仅包含外推轨道元数据的文件，可通过 SDP 工具包读取，二进制格式
【摘要】 Files containing only extrapolated orbital metadata, to be read via SDP Toolkit, Binary Format简介AM1EPHNE 是 Terra 近实时 (NRT) 2 小时航天器外推星历数据文件，采用原生格式。文件名格式如下：AM1EPHNE.Ayyyyddd.hhmm.vvv.yyyydddhhmmss，其...
Files containing only extrapolated orbital metadata, to be read via SDP Toolkit, Binary Format
简介
AM1EPHNE 是 Terra 近实时 (NRT) 2 小时航天器外推星历数据文件，采用原生格式。文件名格式如下：AM1EPHNE.Ayyyyddd.hhmm.vvv.yyyydddhhmmss，其中从左到右依次为：E = 外推；N = 原生格式；A = AM1 (Terra)；yyyy = 数据年份，ddd = 儒略数据日，hh = 数据小时，mm = 数据分钟；vvv = 版本 ID；yyyy = 生产年份，ddd = 儒略生产日，hh = 生产小时，mm = 生产分钟，ss = 生产秒。
摘要
Additional Info
| Field | Value |
|---|---|
| Last Updated | September 11, 2025, 8:26 AM (UTC+08:00) |
| Created | April 1, 2025, 11:40 PM (UTC+08:00) |
| accessLevel | public |
| bureauCode | 026:00 |
| catalog_conformsTo | https://project-open-data.cio.gov/v1.1/schema |
| harvest_object_id | a351cfe1-7481-4ed8-be92-981f2e816302 |
| harvest_source_id | b99e41c6-fe79-4c19-bbc3-9b6c8111bfac |
| harvest_source_title | Science Discovery Engine |
| identifier | 10.5067/MODIS/AM1EPHN0.NRT.061 |
| landingPage | https://earthdata.nasa.gov/earth-observation-data/near-real-time/download-nrt-data/modis-nrt |
| modified | 2025-09-10 |
| programCode | 026:000 |
| publisher | NASA/GSFC/SED/ESD/HBSL/BISB/MODAPS |
| resource-type | Dataset |
| source_datajson_identifier | true |
| source_hash | 592020f2d2821ba07a40645c446fc6376d73e41ed9652f4e13ed85d93d9a4f8a |
| source_schema_version | 1.1 |
| spatial | ["CARTESIAN",[{"WestBoundingCoordinate":-180.0,"NorthBoundingCoordinate":90.0,"EastBoundingCoordinate":180.0,"SouthBoundingCoordinate":-90.0}]] |
| temporal | 2016-01-24/2016-01-24 |
| theme | "Earth Science" |
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"
df = pd.read_csv(url, sep="\t")
df
leafmap.nasa_data_login()
results, gdf = leafmap.nasa_data_search(
short_name="AM1EPHNE",
cloud_hosted=True,
bounding_box=(-180.0, -90.0, 180.0, 90.0),
temporal=("2016-01-24", "2016-01-30"),
count=-1, # use -1 to return all datasets
return_gdf=True,
)
gdf.explore()
#leafmap.nasa_data_download(results[:5], out_dir="data")
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