Turbid Inland Waters Research Articles

A Novel Atmospheric Correction for Turbid Water Remote Sensing

For the remote sensing of turbid waters, the atmospheric correction (AC) is a key issue. The “black pixel” assumption helps to solve the AC for turbid waters. It has proved to be inaccurate to regard all water pixels in the SWIR (Short Wave Infrared) band as black pixels. It is necessary to perform atmospheric correction in the visible bands after removing the radiation contributions of water in the SWIR band. Here, the modified ACZI (m-ACZI) algorithm was developed. The m-ACZI assumes the spatial homogeneity of aerosol types and employs the BPI (Black Pixel Index) and PIFs (Pseudo-Invariant Features) to identify the “black pixel”. Then, the radiation contributions of waters in the SWIR band are removed to complete the atmospheric correction for turbid waters. The results showed that the m-ACZI had better performance than the SeaDAS (SeaWiFS Data Analysis System) -SWIR and the EXP (exponential extrapolation) algorithm in the visible band (sMAPE < 30.71%, RMSE < 0.0111 sr−1) and is similar to the DSF (Dark Spectrum Fitting) algorithm in floating algae waters. The m-ACZI algorithm is suitable for turbid inland waters.

Suitability of Sentinel-3 chlorophyll a products based on optical water types

ABSTRACT Analysis of the suitability of water colour products for the reliability of subsequent applications, or improvements of the algorithm at later stages, is important. We used three types of water bodies: Type 1 (Southern Ocean and Qinghai Lake) clean oceanic water bodies dominated by chlorophyll and clean inland water bodies dominated by chlorophyll and coloured dissolved organic matter; Type 2 (Taihu Lake and Chagan Lake) turbid inland water bodies dominated by suspended matter and chlorophyll and Type 3 (Xiaoxingkai Lake and Daxingkai Lake) turbid inland water bodies dominated by suspended matter. The accuracies of two Chl a concentration products data for synchronous Sentinel-3 OLCI Neural Network (NN) and ocean colour 4 band ratio (OC4Me) algorithms were verified using two metrics combined with in situ data (to compare the applicability of Chl a concentration algorithms for the three types of water bodies). The optimal Chl-a concentration algorithm was selected to analyse the spatial and temporal variation characteristics of monthly concentrations of Chl a. Based on the analysis, (1) the NN algorithm had better applicability to Type 1 water bodies ; the NN algorithm had better applicability to Type 2 water bodies, ; and both algorithms had poor applicability to Type 3 water bodies, (2) Limitations: An overall low derivation value was obtained for the Sentinel-3 OLCI NN algorithm with water concentrations of Chl a, higher than 25 mg m−3. The OC4Me Chl a concentration product only applies to the concentrations of Chl a in Type 2 during the summer. (3) When the Sentinel-3 OLCI Chl a concentration product was compared with the moderate-resolution imaging spectroradiometer (MODIS) and visible infrared imaging radiometer (VIIRS) Chl a concentration products, the distribution trends of the Chl a concentrations were consistent. Furthermore, the Sentinel-3 OLCI Chl a concentration product was more dominant in spatial resolution.

A satellite-based hybrid model for trophic state evaluation in inland waters across China

Eutrophication is one of the major threats to the inland water ecosystem. Satellite remote sensing provides a promising way to monitor trophic state at large spatial scale in an efficient manner. Currently, most satellite-based trophic state evaluation approaches have focused on water quality parameters retrieval (e.g., transparency, chlorophyll-a), based on which trophic state was evaluated. However, the retrieval accuracies of individual parameter do not meet the demand for accurate trophic state evaluation, especially for the turbid inland waters. In this study, we proposed a novel hybrid model to estimate trophic state index (TSI) by integrating multiple spectral indices associated with different eutrophication level based on Sentinel-2 imagery. The TSI estimated by the proposed method agreed well with the in-situ TSI observations, with root mean square error (RMSE) of 6.93 and mean absolute percentage error (MAPE) of 13.77%. Compared with the independent observations from Ministry of Ecology and Environment, the estimated monthly TSI also showed good consistency (RMSE=5.91,MAPE=10.66%). Furthermore, the congruent performance of the proposed method in the 11 sample lakes (RMSE=5.91,MAPE=10.66%) and the 51 ungauged lakes (RMSE=7.16,MAPE=11.56%) indicated the favorable model generalization. The proposed method was then applied to assess the trophic state of 352 permanent lakes and reservoirs across China during the summers of 2016–2021. It showed that 10%, 60%, 28%, and 2% of the lakes/reservoirs are in oligotrophic, mesotrophic, light eutrophic, and middle eutrophic states respectively. Eutrophic waters are concentrated in the Middle-and-Lower Yangtze Plain, the Northeast Plain, and the Yunnan-Guizhou Plateau. Overall, this study improved the trophic state representativeness and revealed trophic state spatial distribution of Chinese inland waters, which has the significant meanings for aquatic environment protection and water resource management.

