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Sentinel-1 算法
使用集合让一切井井有条
根据您的偏好保存内容并对其进行分类。
Sentinel-1 是哥白尼计划下的一个由欧盟资助、由欧洲航天局 (ESA) 执行的空间任务。哨兵 1 号可收集多种极化和分辨率的 C 频段合成孔径雷达 (SAR) 图像。由于雷达数据需要使用多种专用算法才能获得校准过且经过正射校正的图像,因此本文档介绍了如何在 Earth Engine 中预处理 Sentinel-1 数据。
Sentinel-1 数据是在上升轨道和下降轨道期间使用多种不同的仪器配置、分辨率和波段组合收集的。由于这种异质性,通常需要先将数据过滤为同质子集,然后才能开始处理。下文的元数据和过滤部分概述了此流程。
如需创建 Sentinel-1 数据的均匀子集,通常需要使用元数据属性过滤集合。用于过滤的常见元数据字段包括以下属性:
transmitterReceiverPolarisation
:['VV']、['HH']、['VV', 'VH'] 或 ['HH', 'HV']
instrumentMode
:'IW'(干涉宽幅)、'EW'(超宽幅)或 'SM'(条状图)。如需了解详情,请参阅此参考文档。
orbitProperties_pass
:'ASCENDING' 或 'DESCENDING'
resolution_meters
:10、25 或 40
resolution
:“M”(中)或“H”(高)。如需了解详情,请参阅此参考文档。
以下代码会按 transmitterReceiverPolarisation
、instrumentMode
和 orbitProperties_pass
属性过滤 Sentinel-1 集合,然后为地图中显示的多个观测组合计算复合图,以演示这些特征对数据的影响。
Code Editor (JavaScript)
// Load the Sentinel-1 ImageCollection, filter to Jun-Sep 2020 observations.
var sentinel1 = ee.ImageCollection('COPERNICUS/S1_GRD')
.filterDate('2020-06-01', '2020-10-01');
// Filter the Sentinel-1 collection by metadata properties.
var vvVhIw = sentinel1
// Filter to get images with VV and VH dual polarization.
.filter(ee.Filter.listContains('transmitterReceiverPolarisation', 'VV'))
.filter(ee.Filter.listContains('transmitterReceiverPolarisation', 'VH'))
// Filter to get images collected in interferometric wide swath mode.
.filter(ee.Filter.eq('instrumentMode', 'IW'));
// Separate ascending and descending orbit images into distinct collections.
var vvVhIwAsc = vvVhIw.filter(
ee.Filter.eq('orbitProperties_pass', 'ASCENDING'));
var vvVhIwDesc = vvVhIw.filter(
ee.Filter.eq('orbitProperties_pass', 'DESCENDING'));
// Calculate temporal means for various observations to use for visualization.
// Mean VH ascending.
var vhIwAscMean = vvVhIwAsc.select('VH').mean();
// Mean VH descending.
var vhIwDescMean = vvVhIwDesc.select('VH').mean();
// Mean VV for combined ascending and descending image collections.
var vvIwAscDescMean = vvVhIwAsc.merge(vvVhIwDesc).select('VV').mean();
// Mean VH for combined ascending and descending image collections.
var vhIwAscDescMean = vvVhIwAsc.merge(vvVhIwDesc).select('VH').mean();
// Display the temporal means for various observations, compare them.
Map.addLayer(vvIwAscDescMean, {min: -12, max: -4}, 'vvIwAscDescMean');
Map.addLayer(vhIwAscDescMean, {min: -18, max: -10}, 'vhIwAscDescMean');
Map.addLayer(vhIwAscMean, {min: -18, max: -10}, 'vhIwAscMean');
Map.addLayer(vhIwDescMean, {min: -18, max: -10}, 'vhIwDescMean');
Map.setCenter(-73.8719, 4.512, 9); // Bogota, Colombia
Python 设置
如需了解 Python API 以及如何使用 geemap
进行交互式开发,请参阅
Python 环境页面。
import ee
import geemap.core as geemap
Colab (Python)
