Proba-V and S3-SYN SNAP Toolbox: status and updates

Transcription

1 Proba-V QWG-07 Proba-V and S3-SYN SNAP Toolbox: status and updates Carsten Brockmann

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3 SNAP 6 Release January 2018

4 SNAP Version 6 Direct data access (SciHub) integration Plotting of metadata values Ocean Colour (C2RCC) processor Radiometry tool (Rad2Refl) for SLSTR

5 Proba-V Toolbox Publicly available through ESA STEP Website Distribution with SNAP5, December 2016 Version 2.0 with SNAP6, Jan 2018 Proba-V reader for L2A and L3 synthesis products RGB support (PB-V profiles) Enabling all image visualisation, analysis and processing function Non exhaustive list: band math, projection, collocation, mosaicking, statistics, extraction, filtering, subsetting, binning, resampling, classification, segmentation, format conversion

6 Proba-V in STEP Forum

7 SNAP icor for Sentinel 2 and Landsat-8 VITO development, integrated into SNAP via the Stand-Alone Adapter Distribution through VITO Website Integration into SNAP Desktop Support through STEP Website & VITO

8 icor in SNAP Forum

9 Prioritised features for PBV-TBX Evolution Follow-on from last QWG QWG06: presentation of SNAP processors (S3, S2) which have the potential to be applied to Proba-V Subsequent analysis and feedback by VITO Idepix Cloud Masking: Give the user the option to experiment with different cloud masking algorithm suitable for his or her needs. Soil & Vegetation Radiometric & Water Indices: Quite some indices are available via the Copernicus Global Land Service, so no need to invent the wheel again. Only the indices that are not available are a nice plus for PROBA-V users

10 SNAP Evolution SNAP 4 major releases in next 2 years Time series exploration Cloud access data and processing Improved SNAPPY OLCI Smile Correction OLCI Atmospheric Correction Water Quality Operators OLCI & SLSTR Synergy L1C Tool

11 Cloud Support SNAP Processing Services including SNAP Engine Server backend (WPS) SNAP Desktop GUI frontend. Demo servers with data local WPS, close to Sentinel data. IPython-like Remote SNAP REPL (Java 9) interface

12 Improve integration of Python Support of individual Python environments SNAP Desktop: Python Plugin Manager to add plugin paths and to configure python interpreter and to create distributable Python plugin bundles Add pythonic API to better support the python developers and ease their life Allow Python function in Band Math SNAP s band math expression editor in this case becomes a Python code editor. Changes in the Python code shall be reflected immediately in a target band s pixel data. Displayed image of this band will be immediately updated ( hot deployment ). Python REPL as a command-line interface window in SNAP Desktop. Ideally, this integration would be based on the Jupyter / IPython notebook

13 New Standard IO Format Lazy loading Reuse the source binaries User selectable data format Multi-size images, image tiling, image pyramids Single file ZIP Support different user requirements Support of cloud storage Store operation instead of data Interoperability with GIS software

14 Enhance GPF capabilities Support any workflow step types Operator graph (current state) Operating system commands invoking a remote web processing service Support various output types New product instances (current state) Text files Vector data Plots / images / movies

15 Further exploit multi-size product model Support multi-size products in GPF operator API. Add multi-resolution data support to operators: Subset, Binning, Mosaicking, Reprojection, Colocation Write and read multi-size products to/from other formats than DIMAP Add multi-resolution data support to SNAP Desktop functions: Copy Pixel Info to Clip Board, Create Subset, Export Mask Pixels, Magic Wand Tool, Export Transect Pixels, Transfer Mask

16 Introduce global resources library Including access to online geospatial data: OPeNDAP, WCS, WCPS

17 Time series support Virtual stacks of products Product groups with a certain attribute that orders them (e.g. time) Time series tools Time series operator prerequisite is common spatial grid Visualisation of variables along time axis Time series matrix Scrolling through time in image stacks Managing time series (add, edit, remove products from time series)

18 Improved support for uncertainties Promoting existing functions Error propagation in band maths Visualisation tutorials, training Monte-Carlo Propagator Associate uncertainties to input of an operator Ensemble generation Support sensitivity studies

19 OLCI/SLSTR Level-1C SYN Tool

20 L1C-SYN Tool for SNAP OLCI / SLSTR Level-2 Synergy (will) exists - Why a L1C-SYN Tool? Users need Synergy product on Level 1b in order to perform individual Level-2 processing E.g. Copernicus Global Land Service and ESA CCI projects Requirements shall be fulfilled by a user tool. A tool gives more freedom to the users compared to pre-defined product from the IPF Define and configure own L1C-SYN product

21 L1C-SYN Tool - Stages Combination of existing operations Selection of bands Spatial subsetting & resampling Release: Soon MISR file used for improved co-registration Export to a tile grid Sentinel-2/Proba-V Release: Summer Different co-registration methods Usable with other Sensors Release: End

22 L1C-SYN Tool - Processor

23 Sentinel 3 Land Product Level 3 fapar (OGVI) OLCI Terrestrial Chlorophyll Index (OTCI)

24 Intercomparison of S3 and Proba-V Land Products Sentinel 3 Level 3 products of OTCI and OGVI (fapar) for validation and evaluation Activity started within S3 MPC to support Land Validation Activities Protoype presented at S3VT (March 2018) Continuation under discussion with ESA Product Definition MODIS Level 3 grid to allow easy comparison, analysis and further usage of products from both sensors. Global coverage at spatial resolution of 500m Sinusoidal projection 8-day and monthly temporal aggregation periods Tiling on MODIS tile grid

25 bs fapar (OGVI) monthly mean April 2017

26 October September August Consistency Test with MERIS Work performed by J. Dash/L. Brown USouthampton MTCI (0 to 6) OTCI (0 to 6) Difference (-2 to +2)

27 Proba-V Symposium Demonstration during lunch time on Tuesday and Wednesday Interactive presentation Questions & Answers Topics: Validation with SNAP Generic operations on data SNAP & Python Cloud Screening with IdePix and what it can offer to Proba-V Synergy between Proba-V and S3 OLCI / S2 MSI

28 Standard Neural Net Format

29 Standard Neural Net Format (NNF) Description of the architecture of neural nets Input / output neurons Hidden layers Neurons Activation functions Weights Library for reading and executing neural nets Neural nets become auxiliary data (ADF) Software processor is independet from actual net Independecy Code and ADF kept separate allowing faster updates No technical dependency from a certain net provider

30 Support by NNF Neural Net Types Multilayer Perceptrons (MLP) Radial Basis Function nets (RBF) Activation Functions Sigmoid Additional layers for pre- and postprocessing Input/output scaling Normalisation Linear combinations Library C++ version (used in MERIS and OLCI ground segment) Java version (used in BEAM and SNAP)

31 Example

32 Summary - Recommendations 1. Evolution of Proba-V Toolbox Add Proba-V support to IdePix Add Proba-V support to those Soil & Vegetation Radiometric & Water Indices which are not supported by Global Land Service 2. Add Proba-V Grid to Sentinel 3 Land Level 3 production 3. Use NNF for Proba-V Luis to write his net in NNF format Vito to use NNF library in processor Prerequisite: BC to update the format in order to stay up-to-date with recent developments (CNN, Tensorflow, )