NIR Red Models for Estimation of Chlorophyll a Concentration Application to Aircraft and Satellite Data

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1 NIR Red Models for Estimation of Chlorophyll a Concentration

Povazhnyi 2, Daniela Gurlin 1, and Alexander A.

2 Southern Scientific Center of the Russian Academy of

1 1 NIR Red Models for Estimation of Chlorophyll a Concentration Application to Aircraft and Satellite Data Wesley J. Moses 1, *, Anatoly A. Gitelson 1, Vladislav Saprygin 2, Sergey Berdnikov 2, Vasiliy Povazhnyi 2, Daniela Gurlin 1, and Alexander A. Gilerson 3 1 University of Nebraska Lincoln, NE, USA. 2 Southern Scientific Center of the Russian Academy of Sciences, Rostov on Don, Russia 3 The City College of the City University of New York, NY, USA. *Currently at Naval Research Lab, Washington, D.C.

2 NIR red Algorithms for Satellite Data Required Conditions 2 Algorithm should be Consistently highly accurate over a wide range of chl a concentrations Resistant to variations in biophysical characteristics of water Sufficiently resistant to atmospheric effects or can be coupled with a reliable atmospheric correction procedure

3 Application of NIR Red Model to Airborne Data 3 AISA Eagle (Airborne Imaging Spectrometer for Applications) maximum of 256 continuous spectral channels range 4 97 nm max. spectral resolution 2.3 nm peak signal to noise ratio 49:1 spatial resolution 2 m at 1, ft above ground

4 Data 4 Five data collection campaigns on the Fremont State Lakes 2 July, 14 July, 26 Sep, 25 Oct, and 19 Nov of 8 Date Min. Median Max. Mean Standard Deviation Coefficient of Variation Number of Stations 2 July July Sep Oct Nov Descriptive statistics of chl-a data from the five campaigns

5 5 Two Band NIR Red Model for AISA Data Chl-a (mg m -3 ) 25 y = x r 2 = y = 7.718x r 2 =.89 8 y = x r 2 = Jul 5 14 Jul 26 Sep Chl-a (mg m -3 ) Chl-a (mg m -3 ) R 74 /R 676 R 74 /R 676 R 74 /R 676 Chl-a (mg m -3 ) 8 y = 18.15x r 2 =.87 8 y = 156.6x r 2 = Oct 19 Nov Chl-a (mg m -3 ) R 74 /R 676 R 74 /R 676 Chl-a (mg m -3 ) 8 2-Jul 14-Jul 4 26-Sep 25-Oct 19-Nov R 74 /R 676

6 At Sensor Reflectance Spectra Before Atmospheric Correction Jul(a).5 (e) 19 Nov.5.4 Rrs (Sr -1 ) Rrs (Sr -1 ) Wavelength (nm) Wavelength (nm) Before atmospheric correction After QUAC atmospheric correction Chl-a (mg m -3 ) 8 2-Jul 14-Jul 4 26-Sep 25-Oct 19-Nov R 74 /R 676 Chl-a (mg m -3 ) 8 4 R 2 =.92 8 NE Calibration 19-Nov R 74 /R Jul 14-Jul 26-Sep 25-Oct (Moses 9; Moses et al. 12a)

7 Chlorophyll a Maps from AISA Eagle Data 7

8 Application to Airborne Data Conclusion 8 MERIS NIR red model highly accurate, even without atmospheric correction Relationship affected by non uniform atmospheric effects Atmospheric correction by relative adjustment of input parameters resulted in conformity of the slope and offset for all the AISA images With robust atmospheric correction, the two band MERIS NIR red model can be calibrated to accurately estimate chl a concentration

9 Application of NIR red Algorithms to MERIS Data 9 Data: 18 campaigns between March and October in 8 1 on the Taganrog Bay and Azov Sea Taganrog Bay Azov Sea (maps from

10 Application of NIR red Algorithms to MERIS Data 1 18 stations from 8 and 113 stations from stations for calibration 9 1 stations for validation Min Max Median Mean Descriptive statistics for chl a concentration (mg m 3 ) in the calibration dataset Min Max Median Mean Descriptive statistics for chl a concentration (mg m 3 ) in the validation dataset

11 Calibration of NIR red Algorithms with MERIS bands 11 8 Azov Sea/Taganrog Bay Chl-a (mg m -3 ) 8 Azov Sea 4 MERIS 3 Band 8 NE Line r 2 = (1/R 665-1/R 78 ) x R 753 Chl-a (mg m -3 ) MERIS 2 Band 8 Azov Sea 4 8 NE Line r 2 = R 78 /R 665 Moses et al. 9 Three band MERIS NIR red Algorithm: Two band MERIS NIR red Algorithm: 1 1 R R R ] Chl-a [ Chl a [ R665 R ] 37.94

12 Validation of NIR red algorithms 8 1 Azov Sea 12 MERIS 3 Band MERIS 2 Band In Situ Chl-a (mg m -3 ) :1 Line RMSE = 6.68 mg m -3 MAE = 5.12 mg m Estimated Chl-a (mg m -3 ) In Situ Chl-a (mg m -3 ) :1 Line RMSE = 6.4 mg m -3 MAE = 4.57 mg m Estimated Chl-a (mg m -3 ) (Moses et al. 12b)

13 13

14 Validation of Advanced NIR red Algorithms (Gilerson et al. 1) 8 1 Azov Sea Advanced MERIS 3 Band Advanced MERIS 2 Band Chl-a R R R Chl-a R R :1 Line 1 1:1 Line In Situ Chl-a (mg m -3 ) 9 3 RMSE = 5.91 mg m -3 MAE = 4.71 mg m Estimated Chl-a (mg m -3 ) In Situ Chl-a (mg m -3 ) 9 3 RMSE = 5.92 mg m -3 MAE = 4.32 mg m Estimated Chl-a (mg m -3 ) (Moses et al. 12b)

15 Hyperspectral Imager for the Coastal Ocean (HICO) (taken from Gitelson et al. 11b) 15

16 Challenges & Limitations Temporal Variation of Water Quality 16 Satellite captures image in a few seconds in situ data collection takes hours Problem is magnified when same day cloud free image is not available The effect of temporal variability is not uniform but specific to each water body

17 Challenges & Limitations Within Pixel Spatial Heterogeneity Point in situ data may not represent satellite pixel area (MERIS 2 m x 29 m; MODIS 1 km x 1km) 17 Plot of fluorescence readings along a transect on the Azov Sea/Taganrog Bay Fluorometer Values km 3 m 3 m 3 m Distance (m)

18 Atmospheric Correction Challenges & Limitations 18 No atmospheric correction procedure worked consistently well for MODIS Data Bright Pixel Atmospheric Correction (Moore et al. 1999) gave good results for MERIS data Nevertheless, it yielded negative reflectances for several stations

19 Conclusion 19 The NIR red models are consistently highly accurate over a wide range of chl-a concentrations, including low tomoderate values The NIR red models need not be re parameterized for different water bodies The two band MERIS NIR red model is well suited for estimating chl-a concentration in turbid productive waters using satellite data With well adapted in situ data collection techniques, the NIRred algorithms can be robustly calibrated Results point to the possibility of universal NIR red algorithms that can be broadly applied to satellite data from inland, estuarine, and coastal waters around the globe

20 Acknowledgements Field data collection in the Azov Sea and Taganrog Bay were conducted by teams at the Institute for Environmental Quality, Kiev, Ukraine, and the Southern Scientific Center of the Russian Academy of Sciences, Rostov on Don, Russia Thank You! Contact:

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