Translation from x,y coordinates to latitude and longitude for the "Landsat Satellite Surface Temperature v3" dataset. cdm_data_type=Grid comment=Attribute Accuracy Report: Satellite-derived orthorectified brightness temperature was measured within 0.1 degrees C for Landsat 8, 0.6 degrees C for Landsat 7, and 0.5 degrees C for Landsat 5. Satellite measurements were compared to in situ (buoy) surface temperatures from 2003 to 2019, and the bias between the RI DEM buoy temperatures and the satellite temperatures at the pixel of the buoys was removed from each satellite pixel. See https://www.usgs.gov/land-resources/nli/landsat/landsat-surface-reflectance?qt-science_support_page_related_con=0#qt-science_support_page_related_con for more information. Conventions=COARDS, CF-1.6, ACDD-1.3 defaultGraphQuery=Longitude[0:last][0:last][last]&.draw=surface&.vars=X|Y|Longitude history=Converted from Landsat 5, 7, and 8 Surface Reflectance geotiff products to netCDF. The units were changed from K to degrees C and the average bias determined through RI DEM buoy comparison in Narragansett Bay (2003-2019) to each satellite was removed from all scenes from the corresponding satellite. The errors were determined by using a K-fold cross-validation to minimize error at each satellite pixel. The scenes were also cloud masked, land masked, and stripes in Landsat 7 imagery (due to sensor failure) were masked as well. A buoy comparison was only conducted within Narragansett Bay for Landsat scenes with less than 50% cloud cover, and applied to available scenes back to 1984 for Landsat 5, 1999 for Landsat 7, and 2013 for Landsat 8. As a result, data uncertainties are unknown outside of the Narragansett Bay region, for scenes with greater cloud cover, and scenes before 2003. infoUrl=https://earthexplorer.usgs.gov/ accessed through https://code.earthengine.google.com/ institution=Rhode Island Data Discovery/United States Geological Survey publication=https://doi.org/10.26300/ja0b-xa86 references=Rhode Island Data Discovery/United States Geological Survey source=https://earthexplorer.usgs.gov/ accessed through https://code.earthengine.google.com/ sourceUrl=(local files) standard_name_vocabulary=CF Standard Name Table v55

Massive sargassum influxes into the Wider Caribbean Region and West African coast have negatively affected both social and ecological systems since 2011. Current monitoring efforts using satellite data are being conducted but are mainly limited to offshore waters. This research attempts to address the literature gap by developing a method to characterize sargassum accumulations along the shoreline and nearshore waters, and to assess their spatial and temporal dynamics. Using the online Google Earth Engine platform, we analyzed Sentinel-2 MultiSpectral Instrument (MSI) satellite imagery for sargassum occurrence from 2015-09-16 to 2022-01-22 in La Parguera, Puerto Rico. A combination of MSI reflectance bands and several vegetation and water quality indexes were used with a Random Forest classification algorithm. Field data was collected to calibrate and validate the classification product. Our classification model was able to identify different stages of the sargassum decaying process in the shoreline (e.g., fresh sargassum, decomposing sargassum, and sargassum brown tide) along with other non-sargassum cover classes (e.g., water, mangroves, and clouds). Sargassum accumulation hotspots (SAHs) that persisted throughout the study period were identified and their spatial and temporal dynamics were assessed.

The data package consists of the following datasets: - LeonPerez.et.al_TrainingAndValidation.shp: Shapefile showing the training and validation data used. - LeonPerez.et.al_CoverClassPersistence.tiff: Raster showing the cover classes that persisted for each pixel throughout the timeseries. - LeonPerez.et.al_SAHLocation.shp: Shapefile showing the location of the three SAHs analyzed. - LeonPerez.et.al_SAHTimeseries_IslaCueva.txt: Timeseries of the area covered by each of the six cover classes in Isla Cueva SAH. - LeonPerez.et.al_SAHTimeseries_IslaGuayacan.txt: Timeseries of the area covered by each of the six cover classes in Isla Guayacán SAH. - LeonPerez.et.al_SAHTimeseries_LaPitahaya.txt: Timeseries of the area covered by each of the six cover classes in La Pitahaya SAH.

