Name: raster
Date Completed: 2023-05-15T09:11:26.255412
ID: bc963949-02cd-46c4-9db2-55b942962bad
Details:
Number of Vector Features: 1
Start Date: 01-01-2010
End Date: 12-31-2010
Layers:
Greenup (MCD12Q2.006)
Output Projection: Geographic
Datum: WGS84
EPSG: 4326
PROJ.4: "+proj=longlat +datum=WGS84 +no_defs"
Output Format: geotiff
Version: This request was processed by AppEEARS version 3.28
Supporting Files:
Data Files:
Number of Extracted Data Files: 6 Total Size of Extracted Data Files: 0.06 MB
When an area sample extraction request is successfully submitted, AppEEARS implements a series of tools and services to extract the exact data the user is interested in; or rather, extracts data from specific data layers that intersect with the input vector file's features. AppEEARS first uploads the input vector file and reprojects it to the source projection of the data layer of interest. The PROJ.4 definitions for each data collection available through AppEEARS are listed below.
"+proj=sinu +lon_0=0 +x_0=0 +y_0=0 +R=6371007.181 +units=m +no_defs"
"+proj=longlat +datum=WGS84 +no_defs"
"+proj=sinu +lon_0=0 +x_0=0 +y_0=0 +R=6371007.181 +units=m +no_defs"
"+proj=longlat +datum=WGS84 +no_defs"
"+proj=sinu +lon_0=0 +x_0=0 +y_0=0 +R=6371007.181 +units=m +no_defs"
"+proj=cea +lon_0=0 +lat_ts=30 +x_0=0 +y_0=0 +datum=WGS84 +units=m +no_defs"
"+proj=laea +lat_0=90 +lon_0=0 +x_0=0 +y_0=0 +datum=WGS84 +units=m +no_defs"
"+proj=lcc +lat_0=42.5 +lat_1=25 +lat_2=60 +lon_0=-100 +x_0=0 +y_0=0 +ellps=WGS84 +units=km +no_defs"
"+proj=longlat +datum=WGS84 +no_defs"
"+proj=utm +zone=XX +ellps=WGS84 +units=m +no_defs"
Where "XX" = UTM zone number.
Example:
"+proj=utm +zone=13 +ellps=WGS84 +units=m +no_defs"
"+proj=longlat +datum=WGS84 +no_defs"
"+proj=longlat +datum=WGS84 +no_defs"
"+proj=utm +zone=XX +ellps=WGS84 +units=m +no_defs"
Where "XX" = UTM zone number.
Example:
"+proj=utm +zone=13 +ellps=WGS84 +units=m +no_defs"
Following the reprojection of the vector file, a bounding box for each feature in the vector file is determined. Each corner point is first determined using the minimum and maximum latitude and longitude values. An additional 1 pixel buffer is applied to each corner point to create the feature bounding box. See below for details on how the bounding box is determined.
UpperLeft = (maxLat + cellSize), (minLon - cellSize)
LowerLeft = (minLat - cellSize), (minLon - cellSize)
UpperRight = (maxLat + cellSize), (maxLon + cellSize)
LowerRight = (minLat - cellSize), (maxLon + cellSize)
For each feature, a series of tools and services are used to determine which spatial tiles intersect with the coordinates of the feature bounding box for the data layer of interest. These tiles are extracted (from OPeNDAP) to a server-side work environment where they are mosaicked into an image. Tile extraction and processing is implemented for each requested composite period (e.g., daily, weekly, 8-day, 16-day, monthly, seasonal, or annual) to create a time series image stack. If the user chooses to have the output projection for each extracted data file match, then the image stack is reprojected into the user-requested projection using the PROJ.4 definition described above. The image stacks are finally clipped to the input feature shape to only maintain the data intersecting the feature shape. Data outside of the feature shape are converted to a product-specific NODATA value. Each clipped image in the time series image stack is saved as a CF-compliant NetCDF file or in a series of Geospatial Tagged Image File Format (GeoTIFF) files with a unique name following the naming conventions described in Section 4 of this README.
AppEEARS implements a strict procedure for reprojecting data outputs. Pixel size and resampling methods are non-customizable in AppEEARS. Reprojected data are produced using the Geospatial Data Abstraction Library (GDAL) gdalwarp function in combination with the PROJ.4 string definition for the user-defined output projection type. Reprojection is performed using nearest neighbor resampling. If the projection units are the same between the native and output projections, the native pixel size is used to reproject the image. If the projection units between the native and output projections are different (e.g. sinusoidal (m) to geographic (degrees), pixel size is calculated by reprojecting the center pixel of the original image, calculating its width and height, and then squaring all pixels. The latitude and longitude of the region of interest are maintained in the conversion.
