Access Land Surface Temperature Data From ECOSTRESS

Access Land Surface Temperature Data From ECOSTRESS#

ecostress_diagram

The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) mission measures the temperature of plants to better understand how much water plants need and how they respond to stress. ECOSTRESS is attached to the International Space Station (ISS) and collects data globally between 52° N and 52° S latitudes. Source: NASA Earthdata.

Requirements#

EDL authentication (username/password)

Objectives#

Subset a remote file#

a) By Variables
b) By Spatial selection

Subset multiple remote files#

Stream subset of data

References#

Hook, S., & Hulley, G.(2022). ECOSTRESS Swath Land Surface Temperature and Emissivity Instantaneous L2 Global 70 m v002 [Data set]. NASA Land Processes Distributed Active Archive Center. https://doi.org/10.5067/ECOSTRESS/ECO_L2_LSTE.002

import xarray as xr
import datetime as dt
import earthaccess
import pydap
import matplotlib.pyplot as plt
# import pydap-specific tools
from pydap.client import get_cmr_urls, open_url
import numpy as np
from pydap.client import to_netcdf as dap_to_netcdf

EDL Authentication via earthaccess and OPeNDAP#

You can authenticate via earthaccess as demonstrated below. You must have a valid EDL account. There are two strategies for authenticating with earthaccess:

strategy="interactive". This will promt your edl username-password.
strategy="netrc". Use this if the notebook is running on an environment where a .netrc with your credentials is recoverable. T

Below the default will be netrc, assuming the user has executed the notebook Authenticate.ipynb. If not, you can change the strategy to "interactive".

from earthaccess.exceptions import LoginStrategyUnavailable
try:
    auth = earthaccess.login(strategy="netrc", persist=True) # you will be promted to add your EDL credentials
except LoginStrategyUnavailable:
    auth = earthaccess.login(strategy="interactive", persist=True)

# pass Token Authorization to a new Session.
my_session = session=auth.get_session()

Finding OPeNDAP URLs#

Query opendap urls using NASA’s CMR API

We query NASA’s CMR to identify remote files that intersect the following geographical area (bounding box) covering the following time range

-128.85 < longitude < -107.05, and 41.1 < latitude < 46.68
03/01 - 04/30 (2025)

ECOSTRESS_ccid = "C2076114664-LPCLOUD"
bounding_box = [-128.847656, 41.112469, -107.050781, 46.679594]
time_range = [dt.datetime(2025, 3, 1), dt.datetime(2025, 3, 30)]

cmr_urls = get_cmr_urls(ccid=ECOSTRESS_ccid, bounding_box = bounding_box, limit=1000, time_range=time_range) # limit by default = 50

print("################################################ \n We found a total of ", len(cmr_urls), "OPeNDAP URLS!!!\n################################################")

################################################ 
 We found a total of  188 OPeNDAP URLS!!!
################################################

Accessing Metadata-ONLY with PyDAP#

We can access OPeNDAP-produced metadata to identify the variables of interest. In particular those associated with latitude and longitude values

Below need to request the DAP4 metadata from the remote server.

%%time
pyds = open_url(cmr_urls[0], protocol="dap4", session=my_session)
pyds.tree()

.ECOv002_L2_LSTE_37709_001_20250301T092419_0713_01.h5
├──L2 LSTE Metadata
│  ├──AncillaryNWP
│  ├──BandSpecification
│  ├──CloudMaxTemperature
│  ├──CloudMeanTemperature
│  ├──CloudMinTemperature
│  ├──CloudSDevTemperature
│  ├──Emis1GoodAvg
│  ├──Emis2GoodAvg
│  ├──Emis3GoodAvg
│  ├──Emis4GoodAvg
│  ├──Emis5GoodAvg
│  ├──LSTGoodAvg
│  ├──NWPSource
│  ├──NumberOfBands
│  ├──OrbitCorrectionPerformed
│  ├──QAPercentCloudCover
│  └──QAPercentGoodQuality
├──SDS
│  ├──Emis1
│  ├──Emis1_err
│  ├──Emis2
│  ├──Emis2_err
│  ├──Emis3
│  ├──Emis3_err
│  ├──Emis4
│  ├──Emis4_err
│  ├──Emis5
│  ├──Emis5_err
│  ├──EmisWB
│  ├──LST
│  ├──LST_err
│  ├──PWV
│  ├──QC
│  ├──cloud_mask
│  └──water_mask
└──StandardMetadata
   ├──AncillaryInputPointer
   ├──AutomaticQualityFlag
   ├──AutomaticQualityFlagExplanation
   ├──BuildID
   ├──CampaignShortName
   ├──CollectionLabel
   ├──DataFormatType
   ├──DayNightFlag
   ├──EastBoundingCoordinate
   ├──FieldOfViewObstruction
   ├──HDFVersionID
   ├──ImageLineSpacing
   ├──ImageLines
   ├──ImagePixelSpacing
   ├──ImagePixels
   ├──InputPointer
   ├──InstrumentShortName
   ├──LocalGranuleID
   ├──LongName
   ├──NorthBoundingCoordinate
   ├──PGEName
   ├──PGEVersion
   ├──PlatformLongName
   ├──PlatformShortName
   ├──PlatformType
   ├──ProcessingEnvironment
   ├──ProcessingLevelDescription
   ├──ProcessingLevelID
   ├──ProducerAgency
   ├──ProducerInstitution
   ├──ProductionDateTime
   ├──ProductionLocation
   ├──RangeBeginningDate
   ├──RangeBeginningTime
   ├──RangeEndingDate
   ├──RangeEndingTime
   ├──RegionID
   ├──SISName
   ├──SISVersion
   ├──SceneID
   ├──ShortName
   ├──SouthBoundingCoordinate
   ├──StartOrbitNumber
   ├──StopOrbitNumber
   └──WestBoundingCoordinate
CPU times: user 55.7 ms, sys: 2.74 ms, total: 58.4 ms
Wall time: 281 ms

Subset data#

First, we would like to subset data based on the criteria:

Daylight data
QAPercentCloudCover <30%
and QAPercentGoodQuality> 70%

For that, we need to download only 3 variables:

    ["/L2 LSTE Metadata/QAPercentCloudCover", 
     "/L2 LSTE Metadata/QAPercentGoodQuality",
     "/StandardMetadata/DayNightFlag"]

output_path = 'data/'

Stream data into local directory#

Stream each remote file into an individual file, since these cannot be aggregated.

%%time
dap_to_netcdf(cmr_urls, session=my_session, output_path = output_path, 
              keep_variables=["/L2 LSTE Metadata/QAPercentCloudCover", 
                              "/L2 LSTE Metadata/QAPercentGoodQuality",
                              "/StandardMetadata/DayNightFlag"]
             )

CPU times: user 192 ms, sys: 270 ms, total: 462 ms
Wall time: 17.7 s