基于日期、时间、经纬度下载MODIS数据并批处理

2023-11-08 06:12

本文主要是介绍基于日期、时间、经纬度下载MODIS数据并批处理,希望对大家解决编程问题提供一定的参考价值,需要的开发者们随着小编来一起学习吧!

一、利用python基于日期、时间和经纬度批量下载MODIS数据

我想根据一些实测点下载对应时间和位置的MODIS数据(5min一景的产品)作为对比。

之前想了很多种方法,比如基于GEE什么的,但是我下载的MODIS产品在GEE上没有。

于是后来考虑可以用这个网站

| National Snow and Ice Data Center

这个网站可以搜索比如MYD29是我需要下载的产品,然后搜索MOD29 download就可以进入

MODIS/Terra Sea Ice Extent 5-Min L2 Swath 1km, Version 61 | National Snow and Ice Data Center

这里有很多下载的方式,选第二个Data Access Tool,get data.

然后可以用其中的一条实测点数据,输入到左边的输入框中,这样右边就有数据了,然后点击

Download Script就可以下载到下载这个条件数据的python代码,以下是代码,修改当中函数的username和password即可,然后再修改main函数,设定不同的bounding box(位置)和time什么的按照你的要求搜索并下载数据就行啦。

但是这个程序经常会因为网络不稳定而断掉,所以可能需要自己重启,或者再exception里面修改进行重启。但是值得一提的是,global 变量并不是在函数里修改然后再次进入这个函数就可以接着运行的,必须要重新传入(如果有修改的话),否在就重启后还是最上面的那个值。