# Load the Sentinel-1 ImageCollection, filter to Jun-Sep 2020 observations.
sentinel_1 = ee.ImageCollection('COPERNICUS/S1_GRD').filterDate(
'2020-06-01', '2020-10-01'
)
# Filter the Sentinel-1 collection by metadata properties.
vv_vh_iw = (
sentinel_1.filter(
# Filter to get images with VV and VH dual polarization.
ee.Filter.listContains('transmitterReceiverPolarisation', 'VV')
)
.filter(ee.Filter.listContains('transmitterReceiverPolarisation', 'VH'))
.filter(
# Filter to get images collected in interferometric wide swath mode.
ee.Filter.eq('instrumentMode', 'IW')
)
)
# Separate ascending and descending orbit images into distinct collections.
vv_vh_iw_asc = vv_vh_iw.filter(
ee.Filter.eq('orbitProperties_pass', 'ASCENDING')
)
vv_vh_iw_desc = vv_vh_iw.filter(
ee.Filter.eq('orbitProperties_pass', 'DESCENDING')
)
# Calculate temporal means for various observations to use for visualization.
# Mean VH ascending.
vh_iw_asc_mean = vv_vh_iw_asc.select('VH').mean()
# Mean VH descending.
vh_iw_desc_mean = vv_vh_iw_desc.select('VH').mean()
# Mean VV for combined ascending and descending image collections.
vv_iw_asc_desc_mean = vv_vh_iw_asc.merge(vv_vh_iw_desc).select('VV').mean()
# Mean VH for combined ascending and descending image collections.
vh_iw_asc_desc_mean = vv_vh_iw_asc.merge(vv_vh_iw_desc).select('VH').mean()
# Display the temporal means for various observations, compare them.
m = geemap.Map()
m.add_layer(vv_iw_asc_desc_mean, {'min': -12, 'max': -4}, 'vv_iw_asc_desc_mean')
m.add_layer(
vh_iw_asc_desc_mean, {'min': -18, 'max': -10}, 'vh_iw_asc_desc_mean'
)
m.add_layer(vh_iw_asc_mean, {'min': -18, 'max': -10}, 'vh_iw_asc_mean')
m.add_layer(vh_iw_desc_mean, {'min': -18, 'max': -10}, 'vh_iw_desc_mean')
m.set_center(-73.8719, 4.512, 9) # Bogota, Colombia
m
Sentinel-1 预处理
Earth Engine 'COPERNICUS/S1_GRD'
Sentinel-1 ImageCollection
中的图像由处理为分贝 (dB) 的反射率系数 (σ°) 的 Level-1 地面距离检测 (GRD) 场景组成。回波系数表示每单位地面面积的目标回波面积(雷达截面面积)。由于它可以相差几个数量级,因此会按 10*log10σ° 的公式转换为 dB。它用于衡量辐射的地形是否会优先将入射的微波辐射散射到远离 SAR 传感器的位置 (dB < 0),还是朝向 SAR 传感器的位置 (dB > 0)。这种散射行为取决于地形的物理特性,主要是地形元素的几何图形及其电磁特性。
Earth Engine 使用以下预处理步骤(由 Sentinel-1 Toolbox 实现)来推导每个像素的反射率系数:
- 应用轨道文件
- 使用重建的轨道文件(如果无法使用重建的轨道文件,则使用精确的轨道文件)更新轨道元数据。
- GRD 边界噪声去除
- 移除场景边缘上的低强度噪声和无效数据。
(截至 2018 年 1 月 12 日)
- 热噪声消除
- 移除子扫描区域中的叠加噪声,以帮助减少多扫描区域采集模式下场景的子扫描区域之间的不连续性。
(此操作不适用于 2015 年 7 月之前生成的图片)
- 应用辐射校准值
- 使用 GRD 元数据中的传感器校准参数计算反射强度。
- 地形校正(正射校正)
数据集备注
- 由于山坡上存在伪影,因此未应用射电地形平坦化。
- 无单位的回波系数会转换为 dB,如上所述。
- 目前无法提取 Sentinel-1 SLC 数据,因为 Earth Engine 不支持包含复杂值的图片,因为在进行金字塔处理期间无法对这些图片求平均值,而不会丢失相位信息。
- 系统不会提取 GRD SM 资产,因为 S1 工具箱中的边缘噪声去除操作中的
computeNoiseScalingFactor()
函数不支持 SM 模式。
如未另行说明,那么本页面中的内容已根据知识共享署名 4.0 许可获得了许可,并且代码示例已根据 Apache 2.0 许可获得了许可。有关详情,请参阅 Google 开发者网站政策。Java 是 Oracle 和/或其关联公司的注册商标。
最后更新时间 (UTC):2025-07-25。
[null,null,["最后更新时间 (UTC):2025-07-25。"],[[["\u003cp\u003eSentinel-1, part of the Copernicus Programme, provides C-band SAR data for various applications.\u003c/p\u003e\n"],["\u003cp\u003ePre-processing of Sentinel-1 data in Earth Engine involves filtering by metadata and applying specific algorithms.\u003c/p\u003e\n"],["\u003cp\u003eMetadata filtering is crucial for creating a homogeneous subset of data based on polarization, instrument mode, and orbit properties.