Random forest: An optimal chlorophyll-a algorithm for optically complex inland water suffering atmospheric correction uncertainties

A robust and reliable chlorophyll-a (Chla) concentration algorithm is still lacking for optically complex waters due to the lack of understanding of the bio-optical process. Machine learning approaches, which excel at detecting potential complex nonlinear relationships, provide opportunities to estimate Chla accurately for optically complex waters. However, the uncertainties in atmospheric correction (AC) may be amplified in different Chla algorithms. Here, we aim to select one state-of-the-art algorithm or establish a new algorithm based on machine learning approaches that less sensitive to AC uncertainties. Firstly, nine state-of-the-art empirical, semianalytical, and optical water types (OWT) classification-based Chla algorithms were implemented. These existing algorithms showed good performance by using in situ database, however, failed in actual OLCI applications due to their sensitivity to AC uncertainties. Thus, four popular machine learning approaches (random forest regression (RFR), extreme gradient boosting (XGBoost), deep neural network (DNN), and support vector regression (SVR)) were then employed. Among them, the “RFR-Chla” model performed the best and showed less sensitivity to AC uncertainties. Finally, the Chla spatiotemporal variations in 163 major lakes across eastern China were mapped from OLCI between May 2016 and April 2020 using the proposed RFR-Chla model. Generally, the lakes in eastern China are severely eutrophic, with an average Chla concentration of 33.39 ± 6.95 μg/L. Spatially, Chla in the south of eastern China was significantly higher than those in northern lakes. Seasonally, Chla was high in the summer and autumn and low in the spring and winter. This study provides a reference for water quality monitoring in turbid inland waters suffering certain AC uncertainties and supports aquatic management and SDG 6 reporting.

Remote Estimation of the Particulate Phosphorus Concentrations in Inland Water Bodies: A Case Study in Hongze Lake

Phosphorus is the most important nutrient associated with lake eutrophication and changes in cyanobacterial blooms, and particulate phosphorus (PP) is the main form of phosphorus found in highly turbid inland waters. Therefore, it is urgent to monitor PP concentrations in inland water bodies. In this study, we take Hongze Lake as the research area and establish a semianalytical model to estimate PP concentrations based on the total particle absorption coefficient (ap); the mean absolute percentage error (MAPE) and root-mean-square error (RMSE) values, which indicate the model accuracy, were 14.90% and 0.009 mg/L, respectively. In addition, the construction process and parameter selection criteria of the remote sensing-based PP concentration estimation model were derived using remote sensing data obtained at different spectral resolutions. Sentinel 3 Ocean and Land Color Instrument (OLCI) and Landsat 9 Operational Land Imager version 2 (OLI-2) data were selected as representatives to verify the accuracy of the model; compared to these two datasets, the MAPE values of the models were 16.32% and 26.84%, respectively, while the RMSE values were 0.010 mg/L and 0.014 mg/L, respectively. Finally, the models were applied to Sentinel 3 OLCI and Landsat 9 OLI-2 images obtained on 16 January 2022. The results show that the spatiotemporal distributions of PP concentrations in Hongze Lake estimated from these two images were relatively consistent, but the OLI data reflected overestimations and underestimations in some areas. These research results provide a new methodology for estimating PP concentrations through remote sensing methods and help to further improve the accuracy of remotely sensed PP concentration estimations in inland water bodies.