The datasets included here were used to assess trends and geospatial patterns of river ice extent in the Copper River Basin of southcentral Alaska. Our goals were to document how river ice travel and wintertime access to traditional lands have been impacted by climate change, to support safe ice travel by identifying reaches with persistent open water vs early ice cover, and to understand the physical drivers of the local variation in freeze-up. Trends in ice extent for a section of the Copper River were analyzed using Landsat imagery from water years (WY, Oct. 1-Sep 30) 1973-2021. The direct observations of ice extent classes from each image date are provided (IceExtent_DirectObs.csv). These observations were gap-filled and summarized on a weekly basis, along with local air temperature metrics (IceExtent_AirTemps_Weekly_GapFilled.csv). Geospatial patterns of ice and open water extent for recent winters were assessed in two ways. First, we quantified reach-level late-winter open water extent of the Copper and Chitina rivers by digitizing Sentinel-2 multispectral images from WY 2018, 2020, and 2021, and combined these with river hydrologic and geomorphic characteristics (e.g. unit stream power, number of channels, river width, etc.). These data are provided in spatial (RiverReachData_Spatial.zip) and tabular (RiverReachData_Tabular.csv) formats. Second, we analyzed the progression of freeze-up by visualizing Sentinel-1 SAR imagery and compositing pixel-based classifications of water for Nov-Feb of WY 2018-2020 to show the seasonal and multiyear water occurrence (% season) (waterOccurrenceYYYY.tif, waterOccurrenceMultiYear.tif). The script for this SAR analysis in Google Earth Engine is provided (RiverIceAndOpenWater_SAR.js).

Disclaimer

This resource is currently being under revision. A preprint is available at: https://doi.org/10.21203/rs.3.rs-6343364/v1

The data is available at three resolutions:

• 1000 m: this deposition
• 100 m: see related datasets below
• 20 m: Only on Google Earth Engine (also 1000 m and 100 m available):
  • LAI: ee.ImageCollection('/ee-speckerfelix/assets/open-earth/lai_predictions-mlp_20m_v01')
  • FAPAR: ee.ImageCollection('/ee-speckerfelix/assets/open-earth/fapar_predictions-mlp_20m_v01')
  • FCOVER: ee.ImageCollection('/ee-speckerfelix/assets/open-earth/fcover_predictions-mlp_20m_v01')
  • GEE-App: Open GEE App
  • Visualization Code: Open in GEE Code Editor
  • Example: Mosaicking / Scaling / Filtering Code: Open in GEE Code Editor

Abstract

Environmental restoration projects are crucial for ecosystem recovery and biodiversity conservation but monitoring progress at a global scale poses substantial challenges. Publicly funded satellite missions such as Sentinel-2 have great potential to transform ecosystem monitoring due to their high spatial and temporal resolution if they can be reliably linked to ecosystem characteristics. Here, we present the first global, analysis-ready, decametric maps for three key vegetation biophysical properties on an annual basis, including effective leaf area index (LAIe), fraction of absorbed photosynthetically active radiation (FAPAR), and fractional vegetation cover (FCOVER). We utilize a hybrid retrieval approach of the physically based radiative transfer model PROSAIL to directly estimate biophysical variables from multispectral Sentinel-2 images, making use of multiple observations during the peak of the growing season. All retrievals are aggregated into mean values, standard deviations, and the number of observations taken during this period. The maps are available at 20 m, 100 m, and 1000 m spatial resolution for the years 2019 to 2024, totaling approximately 20 TB of analysis-ready data, and are validated using in-situ data from the Ground-Based Observations for Validation (GBOV). The annual temporal and decametric spatial resolution of these maps provides new opportunities for biodiversity and ecosystem monitoring, enabling more effective assessments of restoration efforts and contributing to the development of standardized global monitoring frameworks.

Description

This data set includes:

• Annual Effective Leaf Area Index (LAIe) (2019 - 2024):
  Includes annual mean (mean of retrievals), variation (standard deviation of retrievals) and count (number of cloud-free observations) for LAI at 1000, and 100 m resolution.
• Annual Fractional Vegetation Cover (FCOVER) (2019 - 2024):
  Includes annual mean (mean of retrievals), variation (standard deviation of retrievals) and count (number of cloud-free observations) for FCOVER at 1000, and 100 m resolution.
• Annual fraction of absorbed photosynthetically activate radiation (FAPAR) (2019 - 2024):
  Includes annual mean (mean of retrievals), variation (standard deviation of retrievals) and count (number of cloud-free observations) for FCOVER at 1000, and 100 m resolution.

