1 U.S. Department of the Interior U.S. Geological Survey LP DAAC Stacie Doman Bennett, LP DAAC Scientist Dave Meyer, LP DAAC Project Scientist

2 OUR GOALS NASA MEaSUREs Expand Understanding of the Earth System Using Consistent Records NASA LP DAAC Preserve NASA’s Earth Science Data For Future Generations

3 LP DAAC Data Preservation Preserve NASA’s Earth Science Data For Future Generations  Three aspects of preservation  Maintain bits  No loss as data move across systems/media/time  Ensure readability  No dependencies  Provide long-term understandability/reprodu cibility  Documentation/data/ancilla ry data & products  Code!

4 A lgorithm Theoretical Basis Document  Each product made available to the users should have the theoretical basis description accessible from the product “landing page”.  The ATBD should:  Be a clear description of the algorithms used to generate the products  Be understandable to the intended users of the products  Be applicable to the version of the product delivered to the DAAC  Datasets that use inputs for which ATBDs are available can just point to those ATBDs; but there needs to be documentation on how the delivered products were generated using those inputs.  For example, using MODIS EVI as in input to a MEaSUREs product algorithm  The form and review process used for an ATBD is at the discretion of the funding NASA Program Manager

5 ATBD’s (con’t)  Content:  Background/Intro  Algorithm descriptions  External dependencies/product interdependencies  Approach to validation/uncertainty analysis  Anticipated/known issues  Submitted manuscripts are acceptable (even preferred) if they address the above.

6 GFSAD ECE Project Space  NASA Earthdata Collaborative Environment  URS Account Required  Project space is restricted to team  View of events, documentation, checklist deliverables received

7 GFSAD ECE Collection Inception Checklist  Project artifacts required  Description of artifacts  Status for delivery of artifacts  Started / completed  Developing reporting for NASA program manager / stakeholders

8 U.S. Department of the Interior U.S. Geological Survey LP DAAC Provisional Dissemination

9 LP DAAC Provisional Data Distribution GFSADCD1KM aka GCE V0.0 The GFSAD Global Crop Dominance at nominal 1KM (GFSADCD1KM), aka Global Cropland Extent 1km Crop Dominance (GCE V0.0), provides information on the spatial distribution of the five major global cropland types (wheat, rice, corn, barley, and soybeans), which occupy 60 percent of the total cropland area at nominal 1km. The map is produced by overlying the five major cropland types of the world produced by Ramankutty et al. (2008), Monfreda et al. (2008), and Portman et al. (2009) over the remote sensing derived global irrigated and rainfed cropland area map of the International Water Management Institute (IWMI; Thenkabail et al., 2009a, 2009b, 2011). The GFSADCD1KM product is an 8 class digital product that provides, at nominal 1 km, information on global: 1. Cropland extent\areas; 2. irrigated versus rainfed cropping; 3. Crop dominance; and 4. Cropping intensity (single, double, triple, and continuous crops). Geographic Extent: Global Map Projection: Geographic (Lat/Long) Datum: World Geodetic System 84 Raster Type: Thematic Spatial Coordinates: Min/Max Longitude -180 to 180, Min/Max Latitude -90 to 90 Temporal Coverage: Nominal Year 2000 Columns: 40430, Rows: Size: 845MB

10 LP DAAC Provisional Data Distribution GFSADCM1KM aka GCE V1.0 The GFSAD Multi-Study Global Crop Mask (GFSADCM1KM), aka Global Crop Extent (GCE V1.0), provides the spatial distribution of a disaggregated five class global cropland extent map derived at nominal 1km based on four major studies: Thenkabail et al. (2009a, 2011), Pittman et al. (2010), Yu et al. (2013), and Friedl et al. (2010). Classes 1 to Class 5 are cropland classes, that are dominated by irrigated and rainfed agriculture. Class 4 and Class 5 have minor/very minor fractions of croplands. GCE 1KM Multi-study Crop Mask is a 5 class digital product that provides, at nominal 1 km, information on global: 1. cropland extent\areas, 2. irrigated versus rainfed cropping. There is no crop type or crop type dominance information. Cropping intensity (single, double, triple, and continuous crops) can be obtained for every pixel using time- series remote sensing data. Geographic Extent: Global Map Projection: Geographic (Lat/Long) Datum: World Geodetic System 84 Raster Type: Thematic Spatial Coordinates: Min/Max Longitude -180 to 180, Min/Max Latitude -90 to 90 Temporal Coverage: Nominal Year 2000 Columns: 40457, Rows: Size: 25.67MB

11  EMS Tool  Capture provisional data distribution  Reporting to NASA stakeholders

12 U.S. Department of the Interior U.S. Geological Survey LP DAAC Products / Algorithms

13 GCE Products + Versions 1km DataMaskVersion 1.0DominanceVersion 0.0Version x.x 250m DataDominanceVersion 2.0V x.x30m DataDominanceVersion 3.0V x.x

14 GCE Products + Versions

15 GCE Unique Algorithms

16 GCE  GFSAD Products & Algorithms Discussion  Quality / Accuracy  Will be assessed per region  How do products relate to versions  One global product (current GCE path)  How do algorithms relate to versions?  Potentially 10+ algorithms w/in one version  How is/are version(s) updated when an algorithm is updated?  How are data archived?  Three global products at three versions (current GCE 1km/250m/30m)  Three global products at one version (1km 1.0, etc)  Ten regional products with unique versions?  How are data discovered/delivered?  By resolution  By region

17 GFSAD Team Field & Crowdsourced Data Discussion  How are quality/accuracy determined?  Included in Product Quality assessment?  How do data relate to versions?  Unrelated to algorithms = not included in versioning  Related to algorithms = included in versioning  How are data archived?  Ancillary files? QA files?  Are / How are data discovered/delivered?  Embedded/Layered in product download?  Optional / additional product download?  What are data provenance?  Can data be filtered out of final product?  What are the impacts of filtering regarding training / validation?

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