1. Introduction
More than 40% of the world’s population lives in coastal areas or along rivers and lakes [
1], while the water quality of numerous water bodies has deteriorated in recent decades owing to intensified human activities. In particular, the widespread problem of microplastics in water bodies poses a potential threat to the health of people [
2]. Inland waters are more fragile than marine ecosystems because of their more confined nature and lower ecological stability [
3]. Total suspended matter is a widely used water quality parameter in aquatic environmental studies, which mainly refers to solids suspended in water bodies, including inorganic substances that are insoluble in water, organic substances, sediment, and microorganisms [
4]. The presence of suspended matter alters the distribution of light intensity in the water body, affecting the growth of aquatic vegetation and thus the distribution of primary productivity and biomass in the water body [
5].
Water quality monitoring based on in-situ measurements usually only represents the status of stations. It is costly and difficult to provide continuous large-scale monitoring over time. With the rapid development of remote sensing technology, large-scale and long time-series remote sensing products are increasingly being used [
6,
7]. Satellite image-based
CTSM retrieval can effectively overcome the shortcomings of traditional methods and is being increasingly used for reflecting the water quality of the water body [
8,
9,
10]. At present, the main methods for estimating
CTSM utilizing remote sensing products are empirical [
7,
11,
12,
13,
14], semi-analytical [
15], and analytical [
16]. Among them, empirical methods, with advantages of simple steps and fast processing, are mainly based on linear regression of a single band or a combination of bands [
5,
8,
9,
17,
18]. However, the applicability of empirical models varies widely across water bodies [
19], and it is difficult to find a general empirical model that can be applied to most water bodies. The semi-analytical method can avoid the shortcomings of empirical models to a certain extent and has better applicability to different water bodies. Nechad [
15] developed a single-band semi-analytic
CTSM retrieval model based on a bio-optical model and provided calibration results for MODIS, MERIS, and SeaWiFS sensors. This method was later successfully applied to Landsat8-OLI and Sentinel2-MSI sensors [
20] with good generalizability. The analytical method mainly simulates the light field distribution of the water body based on the radiative transfer model, and uses the relationship between the water color signal and the spectral properties of the water body to estimate the content of each component of the corresponding water body. The physical meaning of this process is clearer and more general in space and time, but it is less commonly used in practical applications because the theoretical application of absorption and scattering in different water bodies is still immature and the arithmetic process is relatively more complicated [
21].
The spatial, temporal, spectral, and radiometric resolutions of the satellite sensors (
Table 1) can affect their capabilities in water-color remote sensing [
22]. Different types of sensors have different limitations for estimating total suspended matter concentrations [
19]. First, multispectral sensors with broadbands, such as Landsat8 OLI and Sentinel-2 MSI, generally have relatively high spatial resolution, but with only a few numbers of bands. Second, multi-spectral sensors with narrow widths, (e.g., Sentinel-3 OLCI and MERIS) can capture the spectral characteristics of suspended matter more accurately. However, the spatial resolution of this type of data is usually very low (300–1200 m), making it hard to be applied to small- and medium-sized water bodies. In comparison, hyperspectral data (e.g., GF-5 AHSI and ZY1-02D AHSI) have relatively spatial resolution (i.e., 30 m) and abundant narrow bands, thereby serving as a suitable data source for total suspended matter monitoring of inland waters [
23,
24].
