1. SEO Report to WGISS Brian Killough CEOS Systems Engineering Office (SEO) WGISS-41 Meeting March 14-18, 2016

2. COVE Reminder...  The CEOS Visualization Environment (COVE) is a browser-based suite of tools for searching, analyzing and visualizing actual and potential satellite sensor coverage.  COVE is FREE and OPEN for anyone to use!  COVE includes 260 missions, 705 mission- instrument combinations.  COVE is linked to several mission archives to get metadata and browse images for past acquired data.  There is a large international user base... 5800+ unique users in 2015.  The NASA SEO maintains COVE is constantly assessing inquiries from the user community to add new missions, instruments, mode and features to the tool for expanded capability. The COVE suite of tools includes:  COVE – The main tool for global visualizations  Rapid Acquisition Tool – A tabular tool for analyses  Coverage Analyzer - A tool for long-term coverage analysis  Mission and Instrument Browser – Details on the COVE mission database

3. Recent COVE Updates  January 2016: Changed from Google Earth to Cesium for globe interface  New Missions: Sentinel-1A/2A, CBERS-4, RCM 1-3 (notional), FY-3C, PROBA-V, TET-1.  New Overlays: Landsat/Sentinel constellation revisit performance, Global Phenology (2001/2014 monthly NDVI Min/Max)  New Analysis Tools: Custom Mission Tool allows the creation of a notional mission for analyses.  New Archive Link: Sentinel-1A !!!!  Future Archive Links: Sentinel-2A... coming soon Example of COVE connection to the Sentinel-1A data archive. COVE now shows actual acquired data locations, quick-look images, and links to order the data.

4. The COVE Dream... COVE will become a popular international free/open tool for conducting historic satellite coverage analyses (where data was taken) and assessing future potential coverage (where data could be taken). The tool will include most CEOS missions and connect to every open/free mission archive. Users will make connections (through COVE) to link to the data ordering tools of any agency.

5. COVE Archive Connections  The SEO would like to expand the number of links between CEOS missions and the COVE tool. We believe that WGISS can help since they have successfully connected to many data archives through CWIC. Here is the metadata we need for COVE...  Common to all missions: Scene ID, Date and time (UTC) of acquisition, Processing Level(s), Scene boundary  Optical only: Cloud cover % (if available), Solar angles, Viewing angles  Radar only: Mode, Polarization, Ascending or Descending, Incidence angles  Some CWIC questions...  Is there a unique “ID list” for mission and instrument combinations in CWIC?  Can CWIC give us the needed metadata for more misssions?

6. Why Data Cubes? Proven concept in Australia by Geoscience Australia and the Australian Space Agency (CSIRO). A multi-dimensional (space, time, data layers) Data Cube is an efficient and effective data storage/access solution! Shift in Paradigm... Pixels vs Scenes (no pixels lost) Analysis Ready” Data Products vs. Unprocessed Data (leave processing to the Space Agencies). Data Cube approach supports an infinite number of applications, makes it easier for users to access and use space-based data, and allows efficient time series analyses and data assimilation. Open source software approach expands capabilities, data use and capacity. TIME Data Layer #1 Data Layer #2 Data Layer #3 Data Layer #4 www.datacube.org.au

7. The Data Cube Vision...  The CEOS Data Cube infrastructure will become a commonly used free and open source software toolset for creating local, regional or national pixel- based time-series of multiple datasets that are spatially aligned according to user needs (spatial region, time period, data layers, grid projection).  Users will connect free/open user interface tools to the Data Cube for common analyses (cloud-free mosaics, time series change detection) or utilize APIs to develop their own tools to query the content.  Scene-based tools will be used less frequently as users move toward a preference for Data Cubes  Space Agencies will systematically supply Analysis-Ready Data (ARD) products that are easily ingested into Data Cubes

8. What are we doing? Working with CEOS Space Agencies to develop plans for sustained provision of Analysis Ready Data (ARD). Sentinel-1A and Sentinel-2A are the highest priority. User Interface Data Cubes Analysis-Ready Data Products Developing and testing prototype user interfaces for custom mosaic creation. Investigate connections to other tools... QGIS Developing Advanced Programming Interfaces (APIs) for users to create their own user interfaces Testing prototype Data Cubes for Kenya and Colombia. Testing local, regional hub and cloud deployment. Developing ingestors to add more datasets: Priority on Sentinel-1A/2A. API Ingestor

