- Open Access
Near infrared band of Landsat 8 as water index: a case study around Cordova and Lapu-Lapu City, Cebu, Philippines
© The Author(s) 2019
- Received: 13 March 2018
- Accepted: 14 March 2019
- Published: 30 April 2019
Monitoring water bodies by extraction using water indexes from remotely sensed images has proven to be effective in delineating surface water against its surrounding. This study tested and assessed the Normalized Difference Water Index, Modified Normalized Difference Water Index, Automated Water Extraction Index, and near infrared (NIR) band using Landsat 8 imagery acquired on September 3, 2016. The threshold method was adapted for surface water extraction. To avoid over and under-estimation of threshold values, the optimum threshold value of each of the water indexes was obtained by implementing a geoprocessing model. Examining images of Landsat 8, NIR band has the largest difference in reflectance values between water and non-water bodies. Thus, NIR band exhibits the highest contrast between water and non-water bodies. An optimum threshold value of 0.128 for NIR band achieved an overall accuracy (OA) and kappa hat (Khat) coefficient of 99.3% and 0.986, respectively. NIR band of Landsat 8 as water index was found more satisfactory in extracting water bodies compared to the multi-band water indexes. This study shows that the optimum threshold values of each of the water indexes considered in this study were determined conveniently, where highest value of OA and Khat coefficient were obtained by creating and implementing a graphical modeler in Quantum Geographic Information System that automates from setting threshold value to accuracy assessment. This study confirms that remote sensing can extract or delineate water bodies rapidly, repeatedly and accurately.
- Water index
- Landsat 8
- Remote sensing
Remote sensing is an observation method in obtaining information about several objects on Earth’s surface (that generally includes water, vegetation, built-up, and bare soil), without having contact with the use of sensors . Optical remote sensing sensors are the vital devices that measure distinct spectral signatures, concerning wavelengths, that each sensor measures reflected or emitted energy [2–4]. However, clouds or haze and cloud shadows affect optical remote sensing images [5–8] which makes it challenging to discriminate them from dark objects like water and shadows [5, 7, 8]. Thus, cloud and haze-free images were used for this study. Recent surface water mapping methods using optical imagery are generally categorized as supervised classification [9–11], unsupervised classification [11, 12], and water spectral indexes [13–17].
Remote sensing is essential in several studies on surface water mapping including but not limited to water bodies extraction [13–16, 18], flood management [19, 20], and water quality [21–23]. Delineation of water bodies from remotely sensed imagery by extraction techniques has long been applied [13–16, 18]. The methods involved comfort with the number of bands used mainly single-band and multi-band [18, 24]. Water body extraction by multi-band water index threshold methods was introduced by McFeeters  from Landsat 4 Multispectral Scanner using green and near-infrared (NIR) bands, by Rogers and Kearney  from Landsat Thematic Mapper (TM) using red and green and shortwave infrared (SWIR) bands, by Xu  from Landsat 5 TM and Landsat 7 Enhanced TM using SWIR bands, and by Feyisa et al.  from Landsat 5 TM using green, blue, NIR, and SWIR bands. Such methods examine comprehensively the bands considered  in order to determine the threshold that categorizes water from non-water bodies . Threshold values both in single-band and multi-band water indexes are determined based on surface reflectance between water and non-water bodies . However, Xu  emphasized that the subjective threshold value determination could lead to under- or over-estimation of open water areas. Additionally, determination of threshold value that is producing optimum accuracy is perplexing, time-consuming, and image dependent [16, 25]. Furthermore, Feyisa et al.  made a comparison of optimum thresholds and found variations at different test sites.
Knowing that Landsat missions have been implemented for the past four decades, Landsat satellites performances improve a great deal. In fact, Landsat 8 is considered “robust, high performing, and of extremely high data quality” . Similarly, Landsat 8 has a different position of central wavelength with narrower bandwidth particularly bands 5 and 7 [25, 27].