IMPROVED RRS AND TURBIDITY RETRIEVAL FROM OLI IMAGES IN COMPLEX INLAND WATERS: A CASE STUDY FOR LAGUNA DE BAY

Abstract. Spatiotemporal monitoring of water quality parameters such as turbidity in inland waters is desirable to better understand productivity and mitigate the negative impacts of pollution induced by increasing anthropogenic activities. However, precise retrieval of water quality parameters in complex turbid waters from the remote sensing reflectance Rrs, remains a challenging task due to the varying optical complexity of the water body. In this study, a modified version of the Atmospheric Correction for OLI-lite (ModACO) scheme for turbid inland waters, which implements a linear extrapolation of NIR aerosol reflectance in the visible bands instead of a nonlinear function, is presented. The performance of the proposed method and other existing algorithms such as the Atmospheric Correction for OLI-lite (ACOLITE), Management Unit of the Noth Seas Mathematical Models (MUMM) scheme, SeaDAS standard processing, and SWIRE were evaluated. The Rrs retrievals from these models were then used as input for turbidity estimation and mapping of Laguna de Bay. Results show lowest Rrs error in all five spectral bands (443, 482, 561, 655, and 865 nm) on ModACO-based retrievals. Relative to the ACOLITE and other atmospheric correction schemes, the proposed method reduced the Rrs retrieval errors in terms of RMSE and MAPE by more than 50%. Similarly, significant improvements in turbidity retrievals were achieved from ModACO-based Rrs values, wherein comparable accuracy was observed from red/green ratio and the single NIR band turbidity models. Turbidity maps of Laguna de Bay show elevated values from the mid of dry season, which may be associated with point source discharge and wind-induced resuspension of bottom sediments. The lake turbidity then drops by the end of dry season, which is linked to the absence of prevailing strong winds that may increase in-water mixing. Using the aforementioned method, accurate monitoring of turbidity can be done to determine and mitigate possible degradation on the water quality of Laguna de Bay and other productive turbid inland waters.

Semi-Empirical Models for the Bidirectional Water-Leaving Radiance: An Analysis of a Turbid Inland Lake

When a precise quantitative analysis of satellite measurements over bodies of water is required, the bidirectional effects of water-leaving radiance must be considered. The bidirectional reflectance distribution function (BRDF) is used to estimate the directional dependency of the radiance. Previous research on BRDF has focused on oceanic waters; few studies on turbid inland waters have been conducted. In this article, using multi-angle MISR measurements, five semi-empirical BRDF models (MAG2002, Lee2004, Park-Ruddick 2005, Woerd-Pasterkamp2008, and Lee2011) were quantitatively compared, and an adaptive algorithm was proposed over a typical turbid inland lake, Taihu Lake, China. Our results reveal the following: 1) the Woerd-Pasterkamp2008 and Lee2011 models provide the best fits with correlation coefficients greater than 0.8; 2) when prior modeling parameters were used, the Lee2011 model was still the most accurate with RMSEs less than 1.1%, while the accuracy of the Woerd-Pasterkamp2008 model varied; and 3) the use of an adaptive algorithm including an empirical rule based on the ratio ofbb/aimproved the accuracy. The results provide a theoretical basis for BRDF models and BRDF effects over inland Case II waters. They also providea prioriknowledge for future studies on water constituents and the quantitative inversion of atmospheric parameters.

Deriving VIIRS High-Spatial Resolution Water Property Data over Coastal and Inland Waters Using Deep Convolutional Neural Network

The Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar-orbiting Partnership (SNPP) satellite has been a reliable source of ocean color data products, including five moderate (M) bands and one imagery (I) band normalized water-leaving radiance spectra nLw(λ). The spatial resolutions of the M-band and I-band nLw(λ) are 750 m and 375 m, respectively. With the technique of convolutional neural network (CNN), the M-band nLw(λ) imagery can be super-resolved from 750 m to 375 m spatial resolution by leveraging the high spatial resolution features of I1-band nLw(λ) data. However, it is also important to enhance the spatial resolution of VIIRS-derived chlorophyll-a (Chl-a) concentration and the water diffuse attenuation coefficient at the wavelength of 490 nm (Kd(490)), as well as other biological and biogeochemical products. In this study, we describe our effort to derive high-resolution Kd(490) and Chl-a data based on super-resolved nLw(λ) images at the VIIRS five M-bands. To improve the network performance over extremely turbid coastal oceans and inland waters, the networks are retrained with a training dataset including ocean color data from the Bohai Sea, Baltic Sea, and La Plata River Estuary, covering water types from clear open oceans to moderately turbid and highly turbid waters. The evaluation results show that the super-resolved Kd(490) image is much sharper than the original one, and has more detailed fine spatial structures. A similar enhancement of finer structures is also found in the super-resolved Chl-a images. Chl-a filaments are much sharper and thinner in the super-resolved image, and some of the very fine spatial features that are not shown in the original images appear in the super-resolved Chl-a imageries. The networks are also applied to four other coastal and inland water regions. The results show that super-resolution occurs mainly on pixels of Chl-a and Kd(490) features, especially on the feature edges and locations with a large spatial gradient. The biases between the original M-band images and super-resolved high-resolution images are small for both Chl-a and Kd(490) in moderately to extremely turbid coastal oceans and inland waters, indicating that the super-resolution process does not change the mean values of the original images.

Estimation of Particulate Backscattering Coefficient in Turbid Inland Water Using Sentinel 3A-OLCI Image

The particulate backscattering coefficient ( b bp) plays an important role in the underwater light field. However, it is difficult to accurately estimate b bp( λ ) in turbid inland water with complex optical properties. To accurately estimate the backscattering coefficients in inland water, a simple classification method based on the shape of remote sensing reflectance was first proposed to distinguish two water types (i.e., water type 1 and water type 2) with different backscattering characteristics. Then, trigonometric functions were developed to simulate the backscattering coefficients at all bands in water type 1 and the backscattering coefficients in the visible band of water type 2, whereas a linear function was built to estimate the backscattering coefficients in the near-infrared band of water type 2. The proposed algorithm was compared with four state-of-the-art methods and validated by an independently measured dataset of three lakes in the middle and lower reaches of the Yangtze River in 2020. The results showed that the proposed algorithm performed well in inland waters, with all mean absolute percentage errors b bp(676) in Lake Hongze began to decrease since 2017, whereas no obvious interannual variation was observed in Taihu Lake in recent five years.

A semi-analytical algorithm for deriving the particle size distribution slope of turbid inland water based on OLCI data: A case study in Lake Hongze.

The particle size distribution (PSD) slope (ξ) can indicate the predominant particle size, material composition, and inherent optical properties (IOPs) of inland waters. However, few semi-analytical methods have been proposed for deriving ξ from the surface remote sensing reflectance due to the variable optical state of inland waters. A semi-analytical algorithm was developed for inland waters having a wide range of turbidity and ξ in this study. Application of the proposed model to Ocean and Land Color Instrument (OLCI) imagery of the water body resulted in several important observations: (1) the proposed algorithm (754nm and 779nm combination) was capable of retrieving ξ with R2 being 0.72 (p<0.01, n=60), and MAPE and RMSE being 4.37% and 0.22 (n=30) respectively; (2) the ξ in HZL was lower in summer than other seasons during the period considered, this variation was driven by the phenological cycle of algae and the runoff caused by rainfall; (3) the band optimization proposed in this study is important for calculating the particle backscattering slope (η) and deriving ξ because it is feasible for both algae dominant and sediment governed turbid inland lakes. These observations help improve our understanding of the relationship between IOPs and ξ, which are affected by different bio-optic processes and algal phenology in the lake environment.

