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Article

A MODIS-Based Retrieval Model of Suspended Particulate Matter Concentration for the Two Largest Freshwater Lakes in China

by
Fangyuan Chen
1,
Guofeng Wu
1,2,3,*,
Junjie Wang
2,3,
Junjun He
1,4 and
Yihan Wang
1,5
1
School of Resource and Environmental Sciences & Key Laboratory of Geographic Information System of the Ministry of Education, Wuhan University, Wuhan 430079, China
2
Key Laboratory for Geo-Environmental Monitoring of Coastal Zone of the National Administration of Surveying, Mapping and GeoInformation & Shenzhen Key Laboratory of Spatial Smart Sensing and Services, Shenzhen University, Shenzhen 518060, China
3
College of Life Sciences and Oceanography, Shenzhen University, Shenzhen 518060, China
4
Fourth Surveying and Mapping Institute of Anhui Province, Hefei 230031, China
5
Surveying and Mapping Engineering Institute of Hubei Province, Wuhan 430074, China
*
Author to whom correspondence should be addressed.
Sustainability 2016, 8(8), 832; https://doi.org/10.3390/su8080832
Submission received: 15 May 2016 / Revised: 13 August 2016 / Accepted: 17 August 2016 / Published: 22 August 2016
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Abstract

:
Suspended particulate matter concentration (CSPM) is a key parameter describing case-II water quality. Empirical and semi-empirical models are frequently developed and applied for estimating CSPM values from remote sensing images; however, they are usually region- or season-dependent. This study aimed to develop a Moderate Resolution Imaging Spectroradiometer (MODIS)-based retrieval model of CSPM for Poyang and Dongting Lake together. The 89 CSPM measurements in Poyang and Dongting Lake as well as their corresponding MODIS Terra images were used to calibrate CSPM retrieval models, and the calibration results showed that the exponential models of MODIS red band and red minus shortwave infrared (SWIR) band at 1240 nm both explained about 76% of the variation of CSPM of Poyang and Dongting Lake together. When the two models were applied to the validation datasets, the results indicated that the exponential model of red band obtained more stable CSPM estimations with no bias at a significance level of 0.05 in both lakes. The MODIS red-band-based model achieved acceptable results for estimating CSPM in both Poyang and Dongting Lake, and it provided a foundation for obtaining comparable spatiotemporal information of CSPM, which will be helpful for comparing, understanding, managing, and protecting the two aquatic ecosystems.

1. Introduction

The inland and coastal case-II waters are important for social and economic development, tourism, recreation, and biodiversity conservation, and they are being paid close attention due to the increasing impacts of local or regional social and economic development as well as global environmental change. Suspended particulate matter (SPM) is a primary constituent of case-II water, and it increases water turbidity, reduces water quality, and further affects the whole aquatic ecosystem [1,2,3]. SPM concentration (CSPM) is a key parameter commonly used to describe water quality [4,5], and its spatiotemporal distributions are useful for monitoring and understanding water quality dynamics and further for managing and protecting aquatic ecosystems.
The spatiotemporal distributions of CSPM for case-II waters are frequently obtained using remote sensing techniques, in which Moderate Resolution Imaging Spectroradiometer (MODIS) images have great potentials for large water bodies due to their advantages of daily coverage, high sensitivity, and cost-free distribution. Many studies have employed MODIS images to estimate the CSPM values of water bodies, such as the Roosevelt, Bartlett, and Pleasant Lakes [6] and Chesapeake Bay [7,8] in the United States; the Pakri Bay in Finland [9]; as well as the Yangtze River [10] and Taihu Lake [11,12] in China (Table 1).
Empirical and semi-empirical models are commonly employed methods for estimating CSPM values since they are simple and easily developed; however they are often region- and time-dependent considering model types (e.g., linear, cubic, and exponential) and estimation accuracies, because they have no strict theoretical foundations and different optical properties appear at different water bodies or even at different time periods [13]. Therefore, the empirical and semi-empirical models developed for a specific water body or season cannot be directly applied to other water bodies or seasons; therefore, the CSPM values retrieved with different models are not comparable, which might limit the simultaneous analysis of water quality of different water bodies [30,31].
Poyang and Dongting Lake are the first and second largest freshwater lakes in China, respectively. So far, several MODIS-based empirical or semi-empirical models have been developed for retrieving the CSPM values in Poyang Lake, including a cubic model of red band [27], a quadratic model of blue plus green band as well as two power models of red band [21], exponential models of red minus shortwave infrared (SWIR) band [13,20], and an exponential model of red band [32]. However, few MODIS-based studies have been carried out to estimate the water quality in Dongting Lake, based on our latest literature review and knowledge; for example, Wu et al. [16] developed an exponential model of MODIS red minus SWIR band at 1240 nm for estimating the CSPM values in this lake.
Poyang and Dongting Lake both connect to the Yangtze River, and they have some similar hydrological characteristics. Thus, it is theoretically and practically meaningful to develop a common water quality retrieval model for Poyang and Dongting Lake in order to compare their water quality properties for lake study and management. The objective of this study was to develop a MODIS-based retrieval model of CSPM for Poyang and Dongting Lake together, which will lay the foundation for obtaining comparable CSPM values from MODIS images and for further comparing the water quality properties of the two lakes.

