SENTINEL 2 WATER QUALITY APPLICATIONS IN SOUTH AFRICA: ASSESSMENT OF USER NEEDS AND POTENTIAL ALGORITHMIC APPROACHES

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1 SENTINEL 2 WATER QUALITY APPLICATIONS IN SOUTH AFRICA: ASSESSMENT OF USER NEEDS AND POTENTIAL ALGORITHMIC APPROACHES Stewart Bernard, Mark Matthews, Derek Griffith, Lisl Robertson-Lain, Hayley Evers-King May 2014, Sentinel 2 for Science, ESRIN

2 Earth Observation and Water Quality: The South African User Need On current trends, South Africa is headed for a crisis of water security and quality that will hamper the country s social and economic development, Centre for Development and Enterprise 2010 "Any decrease in the quality, and therefore usability, of water in South Africa by 1% may result in the loss of 200,000 jobs, a drop of 5,7% in disposable income per capita, and an increase of 5% or R18,1 billion in government spending, PlusEconomics/UASA (2010) Remote sensing based water quality measurements can play a major role in better managing the vital national water resource. Critical in a South African context is the need for water quality observations in small and typically eutrophic water bodies. This need is not currently met by existing earth observation sensors and programmes. South African Department of Water Affairs

3 South African User Requirements for EO Water Quality Table 1. Summary of key user needs for water quality products, driven primarily by the National Eutrophication Monitoring Programme (NEMP) of the Resource Quality Services and Water Management System of the South African Department of Water Affairs.

4 First order study of two algorithms to simulated MSI and OLCI data for Case 1 eutrophic waters Two approaches were taken to make a preliminary assessment of the potential performance of Sentinel 2/MSI and Sentinel 3/OLCI for South African inland water types. 1.Combined Hydrolight/MODTRAN modelling approach to derive hyperspectral remotesensing reflectance R rs and TOA radiance L TOA for a range of water with increasing algal biomass in Case 1 conditions and atmosphere types, and the multi-spectral R rs and L TOA subset to MSI and OLCI bands. R rs data have 10% random noise added to simulate atmospherically corrected L2 data. 2.The second approach used the MERIS bottom-of-rayleigh reflectance & match-up data set used for the original MPH derivation (Matthews et al 2012) and produced an approximate simulation of the 30 nm wide MSI band centred at 665 nm by averaging the MERIS bands centred at 665 and 681 nm. Application of MPH and MPH-S2 (665, 705, 865 nm line height type algorithm) applied to top-of-atmosphere & bottom-of-rayleigh data from 1. and 2. Application of Equivalent Algal Population semi-analytical inversion algorithm to simulated R rs + 10% noise data

5 New models for eutrophic waters & phytoplankton type Robertson-Lain, Bernard and Evers- King, Optics Express, 2014 (forthcoming)

6 New models for eutrophic waters & phytoplankton type Nadir viewing TOA spectral ranges for three typical target cases: A) Moderate biomass diatom-type waters with a pronounced fluorescence peak at ± 683 nm B) High biomass red tide type waters with pronounced reflectance peak at ± 709 nm C) Surface scum of cyanobacteria with vegetation type spectra D) The relative contribution of the water signal to that measured at TOA, clearly demonstrating the need for the suite of narrow bands in the nm region.

7 Example modelled eutrophic data for MSI and OLCI Hyperspectral remote-sensing reflectance and extracted Sentinel 2/MSI bands (left) and Sentinel 3/OLCI bands (right) for Case 1 waters with Chl a = 8.7 mg m -3 highlighting relative spectral coverage for mesotrophic waters with prominent fluorescence at ± 685 nm Hyperspectral remote-sensing reflectance and extracted Sentinel 2/MSI bands (left) and Sentinel 3/OLCI bands (right) for Case 1 waters with Chl a = 92 mg m-3, highlighting relative spectral coverage for a highly eutrophic water type with prominent backscattering/absorption related peak at ± 705 nm.

8 Algorithms: MPH Chlorophyll-a product Objective: Operational trophic status determination Matthews et al., 2012 Maximum Peak Height (MPH) algorithm (Matthews et al., 2012) Scu m Source: MERIS FR data Temporal resolution: 3/5 days Availability: 2002 to 04/2012 Spatial scale: 300 m Range: 1 to 1000 mg/m 3 Accuracy: +- 30% Sterkfontein Heyshope Vaal Hartbeespoort Oligotrophic Chl < 10 Mesotrophic 10 < Chl <20 Eutrophic 20 < chl < 30 Hypertrophic Chl > 30 Validated? Yes. Using DWA data, global dataset. Matthews et al 2012

9 Algorithms: MPH Cyanobacteria product Objective: Health, water treatment, early warnings, toxicity warnings. WARNING: CYANOBACTERIA DETECTED! Dark green = Cyano scum White = cyanobacteria Black = other water Winter cyano bloom in Midmar in 2005 identified by Oberholster and Botha (2007) was validated by product. VALIDATE! Surface scum Description: Detects cyanobacteria for chla > +-20 mg/m3 Source: MERIS FR data Temporal resolution: 3/5 days Availability: 2002 to 2012/04 Spatial scale: 300 m Validated? Using local and global case studies. Matthews et al 2012