Water absorbs more energy (low reflectance) in NIR and SWIR wavelengths, while non-water reflects more energy (high reflectance) [11, 16, 25, 28]. Considering that the narrower bandwidth has the advantage of effectively discriminating specific objects , NIR as single-band water index and multi-band water indexes of McFeeters , Rogers and Kearney , Xu , Feyisa et al.  using Landsat 8 was investigated in the present study. It is worth noting that single-band water index using NIR band was probably last investigated by Work and Gilmer  in 1976. Hence, this study focuses on extracting water bodies applying both the single-band and multi-band water indexes by threshold method using Landsat 8 operational land imager (OLI). The study also aims at avoiding under- or over-estimation of extracted water bodies by obtaining an optimum threshold value where the highest values for overall accuracy (OA) and Kappa hat (Khat) coefficient were reached by creating and implementing a graphical modeler in Quantum Geographic Information System (QGIS) that automates the workflow from setting threshold value to accuracy assessment.
Study area and data description
Bands, spectral wavelengths, and resolution of Landsat 8 OLI
Wavelength Range (µm)
Center Wavelength (µm)
Spatial Resolution (m)
Workflow for water extraction from images without clouds
Selection of Landsat 8 OLI imagery
Landsat 8 image was selected and downloaded from the USGS data archive (https://earthexplorer.usgs.gov/). A cloud free image of September 3, 2016 at the study area was selected and downloaded.
Clipping of study area
Before implementing pre-processing of the selected image, clipping bands 2 to 8 to the extent of the study area was applied. Clipping of band 8 was included for pan-sharpening purposes. This step was necessary to reduce memory requirement and speed up further processes like classification, band calculation, accuracy assessment, and building of virtual raster that were implemented in this study.
Spectral radiance at the sensor’s aperture
where Lλ is the spectral radiance (W sr− 1 m− 2 μm− 1); ML is the radiance multiplicative scaling factor for the band (Radiance_Multi_Band_n from the metadata, where n is the band number); AL is the radiance additive scaling factor for the band (Radiance_Add_Band_n from the metadata); and Qcal is the quantized and calibrated standard product pixel value (DN).
where d is the Earth-Sun distance in astronomical units (provided in Landsat 8 metadata file); Esunλ is the mean solar exo-atmospheric irradiances; θs is the solar zenith angle in degrees which is expressed as θs = 900 − θe where θe is the Sun elevation angle (provided in Landsat 8 metadata file); and Lλ is the spectral radiance at the sensor’s aperture.
Atmospheric correction using DOS1 method
Lλ does not consider the effects of the atmosphere; thus, spectral radiance was translated into surface reflectance where atmospheric correction method was further applied [30, 34]. To maximize the use and achieve the full potential of optical satellite data, an accurate, cost-effective and easy to apply atmospheric correction method, that does not require in-situ field measurements particularly of historical image or image that have been collected before its examination, is necessary [32, 33, 36]. Thus, an image based DOS radiometric calibration and correction method is applicable for historical data. The DOS method was confirmed effective and accurate where it is successfully applied among several Landsat studies regardless of location in cases when atmospheric measurements are unavailable [33, 35, 37, 39–42].
where Lmin is the “radiance that corresponds to a digital count value for which the sum of all the pixels with digital counts lower or equal to this value is equal to the 0.01% of all the pixels from the image considered”  of which the corresponding DNmin was obtained; LDO1% is the radiance of dark object.
where MSP is the pan-sharpened multispectral band; MS is the multispectral band with lower resolution; P is the panchromatic band with higher resolution; I is the intensity as a function of the MS bands.
Water indexes from Landsat images
Multi-band water indexes
NDWI = (Green − NIR)/(Green + NIR)
Band 2: Blue
Band 3: Green
Band 4: Red
Band 5: NIR
Band 6: SWIR1
Band 7: SWIR2
Rogers and Kearney 
NDWI = (Red − SWIR1)/(Red + SWIR1)
MNDWI = (Green − SWIR1)/(Green + SWIR1)
Feyisa et al. 