\u003c/p\u003e\n"],["\u003cp\u003eEarth Engine automatically applies preprocessing steps including orbit file application, noise removal, radiometric calibration, and terrain correction to Sentinel-1 GRD data.\u003c/p\u003e\n"],["\u003cp\u003eThe data represents backscatter coefficient (σ°) in decibels (dB) and undergoes several processing steps to derive this value.\u003c/p\u003e\n"]]],["Sentinel-1 data, collected by the European Space Agency, is pre-processed in Earth Engine to obtain calibrated imagery. Key actions include filtering the heterogeneous data using metadata properties like `transmitterReceiverPolarisation`, `instrumentMode`, `orbitProperties_pass`, `resolution_meters`, and `resolution`. This is demonstrated in code examples using JavaScript and Python, calculating temporal means for visualization. Preprocessing steps involve applying orbit files, removing noise, radiometric calibration, and terrain correction to derive the backscatter coefficient in decibels (dB).\n"],null,["# Sentinel-1 Algorithms\n\n[Sentinel-1](https://earth.esa.int/web/sentinel/missions/sentinel-1) is a\nspace mission funded by the European Union and carried out by the European Space Agency\n(ESA) within the Copernicus Programme. Sentinel-1 collects C-band synthetic aperture\nradar (SAR) imagery at a variety of polarizations and resolutions. Since radar data\nrequires several specialized algorithms to obtain calibrated, orthorectified imagery,\nthis document describes pre-processing of Sentinel-1 data in Earth Engine.\n\nSentinel-1 data is collected with several different instrument configurations,\nresolutions, band combinations during both ascending and descending orbits. Because\nof this heterogeneity, it's usually necessary to filter the data down to a\nhomogeneous subset before starting processing. This process is outlined below in the\n[Metadata and Filtering](/earth-engine/guides/sentinel1#metadata-and-filtering) section.\n\nMetadata and Filtering\n----------------------\n\nTo create a homogeneous subset of Sentinel-1 data, it will usually be necessary to\nfilter the collection using metadata properties. The common metadata fields used for\nfiltering include these properties:\n\n1. `transmitterReceiverPolarisation`: \\['VV'\\], \\['HH'\\], \\['VV', 'VH'\\], or \\['HH', 'HV'\\]\n2. `instrumentMode`: 'IW' (Interferometric Wide Swath), 'EW' (Extra Wide Swath) or 'SM' (Strip Map). See [this\n reference](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-1-sar/acquisition-modes) for details.\n3. `orbitProperties_pass`: 'ASCENDING' or 'DESCENDING'\n4. `resolution_meters`: 10, 25 or 40\n5. `resolution`: 'M' (medium) or 'H' (high). See [this\n reference](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-1-sar/resolutions/level-1-ground-range-detected) for details.\n\nThe following code filters the Sentinel-1 collection by\n`transmitterReceiverPolarisation`, `instrumentMode`, and\n`orbitProperties_pass` properties, then calculates composites for several\nobservation combinations that are displayed in the map to demonstrate how these\ncharacteristics affect the data.\n\n### Code Editor (JavaScript)\n\n```javascript\n// Load the Sentinel-1 ImageCollection, filter to Jun-Sep 2020 observations.\nvar sentinel1 = ee.ImageCollection('COPERNICUS/S1_GRD')\n .filterDate('2020-06-01', '2020-10-01');\n\n// Filter the Sentinel-1 collection by metadata properties.