1. Introduction
Lake Taihu is China’s third largest fresh water lake, and it is located in the Yangtze River Delta (
Figure 1). It covers an area of ~2300 km
2 with an average water depth of ~2 m. Lake Taihu features constantly turbid waters and frequent breakouts of algal blooms in the spring-summer. For example, a massive blue-green algae bloom broke out in the spring of 2007 and became an environmental crisis, which polluted the water supply for nearby urbanized and heavily populated regions [
1,
2,
3].
Numerous studies have been conducted with both in situ measurements and satellite ocean color observations in order to develop satellite algorithms for geophysical and biological parameter retrievals, study the environmental changes, and to characterize and quantify the physical, biological, and biogeochemical dynamics, such as water quality, phytoplankton algal blooms, lake eutrophication, chlorophyll-a (Chl-a) concentration, water diffuse attenuation coefficient at the wavelength of 490 nm
Kd(490) [
4] or at the domain of photosynthetically available radiation (PAR)
Kd(PAR) [
5], primary production and color dissolved organic matters (CDOM), etc. Using the Moderate Resolution Imaging Spectroradiometer (MODIS) observations, Wang et al. [
2] analyzed the massive blue-green algae bloom event during the spring of 2007, and derived the water properties for water quality monitoring, assessments, and management in Lake Taihu [
2]. The intense blooms of cyanobacteria (primarily
Microcystis aeruginosa) in Lake Taihu were also characterized with MODIS observations [
6]. The annual frequency of significant blooms was found to increase from the time period of 2000–2004 to 2006–2008 [
6]. The total suspended matter (TSM) concentration can also be retrieved and evaluated in Lake Taihu with satellite observations [
7,
8]. The long-term variability of the TSM concentration in Lake Taihu was characterized and quantified [
7].
In recent years, the environment in Lake Taihu has been deteriorating significantly [
9]. The water quality in northern Lake Taihu is getting worse due to urban pollutant discharge. The eutrophication in eastern Lake Taihu is caused by intensive aquaculture [
9]. Climate variability and poor lake management are attributed to the crisis of the blue-green algal bloom in 2007. Similar to the lakes in the middle and low reaches of the Yangtze River, Lake Taihu also experienced eutrophication and the corresponding ecosystem responses, including the extinction of underwater plants and frequent cyanobacterial blooms [
10]. The annual average total nitrogen concentration in Lake Taihu is ~2–3 mg L
−1, and the phosphorus concentration is ~0.2 mg L
−1, with significant spatial and temporal variations. The phytoplankton growth in Lake Taihu is controlled by nitrogen and phosphorus inputs, as well as by climatic factors [
6,
11].
In Lake Taihu, Chl-a concentration typically peaks in the summer and reaches its minimum in the winter. The maximum primary production usually occurs in the spring and summer seasons [
12]. The CDOM shows the spatial and seasonal dynamics in Lake Taihu [
13]. CDOM absorption is significantly higher in the winter than in the summer [
14]. This is caused by the degradation and release of fixed carbon in the phytoplankton and the underwater vegetation [
15]. The spectral slope for the exponential decrease of the CDOM absorption
ag(
λ) is ~0.015 nm
−1 [
16] with seasonal variations. The backscattering spectra from the blue to near-infrared (NIR) wavelengths in Lake Taihu are flat [
17]. In fact, they are highly related to the TSM concentrations [
7,
17,
18]. Shi et al. [
7] show that TSM concentrations in Lake Taihu have significant spatial and temporal variability. Seasonal changes in the TSM concentration for all parts of Lake Taihu are in the range of 25–80 mg L
−1. The TSM in southern Lake Taihu can reach over ~100 mg L
−1 in the wintertime, while low TSM concentrations of ~20–30 mg L
−1 are located in the northern parts of Lake Taihu, such as Meiliang Bay and Gonghu Bay [
7].
The lake water inherent optical properties (IOPs) include the absorption and scattering of the pure water, color dissolved and detrital organic matter, and particles in the water column. These are the intrinsic optical properties, which determine the normalized water-leaving radiance spectra
nLw(
λ), that can be measured or retrieved from the in situ or satellite radiometry sensors [
19,
20,
21,
22]. In comparison to these
nLw(
λ) spectra, retrievals of biological and biogeochemical parameters such as Chl-a, TSM,
Kd(490) and IOPs can provide comprehensive information about the constituents in the water column, and interaction between different constituents in order for researchers to better understand the ecosystem dynamics in both regional and global ocean waters [
23,
24].