2. Methods

2.1. Study Area

Poyang Lake (115°47′–116°45′ E, 28°22′–29°45′ N) (Figure 1) receives the water from five rivers (Raohe, Xinjiang, Fuhe, Ganjiang, and Xiushui) and drains into the Yangtze River. The lake has large fluctuant areas from <1000 km2 in dry seasons (December–February) to around 4000 km2 in flood seasons (July–August) [33,34]. Poyang Lake plays very important roles in local economic and social developments as well as in global ecological conservation, and it is also an internationally important wetland and one of the largest bird conservation areas in the world [35]. SPM is a dominant factor affecting the water quality of Poyang Lake; especially, since 2001 the intensive sand dredging activities have been causing sediments to become resuspended and increase water turbidity [32,36].
Dongting Lake (110°40′–113°10′ E, 28°30′–30°20′ N) (Figure 1) changes from <500 km2 in the dry seasons to around 2500 km2 in the flood seasons [37], and it includes three parts (East, South and West), of which the East Dongting is the biggest one. The waters of Dongting Lake are mainly from the Xiang, Zi, Yuan, Li, and Yangtze River, flow into the western and southern parts, and drain into the Yangtze through the eastern part. Dongting Lake is also an internationally important wetland with a rich biodiversity [38], and its biggest environmental problem is rapid sedimentation [16,39].

2.2. Fieldwork and CSPM Measurement

Four fieldworks were carried out in 2007, 2012, and 2013 in Poyang and Dongting Lake (Table 2), and a total 197 water samples were collected. In each fieldwork, the sampling work started around 10 a.m. local time when the sunlight and weather condition were relatively steady, the selected sampling sites were distributed from clear to turbid water, and the distance between any two sampling sites was not less than 1.5 km. At each sampling site, a global positioning system receiver (Garmin International, Inc., Olathe, KS, USA) was used to record the location coordinates, and about 1000–1500 mL of surface water was collected from a water depth of around 0–50 cm for CSPM measurement in laboratory [16,27,32].
The CSPM values of water samples were measured in the laboratory according to the investigation criteria about the lakes of China [40] as follows: one 0.45 μm nucleopore membrane filter was first weighed, wetted with distilled water, dried for 1 h at 110 °C in a drying oven, and reweighed after recovering to room temperature in a desiccator. The water sample was then shaken to suspend the sediments, and poured through the membrane filter in a vacuum pump filtration apparatus; the membrane filter was dried for 2 h at 110 °C and reweighed after cooling to room temperature in the desiccator. Finally, the CSPM was calculated by dividing the weight difference of membrane filter before and after filtering by the water sample volume.

2.3. Image Acquisition and Pre-Processing

The Terra satellite, as the flagship Earth Observing Satellite, travels in a circular sun-synchronous polar orbit with the descending node at 10:30 a.m. local time, and the MODIS instrument aboard the Terra satellite observing every point on the Earth every 1–2 days [41]. MOD09GQ and MOD09GA data are the surface reflectance products (Collection 5) derived from MODIS Terra level 1B images using the 6S atmospheric algorithm [42,43], in which several key parameters including geometrical conditions, atmospheric model, aerosol model, and aerosol concentration are inputted to correct the effect of atmospheric gases, aerosols, and thin cirrus clouds [44]. The data provide an estimation of the spectral reflectance as it would be measured at ground level in the absence of atmospheric scattering or absorption. The MOD09GQ product contains two 250 m bands at 645 and 858 nm, and the MOD09GA stores seven 500 m or 1 km bands centred at 645, 858, 470, 555, 1240, 1640, and 2130 nm [45]. Considering the uncertainty of current atmospheric algorithms for case-II waters, the MOD09GQ and MOD09GA images—which have been frequently used by Wang et al. [46], Ayana et al. [47], Chen et al. [15], Doxaran et al. [26], and Alcântara et al. [48]—were employed in this study directly.
The MOD09GQ and MOD09GA images corresponding to the four fieldworks in this study (Table 2) and on 1 August 2013 were downloaded from the Land Processes Distributed Active Archive Centre (LP DAAC) [49]. The pre-processes used in Cui et al. [32] and Wu et al. [16] were employed in this study as follows: all downloaded images were projected into WGS 84/UTM (zone 49N for Dongting Lake, and zone 50N for Poyang Lake) using nearest-neighbor resampling method to preserve original reflectance value; for each image, a sub-image covering the study area was cut from the original image, and the land areas and small water bodies were removed using a binary mask created by visually interpreting its unsupervised classification image.