Higher-resolution 20-meter maps are only available on Google Earth Engine (see this link).

Data Format and Scaling

• Data Types: Mean and standard deviation maps are stored as int16. Count maps are stored as uint8.
• NoData Values:
  • Mean and standard deviation maps: -9999
  • Count maps: 255
• Scaling Factors (applies only for int16 maps):
  • FAPAR and FCOVER: 0.0001
  • LAI: 0.001

Children depositions (100 m versions)

To download all at once, you can use the following bash script: Link GitHub

Code

The code used to generate the data is available at Zenodo, and at the GitHub repository.

Naming Convention

To ensure consistency and ease of use across and within the projects, we follow the standard Open-Earth-Monitor file-naming convention. The convention works with 10 fields that describe important properties of the data. In this way, users can search files, prepare data analysis etc, without needing to open files. The fields are:

• Variable name: [lai, fapar, fcover]
• Method: rtm.mlp (Radiative transfer model inversion, using multi-layer perceptron regression model)
• Position in probability distribution: [mean, std, count]
• Spatial resolution: [20m, 100m, 1000]
• Depth reference or depth interval s ("s": surface)
• Time reference begin: 20190101
• Time reference end:

Translation from x,y coordinates to latitude and longitude for the "Landsat Satellite Surface Temperature v3" dataset. cdm_data_type=Grid comment=Attribute Accuracy Report: Satellite-derived orthorectified brightness temperature was measured within 0.1 degrees C for Landsat 8, 0.6 degrees C for Landsat 7, and 0.5 degrees C for Landsat 5. Satellite measurements were compared to in situ (buoy) surface temperatures from 2003 to 2019, and the bias between the RI DEM buoy temperatures and the satellite temperatures at the pixel of the buoys was removed from each satellite pixel. See https://www.usgs.gov/land-resources/nli/landsat/landsat-surface-reflectance?qt-science_support_page_related_con=0#qt-science_support_page_related_con for more information. Conventions=COARDS, CF-1.6, ACDD-1.3 defaultGraphQuery=Longitude[0:last][0:last][last]&.draw=surface&.vars=X|Y|Longitude history=Converted from Landsat 5, 7, and 8 Surface Reflectance geotiff products to netCDF. The units were changed from K to degrees C and the average bias determined through RI DEM buoy comparison in Narragansett Bay (2003-2019) to each satellite was removed from all scenes from the corresponding satellite. The errors were determined by using a K-fold cross-validation to minimize error at each satellite pixel. The scenes were also cloud masked, land masked, and stripes in Landsat 7 imagery (due to sensor failure) were masked as well. A buoy comparison was only conducted within Narragansett Bay for Landsat scenes with less than 50% cloud cover, and applied to available scenes back to 1984 for Landsat 5, 1999 for Landsat 7, and 2013 for Landsat 8. As a result, data uncertainties are unknown outside of the Narragansett Bay region, for scenes with greater cloud cover, and scenes before 2003. infoUrl=https://earthexplorer.usgs.gov/ accessed through https://code.earthengine.google.com/ institution=Rhode Island Data Discovery/United States Geological Survey publication=https://doi.org/10.26300/ja0b-xa86 references=Rhode Island Data Discovery/United States Geological Survey source=https://earthexplorer.usgs.gov/ accessed through https://code.earthengine.google.com/ sourceUrl=(local files) standard_name_vocabulary=CF Standard Name Table v55

