第30回日本情報地質学会講演会 講演要旨集 013-014頁 2019年

Satellite remote sensing for estimating water content and organic matter
distributions in estuary sediments
Vu Thi Thu Thuy*, Nguyen Thi Thu Ha**, Nguyen Thuy Linh** and Katsuaki Koike***Graduate School of Engineering, Kyoto University, Katsura C1–2–215, Kyoto 615–8540, Japan.

E–mail: koike.katsuaki.5x@kyoto–u.ac.jp (K. Koike)**Department of Geology, VNU University of Science, Hanoi, Vietnam.

Key words: Remote sensing, Water content, Organic matter, Estuary sediment

1. Introduction
Tidal flat, the submerged and exposed daily coastal
wetland, is a deposition field of sedimentary materials bytides or rivers. It also has an important role for trappingand filtering sediments and suspended contaminantsfrom discharged river water. Recently, several studies
have addressed a significant relationship between thereflectance of electromagnetic waves and tidal flats’sediment properties such as sediment organic matter(Kaplan and Milliken, 2014) and water content (Lobelland Asner, 2002; Small et al., 2009; Ngoc et al., 2013).
Additionally, the use of satellite imagery and different
classification techniques have demonstrated effectively tomap the distribution of water content of sediments(Rainey et al., 2000). Therefore, satellite remote sensing
is a promising method for monitoring the changes ofsediment properties and coastal geo-environments.

2. Materials and methods
The Van Uc estuary belonging to the Thai Binh river issituated in the northern part of the Red River Delta
coastal zone in the north of Vietnam. As the discharged
area of the Thai Binh river system, this area has complex
environment components with mangrove and intertidaleco-systems (Fig. 1).
Total 20 sediment samples were taken from the tidal
flat of Van Uc estuary on 17 December 2017 by spoon andpreserved in plastic bags under cool condition after
measuring their reflectance spectra by the field
spectrometers RS3. In a laboratory, all samples weremeasured continually for their reflectances under dry andwet conditions.
All samples were dried at 105oC for 24h in an electricoven to detect the water content as follow:
𝑤=𝑚1 − 𝑚0
× 100
𝑚0 − 𝑚 𝑐

where w is water content of sample (%), mc is the weightof cup containing sample (g), 𝑚1 is the weight of wet
sediment with cup (g), and 𝑚0 is the weight of dry sample
with cup (g).

Figure 1. Land covers in the Van Uc estuary in which mudflat
area is colored yellow.

Total samples were also divided into several parts foradding water to take more samples at different situations
from dry to saturation. Then, they were dried at 5500C for
5h in an electric oven to measure the organic matter fromthe next equation:
𝑚1 − 𝑚2𝑜𝑚 =× 100
𝑚1 − 𝑚𝑐
where om is organic matter of sample (%), mc is theweight of cup containing sample (g), 𝑚1 is the weight ofdry sediment with cup before dry at 5500C (g), and 𝑚2 is
the weight of dry sample with cup after dry at 5500C (g).
Sentinel 2A (S2A), one of the Earth observation satellites
has a multi-spectral imager including 13 spectral bands
spanning from the visible blue to shortwave infrared (SWIR)
region. The S2A image used in this study was acquired at
17:46 GMT on 25 September 2016 at the UTM zone 48Nwith 10, 20, and 60 m resolutions. The S2A Toolbox in theSentinel Application Platform ver. 5.0 on Windows 10
was used to resample the image at the 10 m resolution.
Then, a traditional empirical line method was applied as
an atmospheric correction of the S2A scene. Finally, the14

distribution map of sediment properties was generated by
the density slicing tool of ENVI 5.3 and ArcGIS 10.5.

3. Results and discussion
The measured water contents of all samples were
distributed widely from dry condition (0 %) to saturated
condition (54.7 %). The organic matter contents were also
ranged from 2.91 % to 7.67 %. The measured reflectancespectra of all the sediment samples at different watercontents were in the range from visible to SWIR region.
Obviously, the reflectance spectra of dry samples arehigher than the wet samples, and the reflectance spectraof samples at dry condition have the same trend in thatthey increase with the wavelength.

where WC is the water content of the surface sediment oftidal flats (%).
The organic matter had weak correlated with thesimulated S2A single band reflectance, because their Rs
were from 0.1 to 0.4. On the contrary, the organic matterswere well correlated with the in-situ S2A band ratios withhigher R. The maximum R was specified for therelationship between the organic matter and the ratio of
the band in NIR (B7) versus the band in visible deep blue(B1) with R = 0.9. Accordingly, the most suitable equation
to estimate the organic matter from the ratio of S2A band
7 versus band 1 is expressed as:

OM (%) = 0.0134 e 1.0045(B7/B1)(2)

where OM is the organic matter in the tidal flat sediment (%).
Applying Equations (1) and (2) to the S2A image sceneacquired on 25th June 2018, distributions of water content
and organic matter in the surface sediments in the study
area were mapped (Fig. 2). The water contents are
distributed from 0 to 50.1 % and have the trend with thehighest value near the shoreline Tien Lang, while thelowest value was located in the north tidal flat and somesmall areas in the middle estuary. The organic matters areranged from 2% to 8.2% with the highest along the inlandand decreasing toward the off-shore.

4. Conclusion

A strong correlation was clarified between the in-situreflectance spectra data and the sediment features of thesurface sediment samples in the Van Uc estuary. The
result confirmed the strong effect of the water content andorganic matter on the reflectance spectra and the potentialof Sentinel 2A band ratio to estimate the distribution ofthem in the surface sediments in tidal flat of the estuary.

References
David, B.L., Gregopy, P.A. (2002) Moisture effects on soilreflectance. Soil Science Society of America Journal,
66(3), pp.722-727.
Kaplan H. H., Milliken R. E., Knoll A. H. (2014) Detectionof organic matter in sediments with near-infrared
reflectance spectroscopy. Effects of Mineralogy, Albedo
Figure 2. Distribution map of organic matter estimated from
Sentinel 2A acquired on 25th September 2016.

There was a small correlation between the watercontent and the simulated S2A single band reflectance inthe total wavelength range with R (correlation coefficient)smaller than 0.5. On the other hand, most band ratios ofthe simulated S2A had strong correlation with the watercontent. The highest R was for the relationship of watercontent with the band ratio of NIR (band 8a, B8a) andSWI (band 11, B11) with R = 0.84. Therefore, it can be
estimated water content from the ratio of S2A band 8aversus band 11 by the next equation:

WC (%) = [ln (B8a/B11) - ln0.9135)]/0.0143(1)

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