Semi-analytical prediction of Secchi depth transparency in Lake Kasumigaura using MERIS data
To investigate the long-term trend of light conditions in Lake Kasumigaura (a shallow eutrophic lake with high turbidity) in Japan, 215 images of MERIS data from the period 2003–2012 were processed at four stations using a semi-analytical algorithm to retrieve their inherent optical properties after atmospheric correction. Previously obtained Secchi depths (SDs) were somewhat underestimated by the proposed algorithms, and the ratio of the predicted SD to the measured SD changed with the ratio of tripton to chlorophyll a. A submodel was then built describing the ratio of scattering to backscattering based on the ratio of tripton to chlorophyll a (a trend supported by a number of previous reports) and applied to the prediction of SD in this lake. The model showed a gradually increasing trend at all stations in the predicted SD over the period, which was validated by the observations. The relationship between the measured and predicted SDs within a 2-day period was scattered, but showed a positive correlation at a significant level. In addition, this proposed method with the submodel describing the ratio of scattering to backscattering was applied to in situ reflectance spectra, and a correlation at a significant level was confirmed between the measured and predicted SDs.
KeywordsSecchi depth Lake Kasumigaura Semi-analytical prediction of IOPs MERIS Scattering/backscattering
Assessing the transparency of water bodies is one of the key issues in environmental monitoring and management, because it can affect both ecosystems and water amenities. The Secchi depth (SD) is an optical measure of water transparency assessed with the naked eye. It is determined by all optically active substances in the water; i.e., phytoplankton (represented by chlorophyll a), tripton (non-planktonic suspended solids), colored dissolved organic matter (CDOM), and pure water (Mancino et al. 2009). Although the SD measurement is easy, the frequent collection of these data for high numbers or large water bodies is costly and challenging for monitoring agencies (Nelson et al. 2003). Remote sensing using satellite images offers several advantages, including spatially and temporally extensive coverage and the possibility of measuring many water bodies simultaneously (Koponen et al. 2002). Therefore, combining remote sensing techniques and precise knowledge of water optics might be an efficient method of monitoring water transparency.
Many research groups have investigated the relationship between in situ measurements of SD and the spectral response of satellite sensors such as Landsat, MODIS (MODerate resolution Imaging Spectroradiometer on NASA’s Terra/Aqua), SeaWiFS (Sea-viewing-Wide-Field-of-view Sensor on the GeoEye’s OrbView-2 satellite), and MERIS [Medium Resolution Imaging Spectrometer on the European Space Agency’s (ESA) Envisat], as mentioned in Fukushima et al. (2016). However, most of these studies focused on empirical approaches, and thus the final applications tended to be time- and site-specific. Thus, an algorithm developed for a particular set of conditions cannot be applied to a water region/period with different conditions.
At the same time, significant efforts have been made to predict SD using quasi-analytical algorithms. For example, Chen et al. (2007) estimated the SD in Tampa Bay, Florida, by processing SeaWiFS satellite imagery with a two-step process: first, they estimated the diffuse attenuation coefficient at 490 nm [Kd (490)] using a semi-analytical approach; then, they assessed the SD using the empirical relationship with Kd (490) to obtain an excellent estimate of in situ SD values. Doron et al. (2007) proposed a semi-analytical algorithm that can predict the absorption coefficient at 490 nm [a (490)] and the backscattering coefficient at 490 nm [bb (490)]. They then obtained the relationship between (Kd (490) + c (490)) and (Kd (ν) + c (ν)), where c is the attenuation coefficient, and ν indicates the average over-photic range (400–750 nm; Doron et al. 2007), and they successfully applied these algorithms to MERIS, MODIS and SeaWiFS data in oceans (Doron et al. 2011).
For lakes with a wide range of turbidity conditions, Fukushima et al. (2016) used the maximum chlorophyll index (MCI) in semi-analytical retrieval procedure of inherent optical properties (IOPs) in clear (MCI < MCIthreshold; MCIthreshold = 0.0001 or 0.0016) or turbid waters (MCI ≥ MCIthreshold), and applied the chosen procedure to the targeted lake. SD values were then predicted based on the transmittance theory (Tyler 1968; Preisendorfer 1986), which integrated (Kd (λ) + c (λ)) over the photic range with the weights of both the photopic response of the human eye and the downwelling irradiance at the water surface. Further, the coupling constant was determined using the data from Davies-Colley (1988). Fairly good agreement was obtained between the predicted and observed SD values, but two problems remained in our previous study (Fukushima et al. 2016). The first problem was that only in situ-measured remote-sensing reflectance was used for our previous analysis, indicating the need for further testing of our previous method using space-borne data. The second problem was the underestimation of the SD prediction that was generally observed using the previous method in Lake Kasumigaura.
