MULTIPLY - Prior Engine

Scope of MULTIPLY

The MULTIPLY project will “develop a new platform for joint and consistent retrieval of Copernicus SENTINEL data and beyond”.

This documentation covers the prior engine for the MULTIPLY main platform. This module provides a priori information to the Inference Engine to support land surface parameter retrieval.

The prior engine specific documentation is hosted on ReadTheDocs. It is part of the MULTIPLY core documentation. Please find the latest pdf version of this documentation here.

First Steps

Getting Started

Please find instructions on how to download and install the prior engine in the Installation section.

Note

TBD: Getting started with python, bayes theorem, ..

Testing and Contribution

You are welcome to test and contribute to the MULTIPLY Prior Engine.

Please find corresponding guidelines and further information on how to do so in the How to contribute section and on the project GitHub page.

Content

Introduction

Priors are an essential component in the MULTIPLY inference engine as they provide a priori information on different components of the unknown state vector of the system, helping to constrain the ill-posed problem given that the information content from the observations alone is insufficient. A series of prior models with different levels of complexity is therefore required and will be developed and implemented as part of the MULTIPLY platform.

The priors to be implemented are:

• Differential characterisation of the traits of vegetation types or (crop) species

• Vegetation phenology

• Surface soil moisture dynamics

• Surface disturbances

Background

A seamless and gap free integration of SENTINEL data streams requires the transfer of information across temporal and spatial scales. Typically data gaps are filled using low pass filters and different interpolation techniques (e.g. Savitzky-Golay filter; Savitzky & Golay, 1964) directly on parameter space (e.g. Yuan et al., 2011; Kandasamy et al. 2013). However, this approach is inconsistent, as the ill-posed nature of the inversion problem results in strong correlations between parameters: smoothing one parameter breaks that relationship with other retrieved parameters. Additionally, the role of uncertainty is usually ignored in filtering. Given that filtering methods originate from a prior belief in the smoothness of the processes that control the evolution of the parameters, it makes sense to implement these smoothness constraints consistently as priors within the retrieval process. These so-called regularisation constraints encompass our prior belief in the spatial and temporal correlation of the parameter fields. These constraints are implemented within the MULTIPLY platform as a weak constraint. The added benefit of having these constraints is that they not only result in smoother and more consistent series (an added benefit is an important reduction in parameter uncertainty), but also in spatially and temporally gap free estimates of biophysical parameters.

However, other prior information should be used to better constrain the inversion, and make sure that the inferences on the parameters are consistent with our understanding of biogeochemical processes and their effect on the state of the land surface.

Goal

The major objectives of this software are i) to implement the required technical infrastructures to provide the prior information at appropriate temporal and spatial scales in relation to the SENTINEL observations, and ii) implement a flexible user interface which allows user to integrate own prior models as a MULTIPLY plugin.

Prior Data

Vegetation Prior Data

Note

TBD

Soil Moisture Prior Data

The provided prior data for the soil moisture domain is twofold. Mattia et al. [Mattia] show that the usage of climatological mean soil moisture information significantly improves soil moisture estimates from active microwave observations. Therefore, a soil moisture climatology is used as prior to get a general idea of the amplitude, variability and seasonal behaviour of the in situ soil moisture. Furthermore, a dynamic daily coarse resolution product is consulted for an a priori estimation of the current state.

The climatological prior data set has been generated from the global ESA CCI SM v04.4 COMBINED product which is derived from a combination of active and passive satellite sensors over the period 1978 - 2018. Originally, the data set provides daily surface soil moisture with a spatial resolution of 0.25 degree ([Dorigo]; [Gruber]; [Liu]). The data was aggregated to monthly means. Uncertainty is given by the intra-monthly standard deviation.

Data from the Soil Moisture Active Passive (SMAP) project is used as dynamic prior ([Reichle]). Specifically, the model-derived value-added Level 4 data product with 3-hourly estimates of soil moisture and respective error estimates at a 9 km resolution are averaged to daily values as the MULTIPLY platform assimilates data at this temporal resolution.

