APEX EnMAP

Sentinel-3 PRISMA

1. Abstract

Mantua Lakes are formed by three shallow fluvial lakes (surface 6.2 km2, averaged depth 3 m), located in the centre of the Padana plain, their water status is strongly affected by ag-riculture. Excess growth of macrophyte vegetation and dys-trophic water conditions makes the waters very productive; the most intense phytoplankton blooms, characterized by high biomass in surface, occur in the summer *1+.

4. Method

2. Study Area

Fig. 1: Location of the study area.

Fig. 2: Spectral absorption coefficient aph*() per each functional group (i). Fig. 4: Processing scheme, input simulation, model run, output decomposition and sensitivity index retrieval (R language - R 2014).

3. Data

1. Bresciani M., Rossini M., Cogliati S., Colombo R., Morabito G., Matta E., Pinardi M., Giardino C. (2013) - Analysis of intra- and inter-daily chlorophyll-a dynamics in Mantua Superior Lake with spectroradiometric continuous measures. Marine and Freshwater Research, 64: 1-14.

2. Giardino, C., Bresciani, M., Valentini, E., Gasperini, L., Bolpagni, R., & Brando, V. E. (2015). Airborne hyperspectral da-ta to assess suspended particulate matter and aquatic vegetation in a shallow and turbid lake. Remote Sensing of Environment, 157, 48-57.

3. Manzo C., Bresciani M., Giardino C., Braga F., Bassani C. (2015) Sensitivity analysis of a bio-optical model for Italian

lakes focused on Landsat-8, Sentinel-2 and Sentinel-3. European Journal of Remote Sensing, 48, 17-32

4. R (2014). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL http://www.R-project.org/.

5. Saltelli A., Annoni P., Azzini I., Campolongo F., Ratto M., Tarantola S. (2010) - Variance based sensitivity analysis of model output. Design and estimator for the total sensitivity index. Comp. Phys. Comm., 181(2): 259-270.

6. Zambrano-Bigiarini M. (2013) - Sobol_sensitivity.R. Available online at: http://ipsc.jrc.ec.europa.eu/fileadmin/repository/eas/sensitivity/software/sobol_sensitivity.R (active at 7th of July 2013).

OutputOutput decompositiondecomposition

SENSITIVITY INDEX RETRIEVALSENSITIVITY INDEX RETRIEVAL

The model works in the 400-750 nm wavelength range, while the model parametrization is based on WQP concen-trations and SIOPs. The SA is performed according *3,4,5+

The results provided information relating the sensitivity of water reflectance as observable with band setting of the latest generation sensors depending on different PFT.

References

6. Conclusion

Table 1: Spectral ranges with Si values of principal main functional groups

Output variance due to Single functional group Vi

Total output variance V

MainMain Effect (SEffect (S ii))

These findings shows that the combined spectral response of sensor and the PFT interaction have a spectral effect that must be considered for the retrieval of these parameters by means of semi-empirical indices based on the band combinations. The research activity is part of the EU FP7 INFORM (Grant No. 606865, www.copernicus-inform.eu/).

The spectral interaction between PFT is

between 5 and 15% of total output. In particu-

lar PRISMA was the best in the spectral sensiti-

vity definition in the first part of the spectrum,

while APEX in the second and third domain.

The Sentinel-3 showed lower performances al-

though in the third domain it was able to iden-

tify some spectral features.

PFT with their specific absorption coefficients, aph*i(), were obtained by field survey carried out from May

to September of 2011 and 2014 and referred to samples in which these were dominant in the waters.

Chlorophyta (Green Algae), Cyanobacteria with phycocyanin (PC), Cyanobacteria and Cryptophytes with

phycoerythrin (PE), Diatoms with carotenoids and mixed phytoplankton (Phyto).

Range (nm) Main Funciontal

Group PRISMA APEX EnMAP Sentintel-3

450<wl<500 Phyto 0,7 0,69 0,7 0,72

560<wl<565 PE 0,47 0,47 0,51 0,46

585<wl<595 PE-PC 0,46 0,52 0,5 -

615<wl<625 PC 0,42 0,41 0,45 0,44

635<wl<650 PC 0,33 - 0,44 0,36 - 0,44 0,39 - 0,40 -

690<wl<715 Phyto- PE - Diatom 0,28 - 0,25- 0,23 0,31 - 0,28- 0,19 0,29 - 0,27- 0,21 0,17 - 0,25 - 0,28

Fig. 6: PFTs Interaction graphs.

The analysis of Main Effect show (Table 1) that the contribution of Phyto in all sensors is high in the first part of the spectum up to 500 nm. PC have a sensitivity peak in the range 600 - 650 nm visible in all sensors. PE have a peak in the range 560 - 590 and 670 - 690 nm. APEX is the best in the spectral shape definition due to its higher spectral resolution. In the range 690—715 nm the Phyto, PE and Diatom have high main effects. The graph of Figure 5 show an example for Sentinel-3. Fig. 5: Spectral Main effect of PFTs for Sentinel –3 sensor

The types of phytoplankton and their concentrations are used to describe the status of water and the processes inside of this. This work investigates bio-optical modeling of Phytoplankton Functional Types (PFT) in terms of pigment composition demonstrating the capability of remote sensing to recognize freshwater phytoplankton. We studied the sensitivity of simulated water reflec-tance (with band setting of APEX, EnMAP, PRISMA and Sentinel-3 (S3)) of Mantua lakes (Italy) by variance based method *3,4,5+. The bio-optical model takes into account the reflectance dependency on PFT (which affect the absorption coefficients) and fixing the geometric conditions of light field, concentrations of water quality parameters (WQPs) and the other inherent optical proper-ties (IOPs). Results highlight the sensitivity of new generation sensors to different pigments of PFT in the water.

INTERACTION (1- Si) EnMAP APEX PRISMA Sentinel-3

5. Results

Max

Average

Min

Wavelength (nm)

Wavelength (nm)

Ab

sorp

tio

n c

oeffi

cie

nt

Wavelength (nm)

Wavelength (nm) Wavelength (nm)

Fig. 3: Spectral responses of Sensor Data.

Wavelength (nm)

Spe

ctra

l re

spo

nse

Spe

ctra

l re

spo

nse

Sp

ect

ral r

esp

on

se

Spe

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spo

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APEX

EnMAP

Sentinel-3 Output Reflectance

R (0-;)

PRISMA

Spectral Response

Ciro Manzo1*, Cristiana Bassani1 , Monica Pinardi2, Claudia Giardino2, Mariano Bresciani2

1CNR-IIA, Institute of Atmospheric Pollution Research, Research Area of Rome 1, Via Salaria Km 29,300, 00016-Monterotondo Scalo, Rome, Italy - phone: +39 06 90672712 2Optical Remote Sensing Group, CNR-IREA, Via Bassini 15, 20133-Milan, Italy

Sensitivity in forward modeled hyperspectral reflectance due to phytoplankton groups

The output

is explained only

by single variability

The output

is due also to va-

riable interaction