Google Earth Engine ——数据全解析专辑（COPERNICUS/S1_GRD）20154至今哨兵-1号合成孔径雷达 (SAR) 数据集

The Sentinel-1 mission provides data from a dual-polarization C-band Synthetic Aperture Radar (SAR) instrument at 5.405GHz (C band). This collection includes the S1 Ground Range Detected (GRD) scenes, processed using the Sentinel-1 Toolbox to generate a calibrated, ortho-corrected product. The collection is updated daily. New assets are ingested within two days after they become available.

This collection contains all of the GRD scenes. Each scene has one of 3 resolutions (10, 25 or 40 meters), 4 band combinations (corresponding to scene polarization) and 3 instrument modes. Use of the collection in a mosaic context will likely require filtering down to a homogeneous set of bands and parameters. See this article for details of collection use and preprocessing. Each scene contains either 1 or 2 out of 4 possible polarization bands, depending on the instrument's polarization settings. The possible combinations are single band VV or HH, and dual band VV+VH and HH+HV:

1. VV: single co-polarization, vertical transmit/vertical receive
2. HH: single co-polarization, horizontal transmit/horizontal receive
3. VV + VH: dual-band cross-polarization, vertical transmit/horizontal receive
4. HH + HV: dual-band cross-polarization, horizontal transmit/vertical receive

Each scene also includes an additional 'angle' band that contains the approximate incidence angle from ellipsoid in degrees at every point. This band is generated by interpolating the 'incidenceAngle' property of the 'geolocationGridPoint' gridded field provided with each asset.

Each scene was pre-processed with Sentinel-1 Toolbox using the following steps:

1. Thermal noise removal
2. Radiometric calibration
3. Terrain correction using SRTM 30 or ASTER DEM for areas greater than 60 degrees latitude, where SRTM is not available. The final terrain-corrected values are converted to decibels via log scaling (10*log10(x)).

For more information about these pre-processing steps, please refer to the Sentinel-1 Pre-processing article. For further advice on working with Sentinel-1 imagery, see Guido Lemoine's tutorial on SAR basics and Mort Canty's tutorial on SAR change detection.

This collection is computed on-the-fly. If you want to use the underlying collection with raw power values (which is updated faster), see COPERNICUS/S1_GRD_FLOAT.

Sentinel-1 任务提供来自双极化 C 波段合成孔径雷达 (SAR) 仪器的数据，频率为 5.405GHz（C 波段）。该集合包括 S1 地面范围检测 (GRD) 场景，使用 Sentinel-1 工具箱处理以生成校准的正射校正产品。该系列每天更新。新资产在可用后的两天内被摄取。

此集合包含所有 GRD 场景。每个场景具有 3 种分辨率（10、25 或 40 米）、4 种波段组合（对应于场景极化）和 3 种仪器模式之一。在镶嵌上下文中使用集合可能需要过滤到一组同质的波段和参数。有关集合使用和预处理的详细信息，请参阅本文。每个场景包含 4 个可能的极化波段中的 1 个或 2 个，具体取决于仪器的极化设置。可能的组合是单频段 VV 或 HH，以及双频段 VV+VH 和 HH+HV：

VV：单共极化，垂直发射/垂直接收

HH：单共极化，水平发射/水平接收

VV + VH：双频交叉极化，垂直发射/水平接收

HH + HV：双频交叉极化，水平发射/垂直接收

每个场景还包括一个附加的“角度”带，其中包含从椭球每个点的近似入射角（以度为单位）。该波段是通过插入每个资产提供的“geolocationGridPoint”网格字段的“incidenceAngle”属性来生成的。

每个场景都使用 Sentinel-1 Toolbox 进行预处理，步骤如下：

热噪声去除

辐射校准

使用 SRTM 30 或 ASTER DEM 对纬度大于 60 度的区域进行地形校正，其中 SRTM 不可用。最终的地形校正值通过对数缩放 (10*log10(x)) 转换为分贝。

有关这些预处理步骤的更多信息，请参阅 Sentinel-1 预处理文章。有关使用 Sentinel-1 影像的进一步建议，请参阅 Guido Lemoine 的 SAR 基础教程和 Mort Canty 的 SAR 变化检测教程。

该集合是即时计算的。如果要使用具有原始功率值（更新速度更快）的基础集合，请参阅 COPERNICUS/S1_GRD_FLOAT。

Dataset Availability

2014-10-03T00:00:00 - 2021-09-04T00:00:00

Dataset Provider

European Union/ESA/Copernicus

Collection Snippet

ee.ImageCollection("COPERNICUS/S1_GRD")

Bands Table

* = Values are estimated

影像属性：

代码：

var imgVV = ee.ImageCollection('COPERNICUS/S1_GRD')
        .filter(ee.Filter.listContains('transmitterReceiverPolarisation', 'VV'))
        .filter(ee.Filter.eq('instrumentMode', 'IW'))
        .select('VV')
        .map(function(image) {
          var edge = image.lt(-30.0);
          var maskedImage = image.mask().and(edge.not());
          return image.updateMask(maskedImage);
        });
var desc = imgVV.filter(ee.Filter.eq('orbitProperties_pass', 'DESCENDING'));
var asc = imgVV.filter(ee.Filter.eq('orbitProperties_pass', 'ASCENDING'));
var spring = ee.Filter.date('2015-03-01', '2015-04-20');
var lateSpring = ee.Filter.date('2015-04-21', '2015-06-10');
var summer = ee.Filter.date('2015-06-11', '2015-08-31');
var descChange = ee.Image.cat(
        desc.filter(spring).mean(),
        desc.filter(lateSpring).mean(),
        desc.filter(summer).mean());
var ascChange = ee.Image.cat(
        asc.filter(spring).mean(),
        asc.filter(lateSpring).mean(),
        asc.filter(summer).mean());
Map.setCenter(5.2013, 47.3277, 12);
Map.addLayer(ascChange, {min: -25, max: 5}, 'Multi-T Mean ASC', true);
Map.addLayer(descChange, {min: -25, max: 5}, 'Multi-T Mean DESC', true);

影像