Climatological Soil Moisture July

Mattia

Mattia, F. et al. (2006) Using a priori information to improve soil moisture retrieval from ENVISAT ASAR AP data in semiarid regions. IEEE Trans. Geosci. Remote Sens. 44: 900–912.

Dorigo

Dorigo, W. A., et al., 2017, ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions, Remote Sensing of Environment, 203, 185-215, 2017, doi:10.1016/j.rse.2017.07.001.

Gruber

Gruber, A., et al., 2017, Triple Collocation-Based Merging of Satellite Soil Moisture Retrievals, Transactions on Geoscience and Remote Sensing, 55(12), 1-13. doi:10.1109/TGRS.2017.2734070.

Liu

Liu, Y. Y., et al., 2012, Trend-preserving blending of passive and active microwave soil moisture retrievals, Remote Sensing of Environment, 123, 280-297.

Reichle

Reichle, R. et al. 2014. SMAP Algorithm Theoretical Basis Document: L4 Surface and Root-Zone Soil Moisture Product. SMAP Project, JPL D-66483, Jet Propulsion Laboratory, Pasadena, CA, USA.

Installation

Download

If not already done so, the first step is to clone the latest code and change directory:

git clone https://github.com/multiply-org/prior-engine.git
cd prior-engine

Note

The MULTIPLY platform has been developed against Python 3.6. It cannot be guaranteed to work with previous Python versions.

Installation procedure

The MULTIPLY prior engine can be run from sources directly. To install the MULTIPLY prior engine into an existing Python environment just for the current user, use

python setup.py install --user

To install the MULTIPLY Core for development and for the current user, use

python setup.py develop --user

Using Conda

Note

TBD

Module requirements

from requirements.txt:

numpy==1.16
shapely==1.6
h5py==2.8
pandas==0.22
scipy==0.22
setuptools==40.8
matplotlib==2.2
pytest==4.6
gdal==2.4
netCDF4==1.5
PyYAML==3.12
typing
python_dateutil
recommonmark

Usage

Python Package

MULTIPLY prior engine is available as Python Package. To import it into your python application, use

import multiply_prior_engine

User defined priors

Users are provided the possibility to choose between prior-types, using the configuration file. This configuration file can be modified by both the users directly (using simple text editors), as well as the user-interface described below and in the upcoming MULTIPLY platform user-interface.

The user has three options to add prior data to the retrieval (in addition to choosing priors already made available by MULTIPLY).

• The user can choose to define single values for the prior in terms of transformed ‘mu’ and ‘unc’ values.

• The user can choose to provide a single geolocated tiff file, with both mean and uncertainty values. Here, the mean value should be provided as the first band, while the uncertainty of these values should be provided as the second band.

• Finally, the user can choose to provide a directory with multiple files, following a similar structure as the previous choice. Here, the files should be given a 8 digit date stamp in the filename.

The configuration file then could look like:

Prior
        General:
                directory_data: ‘path 2 prior engine’
        LAI:
                database
                        static_dir: same as general directory_data
        SM:
                user:
                        mu: 0.5
                        unc: 0.02
        CWC:
                user:
                        file: ‘path to geotiff-file’
        ALA:
                user:
                        dir: ‘path to directory with geotiff-files (sorted on date)’

                ...


        output_directory: ‘path to outputdirectory’

Command Line Interface

There is a Command Line Interface (CLI) integrated to allow for the following actions:

• add user defined prior data,

• import user defined prior data,

• remove/un-select prior data from configuration,

• show configuration.

The CLI’s help can be accessed via -h flag:

user_prior -h

and will show:

usage: user_prior.py [-h] {show,S,add,A,remove,R,import,I} ...

Utility to integrate User Prior data in MULTIPLY Prior Engine

positional arguments:
{show,S,add,A,remove,R,import,I}
    show (S)            Show current prior config.
    add (A)             Add prior directory to configuration.
    remove (R)          Remove prior information from configuration.
    import (I)          Import user prior data.

optional arguments:
-h, --help            show this help message and exit

The help and description of the above mentioned sub-commands can be accessed via, e.g.:

user_prior add -h

Note

If installed for the current user only, make sure the directory the prior engine gets installed to is in your PATH variable.