#!/usr/bin/env python
# ----------------------------------------------------------------------------
# NSIDC Data Download Script
#
# Copyright (c) 2023 Regents of the University of Colorado
# Permission is hereby granted, free of charge, to any person obtaining
# a copy of this software and associated documentation files (the "Software"),
# to deal in the Software without restriction, including without limitation
# the rights to use, copy, modify, merge, publish, distribute, sublicense,
# and/or sell copies of the Software, and to permit persons to whom the
# Software is furnished to do so, subject to the following conditions:
# The above copyright notice and this permission notice shall be included
# in all copies or substantial portions of the Software.
#
# Tested in Python 2.7 and Python 3.4, 3.6, 3.7, 3.8, 3.9
#
# To run the script at a Linux, macOS, or Cygwin command-line terminal:
#   $ python nsidc-data-download.py
#
# On Windows, open Start menu -> Run and type cmd. Then type:
#     python nsidc-data-download.py
#
# The script will first search Earthdata for all matching files.
# You will then be prompted for your Earthdata username/password
# and the script will download the matching files.
#
# If you wish, you may store your Earthdata username/password in a .netrc
# file in your $HOME directory and the script will automatically attempt to
# read this file. The .netrc file should have the following format:
#    machine urs.earthdata.nasa.gov login MYUSERNAME password MYPASSWORD
# where 'MYUSERNAME' and 'MYPASSWORD' are your Earthdata credentials.
#
# Instead of a username/password, you may use an Earthdata bearer token.
# To construct a bearer token, log into Earthdata and choose "Generate Token".
# To use the token, when the script prompts for your username,
# just press Return (Enter). You will then be prompted for your token.
# You can store your bearer token in the .netrc file in the following format:
#    machine urs.earthdata.nasa.gov login token password MYBEARERTOKEN
# where 'MYBEARERTOKEN' is your Earthdata bearer token.
#
from __future__ import print_functionimport base64
import getopt
import itertools
import json
import math
import netrc
import os.path
import ssl
import sys
import time
from getpass import getpasstry:from urllib.parse import urlparsefrom urllib.request import urlopen, Request, build_opener, HTTPCookieProcessorfrom urllib.error import HTTPError, URLError
except ImportError:from urlparse import urlparsefrom urllib2 import urlopen, Request, HTTPError, URLError, build_opener, HTTPCookieProcessorshort_name = 'MYD29'
version = '61'
time_start = '2002-07-04T00:00:00Z'
time_end = '2023-11-07T04:01:18Z'
bounding_box = ''
polygon = ''
filename_filter = '*MYD29.A2020001.1855.061.2020321085433*'
url_list = []CMR_URL = 'https://cmr.earthdata.nasa.gov'
URS_URL = 'https://urs.earthdata.nasa.gov'
CMR_PAGE_SIZE = 2000
CMR_FILE_URL = ('{0}/search/granules.json?provider=NSIDC_ECS''&sort_key[]=start_date&sort_key[]=producer_granule_id''&scroll=true&page_size={1}'.format(CMR_URL, CMR_PAGE_SIZE))def get_username():username = ''# For Python 2/3 compatibility:try:do_input = raw_input  # noqaexcept NameError:do_input = inputusername = do_input('Earthdata username (or press Return to use a bearer token): ')return usernamedef get_password():password = ''while not password:password = getpass('password: ')return passworddef get_token():token = ''while not token:token = getpass('bearer token: ')return tokendef get_login_credentials():"""Get user credentials from .netrc or prompt for input."""credentials = Nonetoken = Nonetry:info = netrc.netrc()username, account, password = info.authenticators(urlparse(URS_URL).hostname)if username == 'token':token = passwordelse:credentials = '{0}:{1}'.format(username, password)credentials = base64.b64encode(credentials.encode('ascii')).decode('ascii')except Exception:username = Nonepassword = Noneif not username:username = get_username()if len(username):password = get_password()credentials = '{0}:{1}'.format(username, password)credentials = base64.b64encode(credentials.encode('ascii')).decode('ascii')else:token = get_token()return credentials, tokendef build_version_query_params(version):desired_pad_length = 3if len(version) > desired_pad_length:print('Version string too long: "{0}"'.format(version))quit()version = str(int(version))  # Strip off any leading zerosquery_params = ''while len(version) <= desired_pad_length:padded_version = version.zfill(desired_pad_length)query_params += '&version={0}'.format(padded_version)desired_pad_length -= 1return query_paramsdef filter_add_wildcards(filter):if not filter.startswith('*'):filter = '*' + filterif not filter.endswith('*'):filter = filter + '*'return filterdef build_filename_filter(filename_filter):filters = filename_filter.split(',')result = '&options[producer_granule_id][pattern]=true'for filter in filters:result += '&producer_granule_id[]=' + filter_add_wildcards(filter)return resultdef build_cmr_query_url(short_name, version, time_start, time_end,bounding_box=None, polygon=None,filename_filter=None):params = '&short_name={0}'.format(short_name)params += build_version_query_params(version)params += '&temporal[]={0},{1}'.format(time_start, time_end)if polygon:params += '&polygon={0}'.format(polygon)elif bounding_box:params += '&bounding_box={0}'.format(bounding_box)if filename_filter:params += build_filename_filter(filename_filter)return CMR_FILE_URL + paramsdef get_speed(time_elapsed, chunk_size):if time_elapsed <= 0:return ''speed = chunk_size / time_elapsedif speed <= 0:speed = 1size_name = ('', 'k', 'M', 'G', 'T', 'P', 'E', 'Z', 'Y')i = int(math.floor(math.log(speed, 1000)))p = math.pow(1000, i)return '{0:.1f}{1}B/s'.format(speed / p, size_name[i])def output_progress(count, total, status='', bar_len=60):if total <= 0:returnfraction = min(max(count / float(total), 0), 1)filled_len = int(round(bar_len * fraction))percents = int(round(100.0 * fraction))bar = '=' * filled_len + ' ' * (bar_len - filled_len)fmt = '  [{0}] {1:3d}%  {2}   '.format(bar, percents, status)print('\b' * (len(fmt) + 4), end='')  # clears the linesys.stdout.write(fmt)sys.stdout.flush()def