Landsat-derived water surface temperature in Mount Hope Bay with bias correction from RI DEM buoys. For translation from the x,y coordinates to latitude and longitude see the "Landsat Satellite Coordinates version 2, Narragansett Bay" dataset. cdm_data_type=Grid comment=Attribute Accuracy Report: Satellite-derived orthorectified brightness temperature was measured within 0.1 degrees C for Landsat 8, 0.6 degrees C for Landsat 7, and 0.5 degrees C for Landsat 5. Satellite measurements were compared to in situ (buoy) surface temperatures from 2003 to 2022, and the mean bias between the RI DEM buoy temperatures and the satellite temperatures at the pixel of the buoys was added to or subtrcted from all scenes by satellite. The standard deviation between the buoys and the satellite after adding the bias is 1.9 degrees C for Landsat 5, 1.9 degrees C for Landsat 7 and 1.3 degrees C for Landsat 8. The standard deviation is considered the uncertainty of the satellite measurements. See https://www.usgs.gov/land-resources/nli/landsat/landsat-surface-reflectance?qt-science_support_page_related_con=0#qt-science_support_page_related_con for more information. Conventions=COARDS, CF-1.10, ACDD-1.3 defaultGraphQuery=temperature[0:last][0:last][last]&.draw=surface&.vars=X|Y|temperature description=Landsat-derived water surface temperature in Narragansett Bay with bias correction from RI DEM buoys. history=Converted from Landsat 5, 7, and 8 Surface Reflectance geotiff products to netCDF. The units were changed from K to degrees C and the average bias determined through RI DEM buoy comparison in Mount Hope Bay (2003-2022) to each satellite was added to or subtracted from all scenes from the corresponding satellite. For Landsat 5, 0.45 degrees C was subtracted from all scenes; similarly, 1.094 was subtracted for Landsat 7, and 0.178 was added for Landsat 8. The errors were determined by averaging the five closest temporal buoy readings for each satellite image capture and spatially averaging buoy locations within a 200-square-meter zone. The scenes were also cloud masked, land masked, and stripes in Landsat 7 imagery (due to sensor failure) were masked as well. A buoy comparison was only conducted within Narragansett Bay for Landsat scenes with less than 50% cloud cover, and applied to available scenes back to 1984 for Landsat 5, 1999 for Landsat 7, and 2013 for Landsat 8. As a result, data uncertainties are unknown outside of the Narragansett Bay region, for scenes with greater cloud cover, and scenes before 2003. infoUrl=https://earthexplorer.usgs.gov/ accessed through https://code.earthengine.google.com/ institution=United States Geological Survey keywords_vocabulary=GCMD Science Keywords publication=https://doi.org/10.26300/ja0b-xa86 references=Rhode Island Data Discovery/United States Geological Survey source=https://earthexplorer.usgs.gov/ accessed through https://code.earthengine.google.com/ sourceUrl=(local files) standard_name_vocabulary=CF Standard Name Table v55 time_coverage_end=2022-09-29T00:00:00Z time_coverage_start=1984-05-02T00:00:00Z

This dataset contains cloud free, low tide composite satellite images for the tropical Australia region based on 10 m resolution Sentinel 2 imagery from 2018 – 2023. This image collection was created as part of the NESP MaC 3.17 project and is intended to allow mapping of the reef features in tropical Australia.

This collection contains composite imagery for 200 Sentinel 2 tiles around the tropical Australian coast. This dataset uses two styles:
1. a true colour contrast and colour enhancement style (TrueColour) using the bands B2 (blue), B3 (green), and B4 (red)
2. a near infrared false colour style (Shallow) using the bands B5 (red edge), B8 (near infrared), and B12 (short wave infrared).
These styles are useful for identifying shallow features along the coastline.

The Shallow false colour styling is optimised for viewing the first 3 m of the water column, providing an indication of water depth. This is because the different far red and near infrared bands used in this styling have limited penetration of the water column. In clear waters the maximum penetrations of each of the bands is 3-5 m for B5, 0.5 - 1 m for B8 and < 0.05 m for B12. As a result, the image changes in colour with the depth of the water with the following colours indicating the following different depths:
- White, brown, bright green, red, light blue: dry land
- Grey brown: damp intertidal sediment
- Turquoise: 0.05 - 0.5 m of water
- Blue: 0.5 - 3 m of water
- Black: Deeper than 3 m
In very turbid areas the visible limit will be slightly reduced.


Change log:

This dataset will be progressively improved and made available for download. These additions will be noted in this change log.
2024-07-24 - Add tiles for the Great Barrier Reef
2024-05-22 - Initial release for low-tide composites using 30th percentile (Git tag: "low_tide_composites_v1")


Methods:

