NASA数据集——2018-2024年VIIRS/NOAA20 深蓝 3 级每日气溶胶数据，1x1 度网格

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发布于 2024-05-04 08:49:31

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发布于 2024-05-04 08:49:31

文章被收录于专栏：GEE数据专栏，GEE学习专栏，GEE错误集等专栏GEE数据专栏，GEE学习专栏，GEE错误集等专栏

VIIRS/NOAA20 Deep Blue Level 3 daily aerosol data, 1 degree x 1 degree grid

简介

联合极地卫星系统（JPSS）系列 NOAA-20 仪器的可见红外成像辐射计套件（VIIRS）NASA 标准三级（L3）每日深蓝气溶胶产品提供了全球陆地和海洋上空气溶胶光学厚度（AOT）的卫星衍生测量数据及其网格集合属性。深蓝算法借鉴了以前从陆地和海洋上的海洋观测宽视场传感器（SeaWiFS）和陆地上的中分辨率成像分光仪（MODIS）测量中获取气溶胶光学厚度的应用。这一每日汇总产品（简称：AERDB_D3_VIIRS_NOAA20）源自基于第 2.0 版（V2.0）L2 6 分钟扫描带的产品（AERDB_L2_VIIRS_NOAA20），以 1 x 1 度的水平分辨率网格提供。在大多数情况下，每个数据字段表示所有单元的算术平均值，这些单元的经纬度坐标位于每个网格元素的边界范围内。此外，还提供标准偏差等其他测量方法。该汇总产品仅使用经过质量保证过滤的最佳估计检索结果。该算法仅使用在相关日期测量到的单元格，要求至少有三次检索到的测量结果才能使给定网格在任何给定日期都有效。该每日产品记录始于 2018 年 2 月 17 日。

该 L3 每日产品采用 netCDF 格式，包含 45 个科学数据集 (SDS) 图层，其中包括以下内容：

Aerosol_Optical_Thickness_550_Land_Count
Aerosol_Optical_Thickness_550_Land_Maximum
Aerosol_Optical_Thickness_550_Land_Mean
Aerosol_Optical_Thickness_550_Land_Minimum
Aerosol_Optical_Thickness_550_Land_Ocean_Count
Aerosol_Optical_Thickness_550_Land_Ocean_Maximum
Aerosol_Optical_Thickness_550_Land_Ocean_Mean
Aerosol_Optical_Thickness_550_Land_Ocean_Minimum
Aerosol_Optical_Thickness_550_Land_Ocean_Standard_Deviation
Aerosol_Optical_Thickness_550_Land_Standard_Deviation
Aerosol_Optical_Thickness_550_Ocean_Count
Aerosol_Optical_Thickness_550_Ocean_Maximum
Aerosol_Optical_Thickness_550_Ocean_Mean
Aerosol_Optical_Thickness_550_Ocean_Minimum
Aerosol_Optical_Thickness_550_Ocean_Standard_Deviation
Aerosol_Type_Land_Ocean_Histogram
Aerosol_Type_Land_Ocean_Mode
Aerosol_Types
Angstrom_Exponent_Land_Maximum
Angstrom_Exponent_Land_Mean
Angstrom_Exponent_Land_Minimum
Angstrom_Exponent_Land_Ocean_Maximum
Angstrom_Exponent_Land_Ocean_Mean
Angstrom_Exponent_Land_Ocean_Minimum
Angstrom_Exponent_Land_Ocean_Standard_Deviation
Angstrom_Exponent_Land_Standard_Deviation
Angstrom_Exponent_Ocean_Maximum
Angstrom_Exponent_Ocean_Mean
Angstrom_Exponent_Ocean_Minimum
Angstrom_Exponent_Ocean_Standard_Deviation
Fine_Mode_Fraction_550_Ocean_Mean
Fine_Mode_Fraction_550_Ocean_Standard_Deviation
Land_Bands
Latitude
Latitude_1D
Longitude
Longitude_1D
Ocean_Bands
Spectral_Aerosol_Optical_Thickness_Land_Count
Spectral_Aerosol_Optical_Thickness_Land_Mean
Spectral_Aerosol_Optical_Thickness_Land_Standard_Deviation
Spectral_Aerosol_Optical_Thickness_Ocean_Count
Spectral_Aerosol_Optical_Thickness_Ocean_Mean
Spectral_Aerosol_Optical_Thickness_Ocean_Standard_Deviation
Unsuitable_Pixel_Fraction_Land_Ocean

有关全局属性、数据字段属性、质量标志、处理和使用这些数据产品的软件等更多信息，请查阅 VIIRS 深蓝气溶胶产品用户指南。

数据信息

Shortname:	AERDB_D3_VIIRS_NOAA20
Platform:	NOAA-20
Instrument:	VIIRS
Processing Level:	Level-3
Data Format:	netCDF4
Spatial Resolution:	1 degree
Temporal Resolution:	daily
ArchiveSets:	5200
Collection:	NPP and JPSS1 VIIRS data 2.0 (ArchiveSet 5200)
PGE Number:	NONE
File Naming Convention:	Syntax: ESDT.AYYYYDDD.CCC.YYYYDDDHHMMSS.Format Example: AERDB_D3_VIIRS_NOAA20.A2020001.002.2022245184551.nc ESDT Earth Science Data Type or ShortnameA Stands for AcquisitionYYYYDDD Data acquisition year and Day-of-year per the Julian CalendarCCC Version ID of the data collectionYYYYDDDHHMMSS Processing year, Day-of-year, UTC time (hour, minutes, seconds)Format File format suffix, which in the above case represents netCDF4
Keywords:	NOAA20 VIIRS, L3, Daily Aggregated, Deep Blue Aerosol Optical Thickness

ESDT Earth Science Data Type or Shortname
A Stands for Acquisition
YYYYDDD Data acquisition year and Day-of-year per the Julian Calendar
CCC Version ID of the data collection
YYYYDDDHHMMSS Processing year, Day-of-year, UTC time (hour, minutes, seconds)
Format File format suffix, which in the above case represents netCDF4

Keywords:NOAA20 VIIRS, L3, Daily Aggregated, Deep Blue Aerosol Optical Thickness

代码

!pip install leafmap
!pip install pandas
!pip install folium
!pip install matplotlib
!pip install mapclassify
 
import pandas as pd
import leafmap
 
url = "https://github.com/opengeos/NASA-Earth-Data/raw/main/nasa_earth_data.tsv"
df = pd.read_csv(url, sep="\t")
df
 
leafmap.nasa_data_login()
 
 
results, gdf = leafmap.nasa_data_search(
    short_name="AERDB_D3_VIIRS_NOAA20",
    cloud_hosted=True,
    bounding_box=(-180.0, -90.0, 180.0, 90.0),
    temporal=("2018-01-05", "2024-02-10"),
    count=-1,  # use -1 to return all datasets
    return_gdf=True,
)
 
 
gdf.explore()
 
#leafmap.nasa_data_download(results[:5], out_dir="data")