\nvar vvVhIw = sentinel1\n // Filter to get images with VV and VH dual polarization.\n .filter(ee.Filter.listContains('transmitterReceiverPolarisation', 'VV'))\n .filter(ee.Filter.listContains('transmitterReceiverPolarisation', 'VH'))\n // Filter to get images collected in interferometric wide swath mode.\n .filter(ee.Filter.eq('instrumentMode', 'IW'));\n\n// Separate ascending and descending orbit images into distinct collections.\nvar vvVhIwAsc = vvVhIw.filter(\n ee.Filter.eq('orbitProperties_pass', 'ASCENDING'));\nvar vvVhIwDesc = vvVhIw.filter(\n ee.Filter.eq('orbitProperties_pass', 'DESCENDING'));\n\n// Calculate temporal means for various observations to use for visualization.\n// Mean VH ascending.\nvar vhIwAscMean = vvVhIwAsc.select('VH').mean();\n// Mean VH descending.\nvar vhIwDescMean = vvVhIwDesc.select('VH').mean();\n// Mean VV for combined ascending and descending image collections.\nvar vvIwAscDescMean = vvVhIwAsc.merge(vvVhIwDesc).select('VV').mean();\n// Mean VH for combined ascending and descending image collections.\nvar vhIwAscDescMean = vvVhIwAsc.merge(vvVhIwDesc).select('VH').mean();\n\n// Display the temporal means for various observations, compare them.\nMap.addLayer(vvIwAscDescMean, {min: -12, max: -4}, 'vvIwAscDescMean');\nMap.addLayer(vhIwAscDescMean, {min: -18, max: -10}, 'vhIwAscDescMean');\nMap.addLayer(vhIwAscMean, {min: -18, max: -10}, 'vhIwAscMean');\nMap.addLayer(vhIwDescMean, {min: -18, max: -10}, 'vhIwDescMean');\nMap.setCenter(-73.8719, 4.512, 9); // Bogota, Colombia\n```\nPython setup\n\nSee the [Python Environment](/earth-engine/guides/python_install) page for information on the Python API and using\n`geemap` for interactive development. \n\n```python\nimport ee\nimport geemap.core as geemap\n```\n\n### Colab (Python)\n\n```python\n# Load the Sentinel-1 ImageCollection, filter to Jun-Sep 2020 observations.\nsentinel_1 = ee.ImageCollection('COPERNICUS/S1_GRD').filterDate(\n '2020-06-01', '2020-10-01'\n)\n\n# Filter the Sentinel-1 collection by metadata properties.\nvv_vh_iw = (\n sentinel_1.filter(\n # Filter to get images with VV and VH dual polarization.\n ee.Filter.listContains('transmitterReceiverPolarisation', 'VV')\n )\n .filter(ee.Filter.listContains('transmitterReceiverPolarisation', 'VH'))\n .filter(\n # Filter to get images collected in interferometric wide swath mode.\n ee.Filter.eq('instrumentMode', 'IW')\n )\n)\n\n# Separate ascending and descending orbit images into distinct collections.\nvv_vh_iw_asc = vv_vh_iw.filter(\n ee.Filter.eq('orbitProperties_pass', 'ASCENDING')\n)\nvv_vh_iw_desc = vv_vh_iw.filter(\n ee.Filter.eq('orbitProperties_pass', 'DESCENDING')\n)\n\n# Calculate temporal means for various observations to use for visualization.\n# Mean VH ascending.\nvh_iw_asc_mean = vv_vh_iw_asc.select('VH').mean()\n# Mean VH descending.\nvh_iw_desc_mean = vv_vh_iw_desc.select('VH').mean()\n# Mean VV for combined ascending and descending image collections.\nvv_iw_asc_desc_mean = vv_vh_iw_asc.merge(vv_vh_iw_desc).select('VV').mean()\n# Mean VH for combined ascending and descending image collections.\nvh_iw_asc_desc_mean = vv_vh_iw_asc.merge(vv_vh_iw_desc).select('VH').mean()\n\n# Display the temporal means for various observations, compare them.