Google Earth Engine ——数据全解析专辑（COPERNICUS/S5P/OFFL/L3_AER_AI）紫外线气溶胶指数 (UVAI) 数据集

2024-02-02 09:00:55 浏览数 (1)
OFFL/L3_AER_LH

This dataset provides offline high-resolution imagery of the UV Aerosol Index (UVAI), also called the Absorbing Layer Height (ALH).

The ALH is very sensitive to cloud contamination. However, aerosols and clouds can be difficult to distinguish, and ALH is computed for all FRESCO effective cloud fractions smaller than 0.05. Cloud masks are available from FRESCO and VIIRS, and are strongly recommended to filter for residual clouds. A sunglint mask is also available to screen sunglint regions, which are not filtered beforehand.

It is known that high surface albedos negatively influence the ALH, biasing the ALH towards the surface. In general, the ALH over (dark) oceans is considered reliable to within the requirement of 1000 m or 100 hPa. Over land, especially bright surfaces, the accuracy may be lower, and the use of the ALH product over bright surfaces like deserts is not advisable.

For this L3 AER_LH product, the aerosol_mid_pressure is calculated with a pair of measurements at the 354 nm and 388 nm wavelengths. The COPERNICUS/S5P/OFFL/L3_SO2 product has the absorbing_aerosol_index calculated using the 340 nm and 380 nm wavelengths.

代码语言：javascript复制
该数据集提供了紫外线气溶胶指数 (UVAI) 的离线高分辨率图像，也称为吸收层高度 (ALH)。

ALH 对云污染非常敏感。然而，气溶胶和云很难区分，ALH 是针对所有小于 0.05 的 FRESCO 有效云分数计算的。 FRESCO 和 VIIRS 提供云遮罩，强烈建议过滤残留云。也可使用阳光遮罩来筛选未预先过滤的阳光区域。

众所周知，高表面反照率会对 ALH 产生负面影响，使 ALH 偏向表面。一般而言，（暗）海洋上的 ALH 在 1000 m 或 100 hPa 的要求内被认为是可靠的。在陆地上，尤其是在明亮的表面上，精度可能会较低，并且不建议在沙漠等明亮的表面上使用 ALH 产品。

对于此 L3 AER_LH 产品，使用 354 nm 和 388 nm 波长处的一对测量值计算 aerosol_mid_pressure。 COPERNICUS/S5P/OFFL/L3_SO2 产品具有使用 340 nm 和 380 nm 波长计算的吸收气溶胶指数。
OFFL L3 Product

To make our OFFL L3 products, we find areas within the product's bounding box with data using a command like this:

代码语言：javascript复制
harpconvert --format hdf5 --hdf5-compression 9
-a 'aerosol_height_validity>50;derive(datetime_stop {time})'
S5P_OFFL_L2__AER_LH_20190404T042423_20190404T060554_07630_01_010300_20190410T062552.nc
grid_info.h5

We then merge all the data into one large mosaic (area-averaging values for pixels that may have different values for different times). From the mosaic, we create a set of tiles containing orthorectified raster data.

Example harpconvert invocation for one tile:

代码语言：javascript复制
harpconvert --format hdf5 --hdf5-compression 9
-a 'aerosol_height_validity>50;derive(datetime_stop {time});
bin_spatial(2001, 50.000000, 0.01, 2001, -120.000000, 0.01);
keep(aerosol_height,aerosol_pressure,aerosol_optical_depth,
     sensor_zenith_angle,sensor_azimuth_angle,solar_azimuth_angle,solar_zenith_angle)'
S5P_OFFL_L2__AER_LH_20190404T042423_20190404T060554_07630_01_010300_20190410T062552.nc
output.h5
Sentinel-5 Precursor

Sentinel-5 Precursor is a satellite launched on 13 October 2017 by the European Space Agency to monitor air pollution. The onboard sensor is frequently referred to as Tropomi (TROPOspheric Monitoring Instrument).

All of the S5P datasets, except CH4, have two versions: Near Real-Time (NRTI) and Offline (OFFL). CH4 is available as OFFL only. The NRTI assets cover a smaller area than the OFFL assets, but appear more quickly after acquisition. The OFFL assets contain data from a single orbit (which, due to half the earth being dark, contains data only for a single hemisphere).

Because of noise on the data, negative vertical column values are often observed in particular over clean regions or for low SO2 emissions. It is recommended not to filter these values except for outliers, i.e. for vertical columns lower than -0.001 mol/m^2.

The original Sentinel 5P Level 2 (L2) data is binned by time, not by latitude/longitude. To make it possible to ingest the data into Earth Engine, each Sentinel 5P L2 product is converted to L3, keeping a single grid per orbit (that is, no aggregation across products is performed).

