GEE（4）：计算两个变量（影像）之间的相关性并绘制散点图-Toy模板网

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最近使用Google Earth Engine（GEE）分析了一下高程和NDVI的相关性，并绘制二者的散点图，计算其决定系数。
计算时主要用到了GEE中的图表 ui.Chart.image.byRegion()，将研究区域内的高程和NDVI的散点图先绘制出来，再添加趋势线，计算决定系数，就可以知道二者之间的相关性有多高。
NDVI-高程散点图及决定系数计算实现代码如下：

//研究区域，可自己绘制或导入
var roi = /* color: #d63000 */ee.Geometry.Polygon(
        [[[104.34385678174718, 27.233899188878446],
          [114.80284115674718, 28.477166904461537],
          [117.52745053174718, 34.61402019968164],
          [111.99034115674718, 40.99546927185892],
          [95.11534115674718, 37.87379212761336]]]);

//导入 DEM
var DEM=ee.Image("CGIAR/SRTM90_V4").reproject('SR-ORG:6974',null,500);
//从DEM中抽取样本点，这里选取500个
var rroi = DEM.sample(
    {region: roi, scale: 30, numPixels: 500, geometries: true});

//导入NDVI数据
var ndvi=ee.ImageCollection('MODIS/006/MOD13A1')
                     .filter(ee.Filter.date('2020-01-01', '2020-02-01'))
                     .first()
                     .multiply(0.0001);

// 设置图表属性，包括样式颜色等
var chartStyle = {
  title: 'NDVI-DEM',
  hAxis: {
    title: 'elevation',
    titleTextStyle: {italic: false, bold: true},
    gridlines: {color: 'FFFFFF'}
  },
  vAxis: {
    title: 'NDVI',
    titleTextStyle: {italic: false, bold: true},
    gridlines: {color: 'FFFFFF'},
  },
  pointSize: 4,
  dataOpacity: 0.6,

  chartArea: {backgroundColor: 'EBEBEB'},
  
  //添加趋势线
  trendlines: {
    0: {  // add a trend line to the 1st series
      type: 'polynomial',  // or 'polynomial', 'exponential'
      color: 'green',
      showR2:'true', //show R2 cofficient
      lineWidth: 5,
      opacity: 0.2,
      visibleInLegend: true,
    }
  }
};

//绘制散点图
var charten=ui.Chart.image.byRegion({
  image:ndvi.select('NDVI'),
  regions:rroi,
  reducer:ee.Reducer.mean(),
  scale:500,
  xProperty: 'elevation'
  });
charten.setChartType('ScatterChart').setOptions(chartStyle);
print(charten)

结果如图所示：
影像相关性分析,GEE,ui,python,GEE,决定系数,散点图ui.Chart
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这里还做了温度和高程之间的关系，实现代码：

// Load SRTM elevation data.
var elev = ee.Image('CGIAR/SRTM90_V4').select('elevation');

// Subset Colorado from the TIGER States feature collection.
var colorado = ee.FeatureCollection('TIGER/2018/States')
                   .filter(ee.Filter.eq('NAME', 'Colorado'));

// Draw a random sample of elevation points from within Colorado.
var samp = elev.sample(
    {region: colorado, scale: 30, numPixels: 500, geometries: true});

// Load PRISM climate normals image collection; convert images to bands.
var normClim = ee.ImageCollection('OREGONSTATE/PRISM/Norm81m').toBands();

// Define the chart and print it to the console.
var chartte = ui.Chart.image
                .byRegion({
                  image: normClim.select(['01_tmean', '07_tmean']),
                  regions: samp,
                  reducer: ee.Reducer.mean(),
                  scale: 500,
                  xProperty: 'elevation'
                })
                .setSeriesNames(['Jan', 'Jul'])
                .setChartType('ScatterChart')
                .setOptions({
                  title: 'Average Monthly Colorado Temperature by Elevation',
                  hAxis: {
                    title: 'Elevation (m)',
                    titleTextStyle: {italic: false, bold: true}
                  },
                  vAxis: {
                    title: 'Temperature (°C)',
                    titleTextStyle: {italic: false, bold: true}
                  },
                  pointSize: 4,
                  dataOpacity: 0.6,
                  colors: ['1d6b99', 'cf513e'],
                  trendlines: {
                    0: {  // add a trend line to the 1st series
                    type: 'linear',  // or 'polynomial', 'exponential'
                    color: 'green',
                    showR2:'true', //R2 cofficient
                    lineWidth: 5,
                    opacity: 0.2,
                    visibleInLegend: true,
                    },
                    1: {  // add a trend line to the 1st series
                    type: 'linear',  // or 'polynomial', 'exponential'
                    color: 'green',
                    showR2:'true', //R2 cofficient
                    lineWidth: 5,
                    opacity: 0.2,
                    visibleInLegend: true,
                    }
                  }});
print(chartte);

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结果如图：
影像相关性分析,GEE,ui,python,GEE,决定系数,散点图ui.Chart
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以及绘制植被指数随时间变化的曲线图：

// Import the example feature collection and subset the glassland feature.
var grassland = ee.FeatureCollection('projects/google/charts_feature_example')
                    .filter(ee.Filter.eq('label', 'Grassland'));

// Load MODIS vegetation indices data and subset a decade of images.
var vegIndices = ee.ImageCollection('MODIS/006/MOD13A1')
                     .filter(ee.Filter.date('2010-01-01', '2020-01-01'))
                     .select(['NDVI', 'EVI']);

// Set chart style properties.
var chartStyle = {
  title: 'Average Vegetation Index Value by Day of Year for Grassland',
  hAxis: {
    title: 'Day of year',
    titleTextStyle: {italic: false, bold: true},
    gridlines: {color: 'FFFFFF'}
  },
  vAxis: {
    title: 'Vegetation index (x1e4)',
    titleTextStyle: {italic: false, bold: true},
    gridlines: {color: 'FFFFFF'},
    format: 'short',
    baselineColor: 'FFFFFF'
  },
  series: {
    0: {lineWidth: 3, color: 'E37D05', pointSize: 7},
    1: {lineWidth: 7, color: '1D6B99', lineDashStyle: [4, 4]}
  },
  chartArea: {backgroundColor: 'EBEBEB'},
  trendlines: {
    0: {  // add a trend line to the 1st series
      type: 'linear',  // or 'polynomial', 'exponential'
      color: 'green',
      showR2:'true',
      lineWidth: 5,
      opacity: 0.2,
      visibleInLegend: true,
    }
  }
};

// Define the chart.
var chart =
    ui.Chart.image
        .doySeries({
          imageCollection: vegIndices,
          region: grassland,
          regionReducer: ee.Reducer.mean(),
          scale: 500,
          yearReducer: ee.Reducer.mean(),
          startDay: 1,
          endDay: 365
        })
        .setSeriesNames(['EVI', 'NDVI']);

// Apply custom style properties to the chart.
chart.setOptions(chartStyle);

// Print the chart to the console.
print(chart);