The Seasonal Chlorophyll-a Concentration
Dataset in Poyang Lake, China (2009-2012)

Wang, J. L.^1*  Zhang, Y. J.¹  Yang, F.¹  Cao, X. M.²  Bai, Z. Q.^1,3  Zhu, J. X.^1,3  Chen, E. Y.^1,4  Li, Y. F.^1,3  Ran, Y. Y.⁴

1. State Key Laboratory of Resources and Environment Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences; Beijing 100101, China;

2. Institute of Desertification Studies, Chinese Academy of Forestry; Beijing 100091, China;

3. University of Chinese Academy of Sciences, Beijing 100049, China;

4. China University of Mining & Technology, Beijing 100083, China

Abstract: Chlorophyll-a concentration is an important indicator of eutrophication. The dataset production process is as follows: First of all, water body spectral information of Poyang Lake was measured in the field, and the spectral index was constructed based on the water spectral cha-

racteristics analysis; Then, based on the least squares method and combined with measured chlorophyll-a concentrations data, sensitive band interval was calculated by regression analysis; MODIS data was used to build chlorophyll-a concentration estimation models of Poyang Lake by semi-empirical method and the accuracy of the results was verified; Finally, chlorophyll-a concentration distribution data of Poyang Lake in January, April, July and October from 2009 to 2012 was inversed using these models.

Keywords: Water color remote sensing; Chlorophyll-a concentration; Poyang Lake; inversion model; spatial and temporal distribution

1 Introduction

Poyang Lake is the largest freshwater lake in China, and it plays an important role in flood control regulation and storage and biodiversity protection^[1]. Chlorophyll-a concentration level can reflect the status of water primary productivity, and it is also an important indicator of evaluating the eutrophication degree. Regular chlorophyll-a monitoring and analysis is time and money consuming. In addition, the monitoring is limited to weather and other natural conditions, so it’s difficult to dynamically monitor the distribution and changes of water chlorophyll-a concentration widely^[2–3]. Remote sensing technology is conducive to chlorophyll-a concentration monitoring of Poyang Lake, which has advantages of wide range, low cost and ease of long-term dynamically monitoring.

2 Metadata of Dataset

The metadata summary of the seasonal chlorophyll-a dataset of Poyang Lake, China (2009-2012) is recorded at Table 1. These information include the dataset full name, short name, authors, geographical region of the dataset content, year of the dataset, number of the dataset tiles, dataset spatial and temporal resolution, dataset format and size, data publisher and the data sharing policy.

Table1  Metadata summary of the seasonal chlorophyll-a dataset of Poyang Lake, China (2009-2012)

3 Methods

3.1 Location of Measured Data

There were 3 times water quality sampling and synchronized water spectra measurements^[5–6] in October 2011, July 2012 and October 2012. Geographical location and altitude information of the corresponding sampling sites are shown in the data file (1), (3) and (5). Table 2 shows the geographical location and altitude information of the 25 sampling points measured in October, 2011 (Excerpt).

Table 2  Geographic position information of 25 sampling points in 10/2011 (Excerpt)

Note: All data are shown in “In_SITU_LOCATION_201110.xlsx”.

3.2 Measured Sampling Data

Spectral curves of the three times measurements were shown in Figure 1, Figure 2, and

Figure 3, respectively. The measured data (excerpt) of 25 sampling points in December 2011 was shown in Table 3.

Table 3  Measured data of 25 sampling points in December, 2011 (Excerpt)

Note: All data are shown in “In_SITU_DATA_201110.xlsx”.

3.3 Inversion Method

Inversion models of seasonal chlorophyll-a concentration data from 2009 to 2012 in Poyang Lake were built by semi-empirical method one by one.

Chlorophyll-a sensitive band range was captured, based on the measured water body spectral characteristics analysis. According to the regression analysis on the reflectivity of MODIS channels in the sensitive zone and the measured chlorophyll-a concentration, a regression relationship between chlo rophyll-a concentration and spectral

indices made up of various combinations of MODIS data bands can be built quarter by quarter (January represents winter, April represents spring, July represents summer, and October

represents autumn). Those models which have a maximum coefficient of

determination and minimum root- mean-square error would be chosen as the inversion models of chlorophyll-a concentration.

Chlorophyll-a concentration data inversion method was published in “Estimating chlorophyll-a concentration in Poyang Lake using MODIS based on measured reflectance spectra” and “Spatial and temporal variations of chlorophyll-a concentration from 2009 to 2012 in Poyang Lake, China^[6–9].

Table 4 lists the seasonal inversion models of chlorophyll-a concentration in Poyang Lake (2009-2012).

