Normalized Difference Chlorophyll Index 

/*
Normalized difference chlorophyll index
https://pdfs.semanticscholar.org/2fba/caa14adb43d5bc8d2dd274304f60814d933c.pdf
https://www.sciencedirect.com/science/article/pii/S0380133018301801 
https://www.researchgate.net/publication/297718964_Comparison_of_satellite_reflectance_algorithms_for_estimating_chlorophyll-a_in_a_temperate_reservoir_using_coincident_hyperspectral_aircraft_imagery_and_dense_coincident_surface_observations 
*/
function setup() {
   return {
      input: ["B04", "B05", "dataMask"],
      output: { bands: 4 }
   };
}

const ramp = [
   [-0.2, 0x313695],
   [0, 0xe0f3f8],
   [0.2, 0xfdae61],
   [0.4, 0xa50026],
];

const visualizer = new ColorRampVisualizer(ramp);

function evaluatePixel(samples) {
   let ndci = index(samples.B05, samples.B04);
   let imgVals = visualizer.process(ndci);
   return imgVals.concat(samples.dataMask)
}
/*
Normalized difference chlorophyll index
https://pdfs.semanticscholar.org/2fba/caa14adb43d5bc8d2dd274304f60814d933c.pdf
https://www.sciencedirect.com/science/article/pii/S0380133018301801 
https://www.researchgate.net/publication/297718964_Comparison_of_satellite_reflectance_algorithms_for_estimating_chlorophyll-a_in_a_temperate_reservoir_using_coincident_hyperspectral_aircraft_imagery_and_dense_coincident_surface_observations 
*/
function setup() {
    return {
        input: ["B03", "B04", "B05", "dataMask"],
        output: [
            { id: "default", bands: 4 },
            { id: "index", bands: 1, sampleType: "FLOAT32" },
            { id: "eobrowserStats", bands: 2, sampleType: 'FLOAT32' },
            { id: "dataMask", bands: 1 }
        ]
    };
}

const ramp = [
    [-0.2, 0x313695],
    [0, 0xe0f3f8],
    [0.2, 0xfdae61],
    [0.4, 0xa50026],
];

const visualizer = new ColorRampVisualizer(ramp);

function evaluatePixel(samples) {
    let val = index(samples.B05, samples.B04);
    // The library for tiffs works well only if there is only one channel returned.
    // So we encode the "no data" as NaN here and ignore NaNs on frontend.
    const indexVal = samples.dataMask === 1 ? val : NaN;
    const imgVals = visualizer.process(val);

    return {
        default: imgVals.concat(samples.dataMask),
        index: [indexVal],
        eobrowserStats: [val, isCloud(samples) ? 1 : 0],
        dataMask: [samples.dataMask]
    };
}

function isCloud(samples) {
    const NGDR = index(samples.B03, samples.B04);
    const bRatio = (samples.B03 - 0.175) / (0.39 - 0.175);
    return bRatio > 1 || (bRatio > 0 && NGDR > 0);
}
/*
Normalized difference chlorophyll index
https://pdfs.semanticscholar.org/2fba/caa14adb43d5bc8d2dd274304f60814d933c.pdf
https://www.sciencedirect.com/science/article/pii/S0380133018301801 
https://www.researchgate.net/publication/297718964_Comparison_of_satellite_reflectance_algorithms_for_estimating_chlorophyll-a_in_a_temperate_reservoir_using_coincident_hyperspectral_aircraft_imagery_and_dense_coincident_surface_observations 
*/
function setup() {
    return {
        input: ["B04", "B05"],
        output: {
            bands: 1,
            sampleType: "FLOAT32"
        }
    };
}

function evaluatePixel(samples) {
    return [index(samples.B05, samples.B04)]
}

Evaluate and Visualize

General description of the script

For Sentinel-2, the index looks like this:

\[NDCI = \frac{B5-B4}{B5+B4}\]

NDCI is an index that aims to predict the chlorophyll content in turbid productive waters. It is calculated using the red spectral band B04 with the red edge spectral band B05.

Description of representative images

Visualization of the NDCI index September 2021 over Pyramid Lake Nevada. NDCI

References