The satellite image composites were created by combining multiple Sentinel 2 images using the Google Earth Engine. The core algorithm was:
1. For each Sentinel 2 tile filter the "COPERNICUS/S2_HARMONIZED" image collection by
- tile ID
- maximum cloud cover 0.1%
- date between '2018-01-01' and '2023-12-31'
- asset_size > 100000000 (remove small fragments of tiles)
2. Remove high sun-glint images (see "High sun-glint image detection" for more information).
3. Split images by "SENSING_ORBIT_NUMBER" (see "Using SENSING_ORBIT_NUMBER for a more balanced composite" for more information).
4. Iterate over all images in the split collections to predict the tide elevation for each image from the image timestamp (see "Tide prediction" for more information).
5. Remove images where tide elevation is above mean sea level to make sure no high tide images are included.
6. Select the 10 images with the lowest tide elevation.
7. Combine SENSING_ORBIT_NUMBER collections into one image collection.
8. Remove sun-glint (true colour only) and apply atmospheric correction on each image (see "Sun-glint removal and atmospheric correction" for more information).
9. Duplicate image collection to first create a composite image without cloud masking and using the 30th percentile of the images in the collection (i.e. for each pixel the 30th percentile value of all images is used).
10. Apply cloud masking to all images in the original image collection (see "Cloud Masking" for more information) and create a composite by using the 30th percentile of the images in the collection (i.e. for each pixel the 30th percentile value of all images is used).
11. Combine the two composite images (no cloud mask composite and cloud mask composite). This solves the problem of some coral cays and islands being misinterpreted as clouds and therefore creating holes in the composite image. These holes are "plugged" with the underlying composite without cloud masking. (Lawrey et al. 2022)
12. The final composite was exported as cloud optimized 8 bit GeoTIFF

Note: The following tiles were generated with different settings as they did not have enough images to create a composite with the standard settings:
- 51KWA: no high sun-glint filter
- 54LXP: maximum cloud cover set to 1%
- 54LXP: maximum cloud cover set to 1%
- 54LYK: maximum cloud cover set to 2%
- 54LYM: maximum cloud cover set to 5%
- 54LYN: maximum cloud cover set to 1%
- 54LYQ: maximum cloud cover set to 5%
- 54LYP: maximum cloud cover set to 1%
- 54LZL: maximum cloud cover set to 1%
- 54LZM: maximum cloud cover set to 1%
- 54LZN: maximum cloud cover set to 1%
- 54LZQ: maximum cloud cover set to 5%
- 54LZP: maximum cloud cover set to 1%
- 55LBD: maximum cloud cover set to 2%
- 55LBE: maximum cloud cover set to 1%
- 55LCC: maximum cloud cover set to 5%
- 55LCD: maximum cloud cover set to 1%


High sun-glint image detection:

Images with high sun-glint can lead to lower quality composite images. To determine high sun-glint images, a mask is created for all pixels above a high reflectance threshold for the near-infrared and short-wave infrared bands. Then the proportion of this is calculated and compared against a sun-glint threshold. If the image exceeds this threshold, it is filtered out of the image collection. As we are only interested in the sun-glint on water pixels, a water mask is created using NDWI before creating the sun-glint mask.


Sun-glint removal and atmospheric correction:

Sun-glint was removed from the images using the infrared B8 band to estimate the reflection off the water from the sun-glint. B8 penetrates water less than 0.5 m and so in water areas it only detects reflections off the surface of the water. The sun-glint detected by B8 correlates very highly with the sun-glint experienced by the visible channels (B2, B3 and B4) and so the sun-glint in these channels can be removed by subtracting B8 from these channels.

Eric Lawrey developed this algorithm by fine tuning the value of the scaling between the B8 channel and each individual visible channel (B2, B3 and B4) so that the maximum level of sun-glint would be removed. This work was based on a representative set of images, trying to determine a set of values that represent a good compromise across different water surface conditions.

This algorithm is an adjustment of the algorithm already used in Lawrey et al. 2022


Tide prediction:

To determine the tide elevation in a specific satellite image, we used a tide prediction model to predict the tide elevation for the image timestamp. After investigating and comparing a number of models, it was decided to use the empirical ocean tide model EOT20 (Hart-Davis et al., 2021). The model data can be freely accessed at https://doi.org/10.17882/79489 and works with the Python library pyTMD (https://github.com/tsutterley/pyTMD). In our comparison we found this model was able to predict accurately the tide elevation across multiple points along the study coastline when compared to historic Bureau of Meteorolgy and AusTide data. To determine the tide elevation of the satellite images we manually created a point dataset where we placed a central point on the water for each Sentinel tile in the study area . We used these points as centroids in the ocean models and calculated the tide elevation from the image timestamp.