AWEInsh = 4(Green − SWIR1) − (0.25(NIR) + 2.75(SWIR2))
AWEIsh = Blue + 2.5(Green) − 1.5(NIR + SWIR1) − 0.25(SWIR2)
Analyzing spectral characteristics of water
Threshold method of classification
To acquire all pixel values, a layer of points exactly at the center of the pixels was created. Generation of this point-layer was achieved by creating a Microsoft Excel Visual Basic for Applications (VBA) code that will automate the generation of latitude and longitude coordinates. The VBA code only requires one lower left center of pixel coordinates, intervals along latitude and longitude, and number of columns and rows of pixels to be filled. The generated coordinates were saved as comma separated values (comma delimited) and imported to QGIS. In this way, a credible number of sample points can be generated. Consequently, point sampling tool in QGIS is used to obtain values of pixels for several layers of water indexes under investigation. As the optimum threshold value is estimated visually while aided with its histogram [24, 47], the generated layer of sample points made it more convenient to obtain optimum threshold value.
Knowledge based or supervised classification to generate a reference image
Since reference image was created conveniently using SCP ROI pointer, with only two classes (water and non-water bodies), a large number of samples were considered with 24,422 and 31,803 pixels for water and non-water bodies, respectively, or a total of 56,225 pixels out of 105,600 pixels of the study area. Such number of samples is large enough compared to the suggestion of Congalton and Green  of 50 samples per class or 100 samples if area exceeds 500 km2. If sample size is determined by binomial distribution by N = Z2(p)(q)/E2 , where Z = 2, p is the expected percent accuracy, q = 100 − p and E is the allowable error. Thus, if p = 99%, E = 1%, then N = 22(99)(1)/12 = 396 samples only. Likewise, considering a sampling ratio of 2% for each land use class as applied by Heydari and Mountrakis , it requires only a total of 21,120 pixels. Hence, the sample size of 24,422 and 31,803 pixels for water and non-water bodies, respectively, will most likely achieve a higher probability of getting correct accuracy estimation.
To quantitatively assess accuracy of classified image, user’s accuracy (UA), producer’s accuracy (PA), OA, and Khat coefficient were adapted for evaluation applying “r.kappa” algorithm in QGIS . The following widely adapted equations were used [37, 46, 47, 53, 54]:
where nii is the total number of correctly classified pixels in a particular category or class in i-th row and i-th column, and ni+ is the total number of pixels of i-th row i classified for a particular category.
where n+i is the total number of pixels of i-th column classified for a particular category of the reference data.
where ∑nii is the summation of the correctly classified pixels and n is the total number of testing pixels in the error matrix.
Images of colour composites
Water absorbs more energy (less reflective) at visible red (band 4), NIR (band 5) and short wave infrared (band 6 & 7) wavelengths [11, 16, 25]. In other words, water has “strong absorption in the near-infrared and mid-infrared spectral ranges” . Spectral difference between water and non-water bodies decreases at short wave infrared as presented in Fig. 4. Consequently, contrast between water and non-water bodies narrows down at bands SWIR1 and SWIR2. Hence, NIR band is a better choice over SWIR1 and SWIR2 for water extraction. NIR band spectral image of Landsat 8 reveals large contrast between water and non-water bodies.
A noticeable contrast between shallow and deep water was manifested among NDWI of McFeeters , NDWI of Rogers and Kearney , and MNDWI of Xu , indicating that deep and shallow water absorb energy differently. Furthermore, water index AWEInsh of Feyisa et al.  shows less contrast between water and tidal wetland vegetation (particularly mangroves). Likewise, NDWI of McFeeters , NDWI of Rogers and Kearney , MNDWI of Xu  and AWEIsh of Feyisa et al.  have less contrast between water and built-up white roof or white surfaces as observed in the natural colour composite (Fig. 7a). Similarly, AWEInsh and AWEIsh of Feyisa et al.  and NIR band are having comparable contrast between water and non-water bodies. However, NIR band shows better contrast between water and non-water bodies having no problem with tidal wetland vegetation (particularly mangroves) and built-up white roof or white surfaces.