10 Algorithms: MPH Floating vegetation / macrophytes product Reflectance Typical vegetation spectrum Objectives: Vegetation clearing, open water management, invasives control Source: MERIS FR data Floating vegetation / macrophytes Temporal product Wavelength resolution: 3/5 days Eichhornia crassipes A perennial aquatic plant, free-floating or anchored in shallow water, usually 10-20cm high but up to 1m when growing in dense mats. It has dark shiny green leaves in rosettes with distinctive, swollen, bladder-like petioles. Pale violet or blue flowers in 8-10 flowered spikes appear from November to April. This aquatic plant invades dams and slow-moving rivers. Magenta = floating macrophytes Availability: 2002 to 2012/04 Spatial scale: 300 m Validated: Using local and global case studies. Vaal Dam Matthews et al 2012

11 Albert Falls Barberspan Bloemhof Brandvlei Bronkhorstspruit Chrissiesmeer Darlington Erfenis Fairview Flag Boshielo Gariep Goedertrou Grassridge Grootdraai Hartbeespoort Heyshope Inanda Jericho 50 RESERVOIRS Klipvoor Krugersdrift Kuhlange Kalkfontein Koppies Kwena Lake Msingazi Lake Sibayi Loskop Lubisi Midmar Mokolo Ncora Allemanskraal Ntshingwayo Pongola Roodekoppies Rustfontein Spioenkop Spitskop Theewaterskloof Tzaneen Sterkfontein Umtata Vaal Vaalkop Vanderkloof Voelvlei Witbank Woodstock Xonxa Zaaihoek

12 Algorithm application: decadal characterisation of the trophic status of the inland water resource Loskop Dam. Time series for Chl and area coverage for cyanobacteria and surface scum for Loskop Dam (upper). Deconvolution of Chl into seasonal signal, showing anomalies and yearly averages (lower) 10 years of MPH algorithm and MERIS FR data applied to 50 South African freshwater reservoirs, providing decadal analysis of eutrophication and cyanobacterial occurence Mark Matthews, PhD, awarded May 2014 in prep RSE, SAJS

13 Algorithms: Equivalent Algal Population Chlorophyll a product A semi-analytical model capable of providing algal size/population descriptors used here to assess relative performance of S2 and S3 for algorithms requiring all available spectral bands (and therefore full atmospheric correction) Evers-King, Bernard, Robertson-Lain, & Probyn, Optics Express, 2014

14 Simulated first order MPH performance S2 & S3 Preliminary performance of MPH algorithm for OLCI bands from modelled TOA as expected looks good for Case 1 waters & highlights overly simplistic modelling. Preliminary performance of MPH algorithm for MSI bands (665, 705, 865 nm) from modelled TOA. Looks very promising and width of 665 nm band appears not to be an issue Preliminary performance of MPH algorithm for MSI bands (665, 705, 865 nm) from MERIS averaging. Looks very promising, will need dynamic scaling flags in absence of 620 nm band

15 Simulated first order EAP performance S2 & S3 Correlation between [Chl a] used in the generation of the simulated data and those returned by the EAP algorithm for Sentinel 2 simulated data (blue circles) and Sentinel 3 simulated data (red circles). For comparison, an Algal 1 type approach has also been applied to both Sentinel 2 and Sentinel 3 simulated data, with the 510/560 ratio excluded from the Sentinel 2 application. [Chl a] predicted by the EAP algorithm using the Sentinel 2 and 3 bands. Relatively good performance is achieved for both sensors, despite the large range covered by the simulated data set. Overestimation is seen at lower biomass (<3 mg m 3 ), where IOPS are not sufficiently constrained - performance from Algal 1 type approaches is better at these biomass ranges

16 Adjacency/Atmospheric considerations The MERIS MPH algorithm applied to a clear, high altitude oligotrophic reservoir (Sterkfontein) in the mountainous region of the Drakensberg, South Africa. The chl-a image is on the left and the flags are shown on the right. Red = adjacency flagged, pink = vegetation (shoreline). Adjacency effects on algorithm products will depend on water body size, trophic state and spectral nature of surrounding land. Small, oligotrophic water bodies are likely to be worst affected Some consideration needs to be made of the relative benefits of bottom of Rayleigh vs aerosol corrected products and associated algorithm choice/performance obviously dependent on user needs and nature of target water bodies

17 Adjacency/Atmospheric considerations Planned new adjacency effect modelling based on N surface land classes and synoptically distributed point spread function will allow more precise determination of synoptic adjacency effects for variable water body geometry, synoptic water & land type, and atmosphere type

18 Conclusions This preliminary study shows that Sentinel 2/MSI could provide very valuable data for South African eutrophication monitoring, with potential for the Sentinel 2 constellation to meet many South African user needs.. The MPH-S2 algorithm appears to perform extremely well, and is not impacted by the lack of an explicit fluorescence band at 681nm as Sentinel 3/OLCI has although more work is needed on lower sensitivity ranges. Such an algorithm bypasses the need for an aerosol correction and is therefore much more easily applied to L1C Sentinel 2 data. The EAP algorithm, dependent on an appropriate aerosol correction procedure which could be challenging, also performs very well at least as regards Chl-a retrievals and appears not to suffer greatly from the more restricted band set available with S2 as opposed to S3. Further algorithm performance will be assessed pre-launch with more comprehensive modelled and simulated data (including Case 2 waters, explicit adjacency modelling and regard for the sensitivity and dynamic range of S2), and post-launch with validation measurements and analyses. Optimal spatial binning with regard to SNR, target complexity and product value needs to be determined. Highest value will come from incorporating Sentinel 2 data into a multi-mission water quality ground segment, ideally using minimally platform adapted equivalent algorithms & validation effort.