2.4. Model Development

Four sampling points with high reflectance at 1240 nm (>10%) and two sampling points with high CSPM value (>160 mg·L−1) on 31 August 2012 were removed for Poyang Lake; for Dongting Lake, one sampling point on 31 August 2012 was removed due to its high reflectance at 1240 nm (>40%), and four sampling points close to emergent plants (<1 km) with high reflectance at 1240 nm (>10%) and one point with high CSPM value (63.2 mg·L−1) on 14 June 2013 were also removed; thus, a total of 185 sampling points (90 for Poyang and 95 for Dongting) were kept for model development in this study (Table 2). The remaining CSPM measurements of each fieldwork were statistically described respectively using STATISTICA software package [50].
With the support of STATISTICA, the model calibration process used by Wu et al. [16] was employed to develop the retrieval models of CSPM for Poyang and Dongting Lake, respectively, as follows: (1) the 42 CSPM measurements on 27 September 2007 in Poyang Lake and 47 on 31 August 2012 in Dongting Lake, as well as their corresponding MODIS images, were applied to calibrate the linear, quadratic, and exponential models of CSPM against a single 250 m red and near-infrared (NIR) band, as well as the combinations listed in Table 1 (such as red-NIR, NIR/red, and (red-NIR)/(red + NIR)), respectively, using the least-squares technique; (2) the abovementioned models were calibrated again using the MODIS red or NIR band minus SWIR band at 1240 or 1640 nm, respectively, since adopting the difference between MODIS red or NIR band and SWIR band at 1240 or 1640 nm might refine the atmospheric correction results for turbid waters and thus improve the retrieval models [20,51]. In order to develop common models for Poyang and Dongting Lake, considering at certain levels the spatial and temporal extendibility of the developed models, the datasets of these two lakes (42 CSPM measurements on 27 September 2007 in Poyang Lake and 47 on 31 August 2012 in Dongting Lake) were put together, and the aforementioned calibration process was repeated. By comparing the determination coefficients (R2) and estimated standard errors (SE) of all calibrated models as well as model complexity, the two best-fitting models were selected for Poyang and Dongting Lake as well as their combination, respectively.
The 48 CSPM measurements on 31 August 2012 in Poyang Lake and 48 on 14 June 2013 in Dongting Lake with their corresponding MODIS images were used to validate the above selected two models for Poyang and Dongting Lake together, respectively. Each model was applied to estimate the CSPM values at sampling sites from the MODIS images. The correlation coefficient (r) between the measured and estimated CSPM values as well as the root mean square error (RMSE) and relative root mean square error (RRMSE) of estimation were calculated to assess model performances. Besides, the null hypotheses that the slope and intercept of the linear regression line between the measured and estimated CSPM values were equal to 1 and 0, respectively, were tested to evaluate the model bias.

2.5. Model Application

Considering the aforementioned model validation results, the most stable model was chosen and applied to the MODIS Terra images captured on 1 August 2013 to retrieve the CSPM values of Poyang and Dongting Lake, and their spatial distribution and driving factors were compared and analyzed briefly.

3. Results

3.1. CSPM Measurement

The statistics of remaining CSPM measurements of four fieldworks in Poyang and Dongting Lake are shown in Table 3. The CSPM values do not show a statistically significant difference at a significance level of 0.05 between the two fieldworks for each lake (z-test for mean difference: mean CSPM in Poyang: 38.2 ± 42.0 mg·L−1 on 27 September 2007 and 42.3 ± 43.3 mg·L−1 on 31 August 2012, z = −0.46, p = 0.65; mean CSPM in Dongting: 12.6 ± 13.8 mg·L−1 on 31 August 2012 and 10.8 ± 10.4 mg·L−1 on 14 June 2013, z = 0.69, p = 0.49), and the variations of CSPM values were similar for the two lakes (variation coefficient = 96.2%–110.1%); however, as a whole, the CSPM value in Poyang Lake was higher than that in Dongting Lake.

