Google Earth Engine ——LANDSAT/LC08/C01/T1_ANNUAL_NDVI数据集
This dataset is the atmospherically corrected surface reflectance from the Landsat 8 OLI/TIRS sensors. These images contain 5 visible and near-infrared (VNIR) bands and 2 short-wave infrared (SWIR) bands processed to orthorectified surface reflectance, and two thermal infrared (TIR) bands processed to orthorectified brightness temperature
These data have been atmospherically corrected using LaSRC and includes a cloud, shadow, water and snow mask produced using CFMASK, as well as a per-pixel saturation mask.
Strips of collected data are packaged into overlapping "scenes" covering approximately 170km x 183km using a standardized reference grid.
See also the USGS page on SR QA bands.
SR can only be produced for Landsat assets processed to the L1TP level
Data provider notes:
- Although Surface Reflectance can be processed only from the Operational Land Imager (OLI) bands, SR requires combined OLI/Thermal Infrared Sensor (TIRS) product (LC8) input in order to generate the accompanying cloud mask. Therefore, OLI only (LO8), and TIRS only (LT8) data products cannot be calculated to SR.
- SR is not run for a scene with a solar zenith angle greater than 76°.
- Users are cautioned to avoid using SR for data acquired over high latitudes (> 65°).
- The panchromatic band (ETM+ Band 7, OLI Band 8) is not processed to Surface Reflectance.
- Efficacy of SR correction will be likely reduced in areas where atmospheric correction is affected by adverse conditions:
- Hyper-arid or snow-covered regions
- Low sun angle conditions
- Coastal regions where land area is small relative to adjacent water
- Areas with extensive cloud contamination
This product is generated by Google using a Docker image supplied by USGS.
183公里,使用标准化的参考网格。
另请参见USGS关于SR QA波段的网页。
SR只能为处理到L1TP级别的Landsat资产制作。
数据提供者说明。
虽然表面反射率只能从陆地成像仪(OLI)波段中处理,但SR需要OLI/热红外传感器(TIRS)产品(LC8)的综合输入,以生成相应的云层掩码。因此,只有OLI(LO8)和只有TIRS(LT8)的数据产品不能计算到SR。
对于太阳天顶角大于76°的场景,SR不会被运行。
提醒用户避免对在高纬度地区(>65°)获取的数据使用SR。
全色波段(ETM+波段7,OLI波段8)不处理表面反射率。
在大气校正受到不利条件影响的地区,SR校正的效果将可能降低。
超干旱或被雪覆盖的地区
低太阳角条件
陆地面积相对于邻近水域较小的沿海地区
有大量云层污染的地区
本产品由谷歌使用USGS提供的Docker图像生成。
Dataset Availability
2013-04-11T00:00:00 - 2021-08-29T00:00:00
Dataset Provider
Collection Snippet
Copied
ee.ImageCollection("LANDSAT/LC08/C01/T1_SR")
Resolution
30 meters
Bands Table
Name | Description | Units | Wavelength | Scale |
---|---|---|---|---|
B1 | Band 1 (ultra blue) surface reflectance | 0.435-0.451 μm | 0.0001 | |
B2 | Band 2 (blue) surface reflectance | 0.452-0.512 μm | 0.0001 | |
B3 | Band 3 (green) surface reflectance | 0.533-0.590 μm | 0.0001 | |
B4 | Band 4 (red) surface reflectance | 0.636-0.673 μm | 0.0001 | |
B5 | Band 5 (near infrared) surface reflectance | 0.851-0.879 μm | 0.0001 | |
B6 | Band 6 (shortwave infrared 1) surface reflectance | 1.566-1.651 μm | 0.0001 | |
B7 | Band 7 (shortwave infrared 2) surface reflectance | 2.107-2.294 μm | 0.0001 | |
B10 | Band 10 brightness temperature. This band, while originally collected with a resolution of 100m / pixel, has been resampled using cubic convolution to 30m. | Kelvin | 10.60-11.19 μm | 0.1 |
B11 | Band 11 brightness temperature. This band, while originally collected with a resolution of 100m / pixel, has been resampled using cubic convolution to 30m. | Kelvin | 11.50-12.51 μm | 0.1 |