Assessment of Landsat atmospheric correction methods for water color applications using global AERONET-OC data

With the longest archive of satellite remote sensing images, the Landsat series of satellites have demonstrated their great potential in aquatic environmental studies. However, although various atmospheric correction (AC) methods have been developed for Landsat observations in water color applications, a comprehensive assessment of their accuracies across different AC methods and instruments has yet to be performed. Using in situ spectral data collected by Aerosol Robotic Network-Ocean Color (AERONET-OC) sites, the performances of five types of AC methods over three different Landsat missions (i.e., Landsat 5/7/8) were evaluated. The Landsat 8 Operational Land Imager (OLI) showed more accurate AC retrievals than the other two instruments, and the results for its green and red bands appeared more reliable than those for the other wavelengths (uncertainty levels of ∼30 %). The iterative NIR algorithm with 2-bands (NIR-SWIR2) model selection embedded in SeaDAS showed the best performances for OLI in two blue bands. Moreover, larger residual errors were found for most Landsat 5/7 bands regardless of the AC methods and spectral bands employed with an uncertainty of >50 %. Interestingly, a simple aerosol subtraction method over the Rayleigh-corrected reflectance (Rrc) outperformed the exponential extrapolation (EXP) algorithms, especially for Landsat 5/7. Neither the image-based AC algorithm nor the surface reflectance (SR) products provided by the United States Geological Survey (USGS) showed acceptable performances over coastal environments. The uncertainties in the various Landsat reflectance products over water surfaces could be associated with a relatively poor signal-to-noise ratio (SNR) in addition to radiometric calibration uncertainties, imperfect aerosol removal methods. Future research is required to collect in situ data across a wider range of water optical properties (particularly more turbid inland waters) to examine the corresponding applicability of Landsat-series observations.

Retrieval of Secchi Disk Depth in Turbid Lakes from GOCI Based on a New Semi-Analytical Algorithm

The accurate remote estimation of the Secchi disk depth (ZSD) in turbid waters is essential in the monitoring the ecological environment of lakes. Using the field measured ZSD and the remote sensing reflectance (Rrs(λ)) data, a new semi-analytical algorithm (denoted as ZSDZ) for retrieving ZSD was developed from Rrs(λ), and it was applied to Geostationary Ocean Color Imager (GOCI) images in extremely turbid waters. Our results are as follows: (1) the ZSDZ performs well in estimating ZSD in turbid water bodies (0.15 m &lt; ZSD &lt; 2.5 m). By validating with the field measured data that were collected in four turbid inland lakes, the determination coefficient (R2) is determined to be 0.89, with a mean absolute square percentage error (MAPE) of 22.39%, and root mean square error (RMSE) of 0.24 m. (2) The ZSDZ improved the retrieval accuracy of ZSD in turbid waters and outperformed the existing semi-analytical schemes. (3) The developed algorithm and GOCI data are in order to map the hourly variation of ZSD in turbid inland waters, the GOCI-derived results reveal a significant spatiotemporal variation in our study region, which are significantly driven by wind forcing. This study can provide a new approach for estimating water transparency in turbid waters, offering important support for the management of inland waters.

Spectral Feature Selection Optimization for Water Quality Estimation

The spatial heterogeneity and nonlinearity exhibited by bio-optical relationships in turbid inland waters complicate the retrieval of chlorophyll-a (Chl-a) concentration from multispectral satellite images. Most studies achieved satisfactory Chl-a estimation and focused solely on the spectral regions from near-infrared (NIR) to red spectral bands. However, the optical complexity of turbid waters may vary with locations and seasons, which renders the selection of spectral bands challenging. Accordingly, this study proposes an optimization process utilizing available spectral models to achieve optimal Chl-a retrieval. The method begins with the generation of a set of feature candidates, followed by candidate selection and optimization. Each candidate links to a Chl-a estimation model, including two-band, three-band, and normalized different chlorophyll index models. Moreover, a set of selected candidates using available spectral bands implies an optimal composition of estimation models, which results in an optimal Chl-a estimation. Remote sensing images and in situ Chl-a measurements in Lake Kasumigaura, Japan, are analyzed quantitatively and qualitatively to evaluate the proposed method. Results indicate that the model outperforms related Chl-a estimation models. The root-mean-squared errors of the Chl-a concentration obtained by the resulting model (OptiM-3) improve from 11.95 to 6.37 , and the Pearson’s correlation coefficients between the predicted and in situ Chl- improve from 0.56 to 0.89.

Algorithm to derive inherent optical properties from remote sensing reflectance in turbid and eutrophic lakes.