3.2. Model Development

Based on the 42 CSPM measurements in Poyang Lake and 47 in Dongting Lake, as well as their corresponding MODIS images, model calibration results showed that the red band and the red minus SWIR band at 1240 nm obtained the best fit among all tested models for Poyang and Dongting Lake as well as the two lakes together, respectively (Figure 2). The exponential model of red band explained 71% (SE = 23 mg·L−1) of the variation of CSPM for Poyang and 71% (SE = 7 mg·L−1) for Dongting Lake; the exponential model of red band for Poyang and Dongting Lake together slightly improved fitting accuracy compared with that for each lake alone, considering R2 value (= 0.76) (Figure 2A). The red minus SWIR band at 1240 nm could barely improve the model accuracy for Dongting Lake, and its exponential model explained 72% (SE = 6 mg·L−1) of the variation of CSPM; however, it obtained a higher fitting accuracy than the single red band for Poyang Lake (R2 = 0.75, SE = 21 mg·L−1). The exponential model of red minus SWIR band at 1240 nm for Poyang and Dongting Lake together also slightly improved the fitting accuracy than that for each lake alone, considering R2 value (=0.76) (Figure 2B).
The exponential models of red band and the red minus SWIR band at 1240 nm for Poyang and Dongting Lake together were applied to the validation datasets to estimate their CSPM values, respectively, and the results of comparing the measured and estimated CSPM values are shown in Figure 3. There existed significantly strong correlations between the measured and estimated CSPM values for both models and both lakes at a significance level of 0.05 (r = 0.80–0.87, p < 0.05). The model of red minus SWIR band at 1240 nm (r = 0.87, RMSE = 22 mg·L−1, RRMSE = 54%) obtained a bit higher estimation accuracy than that of red band (r = 0.81, RMSE = 26 mg·L−1, RRMSE = 64%) for Poyang Lake, while the results of null hypothesis tests of intercept being 0 and slope being 1 for the regression line between the measured and estimated CSPM values indicated that the intercept and slope were not significantly different from 0 and 1 for both models at a significance level of 0.05 (Table 4). However, for Dongting Lake, the model of red band (r = 0.85, RMSE = 7 mg·L−1, RRMSE = 55%) obtained higher estimation accuracy than that of red minus SWIR band at 1240 nm (r = 0.80, RMSE = 9 mg·L−1, RRMSE = 76%), while an estimation bias was observed for the model of red minus SWIR band at 1240 nm (Table 4), as the result of null hypothesis test of slope being one for the regression line between the measured and estimated CSPM values showed that the slope was significantly different from one at a significance level of 0.05 (b = 0.816, p = 0.042), which suggests an overestimation of the measured CSPM for this model (Figure 3B). Thus, the exponential model of MODIS red band obtained the best stable result for estimating the CSPM values in both Poyang and Dongting Lake.

3.3. Model Application

The most stable model with MODIS red band (CSPM = 1.98 × Exp (23.47 × Red), R2 = 0.76, SE = 17 mg·L−1) was applied to the MODIS Terra images captured on 1 August 2013 to retrieve the CSPM values of Poyang and Dongting Lake (Figure 4). In general, the CSPM values of Poyang Lake were higher than those in Dongting Lake. A conspicuous turbid region (CSPM values were close to 200 mg·L−1) was observed from the southeast to the north in Poyang Lake, which was caused by the dredging activities and their related vessel shipping in the southern and middle Poyang Lake [52]. The CSPM values in the east were higher than those in the southwest of Poyang Lake, which could be explained by the fact that the shallower water in the east made the sediments suspended and increased CSPM values. The general spatial distribution pattern of CSPM and its driving factors in Dongting Lake were provided by Wu et al. [16] as follows. The CSPM values of South Dongting were higher than those of West Dongting, which could be explained by the fact that the CSPM values of South Dongting were affected by the turbid inflow of Li River (Figure 1). A small turbid region was observed at the western part of East Dongting (region 1 in Figure 4), which was also induced by the turbid river inflow (Figure 1). A large area with high CSPM values (about 40–60 mg·L−1) appeared at the eastern part of East Dongting (region 2 in Figure 4). Dredging activities were also observed at East Dongting Lake (region 3 in Figure 4) from our fieldworks, and they and their related vessel shipping increased the CSPM values at dredging regions and downstream of them. Thus, the turbid inflow from the South Dongting and dredging at East Dongting Lake together could explain the high CSPM values observed at the eastern part of East Dongting Lake.

