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Comparison of Water Extraction Methods Based on Hyperspectral Satellite Imagery
Background Introduction
Water resources are one of the most precious natural resources for humankind. Although my country ranks fourth in the world in total freshwater resources, its per capita share is only one-quarter of the world average. Utilizing remote sensing technology to accurately and rapidly acquire dynamic information on land-based water bodies is an efficient technical means for water resource surveys and area monitoring.
There is a considerable body of research on water body information extraction at present, with most data sources being multispectral. Various methods exist for extracting water bodies of different types and regions, each with its own advantages; however, research on their application in hyperspectral satellite data is relatively limited. Therefore, this paper compares and analyzes different water body extraction methods using the Xi'an Optics-1 05 satellite (also known as the Tianxianpei satellite).
Methods and Principles
This case study used hyperspectral images of Weifang City taken by Xiguang-1 05 satellite (Tianxianpei) on May 21, 2025, to extract water bodies, and compared and analyzed different methods based on the extraction results.
Table 1. Data Introduction
In this case, the index calculation uses atmospherically corrected L2C level reflectance data. The specific operation procedure is as follows:
1. Data Preprocessing
(1) Radiometric calibration: The original DN value data is radiated by using the radiometric calibration coefficients provided by the radiance to obtain the apparent radiance data. This can be done using the Radiometric Calibration tool in ENVI.
(2) Atmospheric correction: Atmospheric correction is performed using atmospheric correction tools based on 6S or MODTRAN models to obtain surface reflectance data, such as the FLAASH atmospheric correction tool in ENVI.
(3) Spectral smoothing: In ENVI, the Savitzky-Golay (SG) algorithm is used to smooth the surface reflectance data, which reduces spectral noise and improves the reliability of subsequent classification. You can use the Savitzky-Golay Filter extension tool in the App Store of ENVI or the built-in THOR Spectral Smoothing tool. When using Savitzky-Golay Filter, the parameters are set as follows:
(4) Image registration (optional): Geographic registration is performed on the processed data based on the base map. Registration can be performed in ENVI, qgis, or arcgis. Google Imagery is recommended as the reference map.
2. Water extraction
(1) Single-band thresholding method
The single-band thresholding method refers to selecting a specific band and extracting water samples by setting an appropriate threshold. The threshold is defined as follows:
In the formula, RNIR represents the near-infrared reflectance value; δ is the threshold. When the reflectance is ≥ δ, the body is judged to be non-water; when it is
(2) Normalized Difference Water Index (NDWI)
Water body extent was extracted using the spectral index method. Specifically, the Normalized Difference Water Index (NDWI) was calculated based on the surface reflectance image processed by FLASH. Then, based on the histogram of the NDWI image, a threshold was set to complete the water body extraction. The calculation formula is as follows:
(3) Improved Shadowed Water Index (MSWI)
The Modified Shade Water Index (MSWI) is a ratio model based on the Shade Water Index that improves the separation between shadows and water bodies. The calculation formula is as follows:
The threshold is set to 0; values greater than 0 indicate water bodies, while others are considered background.
(4) Comprehensive Weighted Water Index (CWWI)
The Comprehensive Weight Water Index (CWWI) utilizes three wavelengths—blue, green, and near-infrared—and assigns different weights to each band to create a band combination. This method further distinguishes water bodies from non-water bodies by highlighting the differences in water reflection and absorption. The definition is as follows:
(5) Shadow Building Index (SBI)
The Shaded Building Index (SBI) is used to extract water bodies by multiplying the NDWI and near-infrared bands and selecting a threshold. Using only NDWI can easily lead to confusion between shadows and water bodies, resulting in significant noise and impurities. In the study area, the NDWI values of typical land features, from highest to lowest, are: water bodies, buildings, mountain shadows, and vegetation, with brightness decreasing in that order. Water bodies and buildings have similar brightness, exhibiting significant overlap in values. Water bodies show significant differences from other land features in the near-infrared band, with reflectance from highest to lowest being: vegetation, mountain shadows, buildings, and water bodies. Therefore, the expression for SBI is:
In the formula, NDWI is the Normalized Difference Water Index; RNIR is the near-infrared reflectance. Multiplying NDWI by the square of the near-infrared band maximizes the numerical difference between water bodies and other land features, while facilitating the identification of grayscale values and the effective extraction of water bodies, especially eliminating the overlap between the grayscale values of water bodies and buildings.
Results Display
Figure 1. True-color composite image of Xiguang-1 05 satellite (Tianxianpei), 20250521
Comparison of spectral curves of different ground features before and after SG filtering (reflectance increased by 10,000 times, Xiguang-1 05 satellite (Tianxianpei), 20250521)
Figure 2. Spectral curves of different ground features after SG filtering
The above water body extraction was performed on the Xiguang-1 05 satellite (Tianxianpei). To minimize the influence of human intervention thresholds, the thresholds for all methods except the single-band method were set to 0, resulting in the following results:
Figure 3. Water distribution map extracted by single-band satellite, Xiguang-1 05 (Tianxianpei), 20250521
Figure 4. NDWI water distribution map, Xiguang-1 05 satellite (Tianxianpei), 20250521
Figure 5. MSWI water distribution map, Xiguang-1 05 satellite (Tianxianpei), 20250521
Figure 6. CWWI water distribution map, Xiguang-1 05 satellite (Tianxianpei), 20250521
Figure 7. SBI water distribution map, Xiguang-1 05 satellite (Tianxianpei), 20250521
As shown in the above water body extraction distribution results and Table 2, single-band threshold extraction is more detailed and covers the largest area than other methods, mainly in the southern section of the Quhe River in Xiashan Reservoir, where the water body is relatively continuous and the river's confluence into the urban area can be clearly seen. Other methods, however, show discontinuities in the lower reaches of the Quhe River. Furthermore, single-band threshold extraction is effective at extracting small water bodies, but it relies heavily on threshold settings, requiring manual intervention. Among the other four water body extraction methods, NDWI and SBI yield very similar results, while MSWI and CWWI methods perform better on large water bodies but may miss smaller ones.
Table 2. Statistical table of water area extracted by different methods