China successfully launched the GF-5 satellite in May 2018. GF-5 carries the Advanced Hyperspectral Imager (AHSI), which is capable of acquiring images with 5 nm and 10 nm spectral resolution in the visible to near-infrared (VNIR) bands and short-wave infrared (SWIR) bands, respectively. The number of bands was 330, and its spatial resolution was 30 m, and the swath width was 60 km. Subsequently, the ZY1-02D satellite was successfully launched in September 2019, carrying the new-generation AHSI, which has the same spatial resolution and swath width as GF-5. However, to improve the signal-to-noise ratio of the data, the spectral resolution in the VNIR and SWIR bands of ZY1-02D AHSI was reduced to 10 nm and 20 nm [
25], respectively. As a result, while maintaining wide swath and coverage capability, the signal-to-noise ratio (SNR) of the ZY1-02D AHSI sensor was improved compared with the GF-5 AHSI. The minimum SNR of the sensor under typical operating conditions exceeds 120, allowing for uninterrupted long strip imaging [
26]. Therefore, ZY1-02D hyperspectral data have a high potential for application in the quantitative information extraction of inland water bodies.
The main purpose of this study is to test the capability of the ZY1-02D AHSI for total suspended matter retrieval in inland waters, and thereby identify the optimal model that can be applied to AHSI data of ZY1-02D.
Section 2 describes the data and retrieval strategy,
Section 3 the data processing methods and alternative models, and
Section 4 the calibration and validation results of the selected model. An experimental discussion is presented in
Section 5 and conclusions are drawn in
Section 6.
4. Results
4.1. Accuracy Assessment of ZY1-02D Image Atmospheric Correction
The accuracy of the image-derived remote sensing reflectance affected the accuracy of the estimated
CTSM. Therefore, the accuracy evaluation of ZY1-02D image-derived
was conducted for the main bands utilized in the
CTSM retrieval models using
R2 and AURE. The results are shown in
Table 5.
Among the major bands utilized by the models, 551 nm had the lowest AURE of 18.84%, which was the band with the highest agreement between image remote sensing reflectance and in-situ remote sensing reflectance. As can be seen from
Figure 2 and
Figure 3, the energies of 551 and 560 nm were higher than that of other wavelengths; therefore, they were relatively less affected by noise. Similarly, the red bands of 645–700 nm were also relatively high in energy and received relatively little noise impact, with AUREs of approximately 30%. It is known from the remote sensing principle that the shorter the wavelength, the weaker the penetration ability, while the atmosphere has a strong scattering effect in the blue band. Therefore, the consistency of the 490 nm satellite spectrum was poor compared with the red and green bands; however, the AURE was still controlled at 32.60%. The NIR band after 750 nm was lower in energy, and there was only a very small reflection peak at 800 nm, which was more affected by noise; therefore, its relative error was also relatively high.
Subsequently, the spectral angle cosine was calculated separately for the satellite-ground spectra of each study area to determine the spectral consistency in different study areas, as shown in
Table 6.
The mean values of the spectral angle cosine in all three regions were >0.9, indicating that the AHSI image spectra were in good agreement with the in-situ spectra.
4.2. CTSM Estimation from In-Situ Measurements by Empirical Models
First, we tested 11 empirical models, which were divided into two categories: Single-band and multi-bands models. Among the empirical models, the selected bands for each model were adapted according to the AHSI bands. For the model calibration, we compared the model equation of the original literature, and four types of fitting functions: linear, exponential, logarithmic, and power functions. The model with the highest R
2 was chosen as the best fitting model. In addition, the fitting function should be monotonous to avoid estimation anomalies. The calibration and validation results of the models are presented in
Table 7 and
Figure 4.
In the empirical models, the calibration of the single-band model based on the NIR band after 700 nm was generally good, with R2 > 0.85. Among the multi-band models, the baseline model was generally better; the Kuster_16_2 model (R2 = 0.83) was the best calibrated multiband model. The results of the other multiband models were generally lower.
From the validation results, the Zhang_10 ratio model had the best validation results, with the lowest AURE of 19.08%. The single-band model was also generally good, with a validation AURE of approximately 20%. Among the multi-band models, Kuster_16 showed better results on the validation set, with AURE < 30%, whereas the validation results of the Liu_18 model were poor and not applicable to the estimation of CTSM in inland waters. The ratio model still performed poorly in the validation set. Determining the optimal empirical model applicable to AHSI images requires further validation.