Logging

For now the Prior Engine has its own logging setup. To set the logging level please adjust the level accordingly in the multiply_prior_engine/__init__.py file. Available options are: NOTSET, DEBUG, INFO, WARNING, ERROR, CRITICAL.

Processing Flow

Priors are provided by the MULTIPLY prior engine for the respective forward operators. The relationships are shown in following figure:

Figure 1: Relationship of priors to their respective forward operators.

Note

For information on user defined prior files please see the section on Usage.

Description of Prior Generation

This prototype is capable of delivering for both vegetation priors as well as soil priors spanning all variables required in the forward operators. The overall processing chain is divided up to two parts (dealing with the soil prior and the vegetation prior).

The optical prior engine is designed to deliver prior information to the inference engine specifically for the leaves and vegetation. The overall flow of the prior-engine is illustrated by Figure 2.

The ‘microwave’ prior engine is designed to deliver prior information for soil parameters. The overall flow of this part of the prior-engine is illustrated by Figure 3.

In these flowcharts a distinction is made between the current implementation of the prototype (green) and the final foreseen version of the prior engine (red). In order for completeness a place-holder (orange) process is embedded into the flowchart. In addition, in the final version of the prior engine the users themselves can choose between how the specific prior are used (see Usage). User-selections are obtained from the configuration-file with which the MULTIPLY framework is run. This is represented in the flowchart by orange selection boxes. Prior data specified by the User is currently not visualized for every prior generator.

Vegetation Priors

Within the prototype version of the module, the values of the priors are consistent with @peak biomass; no dynamical component is integrated into the prototype module.

Figure 2: Flow in ‘optical’ prior engine

Soil Priors

The included priors for soil moisture are currently twofold:

1. a climatological prior based on ESA CCI SM v04.4 data

2. a dynamic prior based on SMAP data

Please see the overall flow of this prior creator sub-engine below:

Figure 3: Flow in ‘microwave’ prior engine

Climatologic Priors

Mattia et al. [Mattia] show that the usage of climatological mean soil moisture information significantly improves soil moisture estimates from active microwave observations. Therefore, a soil moisture climatology is used as prior to get a general idea of the amplitude, variability and seasonal behaviour of the in situ soil moisture. Furthermore, a dynamic daily coarse resolution product is consulted for an a priori estimation of the current state.

The climatological prior data set has been generated from the global ESA CCI SM v04.4 COMBINED product which is derived from a combination of active and passive satellite sensors over the period 1978 - 2018 [GRUBER2019]. Originally, the data set provides daily surface soil moisture with a spatial resolution of 0.25 degree ([Dorigo]; [Gruber]; [Liu]). The data was aggregated to monthly means. Uncertainty is given by the intra-monthly standard deviation. There is also a interpolation routine included to allow for smooth inter monthly transitions.

Figure 4: Climatology soil moisture (bars) at point-scale with interpolated values (line)

Figure 5: Exemplary climatological soil moisture prior (mean) for April

Dynamic Priors

Data from the Soil Moisture Active Passive (SMAP) project is used as dynamic prior ([Reichle]). Specifically, the model-derived value-added Level 4 data product with 3-hourly estimates of soil moisture and respective error estimates at a 9 km resolution are averaged to daily values as the MULTIPLY platform assimilates data at this temporal resolution.