For the global ocean, several algorithms were developed in order to retrieve IOPs, i.e., the particle backscattering coefficient
bbp(
λ), the absorption coefficient of the phytoplankton
aph(
λ), etc. In the Garver-Siegel-Maritorena (GSM) IOP algorithm [
19,
25], a nonlinear least-square scheme is used in order to best fit the modeled remote sensing reflectance
Rrs(
λ) with
Rrs(
λ) spectra from the satellite or in situ measurements. This IOP algorithm uses a fixed
bbp(
λ) power law slope
η, and a constant exponential degradation slope
S for the dissolved and detrital matters (
adg(
λ)). In the Quasi-Analytical Algorithm (QAA) [
21], a couple of empirical formulae are used first to compute backscattering coefficients at a reference wavelength
bbp(
λ0) and the
bbp(
λ) power law slope
η. Then total absorption
at(
λ) is further decomposed into
aph(
λ) and
adg(
λ) using the empirical formulae with
at(410),
at(443), as well as remote sensing reflectance beneath the surface
rrs(λ) at the blue and green bands. On the other hand, the generalized IOP (GIOP) algorithm is a generic IOP algorithm, which allows the researcher to specify the modeling assumption for the IOP parameters, construct and develop new semi-analytical IOP algorithms, and tune into regional IOP algorithms [
22].
These three IOP retrieval algorithms generally work well in the open ocean and less-turbid coastal and inland waters. However, significant errors with these three IOP algorithms can occur in turbid coastal and inland waters, such as Lake Taihu. Specifically, for the QAA algorithm,
rrs(
λ) in the blue and green bands are the major inputs to estimate the
bbp(
λ0) and
bbp(
λ) power law slope
η. The saturation of the
rrs(
λ) in the visible bands over highly turbid waters shows that
rrs(
λ) loses its sensitivity to the change of
bbp(
λ) [
26,
27,
28], thereby resulting in significant errors in coastal and inland turbid waters, such as those in Lake Taihu.
Satellite-measured
nLw(
λ) spectra at the red and NIR wavelengths are rarely used in the open ocean, since their values are close to 0. However, over turbid coastal and inland waters,
nLw(
λ) spectra feature enhanced
nLw(
λ) at the red and NIR wavelengths [
2,
28,
29,
30]. This spectral feature of the coastal and inland waters is caused by the strong water absorption at the red and NIR wavelengths [
31,
32], as well as a significant decrease of absorptions by CDOM at the red and NIR wavelengths and the near zero absorption of the phytoplankton at the NIR wavelengths [
21,
33]. Thus,
nLw(
λ) spectra at the red and NIR wavelengths can provide unique information to address the complexity of turbid coastal and inland waters, while the spectral features at the traditional blue and green wavelengths often fail. Over coastal and inland water regions, Chl-a [
34],
Kd(490) [
4,
35], TSM [
7,
36,
37], the floating algae index [
38] and normalized difference algae index [
39], can all be produced using the optical measurements at the red and NIR wavelengths from the in situ and satellite observations. These products from the ocean color observations can be used to study the long-term environmental variability, characterize and quantify the coastal and lake ecosystems, evaluate the dynamics of the coastal environment, and monitor the natural hazards and environmental events.
In Lake Taihu, several studies were conducted in order to develop an improved algorithm to accurately retrieve the IOPs. By shifting the wavelength reference for
bbp(
λ0) from 551 nm or 640 nm to 701 nm in the QAA algorithm, the IOP retrievals in Lake Taihu are improved [
40]. Another study shows an improved QAA algorithm with double-reference bands, and divides the entire Lake Taihu into two types of waters using the spectral slopes of remote sensing reflectance between 677 and 701 nm [
41].
Even though these two algorithms showed some improvements in comparison with the QAA retrievals, they are in situ focused, and are not designed to be applied to the satellite observations in Lake Taihu.
