ee_export_table_to_Drive

`ee_export_table_to_Drive`通常是一个Google Earth Engine (GEE) 的Python脚本函数，它用于将地球引擎中的表数据导出到Google Drive。GEE是一个强大的开源平台，用于处理、分析全球范围的大规模遥感数据。这个函数允许用户将计算好的结果保存到云端存储服务，便于后续的数据共享和长期访问。

当你运行这样的函数，你需要提供一些参数，比如要导出的表名、目标文件名以及是否加密等选项。例如：

from google.cloud import earthengine
ee = earthengine.connect()

# 假设table_name是你要导出的表的名称
table_id = 'your_table_name'
destination_file_name = 'output.csv'

# 导出到Google Drive
task = ee.batch.Export.table.toDrive(
    table=ee.FeatureCollection(table_id),
    description='Export to Google Drive',
    folder='Your_Google_Drive_Folder',  # 替换为你Google Drive的共享文件夹ID
    fileNamePrefix=destination_file_name,
    fileFormat='CSV'
)

task.start()  # 启动任务

相关问题

ee_export_vector_to_drive用法

`ee_export_vector_to_drive()`是Google Earth Engine (GEE) 提供的一个工具函数，用于将地球引擎中的矢量数据导出到Google Drive。这个函数主要用于地理空间数据分析后的结果可视化或共享，步骤如下：

1. 首先，你需要有一个已经在地球引擎中处理过的矢量图层，比如几何形状或者点云等。

import ee

# 假设你已经有了名为"vector_layer"的图层
vector_layer = ee.FeatureCollection('your/specific/layer')

2. 然后，调用`ee_export_vector_to_drive`函数，传入图层以及一些导出选项，例如文件名、驱动器位置、文件格式（默认是GeoJSON）等。

export_options = {
    'description': 'My exported vector layer',
    'folder': 'Your_Google_Drive_Folder',  # 你的Google Drive目录
    'fileNamePrefix': 'my_layer',  # 导出文件前缀
    'fileFormat': 'GeoJSON',  # 文件格式
}

task = ee.batch.Export.table.toDrive(
    collection=vector_layer,
    description=export_options['description'],
    folder=export_options['folder'],
    fileNamePrefix=export_options['fileNamePrefix'],
    fileFormat=export_options['fileFormat']
)

3. 最后，你可以提交任务以开始导出过程。

task.start()

请注意，由于涉及到GCP服务，可能会有速率限制或需要Google Earth Engine账户有足够的权限。

function maskL8sr(image) { // The third bit is cloud and the fourth bit is cloudshadow。 var cloudShadowBitMask = 1 << 4; var cloudsBitMask = 1 << 3; // Get QA_PIXEL band. var qa = image.select('QA_PIXEL'); // Set the cloud mask to zero var mask = qa.bitwiseAnd(cloudShadowBitMask).eq(0) .and(qa.bitwiseAnd(cloudsBitMask).eq(0)); // Update the cloudmask return image.updateMask(mask) .select("SR_B.*") .copyProperties(image, ["system:time_start"]); } // Map the function over 3 months of data and take the median. // Load Landsat-8 surface reflectance data. var landsat8 = ee.ImageCollection("LANDSAT/LC08/C02/T1_L2") .filterBounds(table) .filterDate('2019-1-01','2020-1-1') // Pre-filter to get less cloudy granules. .filter(ee.Filter.lte('CLOUD_COVER',5)) .map(maskL8sr) .median() .clip(table); landsat8 = landsat8.uint16(); print(landsat8);// visualize the datasets var rgbVis = { min: 8400, max: 15000, gamma:1.5, bands: ['SR_B4', 'SR_B3', 'SR_B2'], }; Map.addLayer(landsat8,rgbVis,'landsat8'); var imgselect = landsat8.select('SR_B2','SR_B3','SR_B4','SR_B5'); //Export image to google drive and then download Export.image.toDrive({ image:imgselect, description:'2019yaogan', scale:30, region:table, folder:"result", crs: "EPSG:4326", maxPixels: 1e13 });将这一段利用Landsat的代码改写成使用哨兵2

Sure here's the modified code to use Sentinel-2 instead of Landsat:

function maskS2clouds(image) {
  var qa = image.select('QA60');

  // Bits 10 and 11 are clouds and cirrus, respectively.
  var cloudBitMask = 1 << 10;
  var cirrusBitMask = 1 << 11;

  // Both flags should be set to zero, indicating clear conditions.
  var mask = qa.bitwiseAnd(cloudBitMask).eq(0)
    .and(qa.bitwiseAnd(cirrusBitMask).eq(0));

  // Return the masked image, scaled to reflectance, without the QA bands.
  return image.updateMask(mask)
    .select('B.*')
    .multiply(0.0001)
    .copyProperties(image, ['system:time_start']);
}

// Load Sentinel-2 surface reflectance data.
var sentinel2 = ee.ImageCollection('COPERNICUS/S2_SR')
  .filterBounds(table)
  .filterDate('2019-01-01', '2020-01-01')
  .map(maskS2clouds)
  .median()
  .clip(table);

sentinel2 = sentinel2.uint16();
print(sentinel2);

// Visualize the datasets
var rgbVis = {
  min: 0.0,
  max: 3000.0,
  gamma: 1.5,
  bands: ['B4', 'B3', 'B2'],
};
Map.addLayer(sentinel2, rgbVis, 'Sentinel-2');

var imgselect = sentinel2.select('B2', 'B3', 'B4', 'B8');

// Export image to Google Drive
Export.image.toDrive({
  image: imgselect,
  description: '2019yaogan',
  scale: 10,
  region: table,
  folder: 'result',
  crs: 'EPSG:4326',
  maxPixels: 1e13
});

Note that Sentinel-2's surface reflectance data has a different band naming convention and scale factor compared to Landsat, so the code has been modified accordingly.