Obtaining an optimum water index threshold values
Extracted water bodies based on optimum threshold value
Confusion matrix of different water indexes. OT means Optimum Threshold
OT = − 0.2592
Feyisa et al.  - AWEInsh
OT = − 0.2982
Rogers and Kearney 
OT = −0.159
Feyisa et al.  - AWEIsh
OT = −0.1905
OT = −0.2105
OT = 0.128
The NDWI of Rogers and Kearney , MNDWI of Xu , and AWEInsh of Feyisa et al.  indicated a narrow range of OA and Khat from 93.0 to 95.1% and 0.857 to 0.900, respectively, indicating that water indexes of those investigators are comparable. Among the multi-band water indexes investigated in this study, AWEIsh of Feyisa et al.  exhibited the highest overall accuracy and kappa hat coefficient of 98.3% and 0.966, respectively. However, single-band water index of NIR band unveiled the highest overall accuracy and kappa hat coefficient of 99.3% and 0.9858, respectively, compared to any multi-band water index applied in this study. Having accounted all accuracy statistics, NIR band performed the best followed closely by the AWEIsh water index of Feyisa et al. , thus, these two water indexes are very comparable.
The non-normalized AWEIsh of Feyisa et al.  adapted 5 out of 6 bands whereby maximizing usage of the different spectral information of Landsat 8 OLI. With this, it performs better than the normalized water indexes. However, results of this study have also indicated that NIR band of Landsat 8 OLI can be adapted more efficiently as a single-band water index compared to the multi-band water index introduced earlier by others [13–16]. The superior performance of NIR band of Landsat 8 OLI as water index can be attributed as having the narrowest bandwidth compared to bands 2, 3, 4, 6 &7 (Table 1). This feature of NIR band contributed to its largest difference in reflectance values between water and non-water bodies making it effective to discriminate non-water to water bodies as revealed in Fig. 8. Furthermore, NIR band is more suitable for elaborating water with considerable vegetation both on coastal and inland areas. The threshold value for NIR band in extracting water bodies is conveniently distinguishable since there is only minimal existence of non-water noise. Thus, a narrower NIR band as a single-band water index has the advantage of effectively discriminating water from non-water bodies. Hence, applying the previous multi-band water indexes of others [13–16] in extracting a water body using Landsat 8 OLI added some noise or that reduces contrast between water and non-water bodies. Additionally, single-band water index using NIR band of Landsat 8 OLI is simpler or less complicated, without requiring raster calculation, compared to the multi-band water indexes introduced by those investigators [13–16]. Moreover, this study shows that an optimum threshold value of the water index, where highest value of OA and Khat coefficient were obtained, is conveniently attainable by creating and implementing a geoprocessing modeler in QGIS that automates the process from setting of threshold value to accuracy assessment. This study likewise confirms that remote sensing can extract or delineate water bodies from non-water bodies rapidly, repeatedly and accurately.
The authors are grateful to the U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center (https://earthexplorer.usgs.gov) for providing free images. The authors also acknowledge the graduate scholarship funding from Department of Science and Technology – Engineering Research and Development Technology (DOST-ERDT), Philippines that have made this research endeavour possible.
JPM conceived the idea of investigating water index using the latest Landsat 8. JPM and AFT conceptualized the research, coordinated in downloading appropriate Landsat 8 image for the study and agreed on the use of an open-source QGIS. AFT initiated image processing and building of a graphical modeler. JPM conducted the image classification by threshold and knowledge based methods. The authors analyzed the spectral signatures of water and non-water bodies. JPM performed the analysis of obtaining threshold values and conducted accuracy assessment. JPM wrote the paper. AFT contributed to the paper revisions. All authors read and approved the final manuscript.
The authors declare that they have no competing interests.
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