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Aquatic chlorophyll a monitoring based on novel hyperspectral index
Background Introduction
Chlorophyll a is the main photosynthetic pigment in phytoplankton and is often used as an indicator of phytoplankton biomass. Chlorophyll a concentration (Chla) is a key indicator for assessing eutrophication levels and studying carbon cycling in aquatic environments. Compared to traditional surface water sampling methods that can only measure a limited number of points, remote sensing technology can provide a wider range of spatial distribution information for chlorophyll a.
Hyperspectral remote sensing holds great potential for monitoring chlorophyll a (Chla) in optically complex water conditions. However, existing hyperspectral indices are easily limited by interference from other water quality parameters, imperfect atmospheric correction, and spectral band dispersion when dealing with high-productivity water bodies. To address this, Yao et al. proposed a novel spectral index—the Red Edge Reflectance Peak Width Index (REPWI). This index:
(1) Make full use of the high spectral resolution of hyperspectral data to reduce the interference of TSM and CDOM in optically complex water bodies, thereby giving full play to the potential of the red edge reflection peak.
(2) Insensitive to uncertainties introduced during the imperfect atmospheric correction process
(3) Minimize the impact of discrete spectral sampling interval
Methods and Principles
This case study used hyperspectral images of the Taihu Lake region taken by Xiguang-1 05 satellite (Tianxianpei) on June 18, 2025, to conduct index calculations and analyze the water quality of the Taihu Lake region based on the index 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) Spectral smoothing: In ENVI, the surface reflectance data is smoothed using the Savitzky-Golay (SG) algorithm, which reduces spectral noise and improves the reliability of subsequent red edge correlation index calculations. Either the Savitzky-Golay Filter from the App Store in ENVI or the built-in THOR Spectral Smoothing tool can be used. When using the Savitzky-Golay Filter, the parameter settings are as follows:
(2) 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.
(3) Water body mask: The Taihu Lake area is extracted by normalizing the water body index by the surface reflectance number and selecting an appropriate threshold based on the histogram of the calculation results.
(4) Index calculation: The index is calculated using the Band Math tool in ENVI. The calculated index is a multi-band or single-band raster. It can be exported as a TIFF format using the New File Builder in ENVI and then analyzed in QGIS or ArcGIS.
(5) Chlorophyll region mask extraction: Using the calculated partial index, chlorophyll region mask extraction was performed in Taihu Lake using a fixed threshold method.
(6) Multi-index comprehensive analysis: Based on the statistical vector or grid index, the distribution of different indices is analyzed by referring to the physical meaning represented by different indices.
The index can also be calculated directly using the original DN value imagery, or by obtaining reflectance data after radiometric calibration and atmospheric correction for index calculation.
Radiometric calibration: Radiometric correction is performed on the raw DN value data using the radiometric calibration coefficients provided by radiance to obtain the apparent radiance data. This can be done using the Radiometric Calibration tool in ENVI.
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.
2. Spectral index calculation
(1) I AM: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:
（2） RESPONSE:According to the bio-optical model, the higher the chlorophyll a content in the water, the stronger its absorption (the deeper the reflection valley) near 670-680 nm, which will cause the reflection peak in the red edge region (about 700 nm) to shift towards longer wavelengths and become wider.
REPWI physical definition: It is defined as the horizontal wavelength distance extending horizontally to the right from a fixed red reflection valley wavelength (678 nm) until it intersects the right side of the red-edge reflection peak curve. The calculation formula is as follows:
in，l'inThe wavelength at the right crossover point is determined by linear interpolation, satisfying R.rs(l'in) = Rrs(678)。
Results Display
Figure 1. True-color composite image Xiguang No. 1 05 (A Perfect Match) 20250618
The following results were obtained by performing the above water body extraction and index calculation on the data of Xiguang-1 05 satellite (Tianxianpei):
Figure 2. REPWI index distribution mapXiguang No. 1 05 (A Perfect Match) 20250618
To analyze the applicability of the REPWI index, a comparative analysis was conducted with the more commonly used Normalized Chlorophyll Index (NDCI). The distribution of the Normalized Chlorophyll Index (NDCI) results is shown in the figure below:
Figure 3. Distribution of Normalized Chlorophyll Index (NDCI)Xiguang No. 1 05 (A Perfect Match) 20250618
Comparison reveals consistency between the REPWI and NDCI indices in terms of macroscopic trends. Both show significantly higher chlorophyll a concentrations in the northwestern and coastal areas of Taihu Lake compared to the central and southeastern parts, indicating a clear spatial differentiation in eutrophication. Overall, algal blooms are severe in the western Taihu Lake region, with higher chlorophyll a concentrations near the blooms. This is because the western part of Taihu Lake is where rivers flow into the lake, bringing in large amounts of nutrients that promote vigorous algal growth, leading to cyanobacterial blooms and increased chlorophyll a concentrations. The central and eastern Taihu Lake regions are less affected, resulting in better water quality than the western Taihu Lake. Aquatic plants grow in the eastern part of the lake, and chlorophyll a concentrations are lower, further validating the effectiveness of the REPWI index in capturing the spatial distribution characteristics of chlorophyll a in water bodies. This provides more reliable data support for subsequent water quality monitoring and algal bloom early warning.