Table 4  Inversion models of chlorophyll-a concentration in Poyang Lake (2009-2012)

4 Results and Validation

4.1 Results

The seasonal minimum, maximum and average value of chlorophyll-a concentration in Poyang Lake are calculated. Its seasonal spatial distribution is shown in Figure 4 and Figure 5.

The maximum value of chlorophyll-a concentration was only 2.9 ug/L in Poyang Lake in January, 2009. There was a relative high value in southern five rivers estuary waters, nor-thern coastal waters, and a low value in the center of lake; The concentration increased in April, and the maximum value was 4.9 ug/L. Relative high value was located in the central west of the lake and northern coastal waters, while low value was in the center of the lake; In July, chlorophyll-a concentration became to increase rapidly, and had a maximum value of 29.8 ug/L, which reached to the maximum value of this year; The maximum value in October was 15.8 ug/L, the overall level of chlorophyll-a concentration fell down as well.

In January 2010, chlorophyll-a concentration was still falling, with a maximum value of 3.3 ug/L. Relative high value appeared in the northern coastal waters and five rivers estuary waters; The average concentration of the entire lake in April increased slowly relative to January, and the maximum value remained at 3.9 ug/L; There was a maximum value which came up to 29.5 ug/L in July, and relative high value area located in the central west and central east waters; The highest value of this year reached in October, which was 31.9 ug/L, relative high value was distributed in the areas near water, five rivers estuary waters, the main channel of lake and the southern waters.

Figure 4  Spatial distribution of chlorophyll-a concentration from 2009 to 2010 in Poyabg Lake

Figure 5  Spatial distribution of chlorophyll-a concentration from 2011 to 2012 in Poyang Lake

Because of a relative high basic point of last year, chlorophyll-a concentration fell down in January, 2011, while had a maximum value of 11.7 ug/L, which was much higher than ever before (0-3 ug/L), the relative high value of this month was located in the northern coastal waters, central west waters and five rivers estuary waters; the chlorophyll-a concentration value declined in April and had a range of 0.9-7.3 ug/L, relative high value was located in five rivers estuary waters, coastal waters and southern waters; The chlorophyll-a concentration rose rapidly in July to the highest level of this year, which was 15.2 ug/L, and relative high value appeared in the five rivers estuary waters, coastal waters and southern waters; In October, the overall level of chlorophyll-a concentration began to decline, with a maximum value of 13.4 ug/L, and it was the inshore waters of the lake where relative high value appeared.

The lowest level of chlorophyll-a concentration in the year 2012 appeared in January, which held a maximum value of 3.6 ug/L, and relative high value was mainly located in southern waters, such as Duchang River; Chlorophyll-a concentration in April increased, the highest value was 8.8 ug/L, and the relative high value was distributed in five rivers estuary waters and the southern waters; In July, the concentration value rose and reached to the highest level of this year, as same as usual, and the highest value came up to 29.5 ug/L. Re-

lative high value areas were distributed in some coastal waters, the main branch of the Ganjiang River and the southern waters; Three months later, the chlorophyll-a concentration value decreased a lot, and the maximum value reached to 13.4 ug/L.

4.2 Data Validation

The MODIS remote sensing data and the independent measured data are used to verify the accuracy of inversion models. The relative error is used to measure the effectiveness of the models:

                     (1)

Where C_m is the measured value and Ci is the inversion value of chlorophyll-a concentration.

  Table 5 shows that the average relative error of chlorophyll-a concentration inversion value was between 16.5% and 52.5%. The average relative error of the seasonal models  was 35.8%.

Table 5  Results verification of inversion models (2009-2012)

5 Discussion

The verification results show that the dataset is reliable, and data analysis show that: chlorophyll-a concentration in Poyang Lake had increased slowly from 2009 to 2012; It had a lowest concentration of each year from December to February, then rapidly rose to peak from March to July and fell down from August to November; In terms of spatial distribution, the high value area of chlorophyll-a concentration were located in the coastal waters, five rivers estuary water area, and the higher the concentration value was, the more significantly the spatial characteristics changed.

The temporal and spatial change characteristic of chlorophyll-a concentration in Poyang Lake is the results of the lake nutritional condition, water temperature, water level changes, storms and human activities affecting together. Its temporal variation is mainly affected by natural factors such as changes in water area and so on, while the differences in spatial distribution is mainly influenced by the surrounding human shipping, sand mining and agricultural activities.

Author Contributions

Wang, J. L. designed the study. Wang, J. L. and Zhang, Y. J. wrote the paper. Yang, F., Cao, X. M., Bai, Z. Q., Zhu, J. X., Chen, E. Y., Li, Y. F., Ran, Y. Y. contributed to the data processing, analysis and validation.

Acknowledgements

Special thanks are due to the Poyang Lake Laboratory for Wetland Ecosystem Research (PLWER) for providing the sampling data and logistics support.

References

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