cmr_read_in_chunks(file_object, chunk_size=1024 * 1024):"""Read a file in chunks using a generator. Default chunk size: 1Mb."""while True:data = file_object.read(chunk_size)if not data:breakyield datadef get_login_response(url, credentials, token):opener = build_opener(HTTPCookieProcessor())req = Request(url)if token:req.add_header('Authorization', 'Bearer {0}'.format(token))elif credentials:try:response = opener.open(req)# We have a redirect URL - try again with authorization.url = response.urlexcept HTTPError:# No redirect - just try again with authorization.passexcept Exception as e:print('Error{0}: {1}'.format(type(e), str(e)))sys.exit(1)req = Request(url)req.add_header('Authorization', 'Basic {0}'.format(credentials))try:response = opener.open(req)except HTTPError as e:err = 'HTTP error {0}, {1}'.format(e.code, e.reason)if 'Unauthorized' in e.reason:if token:err += ': Check your bearer token'else:err += ': Check your username and password'print(err)sys.exit(1)except Exception as e:print('Error{0}: {1}'.format(type(e), str(e)))sys.exit(1)return responsedef cmr_download(urls, force=False, quiet=False):"""Download files from list of urls."""if not urls:returnurl_count = len(urls)if not quiet:print('Downloading {0} files...'.format(url_count))credentials = Nonetoken = Nonefor index, url in enumerate(urls, start=1):if not credentials and not token:p = urlparse(url)if p.scheme == 'https':credentials, token = get_login_credentials()filename = url.split('/')[-1]if not quiet:print('{0}/{1}: {2}'.format(str(index).zfill(len(str(url_count))),url_count, filename))try:response = get_login_response(url, credentials, token)length = int(response.headers['content-length'])try:if not force and length == os.path.getsize(filename):if not quiet:print('  File exists, skipping')continueexcept OSError:passcount = 0chunk_size = min(max(length, 1), 1024 * 1024)max_chunks = int(math.ceil(length / chunk_size))time_initial = time.time()with open(filename, 'wb') as out_file:for data in cmr_read_in_chunks(response, chunk_size=chunk_size):out_file.write(data)if not quiet:count = count + 1time_elapsed = time.time() - time_initialdownload_speed = get_speed(time_elapsed, count * chunk_size)output_progress(count, max_chunks, status=download_speed)if not quiet:print()except HTTPError as e:print('HTTP error {0}, {1}'.format(e.code, e.reason))except URLError as e:print('URL error: {0}'.format(e.reason))except IOError:raisedef cmr_filter_urls(search_results):"""Select only the desired data files from CMR response."""if 'feed' not in search_results or 'entry' not in search_results['feed']:return []entries = [e['links']for e in search_results['feed']['entry']if 'links' in e]# Flatten "entries" to a simple list of linkslinks = list(itertools.chain(*entries))urls = []unique_filenames = set()for link in links:if 'href' not in link:# Exclude links with nothing to downloadcontinueif 'inherited' in link and link['inherited'] is True:# Why are we excluding these links?continueif 'rel' in link and 'data#' not in link['rel']:# Exclude links which are not classified by CMR as "data" or "metadata"continueif 'title' in link and 'opendap' in link['title'].lower():# Exclude OPeNDAP links--they are responsible for many duplicates# This is a hack; when the metadata is updated to properly identify# non-datapool links, we should be able to do this in a non-hack waycontinuefilename = link['href'].split('/')[-1]if filename in unique_filenames:# Exclude links with duplicate filenames (they would overwrite)continueunique_filenames.add(filename)urls.append(link['href'])return urlsdef cmr_search(short_name, version, time_start, time_end,bounding_box='', polygon='', filename_filter='', quiet=False):"""Perform a scrolling CMR query for files matching input criteria."""cmr_query_url = build_cmr_query_url(short_name=short_name, version=version,time_start=time_start, time_end=time_end,bounding_box=bounding_box,polygon=polygon, filename_filter=filename_filter)if not quiet:print('Querying for data:\n\t{0}\n'.format(cmr_query_url))cmr_scroll_id = Nonectx = ssl.create_default_context()ctx.check_hostname = Falsectx.verify_mode = ssl.CERT_NONEurls = []hits = 0while True:req = Request(cmr_query_url)if cmr_scroll_id:req.add_header('cmr-scroll-id', cmr_scroll_id)try:response = urlopen(req, context=ctx)except Exception as e:print('Error: ' + str(e))sys.exit(1)if not cmr_scroll_id:# Python 2 and 3 have different case for the http headersheaders = {k.lower(): v for k, v in dict(response.info()).items()}cmr_scroll_id = headers['cmr-scroll-id']hits = int(headers['cmr-hits'])if not quiet:if hits > 0:print('Found {0} matches.'.format(hits))else:print('Found no matches.')search_page = response.read()search_page = json.loads(search_page.decode('utf-8'))url_scroll_results = cmr_filter_urls(search_page)if not url_scroll_results:breakif not quiet and hits > CMR_PAGE_SIZE:print('.', end='')sys.stdout.flush()urls += url_scroll_resultsif not quiet and hits > CMR_PAGE_SIZE:print()return urlsdef main(argv=None):global short_name, version, time_start, time_end, bounding_box, \polygon, filename_filter, url_listif argv is None:argv = sys.argv[1:]force = Falsequiet = Falseusage = 'usage: nsidc-download_***.py [--help, -h] [--force, -f] [--quiet, -q]'try:opts, args = getopt.getopt(argv, 'hfq', ['help', 'force', 'quiet'])for opt, _arg in opts:if opt in ('-f', '--force'):force = Trueelif opt in ('-q', '--quiet'):quiet = Trueelif opt in ('-h', '--help'):print(usage)sys.exit(0)except getopt.GetoptError as e:print(e.args[0])print(usage)sys.exit(1)# Supply some default search parameters, just for testing purposes.# These are only used if the parameters aren't filled in up above.if 'short_name' in short_name:short_name = 'ATL06'version = '003'time_start = '2018-10-14T00:00:00Z'time_end = '2021-01-08T21:48:13Z'bounding_box = ''polygon = ''filename_filter = '*ATL06_2020111121*'url_list = []try:if not url_list:url_list = cmr_search(short_name, version, time_start, time_end,bounding_box=bounding_box, polygon=polygon,filename_filter=filename_filter, quiet=quiet)cmr_download(url_list, force=force, quiet=quiet)except KeyboardInterrupt:quit()if __name__ == '__main__':main()

