研究目的
The objective of this study is to establish a quantitative model for estimating the Chl-a and the TSS concentrations in irrigation ponds in Higashihiroshima, Japan, using field hyperspectral measurements and statistical analysis.
研究成果
The study successfully developed models for estimating Chl-a and TSS concentrations in irrigation ponds using water surface reflectance spectral data. ISE-PLS regression analysis showed high potential for predicting Chl-a and TSS based on field hyperspectral measurements, with ISE wavebands selection enhancing predictive ability. The findings suggest that ISE-PLS based on field hyperspectral measurements can be used to estimate water Chl-a and TSS concentrations in irrigation ponds.
研究不足
The study is limited to irrigation ponds in Higashihiroshima, Japan, and may not be applicable to other water bodies without further validation. The predictive models may also be affected by the specific conditions of the study area, such as water clarity and the presence of other optically active constituents.
1：Experimental Design and Method Selection:
Field experiments were conducted in six ponds and spectral readings for Chl-a and TSS were obtained from six field observations in
2：Two spectral indices, the ratio spectral index (RSI) and the normalized difference spectral index (NDSI), and a partial least squares (PLS) regression were used for statistical approaches. Sample Selection and Data Sources:
20 Water samples were collected from six irrigation ponds in Higashihiroshima, Japan.
3：List of Experimental Equipment and Materials:
ASD FieldSpec HandHeld-2 spectrometer, spectrophotometer (UVmini-1240, SHIMADZU Co.), oven drier (SANYO Electric Co.).
4：Experimental Procedures and Operational Workflow:
Spectral readings were taken approximately 1 m above the water surface between 10:30 and 13:00 on a day with clear skies. Water samples were collected immediately after spectral reflectance measurements.
5：Data Analysis Methods:
The predictive abilities were compared using the coefficient of determination (R2), the root mean squared error of cross validation (RMSECV) and the residual predictive deviation (RPD).
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