\nm = geemap.Map()\nm.add_layer(vv_iw_asc_desc_mean, {'min': -12, 'max': -4}, 'vv_iw_asc_desc_mean')\nm.add_layer(\n vh_iw_asc_desc_mean, {'min': -18, 'max': -10}, 'vh_iw_asc_desc_mean'\n)\nm.add_layer(vh_iw_asc_mean, {'min': -18, 'max': -10}, 'vh_iw_asc_mean')\nm.add_layer(vh_iw_desc_mean, {'min': -18, 'max': -10}, 'vh_iw_desc_mean')\nm.set_center(-73.8719, 4.512, 9) # Bogota, Colombia\nm\n```\n\nSentinel-1 Preprocessing\n------------------------\n\nImagery in the Earth Engine `'COPERNICUS/S1_GRD'` Sentinel-1\n`ImageCollection` is consists of Level-1 Ground Range Detected\n(GRD) scenes processed to backscatter coefficient (σ°) in\ndecibels (dB). The backscatter coefficient represents\ntarget backscattering area (radar cross-section) per unit ground area. Because it can\nvary by several orders of magnitude, it is converted to dB as\n10\\*log~10~σ°. It measures whether the radiated terrain scatters\nthe incident microwave radiation preferentially away from the SAR sensor\ndB \\\u003c 0) or towards the SAR sensor dB \\\u003e 0). This scattering behavior depends on the\nphysical characteristics of the terrain, primarily the geometry of the terrain elements\nand their electromagnetic characteristics.\n\nEarth Engine uses the following preprocessing steps (as implemented by the\n[Sentinel-1 Toolbox](https://sentinel.esa.int/web/sentinel/toolboxes/sentinel-1))\nto derive the backscatter coefficient in each pixel:\n\n1. **Apply orbit file**\n - Updates orbit metadata with a restituted [orbit file](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-1-sar/pod/products-requirements) (or a precise orbit file if the restituted one is not available).\n2. **GRD border noise removal**\n - Removes low intensity noise and invalid data on scene edges. (As of January 12, 2018)\n3. **Thermal noise removal**\n - Removes additive noise in sub-swaths to help reduce discontinuities between sub-swaths for scenes in multi-swath acquisition modes. (This operation cannot be applied to images produced before July 2015)\n4. **Application of radiometric calibration values**\n - Computes backscatter intensity using sensor calibration parameters in the GRD metadata.\n5. **Terrain correction** (orthorectification)\n - Converts data from ground range geometry, which does not take terrain into account, to σ° using the [SRTM 30 meter DEM](/earth-engine/datasets/catalog/USGS_SRTMGL1_003) or the [ASTER DEM](https://asterweb.jpl.nasa.gov/gdem.asp) for high latitudes (greater than 60° or less than -60°).\n\nDataset Notes\n-------------\n\n- Radiometric Terrain Flattening is not being applied due to artifacts on mountain slopes.\n- The unitless backscatter coefficient is converted to dB as described above.\n- Sentinel-1 SLC data cannot currently be ingested, as Earth Engine does not support images with complex values due to inability to average them during pyramiding without losing phase information.\n- GRD SM assets are not ingested because the `computeNoiseScalingFactor()` function in the [border noise removal operation in the S1 toolbox](https://github.com/senbox-org/s1tbx/blob/master/s1tbx-op-calibration/src/main/java/org/esa/s1tbx/calibration/gpf/RemoveGRDBorderNoiseOp.java) does not support the SM mode."]]