“SMAP measurements provide direct sensing of soil moisture in the top 5 cm of the soil column. However, several of the key applications targeted by SMAP require knowledge of root zone soil moisture in the top 1 m of the soil column, which is not directly measured by SMAP. As part of its baseline mission, the SMAP project will produce model-derived value-added Level 4 data products to fill this gap and provide estimates of root zone soil moisture that are informed by and consistent with SMAP surface observations. Such estimates are obtained by merging SMAP observations with estimates from a land surface model in a data assimilation system. The land surface model component of the assimilation system is driven with observations-based meteorological forcing data, including precipitation, which is the most important driver for soil moisture. The model also encapsulates knowledge of key land surface processes, including the vertical transfer of soil moisture between the surface and root zone reservoirs. Finally, the model interpolates and extrapolates SMAP observations in time and in space, producing 3-hourly estimates of soil moisture at a 9 km resolution. The SMAP L4_SM product thus provides a comprehensive and consistent picture of land surface hydrological conditions based on SMAP observations and complementary information from a variety of sources.” [JPL]

The prior engine will rely on the MULTIPLY data-access component to download the appropriate data sets. These are then converted to be used by the inference engine. A valid registration on NASA’s Earthdata Service is necessary.

Technical Description

The processing chain in the prior engine is defined in a config file. For now this looks like:

General:
    roi: POLYGON ((48.0 11.3, 48.2 11.300, 48.1 11.1, 48.0 11, 48.0 11.3))
    start_time: 2017-01-01
    end_time: 2017-12-31
    time_interval: 1 # 1 day
    spatial_resolution : 10 # metres
    state_mask: /path/to/my/state_mask.tif # Or shape?
    output_directory_root: /some/where/
    # output_prefix: my_test_33

Inference: # inference config
    - parameters:
        - LAI
        - soil_moisture
    - optical_operator_library: some_operator.nc   # Optional
    - sar_operator_library: some_other_operator.nc # Optional
    - a: identity
    - inflation: 1e3

Prior:
# Prior section conventions

# - 1. sub-level contains all potential variables (sm, roughness, lai, ..)
#   which are asked for/being inferred from Orchestrator/Inferrence Engine
#   and for which prior information is provided.
# - 2. sub-level contains prior type (ptype). These can be commented out
#   to be omitted.

  General:
    directory_data: ./aux_data/Static/Vegetation/
  sm:
    climatology:
      dir: ./aux_data/Climatology/SoilMoisture/
    coarse:
      dir: ./aux_data/Coarse/SoilMoisture/
  clay_fraction:
    soil_map:
      file: ./aux_data/Static/SoilTexture/CLYPPT_M_sl1_250m_ll.tif
  sand_fraction:
    soil_map:
      file: ./aux_data/Static/SoilTexture/SNDPPT_M_sl1_250m_ll.tif

#   recent:
#     dir: ""
#   user1:
#     dir: "."
    # dynamic:
    #     type: dynamic
    #     model:
    #         - API
    #         - other
    # recent:
    #   aux_data = ...
    # static:
    #       type: static
  lai:
    database:
  cab:
    database:
    #climatology:
    #  database: ../aux_data/new_geotiff
    # model:
  # veg:
    # veg_pft:
    #   type: pft
    #   database: /aux_data/some_DB
    # veg_spec:
    #   type: species
    #   database: /user_data/some_DB
    # -

Internal Flow

The internal flow and relations can be seen in figure 4.

Figure 6: Prior Engine relationships

References

GRUBER2019

Gruber, A., Scanlon, T., van der Schalie, R., Wagner, W., and Dorigo, W.: Evolution of the ESA CCI Soil Moisture climate data records and their underlying merging methodology, Earth Syst. Sci. Data, 11, 717-739, https://doi.org/10.5194/essd-11-717-2019, 2019

JPL

https://smap.jpl.nasa.gov/data/

Mattia

Mattia, F. et al. (2006) Using a priori information to improve soil moisture retrieval from ENVISAT ASAR AP data in semiarid regions. IEEE Trans. Geosci. Remote Sens. 44: 900–912.

Dorigo

Dorigo, W. A., et al., 2017, ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions, Remote Sensing of Environment, 203, 185-215, 2017, doi:10.1016/j.rse.2017.07.001.

Gruber

Gruber, A., et al., 2017, Triple Collocation-Based Merging of Satellite Soil Moisture Retrievals, Transactions on Geoscience and Remote Sensing, 55(12), 1-13. doi:10.1109/TGRS.2017.2734070.