4. Discussion

It is important to ensure the concurrence of satellite image overpass and fieldwork in order to develop remote-sensing-based water quality parameter retrieval models, because a large time difference between them usually reduces model accuracy due to the water quality parameter change, especially for a very dynamic waterbody. In our study, the MODIS image on 30 August 2012 and the CSPM values measured on 31 August 2012 were involved in model development process, which might influence model development to some extent. However, since there was no rainfall or large changes in wind speed and water level during these two days, we considered that this effect should be very weak.
For the MODIS red and NIR bands, one image pixel covers an area of about 250 by 250 m2, while its corresponding CSPM value for model development is generally derived from the water sample collected at one site. Thus, a scale gap exists between image pixel and CSPM measurement, which has a significant impact on the development of water quality retrieval models. During our fieldworks, sand dredging activities occurred in both lakes, which cause sediments to become resuspended and caused CSPM values anomalies in some regions. Such anomalous CSPM values and scale gap problem could explain why two samples with high CSPM values at the turbid region in Poyang Lake and one sample in Dongting Lake were removed, respectively, from our study. Besides, we found that four sampling points in Poyang Lake were close to the lake shores and five in Dongting Lake were close to the plant regions, and their corresponding MODIS reflectance values at 1240 nm were too high, which indicated that these image pixels might partly cover lake shores and plant regions, because the reflectance values of pure water pixels at 1240 nm are nearly zero. Thus, we removed these nine samples to avoid the influence of mixed pixels on model accuracy.
Many studies reported that the empirical and semi-empirical models for water quality retrievals were often region- and time-dependent, since they have no strict theoretical foundations and the optical properties are often different at different water bodies or even at different seasons. Developing a model for simultaneously estimating water quality parameters of different lakes could thus be meaningful. The suspended particulate inorganic matter (suspended sediment) is dominant within the water constituents of Poyang Lake [53], and the chlorophyll occupied a very low proportion within the CSPM value in Dongting Lake (mean chlorophyll a concentration = 2.8 μg·L−1 on 31 August 2012, n = 48, unpublished result). We considered that the similar water constituent structures of the two lakes might explain why a common model could be developed for the both lakes. The CSPM retrieval model developed in this study might be applicable to other water bodies with similar optical properties as that in Poyang and Dongting, however, a careful model validation should be carried out first.
In this study, we found that the red band of the MODIS image obtained the best result for estimating the CSPM values in Poyang and Dongting Lake (Figure 2 and Figure 3), and such a result is consistent with that of many water bodies (Table 1). The absorption and backscattering characteristics of main water constituents (including phytoplankton, suspended sediment, yellow substance, and water itself) may explain why the MODIS red band has potential for the CSPM estimations in case-II waters, which has been discussed in many studies [1,30,54,55,56]. Repeatedly, the absorption coefficients of main water constituents decrease with the increasing wavelength and are close to zero in red and near infrared regions, especially for the water bodies with low phytoplankton concentration like Poyang and Dongting Lake. Therefore, the backscattering coefficients of phytoplankton and yellow substance can also be neglected over these regions, and thus the water-leaving radiances, which are captured by remote sensing sensor and employed for estimating water quality parameters, are dominated by the backscattering coefficient of suspended sediment in the red or near-infrared regions [16,32].
The exponential model of MODIS red band explained 76% of the CSPM variation in Poyang and Dongting Lake together (Figure 2). Considering that the model is a common model for two lakes, such model fitting accuracy is moderate and acceptable compared with that in other water bodies such as Rhone River, Maderia River, Taihu Lake, and Tampa Bay in Table 1 (R2 = 0.58–0.95). Many factors may affect model fitting accuracy. Wu et al. [13], based on an analysis of 28 MODIS-based CSPM retrieval models, considered that the CSPM value range and sampling number might not be the most important factors affecting model fitting accuracy; thus the CSPM value range (0–144.0 mg·L−1) and sampling number (89 sampling points) employed in this study were large enough. The CSPM measurements and their corresponding MODIS images from two lakes might decrease estimation accuracy in this study, because different water bodies have different water constituents and different inherent optical properties; however, we did not find a decreased model-fitting accuracy when putting the datasets of Poyang and Dongting Lake together (Figure 2). There exists a scale gap between image pixel and CSPM measurement, and it is generally assumed that the CSPM value within a pixel is homogeneous; however, such an assumption is often not valid [16], especially for Poyang and Dongting Lake because of the dredging impacts on CSPM distribution in some regions. We considered that the scale gap between image pixel and CSPM measurement and the large heterogeneity of CSPM value for some pixels could be the most important factors affecting model fitting accuracy.
We also found that, compared with the red band, the red minus SWIR band at 1240 nm did not produce much better fitting results for Dongting Lake as well as for Dongting and Poyang Lake together (Figure 2). Such a result indicated that using the SWIR values at 1240 nm to remove atmospheric effects might not always improve the model-fitting accuracy in all cases. And it showed instability and uncertainty. Such a result might be explained by the fact that (1) the water reflectance at 1240 nm was not always zero [57] due to the changing water constituent structure; and (2) the reflectance at 1240 nm was also not stable and showed some uncertainty because of the lower sensor band signal-to-noise ratio values of the MODIS SWIR bands [58]. Several atmospheric correction methods have been developed for case-II waters [20,58,59,60]; although they also showed uncertainty or even failed in very turbid waters, they or other potential atmospheric methods for the two lakes together should be further explored in order to accurately derive the comparable results of water quality.
Poyang and Dongting Lake are the only remaining large Yangtze-connected lakes at its middle and lower reaches, and they have some similar hydrological characteristics, such as same flooding season and large water level fluctuation. However, we observed a large difference on CSPM between these two lakes (Table 3 and Figure 4), which might be caused by natural or human factors. The dredging activities, which resuspended sediments and increased CSPM, were both found in Poyang and Dongting Lake [20,52,61], and we considered that dredging might explain the large difference on CSPM in the two lakes. The dredging-related CSPM variation is impacted by dredging manner and intensity, water depth, or the size of sediments; however, at present, we do not know which factor(s) might be dominant in causing the large CSPM difference between the two lakes due to limited relevant knowledge.
Poyang and Dongting Lake both play very important roles in local social and economic developments as well as in global ecological conservation; for example, they are the internationally important wetlands and large bird conservation areas in the world. The two lakes are also facing similar water quality degradation due to global climate change and regional or local human activities, such as the impacts of Three Gorges Project and sand dredging activities on water quality and sediment exchanges. Obtaining the comparable spatiotemporal information of CSPM in the two lakes is prerequisite for comparing their water quality and sediment dynamics, analyzing their driving factors, and further improving lake management. The model developed in this study provided a foundation for obtaining the comparable spatiotemporal information of CSPM in Poyang and Dongting Lake together.

