探索墨菲湾海洋生物地球化学：北大西洋时间系列项目数据集分析

此星光明

发布于 2025-02-21 09:04:56

7100

代码可运行

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

运行总次数：0

代码可运行

Ocean Biogeochemistry from Gliders as part of the Gulf of Maine North Atlantic Time Series

简介

这个数据集包含来自海洋潜水器的海洋生物地球化学数据，作为北大西洋时间系列项目中的一部分，涵盖了墨菲湾的部分区域。数据集记录了海洋中不同地点和深度的水体化学成分、生物量和生物多样性等信息，有助于研究海洋生态系统的变化和演化过程。

摘要

Common Core

Publisher	NASA/GSFC/SED/ESD/ESISL/GESDISC
Contact Name	Thomas Hearty
Contact Email	mailto:Thomas.J.Hearty@nasa.gov
Bureau Code	026:00
Program Code	026:001
Public Access Level	public
Geographic Coverage	-69.78 43.35 -66.8 43.9
Temporal Applicability	2008-08-04T14:36:15Z/2021-09-16T12:27:37Z
Theme	CMS, geospatial
Language	en-US
Homepage	GES DISC
Issued	2022-08-04T00:00:00.000Z
Unique Identifier	C2873840825-GES_DISC
Last Update	2022-08-04T00:00:00.000Z

Common Core Extensions

citation	Catherine Mitchell & Sunny Pinkham. 2024-02-27. BGC_glider_GNATS. Version 1. Ocean Biogeochemistry from Gliders as part of the Gulf of Maine North Atlantic Time Series. Greenbelt, MD, USA. Archived by National Aeronautics and Space Administration, U.S. Government, Goddard Earth Sciences Data and Information Services Center (GES DISC). GES DISC. GES DISC. Digital Science Data.
creator	Catherine Mitchell & Sunny Pinkham
data-presentation-form	Digital Science Data
graphic-preview-file	https://docserver.gesdisc.eosdis.nasa.gov/public/project/CMS/GNATS-GoM.png
release-place	Greenbelt, MD, USA
series-name	BGC_glider_GNATS

Topics

Category	This dataset has not been categorized
Tags	earth science,oceans,ocean optics

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="BGC_glider_GNATS",
    cloud_hosted=True,
    bounding_box=(-69.78, 43.35, -66.8, 43.9),
    temporal=("2008-08-04", "2021-09-16"),
    count=-1,  # use -1 to return all datasets
    return_gdf=True,
)
 
 
gdf.explore()
 
#leafmap.nasa_data_download(results[:5], out_dir="data")

本文参与腾讯云自媒体同步曝光计划，分享自作者个人站点/博客。

原始发表：2025-02-20，如有侵权请联系 cloudcommunity@tencent.com 删除

pip