二、批量处理MODIS数据

这个用HEG这个即可,就只需要选择input-file,然后选择第二个,输入一个hdf文件,然后下面各种参数调整,最后选择batch run就行,他就会把文件夹里的MODIS数据都跑了,在HEGOUT文件夹里有输出的tif。但是值得注意的是,我发现似乎最多只能处理900多一些景。然后就不跑了,不知道为什么

这篇关于基于日期、时间、经纬度下载MODIS数据并批处理的文章就介绍到这儿,希望我们推荐的文章对编程师们有所帮助!



http://www.chinasem.cn/article/368282

相关文章

使用MongoDB进行数据存储的操作流程

《使用MongoDB进行数据存储的操作流程》在现代应用开发中,数据存储是一个至关重要的部分,随着数据量的增大和复杂性的增加,传统的关系型数据库有时难以应对高并发和大数据量的处理需求,MongoDB作为... 目录什么是MongoDB?MongoDB的优势使用MongoDB进行数据存储1. 安装MongoDB

Python MySQL如何通过Binlog获取变更记录恢复数据

《PythonMySQL如何通过Binlog获取变更记录恢复数据》本文介绍了如何使用Python和pymysqlreplication库通过MySQL的二进制日志(Binlog)获取数据库的变更记录... 目录python mysql通过Binlog获取变更记录恢复数据1.安装pymysqlreplicat

Linux使用dd命令来复制和转换数据的操作方法

《Linux使用dd命令来复制和转换数据的操作方法》Linux中的dd命令是一个功能强大的数据复制和转换实用程序,它以较低级别运行,通常用于创建可启动的USB驱动器、克隆磁盘和生成随机数据等任务,本文... 目录简介功能和能力语法常用选项示例用法基础用法创建可启动www.chinasem.cn的 USB 驱动

Oracle数据库使用 listagg去重删除重复数据的方法汇总

《Oracle数据库使用listagg去重删除重复数据的方法汇总》文章介绍了在Oracle数据库中使用LISTAGG和XMLAGG函数进行字符串聚合并去重的方法,包括去重聚合、使用XML解析和CLO... 目录案例表第一种:使用wm_concat() + distinct去重聚合第二种:使用listagg,

Python实现将实体类列表数据导出到Excel文件

《Python实现将实体类列表数据导出到Excel文件》在数据处理和报告生成中,将实体类的列表数据导出到Excel文件是一项常见任务,Python提供了多种库来实现这一目标,下面就来跟随小编一起学习一... 目录一、环境准备二、定义实体类三、创建实体类列表四、将实体类列表转换为DataFrame五、导出Da

Python实现数据清洗的18种方法

《Python实现数据清洗的18种方法》本文主要介绍了Python实现数据清洗的18种方法,文中通过示例代码介绍的非常详细,对大家的学习或者工作具有一定的参考学习价值,需要的朋友们下面随着小编来一起学... 目录1. 去除字符串两边空格2. 转换数据类型3. 大小写转换4. 移除列表中的重复元素5. 快速统

Python数据处理之导入导出Excel数据方式

《Python数据处理之导入导出Excel数据方式》Python是Excel数据处理的绝佳工具,通过Pandas和Openpyxl等库可以实现数据的导入、导出和自动化处理,从基础的数据读取和清洗到复杂... 目录python导入导出Excel数据开启数据之旅:为什么Python是Excel数据处理的最佳拍档

在Pandas中进行数据重命名的方法示例

《在Pandas中进行数据重命名的方法示例》Pandas作为Python中最流行的数据处理库,提供了强大的数据操作功能,其中数据重命名是常见且基础的操作之一,本文将通过简洁明了的讲解和丰富的代码示例,... 目录一、引言二、Pandas rename方法简介三、列名重命名3.1 使用字典进行列名重命名3.编

Python 标准库time时间的访问和转换问题小结

《Python标准库time时间的访问和转换问题小结》time模块为Python提供了处理时间和日期的多种功能,适用于多种与时间相关的场景,包括获取当前时间、格式化时间、暂停程序执行、计算程序运行时... 目录模块介绍使用场景主要类主要函数 - time()- sleep()- localtime()- g

Python使用Pandas库将Excel数据叠加生成新DataFrame的操作指南

《Python使用Pandas库将Excel数据叠加生成新DataFrame的操作指南》在日常数据处理工作中,我们经常需要将不同Excel文档中的数据整合到一个新的DataFrame中,以便进行进一步... 目录一、准备工作二、读取Excel文件三、数据叠加四、处理重复数据(可选)五、保存新DataFram