLI | Gao | 5 (NIR), 6 (SWIR) | 0.851–0.879, 1.566–1.651 | 30 m |
| Sentinel-2 MSI | McFeeters | 3 (Green), 8 (NIR) | 0.560 (central), 0.842 (central) | 10 m (B3), 10 m (B8) |
| Sentinel-2 MSI | Gao | 8 (NIR), 11 (SWIR) | 0.842 (central), 1.610 (central) | 10 m (B8), 20 m (B11) |
| MODIS (Terra/Aqua) | McFeeters | 4 (Green), 2 (NIR) | 0.545–0.565, 0.841–0.876 | 500 m |
| MODIS (Terra/Aqua) | Gao | 2 (NIR), 5 (SWIR) | 0.841–0.876, 1.230–1.250 | 500 m |