NOTE:
Output data files returned by AppEEARS have the following naming convention:
<ProductShortName>.<Version>_<LayerName>_doy<Year><JulianDate>_<AppEEARSFeatureID>.<FileFormat>
MOD13Q1.061__250m_16_days_NDVI_doy2005193.aid0002.tif
where:
<ProductShortName> .......... MOD13Q1
<Version> ................... 061
<LayerName> ................. _250m_16_days_NDVI
<Year> ...................... 2005
<JulianDate> ................ 193
<AppEEARSFeatureID> ......... aid0002
<FileFormat> ................ tif
The AppEEARS Feature ID is assigned automatically by the system.
When available, AppEEARS extracts and returns quality assurance (QA) data for each data file returned regardless of whether the user requests it. This is done to ensure that the user possesses the information needed to determine the usability and usefulness of the data they get from AppEEARS. Most data products available through AppEEARS have an associated QA data layer. Some products have more than one QA data layer to consult. See below for more information regarding data collections/products and their associated QA data layers.
All MODIS land products, as well as the MODIS Snow Cover Daily product, include quality assurance (QA) information designed to help users understand and make best use of the data that comprise each product.
SRTM v3 products are accompanied by an ancillary "NUM" file in place of the QA/QC files. The "NUM" files indicate the source of each SRTM pixel, as well as the number of input data scenes used to generate the SRTM v3 data for that pixel.
The GPW Population Count
, Population Density
, and Basic Demographic Characteristics
data layers are accompanied by Data Quality Indicators
datasets. The Data Quality Indicators
were created to provide context for the population count and density grids, and to provide explicit information on the spatial precision of the input boundary data. The data context grid (data-context1) explains pixels with "0" population estimate in the population count and density grids, based on information included in the census documents. The mean administrative unit area grid (mean-admin-area2) measures the mean input unit size in square kilometers. It provides a quantitative surface that indicates the size of the input unit(s) from which the population count and density grids were created.
All S-NPP NASA VIIRS land products include quality information designed to help users understand and make best use of the data that comprise each product. For product-specific information, see the link to the S-NPP VIIRS products table provided in section 6.
NOTE:
SurfReflect_State
and SurfReflect_QC
. Both quality layers are provided to the user with the request results. Due to changes implemented on August 21, 2017 for forward processed data, there are differences in values for the SurfReflect_QC
layer in VNP09A1 and SurfReflect_QC_500m
in VNP09H1. SurfReflect_QC
quality layer for data processed before August 21, 2017. For data processed on or after August 21, 2017, refer to the S-NPP NASA VIIRS Surface Reflectance User's guide Version 1.6: https://lpdaac.usgs.gov/documents/124/VNP09_User_Guide_V1.6.pdf. SMAP products provide multiple means to assess quality. Each data product contains bit flags, uncertainty measures, and file-level metadata that provide quality information. Results downloaded from AppEEARS and/or data directly requested via middleware services contain not only the requested pixel/data values, but also the decoded bit flag information associated with each pixel/data value extracted. For additional information regarding the specific bit flags, uncertainty measures, and file-level metadata contained in this product, refer to the Quality Assessment section of the user guide for the specific SMAP data product in your request: https://nsidc.org/data/smap/smap-data.html.
Daymet station-level daily weather observation data and the corresponding Daymet model predicted data for three Daymet model parameters: minimum temperature (tmin), maximum temperature (tmax), and daily total precipitation (prcp) are available. These data provide information into the regional accuracy of the Daymet model for the three station-level input parameters. Corresponding comma separated value (.csv) files that contain metadata for every surface weather station for the variable-year combinations are also available. https://doi.org/10.3334/ORNLDAAC/1850
V1: Quality information varies by product for the ECOSTRESS product suite. Quality information for ECO2LSTE.001, including the bit definition index for the quality layer, is provided in section 2.4 of the User Guide: https://lpdaac.usgs.gov/documents/423/ECO2_User_Guide_V1.pdf. Results downloaded from AppEEARS contain the requested pixel/data values and also the decoded QA information associated with each pixel/data value extracted. No quality flags are produced for the ECO3ETPTJPL.001, ECO4WUE.001, or ECO4ESIPTJPL.001 products. Instead, the quality flags of the source data are available in the ECO3ANCQA.001 data product and a cloud mask is available in the ECO2CLD.001 product. The ETinst
layer in the ECO3ETPTJPL.001 product does include an associated uncertainty layer that is provided with each request for ‘ETinst’ in AppEEARS. Each radiance layer in the ECO1BMAPRAD.001 product has a linked quality layer (Data Quality Indicators). ECO2CLD.001 and ECO3ANCQA.001 are separate quality products that are also available for download in AppEEARS.
