The use of MOD09 product and in situ data in a reservoir

1. The use of MOD09 product and in situ data in a reservoir Valério, A.M.; Kampel, M.; Stech, J.L. e-mail: alineval, milton, stech [@dsr.inpe.br] COSPAR Training Course – Fortaleza, Brazil.

2. Study Area

3. DATA • MYD09 • Remote Sensing Reflectance • Chl-a

4. Material and Methods 13 samples Feb. 29 day to March 02, 2008 and previous 16 + 17 from past campaings

5. Linear Spectral Mixture Model • Avoid problems of spectral mixing in the spatial resolution of MODIS • Considers the value of the pixel in any spectral band as the linear combination of the response of each component within the pixel. • Endmembers pure pixel:more representative of the spectral response of the constituents that were selected: phytoplankton, dissolved organic matter and inorganic suspended particle

6. Linear Spectral Mixture Model Image fractions and color composition. chlorophyll-a, (b) organic matter, (c) suspension sediment, (d) color composite R(a)G(b)B(c) for the Manso Reservoir, MT.

7. Remote Sensing Reflectance • Provides quantitative and qualitative information of significant optically active components (OAC) in the water • Important link between the images generated by the satellites sensors and the in situ concentration of OAC

8. Remote Sensing Reflectance • The Rrs can be defined by the ratio of upward radiance Luw (v, φ, z) by the downward irradiance Ed (z) where is v the Zenith angle, φ is the azimuth, and z is the upward vertical axis of coordinates.

9. Rrs MYD09 x Rrs FieldSpec • Hyperspectral data was integrated to simulate the values of the bands of multispectral MODIS sensor • MYD09: reflectance of the surface/∏ (Bryan Franz sugestion,2008) • 10 samples avaiable

10. Rrs MYD09 x Rrs FieldSpec • For a range of 95% • The spatial resolution of 500m from MOD09 results in heterogeneity of the pixel, i.e. it is formed by integrating the response of different targets. Thus, it might be noted that there was genuine agreement between the estimates of Rrs orbital with in situ.

11. NIR Rrs MYD09 x in situ chl-a • 27 data sets were obtained from chl-a : ranging from 10 μg/L to 1170 μg/L with an average of 460 μg/L • In the values of chl-a a base-ten logarithm was used • With the MYD09 scenes (250m), on the same campaign days, were extracted from the values of Rrs of the band NIR centered at 856 nm for the same points of chl-a collection made.

12. NIR Rrs MYD09 x in situ chl-a • fourth order polynomial, with R2 of 0.69 (RMSE= 0.34; n=27; p<0.05)

13. Conclusion • The MYD09 product proved to be efficient for classifying the waters of the reservoir, which agrees with the analysis of data obtained from in situ data. With the LSMM it was observed that the largest quantities of chl-a and suspended sediment were located in the arms of the rivers. The organic matter, however, was seen in higher concentration in the main body of the reservoir. • The Rrs obtained in situ through FieldSpec and simulated for the bands of MODIS agreed fairly well with those obtained by the product MYD09, except for the blue band, which due to the shorter wavelengths, is more affected by atmospheric scattering. • The near infrared band provided by the product MYD09 scenes with a resolution of 250m can be used to monitor the chl-a in reservoirs from empirical models of regression.