Using "SENSING_ORBIT_NUMBER" for a more balanced composite:

Some of the Sentinel 2 tiles are made up of different sections depending on the "SENSING_ORBIT_NUMBER". For example, a tile could have a small triangle on the left side and a bigger section on the right side. If we filter an image collection and use a subset to create a composite, we could end up with a high number of images for one section (e.g. the left side triangle) and only few images for the other section(s). This would result in a composite image with a balanced section and other sections with a very low input. To avoid this issue, the initial unfiltered image collection is divided into multiple image collections by using the image property "SENSING_ORBIT_NUMBER". The filtering and limiting (max number of images in collection) is then performed on each "SENSING_ORBIT_NUMBER" image collection and finally, they are combined back into one image collection to generate the final composite.


Cloud Masking:

Each image was processed to mask out clouds and their shadows before creating the composite image.
The cloud masking uses the COPERNICUS/S2_CLOUD_PROBABILITY dataset developed by SentinelHub (Google, n.d.; Zupanc, 2017). The mask includes the cloud areas, plus a mask to remove cloud shadows. The cloud shadows were estimated by projecting the cloud mask in the direction opposite the angle to the sun. The shadow distance was estimated in two parts.

A low cloud mask was created based on the assumption that small clouds have a small shadow distance. These were detected using a 35% cloud probability threshold. These were projected over 400 m, followed by a 150 m buffer to expand the final mask.

A high cloud mask was created to cover longer shadows created by taller, larger clouds. These clouds were detected based on an 80% cloud probability threshold, followed by an erosion and dilation of 300 m to remove small clouds. These were then projected over a 1.5 km distance followed by a 300 m buffer.

The parameters for the cloud masking (probability threshold, projection distance and buffer radius) were determined through trial and error on a small number of scenes. As such there are probably significant potential improvements that could be made to this algorithm.

Erosion, dilation and buffer operations were performed at a lower image resolution than the native satellite image resolution

Landsat-derived water surface temperature near Narragansett Bay with bias correction from RI DEM buoys (lakes and rivers included). For translation from the x,y coordinates to latitude and longitude see the "Landsat Satellite Coordinates version 1" dataset. cdm_data_type=Grid comment=Attribute Accuracy Report: Satellite-derived orthorectified brightness temperature was measured within 0.1 degrees C for Landsat 8, 0.6 degrees C for Landsat 7, and 0.5 degrees C for Landsat 5. Satellite measurements were compared to in situ (buoy) surface temperatures from 2003 to 2015, and the mean bias between the RI DEM buoy temperatures and the satellite temperatures at the pixel of the buoys was added to all scenes by satellite. The standard deviation between the buoys and the satellie after adding the bias is 1.9 for Landsat 5, 1.9 for Landsat 7 and 1.3 for Landsat 8. The standard deviation is considered the uncertainty of the satellite measurements. See https://www.usgs.gov/land-resources/nli/landsat/landsat-surface-reflectance?qt-science_support_page_related_con=0#qt-science_support_page_related_con for more information. Conventions=COARDS, CF-1.6, ACDD-1.3 defaultGraphQuery=Temp[0:last][0:last][last]&.draw=surface&.vars=X|Y|Temp history=Converted from Landsat 5, 7, and 8 Surface Reflectance geotiff products to netCDF. The units were changed from K to degrees C and the average bias determined through RI DEM buoy comparison in Narragansett Bay (2003-2015) to each satellite was added to all scenes from the corresponding satellite. For Landsat 5, 3.36 degrees C was added to all scenes, 3.34 for Landsat 7, and 1.92 for Landsat 8. The errors were determined by calculating the standard deviation of the difference between Landsat and buoy temperature at buoy locations for each satellite. The scenes were also cloud masked, land masked, and stripes in Landsat 7 imagery (due to sensor failure) were masked as well. Data outside of the Narragansett Bay region and for all cloud cover is included, though a buoy comparison was only conducted within Narragansett Bay for Landsat scenes with less than 50% cloud cover. As a result, data uncertainties are unknown outside of the Narragansett Bay region and for scenes with greater cloud cover. infoUrl=https://earthexplorer.usgs.gov institution=United States Geological Survey keywords_vocabulary=GCMD Science Keywords references=United States Geolocical Survey source=https://earthexplorer.usgs.gov/ accessed through https://code.earthengine.google.com/ sourceUrl=(local files) standard_name_vocabulary=CF Standard Name Table v55 testOutOfDate=now-71days time_coverage_end=2021-04-22T15:26:44Z time_coverage_start=1984-05-02T14:54:17Z