In the present study, we first modified the SD prediction algorithm (Fukushima et al. 2016) applicable to MERIS data and checked the performance using in situ-measured remote sensing reflectance. Then, the changes in SD in Lake Kasumigaura were predicted using the MERIS data from 2003 to 2012, processed by a newly developed atmospheric correction method (Jaelani et al. 2015) and compared with the observed values. Finally, we examined a modification of the SD prediction algorithm and compared it to other semi-analytical SD prediction schemes to enhance the accuracy of SD prediction. Because the SD changes observed in this lake for the targeted period were around 50 cm, as shown below, the SD prediction accuracy should be less than this value.
Data and methods
In addition, Lake Kasumigaura is so shallow that vertical stratification is easily destroyed by moderately strong winds (Muraoka and Fukushima 1986). Ishikawa et al. (1989) also reported that diurnal stratification resulted in a weak thermocline with a temperature difference of about 1 °C in summer, but this thermocline did not exist for more than a few days. Thus, we assumed that the characteristics of particle- and dissolved-matter-related optical properties do not change vertically.
Data and atmospheric correction
In-situ-measured remote sensing reflectance data were obtained at 10 lakes in Japan including Lake Kasumigaura, from 2009 to 2014 using a FieldSpec Hand Held spectroradiometer (Analytical Spectral Devices, Boulder, CO, USA) in the range of 325–1075 nm at 1-nm intervals. Details of the surveys were provided in Fukushima et al. (2016). We used only the data for Lake Kasumigaura (SD, chlorophyll a, suspended solids, etc.). SDs were measured using a white circular panel (0.3 m diameter) for all water regions. Reflection and SD measurements were performed from 9:30 to 16:00 h local time. In addition, we used the SD data from four stations (Fig. 1), which have been monitored monthly from 1976 to the present by the National Institute for Environmental Studies (NIES) (2016). SD measurements were performed using the same method [see NIES (2016)] by different observers between 10:00 and 15:00 h local time.
Full-resolution MERIS data (300 m) were obtained from EOLi (ESA’s Link to Earth Observation), the ESA’s client for the Earth Observation Catalogue and Ordering Services (2014). A total of 492 images were taken of all or part of Lake Kasumigaura during the period 2003–2012, and 215 images were used for the subsequent analysis, after cloud-covered images were excluded. The images were photographed between 9:30 and 10:30 h local time. The averaged value of a 3 × 3 pixel window (900 m × 900 m) was used to compare the estimated SD with the corresponding in situ measured SD. The use of the 3 × 3 pixel window rather than a single pixel can reduce the potential error in the geometric correction and in the dynamics of water bodies, as well as the potential error in spatial variability (Han and Jordan 2005). Therefore, NASA provides a protocol for a validation procedure (Bailey and Werdell 2006). In addition, Lake Kasumigaura is very turbid, as explained above, and thus is considered as optically deep. Therefore, it is not necessary to consider bottom contamination in this lake. There is a potential for land contamination, however, as pixels close to the lake shore are possibly affected by land. Since the stations chosen in this study were at least 1 km away from the nearest shores (i.e., using the pixels only located in water region), this influence was not considered.
The new standard Gordon and Wang algorithm with an interactive process and a bio-optical model (N-GWI) (Jaelani et al. 2015) was used for atmospheric correction. This algorithm was developed for application to MERIS data involving very turbid inland waters, such as Lake Kasumigaura, by modifying the GWI (Gordon and Wang 1994). The usefulness of the N-GWI has been confirmed in Lake Kasumigsura (Jaelani et al. 2015).
Semi-analytical SD prediction algorithm
This algorithm is composed of two stages (Fukushima et al. 2016). The first stage is the process for retrieving the IOPs based on the reflectance spectrum as follows.
Step 1: Judgment using MCI for selection between clear and turbid water regions.