V2: Quality information varies by product for the ECOSTRESS product suite. Quality information for ECO_L2_LSTE.002, including the bit definition index for the quality layer, is provided in section 2.4 of the User Guide: https://lpdaac.usgs.gov/documents/423/ECO2_User_Guide_V2.pdf. Results downloaded from AppEEARS contain requested pixel/data values and decoded QA information associated with each pixel/data value extracted. For each Land Surface Temperature and Emissivity product, the quality flags of the source data are available in the ECO_L2_LSTE.002 data product and a cloud mask is available in the ECO_L2_CLOUD.002 product.
Quality information varies by product for the ECOSTRESS product suite. Quality information for ECO_L2T_LSTE.002, including the bit definition index for the quality layer, is provided in section 2.4 of the User Guide: https://lpdaac.usgs.gov/documents/423/ECO2_User_Guide_V2.pdf. Results downloaded from AppEEARS contain requested pixel/data values and decoded QA information associated with each pixel/data value extracted. For each Land Surface Temperature and Emissivity product, the quality flags of the source data are available as a separate science dataset (SDS) layer in the ECO_L2T_LSTE.002 collection, and a separate cloud and water mask layers are also included.
ASTER GDEM v3 data are accompanied by an ancillary "NUM" file in place of the QA/QC files. The "NUM" files refer to the count of ASTER Level-1A scenes that were processed for each pixel or the source of reference data used to replace anomalies. The ASTER Global Water Bodies Database v1 products do not contain QA/QC files.
NASADEM v1 products are accompanied by an ancillary "NUM" file in place of the QA/QC files. The "NUM" files indicate the source of each NASADEM pixel, as well as the number of input data scenes used to generate the NASADEM v1 data for that pixel.
HLS v2.0 Operational Land Imager (OLI) Surface Reflectance and TOA Brightness Daily Global 30m (HLSL30 v002) and Sentinel-2 Multi-spectral Instrument (MSI) Surface Reflectance Daily Global 30m (HLSS30 v002) products have a quality assessment layer enabling per-pixel masking of cloud, cloud shadow, snow, water, and aerosol optical thickness levels. Quality information for HLSL30 v002 and HLSS30 v002 products, including bit definitions for the quality layer can be found in section 6.4 of the User Guide: https://lpdaac.usgs.gov/documents/1326/HLS_User_Guide_V2.pdf.
merge
function from the rasterio
Python package. A subset of the science datasets/variables for VNP22Q2.001 are returned in their raw, unscaled form. That is, these variables are returned without having their scale factor and offset applied. AppEEARS visualizations and output summary files are derived using the raw data value, and consequently do not characterize the intended information ("day of year") for the impacted variables. The variables returned in this state include:
To convert the raw data to "day of year" (doy) for the above variables, use the following equation:
doy = Raw_Data_Value * 1 – (Given_Year - 2000) * 366
merge
function from the rasterio
Python package if they fall within the same UTM zone. The documentation for AppEEARS can be found at https://appeears.earthdatacloud.nasa.gov/help.
Documentation for data products available through AppEEARS are listed below.
AppEEARS sample request outputs are available to download for a limited amount of time after completion. Please visit https://appeears.earthdatacloud.nasa.gov/help?section=sample-retention for details.
AppEEARS Team. (2023). Application for Extracting and Exploring Analysis Ready Samples (AppEEARS). Ver. 3.28. NASA EOSDIS Land Processes Distributed Active Archive Center (LP DAAC), USGS/Earth Resources Observation and Science (EROS) Center, Sioux Falls, South Dakota, USA. Accessed May 15, 2023. https://appeears.earthdatacloud.nasa.gov
We value your opinion. Please help us identify what works, what doesn't, and anything we can do to make AppEEARS better by submitting your feedback at https://appeears.earthdatacloud.nasa.gov/feedback or to LP DAAC User Services at https://lpdaac.usgs.gov/lpdaac-contact-us/.
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.