Customize Scatter Plot Legend (Series)

Question

Currently, I am working with the feature group chart – a scatter plot. The legend (series) displayed on the chart is automatically sorted by the value that first appeared on it, so it looks like a random number (attached image). I wanted to customize the legend by sorting the values from small to large but found nothing (case: 1 - 11). Does anyone have a solution?

My code:

Map.addLayer(target_image);
Map.addLayer(world);

function generatesScatter(feature, xProperty, yProperty, series, title, hAxis_title, xAxis_title, hMinMax, vMinMax) {
    var scatter_chart = ui.Chart.feature.groups(feature, xProperty, yProperty, series)
        .setChartType('ScatterChart')
        .setOptions({
            title: title,
            pointSize: 2,
            dataOpacity: 0.4,
            hAxis: {
                viewWindow: {
                    min: hMinMax[0],
                    max: hMinMax[1],
                },
                'title': hAxis_title,
                titleTextStyle: { italic: false, bold: true },
            },
            vAxis: {
                viewWindow: {
                    min: vMinMax[0],
                    max: vMinMax[1],
                },
                'title': xAxis_title,
                titleTextStyle: { italic: false, bold: true }
            },
        });

    return scatter_chart
}

function stratifiedSampling(img_input, numPoints, classBand, classVal, pts, scale, region) {
    // Sampling
    var str_point = img_input.stratifiedSample({
        numPoints: numPoints,
        classBand: classBand,
        // Class to be sampled
        classValues: classVal,
        // Points each class
        classPoints: [pts, pts, pts, pts, pts, pts, pts, pts, pts, pts, pts],
        scale: scale,
        // region: region,
        geometries: false
    })
    return str_point
}

// Class references 
// Define a dictionary that maps the original values to new numeric values
var classDict = ee.Dictionary({
    "Bush / Shrub": 1,
    "Swamp Shrub": 2,
    "Plantation Forest": 3,
    "Primary Swamp Forest": 4,
    "Secondary Swamp Forest": 5,
    "Primary Mangrove Forest": 6,
    "Dryland Agriculture": 7,
    "Secondary Mangrove Forest": 8,
    "Primary Dry Land Forest": 9,
    "Secondary Dry Land Forest": 10,
    "Shrub-Mixed Dryland Farm": 11
});

// List of class
var classList = classDict.keys()

// List of class values
// Using for remapping raster value from 0 to 10 --> 1 to 11
var valueList = classDict.values().sort()

// SEA
var SEA_select = world.select('VV_mean', 'VV_stdDev', 'VH_mean', 'VH_stdDev');

// K-Means Clustering
// Create training dataset.
var training = SEA_select.sample({
    region: aoi,
    scale: 1000,
    numPixels: 10000
});

// Start unsupervised clusterering algorithm and train it.
var kmeans = ee.Clusterer.wekaKMeans(11).train(training);

// Cluster the input using the trained clusterer.
var result = SEA_select.cluster(kmeans);

// Remapping class raster
var cluster_image = result.select("cluster")
    .remap([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10], valueList)
    .rename("cluster")

// Add SEA Band to image input
var cluster_concat = cluster_image.addBands(world)

// Cluster - Str sampling
var cluster_sample = stratifiedSampling(
    cluster_concat, 1100, "cluster", valueList, 100, 1000, aoi
)

// Cluster - mean chart
var cluster_mean_chart = generatesScatter(
    cluster_sample, 
    "VV_mean", 
    "VH_mean", 
    "cluster", 
    "VV Mean .vs VH Mean - Cluster Results", 
    "VV Mean", 
    "VH Mean",
    [-22, -1.5],
    [-30, -5]
)

print("Cluster Mean:",cluster_mean_chart)

Link:

https://code.earthengine.google.com/893419f54566b127298e523384106f64

Answer 1

I have found a reference to this issue, from a person named Ujaval Gandhi. The point is that there is no built-in feature. The solution is to reformat the data, so we can make the columns sequential. The function used is DataTable Charts. This is done via client-side, with asynchronous, making it a bit slower.

Main code:

var rows = class_id.map(function (cluster) {
    var features = feature.filter(ee.Filter.eq(series, cluster))
    return features.toList(features.size()).map(function (f) {
        var f = ee.Feature(f);
        var cluster = f.get(series);
        var vv = f.get(xProperty);
        var vh = f.get(yProperty);
        var empty = ee.List([{}, {}, {}, {}, {}, {}, {}, {}, {}, {}, {}])
        var row = empty.set(ee.Number(cluster).subtract(1), { v: vh });
        var fullrow = ee.List([{ v: vv }]).cat(row)
        return { c: fullrow }
    })
}).flatten()

rows.evaluate(function (rowsResult) {
    var dataTable = {
        cols: [
            { id: 'vv', label: 'VV', type: 'number' },
            { id: 'vh1', label: '1', type: 'number' },
            { id: 'vh2', label: '2', type: 'number' },
            { id: 'vh3', label: '3', type: 'number' },
            { id: 'vh4', label: '4', type: 'number' },
            { id: 'vh5', label: '5', type: 'number' },
            { id: 'vh6', label: '6', type: 'number' },
            { id: 'vh7', label: '7', type: 'number' },
            { id: 'vh8', label: '8', type: 'number' },
            { id: 'vh9', label: '9', type: 'number' },
            { id: 'vh10', label: '10', type: 'number' },
            { id: 'vh11', label: '11', type: 'number' },
        ],
        rows: rowsResult
    };

Full code: https://code.earthengine.google.com/948d885b2328ec2051216d52dc45eba9