Source products spanning the antimeridian are ingested as two Earth Engine assets, with suffixes _1 and _2.

The conversion to L3 is done by the harpconvert tool using the bin_spatial operation. The source data is filtered to remove pixels with QA values less than:

  • 80% for AER_AI
  • 75% for the tropospheric_NO2_column_number_density band of NO2
  • 50% for all other datasets except for O3 and SO2

The O3_TCL product is ingested directly (without running harpconvert).

Dataset Availability

2018-07-04T13:34:21 - 2021-09-04T00:00:00

Dataset Provider

European Union/ESA/Copernicus

Collection Snippet

ee.ImageCollection("COPERNICUS/S5P/OFFL/L3_AER_LH")

Resolution

0.01 degrees

Bands Table

Name

Description

Min*

Max*

Units

aerosol_height

The aerosol layer pressure is converted into an aerosol layer altitude using an appropriate temperature profile, i.e. the temperature profile used in the retrieval. The value is given relative to the geoid.

-78.91

69370.95

m

aerosol_pressure

Pressure of an aerosol layer with an assumed pressure thickness of (currently) 50 hPa and a constant aerosol volume extinction coefficient and single scattering albedo.

2.06

103896.24

Pa

aerosol_optical_depth

Aerosol optical thickness τ of the assumed aerosol layer. The optical thickness holds for 760 nm.

-0.6

11.56

Pa

sensor_azimuth_angle

Azimuth angle of the satellite at the ground pixel location (WGS84); angle measured East-of-North.

-180

180

degrees

sensor_zenith_angle

Zenith angle of the satellite at the ground pixel location (WGS84); angle measured away from the vertical.

0.098

66.87

degrees

solar_azimuth_angle

Azimuth angle of the Sun at the ground pixel location (WGS84); angle measured East-of-North.

-180

180

degrees

solar_zenith_angle

Zenith angle of the satellite at the ground pixel location (WGS84); angle measured away from the vertical.

12.93

74.7

degrees

* = Values are estimated

影像属性：

Name

Type

Description

ALGORITHM_VERSION

String

The algorithm version used in L2 processing. It's separate from the processor (framework) version, to accommodate different release schedules for different products.

BUILD_DATE

String

The date, expressed as milliseconds since 1 Jan 1970, when the software used to perform L2 processing was built.

HARP_VERSION

Int

The version of the HARP tool used to grid the L2 data into an L3 product.

INSTITUTION

String

The institution where data processing from L1 to L2 was performed.

L3_PROCESSING_TIME

Int

The date, expressed as milliseconds since 1 Jan 1970, when Google processed the L2 data into L3 using harpconvert.

LAT_MAX

Double

The maximum latitude of the asset (degrees).

LAT_MIN

Double

The minimum latitude of the asset (degrees).

LON_MAX

Double

The maximum longitude of the asset (degrees).

LON_MIN

Double

The minimum longitude of the asset (degrees).

ORBIT

Int

The orbit number of the satellite when the data was acquired.

PLATFORM

String

Name of the platform which acquired the data.

PROCESSING_STATUS

String

The processing status of the product on a global level, mainly based on the availability of auxiliary input data. Possible values are "Nominal" and "Degraded".

PROCESSOR_VERSION

String

The version of the software used for L2 processing, as a string of the form "major.minor.patch".

PRODUCT_ID

String

Id of the L2 product used to generate this asset.

PRODUCT_QUALITY

String

Indicator that specifies whether the product quality is degraded or not. Allowed values are "Degraded" and "Nominal".

SENSOR

String

Name of the sensor which acquired the data.

SPATIAL_RESOLUTION

String

Spatial resolution at nadir. For most products this is `3.5x7km2`, except for `L2__O3__PR`, which uses `28x21km2`, and `L2__CO____` and `L2__CH4___`, which both use `7x7km2`. This attribute originates from the CCI standard.

TIME_REFERENCE_DAYS_SINCE_1950

Int

Days from 1 Jan 1950 to when the data was acquired.

TIME_REFERENCE_JULIAN_DAY

Double

The Julian day number when the data was acquired.

TRACKING_ID

String

UUID for the L2 product file.

代码：

代码语言：javascript复制
var collection = ee.ImageCollection('COPERNICUS/S5P/OFFL/L3_AER_LH')
                    .select('aerosol_height')
                    .filterDate('2019-06-01', '2019-06-05');

var visualization = {
  min: 0,
  max: 6000,
  palette: ['blue', 'purple', 'cyan', 'green', 'yellow', 'red']
};

Map.setCenter(44.09, 24.27, 4);

Map.addLayer(collection.mean(), visualization, 'S5P Aerosol Height');
data google string 产品 数据

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