5. Conclusions

It is theoretically and practically meaningful to develop water quality retrieval models for different lakes together in order to compare their water quality properties. A CSPM retrieval model using the 250 m red band of MODIS image was developed for Poyang and Dongting Lake (CSPM = 1.98 × Exp (23.47 × Red), R2 = 0.76, SE = 17 mg·L−1) together, and it obtained an acceptable result in estimating CSPM values. The developed model provides a foundation for obtaining the comparable spatiotemporal information of CSPM, which would be helpful for comparing, understanding, managing, and protecting Poyang and Dongting aquatic ecosystems.

Acknowledgments

This study was supported by the Scientific Research Foundation for Newly High-End Talents of Shenzhen University, the Basic Research Program of Shenzhen Science and Technology Innovation Committee (No. JCYJ20151117105543692).

Author Contributions

Fangyuan Chen and Guofeng Wu conceived and designed the experiments; Guofeng Wu analyzed the data and helped to outline the manuscript structure; Junjie Wang, Junjun He and Yihan Wang contributed equally by conducting fieldwork and laboratory analysis; Fangyuan Chen prepared the draft of the manuscript.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Maps showing the sampling sites in Poyang (circle: 27 September 2007, cross: 31 August 2012) and Dongting (cross: 31 August 2012, circle: 14 June 2013) Lake.
Figure 1. Maps showing the sampling sites in Poyang (circle: 27 September 2007, cross: 31 August 2012) and Dongting (cross: 31 August 2012, circle: 14 June 2013) Lake.
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Figure 2. Scattering plots showing the regression models of suspended particulate matter concentration (CSPM, mg·L−1) against the red band (Red) (A) and the red minus shortwave infrared (SWIR) band at 1240 nm (Red) (B) of MODIS Terra images for Poyang (n = 42), Dongting (n = 47) Lake and their combination (n = 89).
Figure 2. Scattering plots showing the regression models of suspended particulate matter concentration (CSPM, mg·L−1) against the red band (Red) (A) and the red minus shortwave infrared (SWIR) band at 1240 nm (Red) (B) of MODIS Terra images for Poyang (n = 42), Dongting (n = 47) Lake and their combination (n = 89).
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Figure 3. Scattering plots showing the measured suspended particulate matter concentration (CSPM, mg·L−1) against the estimated one using the exponential models of the red band (Red) as well as the red minus SWIR band at 1240 nm (Red″) of MODIS Terra images for Poyang (n = 48) (A) and Dongting (n = 48) (B) Lake. The solid line is the regression line between measured and estimated values, the dashed line is 1:1 line, r is the correlation coefficient between measured and estimated values, p is the probability of significance testing for correlation coefficient, and RMSE and RRMSE are the root mean square error (mg·L−1) and relative root mean square error (%) of estimation, respectively.
Figure 3. Scattering plots showing the measured suspended particulate matter concentration (CSPM, mg·L−1) against the estimated one using the exponential models of the red band (Red) as well as the red minus SWIR band at 1240 nm (Red″) of MODIS Terra images for Poyang (n = 48) (A) and Dongting (n = 48) (B) Lake. The solid line is the regression line between measured and estimated values, the dashed line is 1:1 line, r is the correlation coefficient between measured and estimated values, p is the probability of significance testing for correlation coefficient, and RMSE and RRMSE are the root mean square error (mg·L−1) and relative root mean square error (%) of estimation, respectively.
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Figure 4. Suspended particulate matter concentrations retrieved from the MODIS Terra images captured on 1 August 2013 for Poyang and Dongting Lake. Dashed circles labelled with numbers indicated the different regions in Dongting Lake.
