Google Earth Engine ——数据全解析专辑（COPERNICUS/S5P/OFFL/L3_AER_AI和LH）气溶胶指数数据集

OFFL/L3_AER_AI

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

The AAI is based on wavelength-dependent changes in Rayleigh scattering in the UV spectral range for a pair of wavelengths. The difference between observed and modelled reflectance results in the AAI. When the AAI is positive, it indicates the presence of UV-absorbing aerosols like dust and smoke. It is useful for tracking the evolution of episodic aerosol plumes from dust outbreaks, volcanic ash, and biomass burning.

The wavelengths used have very low ozone absorption, so unlike aerosol optical thickness measurements, AAI can be calculated in the presence of clouds. Daily global coverage is therefore possible.

For this L3 AER_AI product, the absorbing_aerosol_index 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.

OFFL/L3_AER_AI

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，也称为吸收气溶胶指数 (AAI)。

AAI 基于一对波长的 UV 光谱范围内瑞利散射的波长相关变化。观察到的和模拟的反射率之间的差异导致了 AAI。当 AAI 为正值时，表明存在吸收紫外线的气溶胶，如灰尘和烟雾。它可用于跟踪粉尘爆发、火山灰和生物质燃烧引起的偶发气溶胶羽流的演变。

所使用的波长对臭氧的吸收非常低，因此与气溶胶光学厚度测量不同，AAI 可以在有云的情况下计算。因此，每日全球报道是可能的。

对于此 L3 AER_AI 产品，吸收气溶胶指数是通过在 354 nm 和 388 nm 波长处进行的一对测量计算得出的。 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:

harpconvert --format hdf5 --hdf5-compression 9
-a 'absorbing_aerosol_index_validity>50;derive(datetime_stop {time})'
S5P_OFFL_L2__AER_AI_20181030T213916_20181030T232046_05427_01_010200_20181105T210529.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:

harpconvert --format hdf5 --hdf5-compression 9
-a 'absorbing_aerosol_index_validity>50;derive(datetime_stop {time});
bin_spatial(2001, 50.000000, 0.01, 2001, -120.000000, 0.01);
keep(absorbing_aerosol_index,sensor_altitude,sensor_azimuth_angle,
     sensor_zenith_angle,solar_azimuth_angle,solar_zenith_angle)'
S5P_OFFL_L2__AER_AI_20181030T213916_20181030T232046_05427_01_010200_20181105T210529.nc
output.h5

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_AI")

Resolution

0.01 degrees

Bands Table

* = Values are estimated

影像属性：

代码：

var collection = ee.ImageCollection('COPERNICUS/S5P/OFFL/L3_AER_AI')
  .select('absorbing_aerosol_index')
  .filterDate('2019-06-01', '2019-06-06');
var band_viz = {
  min: -1,
  max: 2.0,
  palette: ['black', 'blue', 'purple', 'cyan', 'green', 'yellow', 'red']
};
Map.addLayer(collection.mean(), band_viz, 'S5P Aerosol');
Map.setCenter(-118.82, 36.1, 5);