Recent studies [
42,
43,
44] show that the semi-analytical radiance model [
20] can be simplified for the
nLw(
λ) at the NIR wavelengths because the sea water absorption
aw(
λ) is normally ~1–2 orders higher than the other IOP components at the NIR wavelengths. Consequently, the
bbp(
λ) spectra for all wavelengths between the short blue and NIR can be computed analytically from
bbp(
λ) values at the two NIR bands (745 and 862 nm) in coastal and inland turbid waters [
42]. In comparison, the
bbp(
λ) spectra derived from other IOP algorithms, e.g., QAA [
21], significantly underestimate the true
bbp(
λ) values. Shi and Wang [
42] suggest that the NIR-based
bbp(
λ) retrievals in turbid coastal and inland waters can be extended to the second step of the QAA IOP algorithm to further derive other IOP components, i.e., decompose the total absorption
at(
λ) into phytoplankton absorption
aph(
λ) and absorption coefficient
adg(
λ) for the dissolved and detrital matters in the water column.
In this study, in situ IOP measurements in Lake Taihu are used to tune the NIR-based IOP algorithm for satellite ocean color observations from the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar-orbiting Partnership (SNPP). The performance of the IOP algorithm is evaluated and analyzed. This IOP algorithm is then applied to VIIRS observations between 2012 and 2018 to derive IOP bbp(λ), aph(λ), and adg(λ) products in Lake Taihu. Climatology, seasonal variability of bbp(λ), aph(λ), and adg(λ) are computed, characterized, and quantified. Time series of bbp(λ), aph(λ), and adg(λ) in Lake Taihu between 2012 and 2018 are also evaluated.
5. Discussion
The
nLw(
λ) spectra in this study are derived using the SWIR-based atmospheric correction algorithm from the VIIRS observations. This is similar to the SWIR-based atmospheric correction algorithm for the MODIS observations [
2]. In fact, the same MSL12 ocean color data processing system has been used in both studies. We examined the
nLw(
λ) climatology derived from VIIRS observations [
7] and MODIS observations in Lake Taihu [
2,
28], and concluded that the climatology
nLw(
λ) spectra from these two studies are very close/similar in terms of both the magnitudes and the spatial patterns. This shows that VIIRS-derived
nLw(
λ) spectra are reliable, and can be used to produce the IOP products in Lake Taihu.
On the other hand,
nLw(
λ) bias in highly turbid waters may indeed occur in the blue band. It can reach up to ~0.5 mW cm
−2 μm
−1 sr
−1 at the blue band for extremely turbid waters like Hangzhou Bay [
27], and much less at the other bands. The water reflectance spectra in Lake Taihu [
7,
28] suggest that the winter is the only season when the low biased
nLw(443) can occur. VIIRS-derived
nLw(443) may be biased low ~ < 0.2 mW cm
−2 μm
−1 sr
−1 with the SWIR-based atmosphere correction algorithm. The assessment of the
nLw(
λ) in Lake Taihu implies that there should be little bias for
bbp(
λ),
at(λ),
adg(λ), and
aph(λ) in the spring, summer, and autumn seasons.
In the winter season, however, no or insignificant bias is expected for the bbp(λ) product since bbp(λ) is derived directly from nLw(λ) at the NIR wavelengths. However, VIIRS-derived at(443), adg(443), and aph(443) may be biased high, within ~10%.
It has long been a challenge to derive the IOP products from satellite ocean color observations in turbid coastal and inland waters. This challenge comes from two issues, i.e., an atmospheric correction for deriving accurate
nLw(
λ) spectra, and a valid IOP algorithm to retrieve IOPs in turbid coastal and inland waters. In the studies by Wang et al. [
2,
54], it has been shown that the SWIR-based atmospheric correction algorithm can be used to derive good quality
nLw(
λ) spectra from satellite observations. On the other hand, an NIR-based algorithm was proposed, developed, validated, and demonstrated to derive the backscattering coefficient
bbp(
λ) in turbid coastal and inland waters [
42]. Based on the retrievals of
bbp(
λ) with the NIR-based algorithm, this study shows that the other IOPs such as
adg(
λ) and
aph(
λ) can also be subsequently retrieved after tuning and optimizing the coefficients in the procedure to decompose
at(
λ) into
adg(
λ) and
aph(
λ) with the in situ measurements. With the in situ IOP data, we developed the IOP algorithm in Lake Taihu from the VIIRS-SNPP observations. The comparison between IOP data derived from the NIR-based IOP algorithm and the in situ measurements shows that
at(
λ),
adg(
λ), and
aph(
λ) can be calculated with reasonable accuracy in Lake Taihu. A high determination coefficient between the derived
at(
λ) and in situ-measured
at(λ) also suggests that
bbp(
λ) retrievals from the NIR-based IOP algorithm should also be reasonably accurate.