sr_aerosol | Aerosol attributes | 0 | ||
sr_aerosol Bitmask |
| |||
pixel_qa | Pixel quality attributes generated from the CFMASK algorithm. | 0 | ||
pixel_qa Bitmask |
| |||
radsat_qa | Radiometric saturation QA | 0 | ||
radsat_qa Bitmask |
|
影像属性:
Name | Type | Description |
---|---|---|
CLOUD_COVER | Int | Percentage cloud cover, -1 = not calculated. (Obtained from raw Landsat metadata) |
CLOUD_COVER_LAND | Int | Percentage cloud cover over land, -1 = not calculated. (Obtained from raw Landsat metadata) |
EARTH_SUN_DISTANCE | Double | Earth-Sun distance (AU) |
ESPA_VERSION | String | Internal ESPA image version used to compute SR |
GEOMETRIC_RMSE_MODEL | Double | Combined RMSE (Root Mean Square Error) of the geometric residuals (meters) in both across-track and along-track directions. (Obtained from raw Landsat metadata) |
GEOMETRIC_RMSE_MODEL_X | Double | RMSE (Root Mean Square Error) of the geometric residuals (meters) measured on the GCPs (Ground Control Points) used in geometric precision correction in the across-track direction. (Obtained from raw Landsat metadata) |
GEOMETRIC_RMSE_MODEL_Y | Double | RMSE (Root Mean Square Error) of the geometric residuals (meters) measured on the GCPs (Ground Control Points) used in geometric precision correction in the along-track direction. (Obtained from raw Landsat metadata) |
IMAGE_QUALITY | Int | Image quality, 0 = worst, 9 = best, -1 = quality not calculated. (Obtained from raw Landsat metadata) |
LANDSAT_ID | String | Landsat Product Identifier (Collection 1) |
LEVEL1_PRODUCTION_DATE | Int | Date of production for raw Level 1 data as ms since epoch |
PIXEL_QA_VERSION | String | Version of the software used to produce the 'pixel_qa' band |
SATELLITE | String | Name of satellite |
SENSING_TIME | String | Time of the observations as in ISO 8601 string. (Obtained from raw Landsat metadata) |
SOLAR_AZIMUTH_ANGLE | Double | Solar azimuth angle |
SR_APP_VERSION | String | LaSRC version used to process surface reflectance |
WRS_PATH | Int | WRS path number of scene |
WRS_ROW | Int | WRS row number of scene |
影像使用:
Landsat datasets are federally created data and therefore reside in the public domain and may be used, transferred, or reproduced without copyright restriction.
Acknowledgement or credit of the USGS as data source should be provided by including a line of text citation such as the example shown below.
(Product, Image, Photograph, or Dataset Name) courtesy of the U.S. Geological Survey
Example: Landsat-7 image courtesy of the U.S. Geological Survey
See the USGS Visual Identity System Guidance for further details on proper citation and acknowledgement of USGS products.
代码:
var dataset = ee.ImageCollection('LANDSAT/LC08/C01/T1_ANNUAL_NDVI')
.filterDate('2017-01-01', '2017-12-31');
var colorized = dataset.select('NDVI');
var colorizedVis = {
min: 0.0,
max: 1.0,
palette: [
'FFFFFF', 'CE7E45', 'DF923D', 'F1B555', 'FCD163', '99B718', '74A901',
'66A000', '529400', '3E8601', '207401', '056201', '004C00', '023B01',
'012E01', '011D01', '011301'
],
};
Map.setCenter(6.746, 46.529, 6);
Map.addLayer(colorized, colorizedVis, 'Colorized');
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