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社区首页 >专栏 >Google Earth Engine ——LANDSAT/LC08/C01/T1_SR—NDVI数据集

Google Earth Engine ——LANDSAT/LC08/C01/T1_SR—NDVI数据集

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发布2024-02-02 11:21:48
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发布2024-02-02 11:21:48
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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.

这个数据集是Landsat 8 OLI/TIRS传感器的大气校正表面反射率。这些图像包含5个可见光和近红外(VNIR)波段和2个短波红外(SWIR)波段,这些波段被处理成正交的表面反射率,还有两个热红外(TIR)波段被处理成正交的亮度温度。

这些数据已经用LaSRC进行了大气校正,包括用CFMASK制作的云、影、水和雪掩码,以及每个像素的饱和掩码。

收集的数据条被打包成重叠的 "场景",使用标准化的参考网格,覆盖大约170公里x183公里。

另见美国地质调查局关于SR质量保证带的网页。

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,以生成相应的云层遮挡。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

USGS

Collection Snippet Copiedee.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

Bit 0: FillBit 1: Aerosol retrieval - validBit 2: Aerosol retrieval - interpolatedBit 3: Water pixelBit 4: Water aerosol retrieval failed - needs interpolatedBit 5: Neighbor of failed aerosol retrievalBits 6-7: Aerosol content 0: Climatology1: Low2: Medium3: High

pixel_qa

Pixel quality attributes generated from the CFMASK algorithm.

0

pixel_qa Bitmask

Bit 0: FillBit 1: ClearBit 2: WaterBit 3: Cloud ShadowBit 4: SnowBit 5: CloudBits 6-7: Cloud Confidence 0: None1: Low2: Medium3: HighBits 8-9: Cirrus Confidence 0: None1: Low2: Medium3: HighBit 10: Terrain Occlusion

radsat_qa

Radiometric saturation QA

0

radsat_qa Bitmask

Bit 0: Data Fill Flag 0: Valid data1: Invalid dataBit 1: Band 1 data saturatedBit 2: Band 2 data saturatedBit 3: Band 3 data saturatedBit 4: Band 4 data saturatedBit 5: Band 5 data saturatedBit 6: Band 6 data saturatedBit 7: Band 7 data saturatedBit 8: UnusedBit 9: Band 9 data saturatedBit 10: Band 10 data saturatedBit 11: Band 11 data saturated

  • Bit 0: Fill
  • Bit 1: Aerosol retrieval - valid
  • Bit 2: Aerosol retrieval - interpolated
  • Bit 3: Water pixel
  • Bit 4: Water aerosol retrieval failed - needs interpolated
  • Bit 5: Neighbor of failed aerosol retrieval
  • Bits 6-7: Aerosol content
    • 0: Climatology
    • 1: Low
    • 2: Medium
    • 3: High

pixel_qaPixel quality attributes generated from the CFMASK algorithm.0pixel_qa Bitmask

  • Bit 0: Fill
  • Bit 1: Clear
  • Bit 2: Water
  • Bit 3: Cloud Shadow
  • Bit 4: Snow
  • Bit 5: Cloud
  • Bits 6-7: Cloud Confidence
    • 0: None
    • 1: Low
    • 2: Medium
    • 3: High
  • Bits 8-9: Cirrus Confidence
    • 0: None
    • 1: Low
    • 2: Medium
    • 3: High
  • Bit 10: Terrain Occlusion

radsat_qaRadiometric saturation QA0radsat_qa Bitmask

  • Bit 0: Data Fill Flag
    • 0: Valid data
    • 1: Invalid data
  • Bit 1: Band 1 data saturated
  • Bit 2: Band 2 data saturated
  • Bit 3: Band 3 data saturated
  • Bit 4: Band 4 data saturated
  • Bit 5: Band 5 data saturated
  • Bit 6: Band 6 data saturated
  • Bit 7: Band 7 data saturated
  • Bit 8: Unused
  • Bit 9: Band 9 data saturated
  • Bit 10: Band 10 data saturated
  • Bit 11: Band 11 data saturated

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

代码:

代码语言:javascript
复制
var dataset = ee.ImageCollection('LANDSAT/LC08/C01/T1_32DAY_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');

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.

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