Google Earth Engine（GEE）——如何进行NDVI和EVI指数的图表展示？

阿里云国内75折回扣微信号：monov8

阿里云国际，腾讯云国际，低至75折。AWS 93折免费开户实名账号代冲值优惠多多微信号：monov8 飞机：@monov6

我们如何进行NDVI和EVI指数的图表展示，我们可以通过建立一个函数NDVI和EVI，然后用map遍历每一期影像，从而实现图表的展示，这我们使用sentinel2影像进行分析。

Sentinel-2卫星是由欧洲空间局（ESA）和欧洲联盟开发的一个卫星系统，它可以提供高分辨率和高质量的地球观测数据，特别是在多光谱图像方面。

以下是Sentinel-2卫星影像的相关信息：

分辨率：Sentinel-2卫星有两个多光谱传感器（MSI），分别具有10米、20米和60米的分辨率。其中，10米分辨率的传感器可以提供高精度的地表信息。
波段：Sentinel-2卫星的多光谱传感器可以捕捉13个波段的数据，包括红外和紫外波段。
数据提供方式：Sentinel-2卫星的数据由ESA提供，并且可以通过ESA的Sentinel数据门户进行访问和下载。此外，一些商业公司也提供了Sentinel-2卫星数据的订阅和使用服务。
应用领域：Sentinel-2卫星影像在许多领域都有广泛的应用，包括农业、林业、城市规划、自然资源管理和环境监测等。

Google Earth Engine（GEE）——如何进行NDVI和EVI指数的图表展示？_图表

函数：

setOptions(options)

Sets options used to style this chart.

Returns this chart.

Arguments:

this:ui.chart (ui.Chart):

The ui.Chart instance.

options (Object):

An object defining chart style options such as:

title (string) The title of the chart.
colors (Array) An array of colors used to draw the chart. Its format should follow the Google Visualization API's options: https://developers.google.com/chart/interactive/docs/customizing_charts

Returns: ui.Chart

ui.Chart.image.series(imageCollection, region, reducer, scale, xProperty)

Generates a Chart from an ImageCollection. Plots derived values of each band in a region across images. Usually a time series.

X-axis: Image, labeled by xProperty value.
Y-axis: Band value.
Series: Band names.

Returns a chart.

Arguments:

imageCollection (ImageCollection):

An ImageCollection with data to be included in the chart.

region (Feature|FeatureCollection|Geometry):

The region to reduce.

reducer (Reducer, optional):

Reducer that generates the values for the y-axis. Must return a single value. Defaults to ee.Reducer.mean().

scale (Number, optional):

Scale to use with the reducer in meters.

xProperty (String, optional):

Property to be used as the label for each image on the x-axis. Defaults to 'system:time_start'.

Returns: ui.Chart

代码：

var geometry = 
    /* color: #d63000 */
    /* displayProperties: [
      {
        "type": "rectangle"
      }
    ] */
    ee.Geometry.Polygon(
        [[[113.12962886187917, -8.101032847560129],
          [113.12962886187917, -8.12754392211754],
          [113.16533442828542, -8.12754392211754],
          [113.16533442828542, -8.101032847560129]]], null, false);
var S2A = ee.ImageCollection('COPERNICUS/S2_SR').filterDate('2021-01-01','2022-01-01').filterBounds(geometry);
// print(S2A);
var getIndices=function(image){
  var NDVI = image.normalizedDifference(['B8', 'B4']).rename('NDVI');
  var EVI = image.expression('2.5 * ((NIR - RED) / (NIR + 2.4 * RED + 1))', 
  {
    'NIR': image.select('B8'),
    'RED': image.select('B4')
  }).rename('EVI');
  return image.addBands([NDVI,EVI]);
};

var collection=S2A.map(getIndices);
// print(collection);

var chartEVI = ui.Chart.image.series({
  imageCollection: collection.select('EVI'),
  region: geometry,
  reducer: ee.Reducer.mean(),
  scale: 20
}).setOptions({
      title: 'Original EVI Time Series',
      interpolateNulls: false,
      vAxis: {title: 'EVI', viewWindow: {min: 0, max: 1}},
      hAxis: {title: '', format: 'YYYY-MM'},
      lineWidth: 1,
      pointSize: 4,
      series: {
        0: {color: '#238b45'},
      },

    })
print(chartEVI);

var chartNDVI = ui.Chart.image.series({
  imageCollection: collection.select('NDVI'),
  region: geometry,
  reducer: ee.Reducer.mean(),
  scale: 20
}).setOptions({
      title: 'Original EVI Time Series',
      interpolateNulls: false,
      vAxis: {title: 'EVI', viewWindow: {min: 0, max: 1}},
      hAxis: {title: '', format: 'YYYY-MM'},
      lineWidth: 1,
      pointSize: 4,
      series: {
        0: {color: '#238b45'},
      },

    })
print(chartNDVI);

阿里云国内75折回扣微信号：monov8

阿里云国际，腾讯云国际，低至75折。AWS 93折免费开户实名账号代冲值优惠多多微信号：monov8 飞机：@monov6

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