Inherent optical properties play an important role in understanding the biogeochemical processes of lakes by providing proxies for a variety of biogeochemical quantities, including phytoplankton pigments. However, to date, it has been difficult to accurately derive the absorption coefficient of phytoplankton $[{a_{ph}}(\lambda )]$[aph(λ)] in turbid and eutrophic waters from remote sensing. A large dataset of remote sensing of reflectance $[{R_{rs}}(\lambda )]$[Rrs(λ)] and absorption coefficients was measured for samples collected from lakes in the middle and lower reaches of the Yangtze River and Huai River basin (MLYHR), China. In the process of scattering correction of spectrophotometric measurements, the particulate absorption coefficients $[{a_p}(\lambda )]$[ap(λ)] were first assumed to have no absorption in the near-infrared (NIR) wavelength. This assumption was corrected by estimating the particulate absorption coefficients at 750 nm $[{a_p}({750})]$[ap(750)] from the concentrations of chlorophyll-a (Chla) and suspended particulate matter, which was added to the ${a_p}(\lambda )$ap(λ) as a baseline. The resulting mean spectral mass-specific absorption coefficient of the nonalgal particles (NAPs) was consistent with previous work. A novel iterative IOP inversion model was then designed to retrieve the total nonwater absorption coefficients $[{a_{nw}}(\lambda )]$[anw(λ)] and backscattering coefficients of particulates $[{b_{bp}}(\lambda )]$[bbp(λ)], ${a_{ph}}(\lambda )$aph(λ), and ${a_{dg}}(\lambda )$adg(λ) [absorption coefficients of NAP and colored dissolved organic matter (CDOM)] from ${R_{rs}}(\lambda )$Rrs(λ) in turbid inland lakes. The proposed algorithm performed better than previously published models in deriving ${a_{nw}}(\lambda )$anw(λ) and ${b_{bp}}(\lambda )$bbp(λ) in this region. The proposed algorithm performed well in estimating the ${a_{ph}}(\lambda )$aph(λ) for wavelengths $ > {500}\;{\rm nm}$>500nm for the calibration dataset [${\rm N} = {285}$N=285, unbiased absolute percentage difference $({\rm UAPD}) = {55.22}\% $(UAPD)=55.22%, root mean square error $({\rm RMSE}) = {0.44}\;{{\rm m}^{ - 1}}$(RMSE)=0.44m-1] and for the validation dataset (${\rm N} = {57}$N=57, ${\rm UAPD} = {56.17}\% $UAPD=56.17%, ${\rm RMSE} = {0.71}\;{{\rm m}^{ - 1}}$RMSE=0.71m-1). This algorithm was then applied to Sentinel-3A Ocean and Land Color Instrument (OLCI) satellite data, and was validated with field data. This study provides an example of how to use local data to devise an algorithm to obtain IOPs, and in particular, ${a_{ph}}(\lambda )$aph(λ), using satellite ${R_{rs}}(\lambda )$Rrs(λ) data in turbid inland waters.

Remote Sensing of Secchi Depth in Highly Turbid Lake Waters and Its Application with MERIS Data

The Secchi disk depth (ZSD, m) has been used globally for many decades to represent water clarity and an index of water quality and eutrophication. In recent studies, a new theory and model were developed for ZSD, which enabled its semi-analytical remote sensing from the measurement of water color. Although excellent performance was reported for measurements in both oceanic and coastal waters, its reliability for highly turbid inland waters is still unknown. In this study, we extend this model and its evaluation to such environments. In particular, because the accuracy of the inherent optical properties (IOPs) derived from remote sensing reflectance (Rrs, sr−1) plays a key role in determining the reliability of estimated ZSD, we first evaluated a few quasi-analytical algorithms (QAA) specifically tuned for turbid inland waters and determined the one (QAATI) that performed the best in such environments. For the absorption coefficient at 443 nm (a(443), m−1) ranging from ~0.2 to 12.5 m−1, it is found that the QAATI-derived absorption coefficients agree well with field measurements (r2 &gt; 0.85, and mean absolute percentage difference (MAPD) smaller than ~39%). Furthermore, with QAATI-derived IOPs, the MAPD was less than 25% between the estimated and field-measured ZSD (r2 &gt; 0.67, ZSD in a range of 0.1–1.7 m). Furthermore, using matchup data between Rrs from the Medium Resolution Imaging Spectrometer (MERIS) and in-situ ZSD, a similar performance in the estimation of ZSD from remote sensing was obtained (r2 = 0.73, MAPD = 37%, ZSD in a range of 0.1–0.9 m). Based on such performances, we are confident to apply the ZSD remote sensing scheme to MERIS measurements to characterize the spatial and temporal variations of ZSD in Lake Taihu during the period of 2003–2011.