Step 2: Estimation of the absorption coefficient (a) and backscattering coefficient of particulate matter (bbp) using QAA_v5 (Lee et al. 2009) in clear waters or QAA_turbid (Yang et al. 2013) in turbid waters. In the analysis of MERIS data obtained in Lake Kasumigaura, we compared QAA_turbid (as a standard) and QAA_v5, although large portions of the cases suggested the use of QAA_turbid (e.g., 87% for MCI ≥ 0.0001, 66% for MCI ≥ 0.0016 at St. 9). Underestimation of QAA_v5 was reported in Fukushima et al. (2016).
Correspondence to MERIS data
Comparison of predicted Secchi depths (SDs) using different components for in situ measured reflectance spectra (n = 33)
Case 1 (standard)
Case 6 (proposed)
QAA clear No. 1
QAA clear No. 2
(Y estimation model)
750 vs 780 ratio
440 vs 560 ratio
750 vs 780 ratio
750 vs 780 ratio
440 vs 560 ratio
440 vs 560 ratio
Wavelength used for SD
Standard deviation (m)
r with measured SD
r with case 1 (standard)
Estimation of chlorophyll a and tripton concentrations
The semi-analytical model-optimizing and look-up-table (SAMO-LUT) (Yang et al. 2011) was used to retrieve the concentrations of chlorophyll a and tripton. This method was based on three previous semi-analytical models that estimated chlorophyll a, tripton and CDOM. Look-up tables and an iterative searching strategy were used to obtain the most appropriate parameters in the models. The accuracy of the MERIS-derived chlorophyll a concentration has been evaluated in our previous work by comparing it to the measured chlorophyll a concentrations obtained from the Lake Kasumigaura database (Matsushita et al. 2015). The results showed acceptable accuracy for all test sites (i.e., Sts. 3, 7, 9, and 12) with relative error in the range of 24–34%. In addition, the MERIS-derived chlorophyll a concentrations also showed similar seasonal and yearly variations with the measured chlorophyll a concentrations (correlation coefficient: r between 0.59 and 0.78, n = 63–68, p < 0.001). For the MERIS-derived tripton concentration, the estimation accuracy was only evaluated using the data collected from two field campaigns in Lake Kasumigaura (February 18, 2006 and August 7, 2008) due to the lack of measured tripton data in the Lake Kasumigaura database (with a normalized root mean square error of 23.2%, Yang et al. 2011).
Comparison with previously proposed methods
Another semi-analytical algorithm proposed for MERIS and SeaWiFS sensors (Doron et al. 2011) was also compared in the estimation process of a (490) and bb (490) using two wavelengths at 490 and 560 nm instead of the original one using two wavelengths at 490 and 709 nm (Doron et al. 2007).
Evaluation of predicted SDs using MERIS images
We compared the predicted SDs with the measured SDs at the four stations in Lake Kasumigaura (Fig. 1; station numbers based on NIES). Bailey and Werdell (2006) recommended a time difference between the satellite data and in situ measurements of less than 3 h when the two were compared. However, to increase the available matchups, we relaxed this condition and assumed that water quality does not vary much within 2 days (e.g., only 8 matchups for 0-day difference). Therefore, the condition of a time difference of less than 3 days (i.e., 0, 1 or 2-day difference) was used for further analysis. Their agreement between the sets of SDs was analyzed by considering the wind speed at the time when the MERIS image was taken, as well as characteristics of particulate matter in lake waters. The mean wind speed at Tsuchiura (AMeDAS) (2016) and the ratio of tripton to chlorophyll a, respectively, also estimated by MERIS images, were used for these analyses.
Comparison of SD prediction components using in situ remote-sensing reflectance spectra
A variety of SD prediction components — e.g., the SD prediction algorithm (turbid or clear waters), Y estimation model [original or Eq. (4)], wavelength used for SD prediction (visible range or 490 nm), Kd estimation model (b or bb), reflectance spectra resolution (1-nm resolution or MERIS bands), and the b:bb model [Eqs. (1) or (11) explained below]—were compared with each other and with the measured SD values based on in situ measured-reflectance spectra (n = 33). The methods (cases 1–8) are compared in Table 1. Cases 2–5 were compared with case 1 as a standard in order to evaluate the influence of each component related to the Y estimation model, the wavelength used for SD prediction, the Kd estimation model, and the reflectance spectra resolution, respectively. Case 6 was a variant of case 5 with regard to the b vs bb submodel, as shown below. Cases 7 and 8 were SD prediction models for clear water proposed by Fukushima et al. (2016) and Doron et al. (2011), respectively. We also compared other cases, e.g., two or three components arranged conjointly.
