From the PO.DAAC Cookbook, to access the GitHub version of the notebook, follow this link.

Amazon Estuary Exploration:

Cloud Direct Download Version

This tutorial is one of two jupyter notebook versions of the same use case exploring multiple satellite data products over the Amazon Estuary. In this version, we use data that has been downloaded onto our local machine from the cloud.

Learning Objectives

  • Compare cloud access methods (in tandem with notebook “Amazon Estuary Exploration: In Cloud AWS Version”)
  • Search for data products using earthaccess Python library
  • Access datasets using xarray and visualize using hvplot or plot tools

This tutorial explores the relationships between river height, land water equivalent thickness, sea surface salinity, and sea surface temperature in the Amazon River estuary and coastal region from multiple datasets listed below. The contents are useful for the ocean, coastal, and terrestrial hydrosphere communities, showcasing how to use cloud datasets and services. This notebook is meant to be executed locally.

Cloud Datasets

The tutorial itself will use four different datasets:

1. TELLUS_GRAC-GRFO_MASCON_CRI_GRID_RL06.1_V3

DOI: https://doi.org/10.5067/TEMSC-3JC63

The Gravity Recovery And Climate Experiment Follow-On (GRACE-FO) satellite land water equivalent (LWE) thicknesses will be used to observe seasonal changes in water storage around the river. When discharge is high, the change in water storage will increase, thus highlighting a wet season. 

2. PRESWOT_HYDRO_GRRATS_L2_DAILY_VIRTUAL_STATION_HEIGHTS_V2

DOI: https://doi.org/10.5067/PSGRA-DA2V2

The NASA Pre-SWOT Making Earth System Data Records for Use in Research Environments (MEaSUREs) Program virtual gauges will be used as a proxy for Surface Water and Ocean Topography (SWOT) discharge until SWOT products are available. MEaSUREs contains river height products, not discharge, but river height is directly related to discharge and thus will act as a good substitute.

3. OISSS_L4_multimission_7day_v1

DOI: https://doi.org/10.5067/SMP10-4U7CS

Optimally Interpolated Sea surface salinity (OISSS) is a level 4 product that combines the records from Aquarius (Sept 2011-June 2015), the Soil Moisture Active Passive (SMAP) satellite (April 2015-present), and ESAs Soil Moisture Ocean Salinity (SMOS) data to fill in data gaps.

4. MODIS_AQUA_L3_SST_MID-IR_MONTHLY_9KM_NIGHTTIME_V2019.0

DOI: https://doi.org/10.5067/MODAM-MO9N9

Sea surface temperature is obtained from the Moderate Resolution Imaging Spectrometer (MODIS) instrument on board the Aqua satellite. 

More details on available collections are on the PO.DAAC Cloud Earthdata Search Portal. For more information on the PO.DAAC transition to the cloud, please visit: https://podaac.jpl.nasa.gov/cloud-datasets/about

Note: NASA Earthdata Login Required

An Earthdata Login account is required to access data, as well as discover restricted data, from the NASA Earthdata system. Thus, to access NASA data, you need Earthdata Login. Please visit https://urs.earthdata.nasa.gov to register and manage your Earthdata Login account. This account is free to create and only takes a moment to set up. We use earthaccess to authenticate your login credentials below.

Needed Packages

import glob
import numpy as np
import pandas as pd
import xarray as xr
import hvplot.xarray
import matplotlib.pyplot as plt
import cartopy.crs as ccrs
import cartopy
from datetime import datetime
import os
from os.path import isfile, basename, abspath
import dask
dask.config.set({"array.slicing.split_large_chunks": False})
import earthaccess
from earthaccess import Auth, DataCollections, DataGranules, Store