Regional Vicarious Calibration of the SWIR-Based Atmospheric Correction Approach for MODIS-Aqua Measurements of Highly Turbid Inland Water

Water color remote sensing requires accurate atmospheric correction but this remains a significant challenge in highly turbid waters. In this respect, the shortwave infrared (SWIR) band-based atmospheric correction approach has proven advantageous when applied to the moderate resolution imaging spectroradiometer (MODIS) onboard the Aqua satellite. However, even so, uncertainties affect its accuracy. We performed a regional vicarious calibration of the MODIS-Aqua SWIR (1240, 2130)-based atmospheric correction using in situ water surface reflectance data measured during different seasons in Lake Taihu, a highly turbid lake. We then verified the accuracy of the (1240, 2130)-based atmospheric correction approach using these results; good results were obtained for the remote sensing reflectance retrievals at the 555, 645, and 859 nm, with average relative errors of 15%, 14%, and 22%, respectively, and no significant bias. Comparisons with the (1240, 2130)-based iterative approach and (1640, 2130)-based approach showed that the vicarious calibrated (1240, 2130)-based approach has the best accuracy and robustness. Thus, it is applicable to the highly turbid Lake Taihu. It may also be applicable to other highly turbid inland waters with similar optical and aerosol optical properties above water, but such applications will require further validation.

An improved algorithm for estimating the Secchi disk depth from remote sensing data based on the new underwater visibility theory

The Secchi disk depth (ZSD) is a widely used parameter for evaluating water clarity. Here we propose an improved algorithm, which is based on a new underwater visibility theory, for retrieving more accurate ZSD from remote sensing reflectance (Rrs) in various waters. Two improvements were carried out in the new algorithm. First, we used a hybrid quasi-analytical algorithm (QAA_hybrid) instead of the sixth version of QAA (QAA_v6) for retrieving more accurate total absorption coefficient (aλ) and total backscattering coefficient (bbλ) even in turbid inland waters. Second, we used a dynamic KT/Kd ratio (i.e., ratio of diffuse attenuation coefficient of upwelling radiance and diffuse attenuation coefficient of downwelling irradiance) instead of using the fixed ratio (i.e., 1.5). The results obtained from in situ Rrs show that the improved ZSD estimation algorithm gave more accurate ZSD estimations, with the root mean square error (RMSE) reduced from 0.2 to 0.1 in log10 unit, mean absolute percentage error (MAPE) reduced from 39% to 20% (N = 178 with in situ ZSD values between 0.3 and 20.8 m). We then applied the improved ZSD estimation algorithm to the 2003–2012 MERIS images for Lake Kasumigaura to further confirm the performance of the improved ZSD estimation algorithm. The results obtained from 19 matchups demonstrate that the estimated ZSD matched well with the in situ ZSD, with the RMSE of 0.11 m and the MAPE of 15%. The improved ZSD estimation algorithm shows a potential to estimate more accurate ZSD values from remote sensing data in various waters.

Improvement of scattering correction for in situ coastal and inland water absorption measurement using exponential fitting approach

The absorption coefficient of water is an important bio-optical parameter for water optics and water color remote sensing. However, scattering correction is essential to obtain accurate absorption coefficient values in situ using the nine-wavelength absorption and attenuation meter AC9. Establishing the correction always fails in Case 2 water when the correction assumes zero absorption in the near-infrared (NIR) region and underestimates the absorption coefficient in the red region, which affect processes such as semi-analytical remote sensing inversion. In this study, the scattering contribution was evaluated by an exponential fitting approach using AC9 measurements at seven wavelengths (412, 440, 488, 510, 532, 555, and 715 nm) and by applying scattering correction. The correction was applied to representative in situ data of moderately turbid coastal water, highly turbid coastal water, eutrophic inland water, and turbid inland water. The results suggest that the absorption levels in the red and NIR regions are significantly higher than those obtained using standard scattering error correction procedures. Knowledge of the deviation between this method and the commonly used scattering correction methods will facilitate the evaluation of the effect on satellite remote sensing of water constituents and general optical research using different scatteringcorrection methods.