9. Kenya Pilot Project The project is led by NASA-SEO and supported by the Australian Government and the Clinton Foundation (CCI and SLEEK). Two operating versions of the Kenya Data Cube: Amazon Cloud and a local SEO computer. Considering options for the SERVIR-Africa hub. 11.5 TB of Landsat data (7500+ scenes back to 2000). Pixel data from 42 path-row regions were extracted and reformatted into a cube. 68 time-series stacked tiles (1-deg square). 933 million pixels. The Kenya team is currently utilizing scene-based methods to develop historic forest maps. Future testing of Data Cube is planned in 2017.

10. Colombia Pilot Project The project is led by NASA-SEO and the Colombian Government. Supported by CSIRO and IDEAM (Institute of Hydrology, Meteorology and Environmental Studies). A mini-cube (4 Landsat path-row regions) was delivered in Oct 2015. Cube includes data since 2000. 20 time-series stacked tiles (1-deg square). 273 million pixels. The Colombia government presented the Data Cube concept to the Ministry in late 2015 and was approved to implement the architecture for national scale applications in 2016. The Colombia team is very interested in user interface tools to produce custom mosaics, detect change, and conduct time series analyses (land and water). Capacity building... Colombia has already demonstrated use of the ingestor software to expand the data in their cube and they have modified the user interface to add median mosaics, PCA change detection and NDVI-based forest/non-forest maps.

11. Custom Mosaic Tool Product: Landsat 7 Region: Southern Colombia Output: RGB Bands-7,4,2 (SWIR2, NIR, GREEN) Filter (RED): = Cloud, Shadow, Water, No Data January-February 2014 16 scenes 70% no data (mostly clouds) January-April 2014 30 scenes 36% no data (mostly clouds) January-June 2014 46 scenes 27% no data (mostly clouds) <2 minutes run time for each mosaic with output in GEOTIFF

12. Water Detection Tool Western Kenya, County of Baringo Lake Bogoria Nature Preserve Year 2013 Flooding is seen in the northern tip of the lake * Counts water / non-water QA flags * Landsat-7 “banding” needs to be removed * Blue = Often water, Yellow = Infrequent * Able to assess drought/flood risk extent

13. Analysis Ready Data (ARD)  What is ARD? The term has yet to be defined clearly in CEOS, but most agree on these concepts... ARD is data that has been processed for calibration (e.g. offsets, biases, normalization), corrections (e.g. topography, atmosphere, solar angles, viewing angles) and spatial positioning (e.g. georectification, grid projection) to reduce the computational burden on end users and allow immediate analyses to support user applications. Examples of ARD could be typical “Level-2” products (e.g. optical surface reflectance, radar gamma-nought) or it could be typical “Level-3” products (e.g. mosaics, merged datasets).  The problem... we do not have a global, systematic solution for creating and serving ARD for all CEOS missions. This is desparately needed to support the CEOS Data Cube architecture. Countries spend most of their time preparing data for applications, rather than using the data.  Current situation … Space Agencies and Hosting Sites only provide raw data and point users to toolsets for local processing. Developing nations will have significant issues with this solution as they will not have the knowledge and capacity to perform processing.  OPTIONAL solution #1 … processing is performed systematically by non-space agency groups, such as the hosting or “mirror” sites or cloud-based commercial providers (e.g. Amazon, Google). It may be possible to simplify the processing approach using batch scripts to create common ARD products. This solution puts the entire processing burden on the hosting sites or cloud services.  OPTIONAL solution #2 … Space Agencies utilize their algorithms to process ARD and systematically provide the data for download. This solution puts the entire processing burden on the space agency, but they are the best ones to understand processing. This is how USGS handles Landsat data, but that may not be feasable for other Agencies.

14. (1) Expand the connections from mission archives to the COVE tool. There are many more archives that could be displayed in COVE. (2) Promote the systematic production of “analysis ready” datasets and discuss options for implementation. (3) Support the SEO as it investigates BRDF corrections to remove “scene edge effects” from Landsat data. This issue is commonly seen with dark forests and impacts scene classification. The cause is solar angle variation due to varying distance of each pixel from the “center line” location. Might also discuss with the CEOS WGCV and its LPV subgroup. Proposed WGISS Support