Comparison of SD prediction components using in situ remote-sensing reflectance spectra
The algorithm for clear waters (cases 7 and 8) gave significantly higher SD values that were only weakly correlated (r = 0.161 for case 7 and r = 0.415* for case 8; no asterisk: p ≥ 0.05, *: p < 0.05) with the measured ones (Table 1). In contrast, the SD values predicted using the algorithm for turbid waters (cases 1–6) were underestimated, but were well correlated with the measured ones, similarly to Fukushima et al. (2016). Differences in the Y estimation model (case 1 vs case 2), the wavelength used for calculation (case 1 vs case 3), the Kd estimation model (case 1 vs case 4), and the reflectance spectra resolution (case 2 vs case 5) had negligible effects on the predicted SD values, and the cases with two or three components arranged showed results quite similar to case 1 (data not shown). Thus, the following analyses using MERIS images were conducted using Y estimation with Eq. (4), the whole visible range, Kd estimation by scattering coefficient (b), and the MERIS bands (corresponding to case 5, and subsequently to case 6).
SD prediction using MERIS data
Submodel for the relationship between bp and bbp
Predicted SD time series
Evaluation of our proposed model
Semi-analytical algorithms for retrieving IOPs in clear waters, i.e., QAA_v5 clear No. 1 and No. 2, gave SD values that were considerably higher than the observed ones. This result indicates that semi-analytical algorithms for clear waters are not applicable to turbid waters, although they give an excellent estimation of SD in clear waters (Fukushima et al. 2016; Doron et al. 2011). QAA_v5 uses reflectance information at shorter wavelengths of around 490–560 nm as the reference, while QAA_turbid uses longer wavelengths of around 750 nm. This means that QAA_v5 is not able to accurately predict the values of the absorption and scattering coefficients at longer wavelengths in turbid waters, and then gives overestimated SD values.
In contrast, the use of Eq. (7) for integrating the visible wavelength gave results similar to those from the integration using Eq. (2), even in turbid waters, when we used the semi-analytical algorithm for IOPs in turbid waters. Doron et al. (2007) proposed Eq. (7) using in situ COASTIOOC data covering a wide range of Kd (490) + c(490) from 0.1 to 30 m−1, probably resulting in its good performance for Lake Kasumigaura. The selection of Kd corresponded to negligible differences in SD prediction, probably because the estimation equations for Kd were obtained by numerical simulations for wide ranges of Kd values. With regards to the Y estimation model, we hope to perform a future estimation using the reflectance at 750 and 780 nm after a new method of atmospheric correction at 780 nm is developed.
Although our proposed model (case 6 in Table 1) solved the underestimation problem in the previous model (corresponding to cases 1 to 5), the SD values predicted by it were not in fairly good agreement with the measured ones, particularly for MERIS images (Fig. 6). These discrepancies may have been partly due to the time differences between the in situ measurements and satellite images. There were at most 2% differences among the obtained SD values when we predicted SD by changing the sampling times of the image from 9:30 to 15:00 h local time corresponding to the SD measurement. Further, the variability of the observed SD values was usually observed due to differences in observer eyesight and/or observation experience (Fukushima et al. 2016). Therefore, it might be more important that our proposed model was able to describe the long-term change in SD accompanied by the change in particulate matter content.
The predicted SD values for MERIS images were overestimated at St. 7 (Fig. 5b; RSD: 1.21), while fairly good agreement between them was obtained at other stations. A slight overestimation (less than 2%) of the predicted SD was expected because the SD measurement at this station was generally done from 14:00 to 15:00 h local time while the image was taken from 9:30 to 10:30 h local time. In addition, this station was the most turbid station in this study (averaged measured SD: 0.56 m at St. 3, 0.51 m at St. 7, 0.68 m at St. 9 and 0.63 m at St. 12), probably due to its close proximity to a large influent river (the River Sakura) and/or to gravel digging activity. The difference in tripton characteristics may affect Eq. (11), but a further study is needed to solve this problem.