Figure 4. Suspended particulate matter concentrations retrieved from the MODIS Terra images captured on 1 August 2013 for Poyang and Dongting Lake. Dashed circles labelled with numbers indicated the different regions in Dongting Lake.
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Table
Table 1. Some published regression models of suspended particulate matter concentration (CSPM, mg·L−1) based on Moderate Resolution Imaging Spectroradiometer (MODIS). Rrs is the remote sensing reflectance at the wavelength of λ nm, R2 is the determination coefficient of calibrated model, and n is the number of sampling. Source: modified based on Wu et al. [13].
Table 1. Some published regression models of suspended particulate matter concentration (CSPM, mg·L−1) based on Moderate Resolution Imaging Spectroradiometer (MODIS). Rrs is the remote sensing reflectance at the wavelength of λ nm, R2 is the determination coefficient of calibrated model, and n is the number of sampling. Source: modified based on Wu et al. [13].
Author(s)ModelR2nCSPM RangeLocation
Ody et al. (2016) [14]CSPM = 1400 Rrs(645)0.61273–60Rhone River, France
Chen et al. (2015) [15]log10(Rrs(859))/log10(Rrs(645)) = −0.334 log10(CSPM)2 + 1.0046 log10(CSPM) + 0.82510.75605.8–577.2Etuary of Yangtze River and Xuwen Coral Reef Protection Zone, China
Wu et al. (2014) [16]CSPM = 3.04 exp(20.23 (Rrs(645) − Rrs(1240)))0.68480.1–40.4Dongting Lake, China
Qiu (2013) [17]log10(CSPM) = 1.932 exp(0.875 Rrs(678)/Rrs(551))0.951221.9–1896.5Yellow River Estuary, China
Villar et al. (2013) [18]CSPM = 1020 (Rrs(859)/Rrs(645))2.940.6228225–622Maderia River, Brazil
Long and Pavelsky (2013) [19]CSPM = 12.996 exp(Rrs(859)/(0.189Rrs(645)))0.941473.9–3602Peace-Athabasca Delta, Canada
Wu et al. (2013) [13]CSPM = 0.0365 exp(63.2 (Rrs(645) − Rrs(1240)))0.76420–141.9Poyang Lake, China
Feng et al. (2012) [20]CSPM = 0.6786 exp(34.366 (Rrs(645) − Rrs(nearest1240)))0.87383–200Poyang Lake, China
Jiang and Liu (2011) [21]CSPM = 1365.5 (Rrs(470) + Rrs(555))2 − 369.08 (Rrs(470) + Rrs(555)) + 27.2160.81270–40Poyang Lake, China
Chen et al. (2011) [22]log10(Rrs(859))/log10(Rrs(645)) = −0.1325 log10(CSPM)2 + 0.7429 log10(CSPM) + 0.67680.86321.29–208Apalachicola Bay, United States
Zhao et al. (2011) [23]CSPM = 2.12 exp(45.92 Rrs(645))0.78630–87.8Mobile Bay Estuary, Alabama
Tarrant et al. (2010) [6]CSPM = 0.0213 (Rrs(645) − Rrs(859)) + 0.2320.821050.30–13.4Roosevelt, Bartlett Pleasant Lake, United States
Zhang et al. (2010) [11]ln(CSPM) = 0.015 (Rrs(645)) + 0.003 (Rrs(645))2 − 0.2820.871664.32–311.4Taihu Lake, China
ln(CSPM) = 166.960/(Rrs(470) − Rrs(645)) − 2.1920.79
ln(CSPM) = –16.997 Rrs(470)/Rrs(645) + 3.326 (Rrs(470)/Rrs(645))2 + 23.6810.87
ln(CSPM) = –29.707 (Rrs(470) − Rrs(645))/(Rrs(470) + Rrs(645)) + 41.886 (Rrs(470) − Rrs(645))/(Rrs(470) + Rrs(645))2 + 11.3580.87
Wang and Lu (2010) [24]ln(CSPM) = 0.262 (Rrs(859) − Rrs(1240)) + 4.1170.783574–881Lower Yangtze River, China
Wang et al. (2010) [25]log10(CSPM) = 1.5144 log10(Rrs(859))/
log10(Rrs(645)) − 0.5755		0.72161–64Apalachicola Bay, United States
log10(CSPM) = 0.1497 exp(1.5859 log10(Rrs(859))/log10(Rrs(645)))0.6111
Jiang et al. (2009) [12]log10(CSPM) = 0.3568 ln(Rrs(859)) + 3.34310.81560–170Taihu Lake, China
Doxaran et al. (2009) [26]CSPM = 12.996 exp(Rrs(859)/(0.189Rrs(645)))0.892040–2250Gironde Estuary, France
Wu and Cui (2008) [27]CSPM = 86236.23 Rrs(645)3 − 15858.70 Rrs(645)2 + 1005.29 Rrs(645) − 15.670.92420–142Poyang Lake, China
Liu et al. (2006) [28]ln(CSPM) = 2.495 (Rrs(645) − Rrs(859))/(Rrs(645) + Rrs(859)) + 1.8100.724123.4–61.2Middle Yangtze River, China
Sipelgas et al. (2006) [9]CSPM = 110.3 Rrs(645) + 20.58483–10Pakri Bay, Finland
Hu et al. (2004) [29]CSPM = 0.00522 exp(1002 (Rrs(645) − Rrs(859)))0.90312–11Tampa Bay, United States
Table