In this NIR-based IOP algorithm, the
bbp(
λ) spectral slope
η is computed from
bbp(745) and
bbp(862), as shown in Equation A8 in
Appendix A. Even though it is not an input/output parameter,
η is critical in defining the spectral shapes of the IOPs and in determining the accuracy of the IOP retrievals. In Lake Taihu, the
η calculated from the in situ
Rrs(λ) range between −0.2 and 2.5 with the mean
η of 1.13. Seasonal change of
η is significant. Low
η ~ 0 normally occurs in the winter season with enhanced
Rrs(λ), while
η is generally high in the summer and autumn seasons with low
Rrs(λ). Examination of the VIIRS-SNPP observations also shows the similar seasonal variability in Lake Taihu.
The spatial patterns and temporal variations of the IOPs in Lake Taihu are driven by the physical and biogeochemical dynamics in Lake Taihu. In northern Lake Taihu, the enhanced
aph(
λ) in the summer and autumn seasons can be attributed to the frequent occurrence of the cyanobacterial blooms in that region [
10]. In the spring and winter seasons, the enhanced
bbp(
λ) in southern and western Lake Taihu is consistent with the enhanced TSM concentrations caused by the sediment resuspensions due to high winds in these two seasons [
7]. The spatial and temporal variations of
adg(
λ) in Lake Taihu are also driven by the physical and biogeochemical changes. The enhanced
adg(
λ) in the spring and winter seasons can be attributed to the high TSM in the water column [
7] and degradation and release of fixed carbon in the phytoplankton and the underwater vegetation [
15].
Both
ad(
λ) and
bbp(
λ) are proportional to the TSM concentration.
Figure 5,
Figure 6,
Figure 7,
Figure 8,
Figure 9 and
Figure 10 show that the changes of
adg(
λ) and
bbp(
λ) are different from each other. The main reason for the difference of the changes in
adg(
λ) and
bbp(
λ) is the role of
ag(
λ). In a turbid region like southern Lake Taihu, the
ad(
λ), which is proportional to the TSM concentration just like
bbp(
λ), is dominant in the
adg(
λ). However, in the less turbid waters of northern Lake Taihu, e.g., Meiliang Bay,
ag(
λ) is significantly enhanced due to a high CDOM centration from the phytoplankton decay. Thus,
ag(
λ) can be larger than
ad(
λ) in this region. This leads to the different changes in
adg(
λ) and
bbp(
λ).
It is also noted that no obvious seasonality of mean
aph(
λ) in the entire Lake Taihu shown in
Figure 10c does not necessarily represent the regional
aph(
λ) seasonality in Lake Taihu.
Figure 6,
Figure 7,
Figure 8 and
Figure 9 clearly show that the northern and northwestern Lake Taihu regions experience notable seasonal variations of
aph(
λ). Enhanced
aph(
λ) can be observed in the summer-autumn seasons, while low
aph(
λ) occurs in the winter season. In the summer,
aph(443) reaches over ~1.5 m
−1, while
aph(443) is generally below ~0.5 m
−1 in the winter season.
Even though one of the purposes in this study is to develop the IOP algorithm for satellite observations in Lake Taihu, the approach for the regional NIR-based IOP algorithm can be further expanded to develop similar regional IOP algorithms for other coastal and inland water regions from a broad global perspective. On the other hand, the particles in Lake Taihu, the Yangtze River Estuary, and the Hangzhou Bay are all from the Yangtze River [
56]. Thus, the
nLw(
λ) spectral shapes for these waters are similar [
28]. Since the coefficients, such as the exponential decay coefficient
S, are determined by the mineral type, composition, texture, particle refractive index, etc., this further implies that the particle type and composition for these waters are similar. Thus, the IOP algorithm for Lake Taihu in this study can be correspondingly applied to the other similar waters, such as the Yangtze River Estuary and Hangzhou Bay, in order to decompose
at(
λ) into
adg(
λ) and
aph(
λ) in those regions.