Evaluation of the sensitivity of China’s next-generation ocean satellite sensor MWI onboard the Tiangong-2 space lab over inland waters

The Moderate-resolution Wide-wavelengths Imager (MWI) is the next-generation ocean-color sensor independently designed by China and was successfully launched on September 15, 2016 onboard the Tiangong-2 (TG-2) Space Lab. It is currently urgent to assess the capability and data quality of the MWI sensor to determine its future applications. In this study, we first assessed the signal-to-noise ratio (SNR) and the radiometric sensitivity of the MWI. With this goal, we chose the third largest freshwater lake in China, Lake Taihu, as our test area and assessed the capability of the MWI to monitor the turbid inland waters by comparing its consistency with that of Moderate Resolution Imaging Spectroradiometer (MODIS) data and Geostationary Ocean Color Imager (GOCI) data. Our results show that (1) the SNR values of the MWI are greater than 400 in the visible band and are sufficient to effectively avoid ocean-color data errors caused by the sensor noise; however, the SNR values of the MWI are below 200 in the near-infrared (NIR) band and are lower than the theoretical expectation for the design of ocean-color sensors; (2) on the one hand, the MWI-derived remote sensing reflectance (Rrs) data have large errors in the blue band (R2 < 0.20, MAPE < 50%) but reduced errors in the red and NIR bands with a high correlation (R2 > 0.90, MAPE < 10%) compared with in situ Rrs; on the other hand, the MWI-derived Rrs data are generally consistent with the MODIS- and GOCI-derived Rrs data (R2 > 0.90, MAPE < 10%); and (3) the MWI-derived concentration distributions of cyanobacterial algal blooms and suspended particulate matter (SPM) in Lake Taihu exhibit high spatial resolutions and good accuracies and are in good agreement with the corresponding distributions yielded by the MODIS and GOCI satellites (R2 > 0.90, MAPE < 10%). However, the MWI cannot detect signals at the important 700–710 bands and consequently has a relatively weak capability to detect chlorophyll-a (Chla) concentrations in inland waters. In summary, as the Chinese next-generation ocean-color sensor, the overall performance metrics of the MWI approach the present international level. Meanwhile, we suggest that the band of the MWI can be further optimized to improve the derived SNR values in the near-infrared band in order to satisfy the requirements of satellite observations over inland waters.

A Four-Band Semi-Analytical Model for Estimating Phycocyanin in Inland Waters From Simulated MERIS and OLCI Data

Existing remote-sensing algorithms to estimate the phycocyanin (PC) concentration in turbid inland waters have high associated uncertainties, especially at low PC concentrations in diverse phytoplankton communities. This paper provides the theoretical framework for a four-band semi-analytical algorithm (FBA_PC) which isolates PC absorption from second-order variability caused by yellow matter and other phytoplankton pigment absorption. The algorithm suits the band configuration of both the Medium Resolution Imaging Spectrometer (MERIS) and Sentinel-3 Ocean and Land Color Instrument (OLCI). Calibration of the algorithm was based on absorption data from 12 inland water bodies in the USA, The Netherlands, and China, combined with measurements from laboratory-grown cultures, which demonstrated that the assumptions underlying FBA-PC are an improvement over existing three-band approaches. Validation of FBA_PC in seven inland water bodies in the USA, The Netherlands, and China showed good agreement of FBA_PC adjusted to the MERIS/OLCI band configuration with measured PC, with root-mean-square error =27.691 $\text {mg} \cdot \text {m}^{-3}$ , mean absolute percentage error = 172.863%, and coefficient of determination ( $R^{2}) = 0.730$ . FBA_PC outperformed previously proposed PC algorithms that can be applied to MERIS or OLCI data, and is expected to be more robust when applied to a wider range of water bodies.