4.3. CTSM Estimation from In-Situ Measurements by Semi-Analytical Models
values were calculated using QAA and its improved models for indirect estimation of the total suspended matter concentration. The Jiang_21 model established the relationship between the
and
CTSM over a wider range of
CTSM. Therefore, the parameters were not re-rated. Other models re-rated the relationship between
and
CTSM based on calibrated datasets to enable the estimation of
CTSM. The Nechad_10 model showed the best calibration results at 697 nm with parameters
and
: 934.09 (g/m
3) and 4.39 (g/m
3), respectively.
CTSM was then retrieved based on image-derived
using various semi-analytical models. The calibration and validation results of
CTSM estimated on the ZY1-02D match-ups are shown in
Table 8 and
Figure 5.
In the QAA-based CTSM retrieval method, even though the Le_09 and Mishra_14 models achieved the highest R2 in the calibration dataset, QAA_v5 achieved the optimal performance in the validation dataset. This was mainly due to the utilization of the 551 nm band, for which the image-derived was reasonably accurate.
The accuracy of the Nechad_10 model gradually improved as the wavelength shifted toward the long wave direction, and the highest validation accuracy was achieved at 697 nm. Generally, the difference in the CTSM estimation accuracy between the two types of semi-analytical models was insignificant.
4.4. CTSM Estimation from AHSI Images
To investigate the applicability of different models to satellite images, optimal single-band and multi-band empirical models, the QAA_V5 based model, and Nechad_10 (697 nm) model were applied to the image-derived
of match-ups, and the validation accuracy results are shown in
Table 9. The best results are shown in
Figure 6.
Based on the Nechad_10 (697 nm) model, the total suspended matter concentration distributions were produced for the three study areas (
Figure 7). The overall trend of
CTSM in Taihu Lake decreased from northwest to southeast, while most rivers entering the lake are located along the northwestern coast of Taihu Lake. The confluence of the rivers increases the movement of the lake, resulting in higher
CTSM at the northwest of Taihu Lake, while the central and eastern parts are less affected by this trend [
41]. The
CTSM in Yuqiao Reservoir did not vary considerably, with low
CTSM values in the center of the reservoir and high
CTSM values along the northern coast due to human activities [
31]. The highest
CTSM existed in the eastern part of the reservoir, where the Lin River entered the lake, reaching >13 mg/L. Due to the limited coverage of the AHSI image, only the
CTSM in the central region of Qinghai Lake was estimated. The overall
CTSM in Qinghai Lake was very low, mostly approximately 3 mg/L. The suspended matter concentration was low in the middle of the lake, and showed an increasing trend from the center of the lake to the lake shore.
6. Conclusions
The new-generation hyperspectral imaging spectrometer AHSI onboard the ZY1-02D satellite has continuous narrow spectral bands of 400–2500 nm, and can capture fine spectral features, thereby showing great potential for inland water CTSM retrieval. In this study, we recalibrated 13 widely used empirical and semi-analytical CTSM retrieval models using in-situ AHSI equivalent spectra of six typical inland water bodies in China. Validations based on in-situ spectra showed that the Zhang_10 model in the empirical model achieved the lowest AURE, (i.e., 19.08%). Two semi-analytical models established with the green and red bands, the QAA_V5-based model and the Nechad_10(697) model, had similar accuracy with AUREs of 25.96% and 28.92%, respectively. In terms of the validation based on the AHSI image-derived of 36 match-ups, Nechad_10(697) achieved the best retrieval accuracy (AURE of 34.43%). This is owing to the robustness of the model, as well as the highly accurate AHSI band at 697 nm.
Overall, the spectral and spatial resolution of ZY1-02D AHSI images makes it a useful data source for CTSM retrieval of inland water bodies. With the advancement of atmospheric correction accuracy and model optimization based on a larger range of in-situ data, the accuracy of CTSM retrieval based on ZY1-02D AHSI images will be further improved.