Table 2. Description of four fieldworks and their corresponding MODIS images in Poyang and Dongting Lake. Original sampling number is the number of samples collected in the fields, and employed sampling number is the number of samples employed in this study after removing outliers.
Table 2. Description of four fieldworks and their corresponding MODIS images in Poyang and Dongting Lake. Original sampling number is the number of samples collected in the fields, and employed sampling number is the number of samples employed in this study after removing outliers.
LakeFieldwork DateOriginal Sampling NumberEmployed Sampling NumberMODIS Date
Poyang Lake27 September 2007424227 September 2007
31 August 2012544830 August 2012
Dongting Lake31 August 2012484730 August 2012
14 June 2013534814 June 2013
Table
Table 3. Statistics describing the variation of suspended particulate matter concentrations (CSPM, mg·L−1) of the water samples collected in Poyang (n = 90) and Dongting (n = 95) Lake.
Table 3. Statistics describing the variation of suspended particulate matter concentrations (CSPM, mg·L−1) of the water samples collected in Poyang (n = 90) and Dongting (n = 95) Lake.
CSPMPoyang LakeDongting Lake
27 September 2007
(n = 42)		31 August 2012
(n = 48)		31 August 2012
(n = 47)		14 June 2013
(n = 48)
Minimum0.00.00.00.7
Maximum141.9144.040.444.8
Mean38.242.312.610.8
Standard deviation42.043.313.810.4
Variation coefficient110.1102.3109.696.2
Table
Table 4. Null hypothesis tests of the intercept (a) = 0 and slope (b) = 1 of the regression line between the measured and estimated suspended particulate matter concentrations (CSPM, mg·L−1) of the water samples collected in Poyang (n = 48) and Dongting (n = 48) Lake. SE is standard error, t is the value of Student’s t-test, p is the probability in null hypothesis significance testing and the bold p value suggests that the null hypothesis test is statistically significant at a significance level of 0.05. (A)-(B) correspond to the model number used in Figure 3.
Table 4. Null hypothesis tests of the intercept (a) = 0 and slope (b) = 1 of the regression line between the measured and estimated suspended particulate matter concentrations (CSPM, mg·L−1) of the water samples collected in Poyang (n = 48) and Dongting (n = 48) Lake. SE is standard error, t is the value of Student’s t-test, p is the probability in null hypothesis significance testing and the bold p value suggests that the null hypothesis test is statistically significant at a significance level of 0.05. (A)-(B) correspond to the model number used in Figure 3.
Intercept (a)Slope (b)
a ± SEtpb ± SEtp
(A)-(1)−5.470 ± 6.470−0.8450.4031.186 ± 0.1321.4050.160
(A)-(2)−4.660 ± 5.019−0.9280.3581.196 ± 0.1011.9360.053
(B)-(1)−0.029 ± 1.447−0.0200.9840.975 ± 0.089−0.2840.776
(B)-(2)−1.554 ± 1.840−0.8440.4030.816 ± 0.090−2.0350.042

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Chen, F.; Wu, G.; Wang, J.; He, J.; Wang, Y. A MODIS-Based Retrieval Model of Suspended Particulate Matter Concentration for the Two Largest Freshwater Lakes in China. Sustainability 2016, 8, 832. https://doi.org/10.3390/su8080832

AMA Style

Chen F, Wu G, Wang J, He J, Wang Y. A MODIS-Based Retrieval Model of Suspended Particulate Matter Concentration for the Two Largest Freshwater Lakes in China. Sustainability. 2016; 8(8):832. https://doi.org/10.3390/su8080832

Chicago/Turabian Style

Chen, Fangyuan, Guofeng Wu, Junjie Wang, Junjun He, and Yihan Wang. 2016. "A MODIS-Based Retrieval Model of Suspended Particulate Matter Concentration for the Two Largest Freshwater Lakes in China" Sustainability 8, no. 8: 832. https://doi.org/10.3390/su8080832

APA Style

Chen, F., Wu, G., Wang, J., He, J., & Wang, Y. (2016). A MODIS-Based Retrieval Model of Suspended Particulate Matter Concentration for the Two Largest Freshwater Lakes in China. Sustainability, 8(8), 832. https://doi.org/10.3390/su8080832

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