bp:bbp ratio (coefbp)
Twardowski et al. (2001) reported an increase in the dimensionless backscattering ratio at 532 nm from 0.004 to 0.02 (bbp/bp; i.e., the reciprocal of coefbp) with depth in the Gulf of California and considered that the proportion of inorganic tripton (biogenetic and/or nonbiogenetic minerals) in the particulate matter raised the ratio (lowered the coefbp). They indicated that the change in the backscattering ratio was due to the change in the bulk particulate refractive index np from 1.04 to 1.05 in phytoplankton to 1.14–1.18 in inorganic matter. Loisel et al. (2007) reported low bbp/bp values (i.e., high coefbp) for a particle population made up of low refractive material such as phytoplankton, whereas high bbp/bp values were generally observed in the presence of a relatively high concentration of inorganic particles (this ratio at 650 nm ranged from 0.003 to 0.05) in the eastern English Channel and southern North Sea. Bowers et al. (2014) also reported that the backscattering ratio (bbp/bp; i.e., the reciprocal of coefbp) increased with the ratio of minerals to total suspended solids in the Irish Sea, Celtic Sea and English Channel and proposed a model describing this relationship. Using a global data set, Whitmire et al. (2007) indicated that the bbp/bp values at several wavelengths decreased with the chlorophyll a concentration.
In inland waters, characteristics similar to seawater have been shown. Shi et al. (2014) found that the value of the mass-specific scattering coefficient at 532 nm for inorganic suspended materials (0.71 m2 g−1) was approximately 1.6 times greater than that for organic suspended materials (0.45 m2 g−1). Thus, these results support the tendency of Eq. (11) theoretically.
Nakamura and Aizaki (2016) estimated the concentrations of tripton and particulate organic materials using the concentrations of particulate organic nitrogen and suspended solids, and showed that the tripton concentration decreased while particulate organic materials increased from 2004 to 2010. Our analysis period corresponds to this transition timing; therefore, the submodel related to bbp/bp should be involved. When the characteristics of particulate materials change substantially, it is necessary to understand and model the relevant material constituents to perform long-term monitoring of lake light conditions. Because the materials in the respective lakes differ, the submodel should be arranged based on site-specific characteristics.
Applicability of our SD prediction system
Our proposed SD prediction model (case 6 in Table 1) is composed of atmospheric correction, estimation of IOPs, prediction of tripton and chlorophyll a concentrations, and the SD prediction model. All of these activities should be applicable to turbid waters and can be obtained solely from satellite data. Methods for estimating the diffuse attenuation coefficient and euphotic zone depth, which are also important parameters describing lake light environments, are available for turbid waters (Yang et al. 2014, 2015).
The proposed method was only applied to MERIS data in this study. Its applicability to other ocean color sensors such as MODIS, SeaWiFS, and VIIRS should be tested in a future study to increase the frequency of estimated SD values. The applicability to other turbid water regions should be tried because the light characteristics of those regions may differ significantly. It is likely that the optical properties of inorganic particles and/or dissolved organic substances could affect the light regimes. Thus, the submodel relating to bbp/bp and/or the estimation model of chlorophyll a and tripton concentrations should be arranged corresponding to the light regimes. The indices expressing the characteristics, e.g., Kd × SD (Koenings and Edmundson 1991) may be useful for evaluating the applicability of the submodel, but a deliberate examination of plenty of data is needed.
The long-term trends of SD in a turbid lake were successfully predicted based on satellite images. A semi-analytical algorithm for retrieving IOPs in turbid waters and a submodel describing the relationship between bp and bbp were necessary to obtain reasonable SD values and their trend. Further studies on the light characteristics of particulate materials and their modeling are necessary to achieve accurate SD estimation in a wide variety of turbid water regions.
This research was supported in part by Grants-in-Aid for Scientific Research from the Ministry of Education, Culture, Sport, Science and Technology (MEXT), Japan (Nos. 23404015 and 26281039), the Global Environment Research Fund (S9-4) of the Ministry of Environment, Japan, and the River Fund (27-1271-001) in charge of The River Foundation, Japan. Monitoring data on SD were provided by the National Institute for Environmental Studies (NIES). We express our appreciation to 2 anonymous reviewers for constructive criticisms on earlier versions of the manuscript.
Compliance with ethical standards
Conflict of interest
The authors declare that they have no conflict of interest.
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