Liu

Liu, Y. Y., et al., 2012, Trend-preserving blending of passive and active microwave soil moisture retrievals, Remote Sensing of Environment, 123, 280-297.

Reichle

Reichle, R. et al. 2014. SMAP Algorithm Theoretical Basis Document: L4 Surface and Root-Zone Soil Moisture Product. SMAP Project, JPL D-66483, Jet Propulsion Laboratory, Pasadena, CA, USA.

Developer Documentation

Changelog

All notable changes to this project will be documented in this file.

Unreleased changes

Version 0.5.0 - 2019-09-19

Added

• User defined vegetation priors from the TRYdatabase

• User prior generation CLI

• helper functions to manually create soil moisture prior data from SMAP and ESA CCI data

Changed

• Documentation and README update

• documentation requirements integrated in module requirements.txt file (necessary for building on RTD)

• Bugfixes

Version 0.4.2 - 2018-11-05

Changed

• minor fixes in README and documentation

Version 0.4.1 - 2018-11-02

Added

• In code documentation Vegetation Prior

Changed

• big update on general documentation

• config file is read from package_ressources

• prior .vrt files are now always global

Version 0.4 - 2018-09-01

Added

• command line interface to allow user to add prior data

• first implementation of coarse resolution soil moisture prior based on SMAP L4 data

• averaging and aggregation of output if multiple rasters are available for one date or variable

• logging in prior engine

Changed

• prior engine framework

  • sub-engine from entry points in setup.py

  • conventions through abstract base class implementation in prior creator

• in-code documentation

• fixed travis installation

Removed

Version 0.3 - 2018-03-07

Added

• get_mean_state_vector returns path to prior files and routes to specific submodule for soil and vegetation related priors respectively to produce/provide information.

• Vegetation prior:

  • global vegetation trait maps as static prior implemented

• Soil moisture prior:

  • basic implementation of ESA CCI soil moisture climatology based prior

Changed

Removed

---

The format is based on `Keep a Changelog <http://keepachangelog.com/en/1.0.0/>`_ and this project adheres to `Semantic Versioning <http://semver.org/spec/v2.0.0.html>`_.

How to contribute

You are very welcome to contribute to the MULTIPLY prior engine and we would love to see your ideas. Wether you want to make changes, allowing the engine to work in your environment or to extend the functionality of it, it should be straightforward and as easy as possible. The few guidelines which need to be followed by the contributor are listed below. To keep it simple we follow the ‘standard procedure’ on github.

Introduction to git and Github

Resources for learning git:

• Git Handbook

• Understanding the GitHub Flow

• Further Git and GitHub learning resources

Getting Started

• Make sure you have a GitHub account.

• Fork (guide) the repository on GitHub (and git clone your fork locally).

• Check out the repository.

• Read How to submit a contribution.

General Information on pull requests

from https://opensource.guide:

You should usually open a pull request in the following situations:

• Submit trivial fixes (for example, a typo, a broken link or an obvious error)

• Start work on a contribution that was already asked for, or that you’ve already discussed, in an issue

Tips and guidelines:

• sync your fork (guide) often with the upstream repository to avoid merge conflicts.

• adhere to the GitHub Flow and create a meaningful branch for your changes

• reference relevant issues in your pull request (e.g. ‘Closes #21.’)

Contributing to Issues

You can contribute either by helping to solve existing issues and provide the code updates via pull request or by filing new issues.

from https://opensource.guide:

You should usually open an issue in the following situations:

• Report an error you can’t solve yourself

• Discuss a high-level topic or idea (for example, community, vision or policies)

• Propose a new feature or other project idea

In any case:

• Check if the issue you are going to file already exists in our open issues .

• If you can’t find your issue already, open a new one.

Contributing to Code

New features and bug fixes are very welcome. But, pull requests can only be accepted if:

• all continuous integration builds pass and

• tests for new code sections are included.

Contributing to Documentation

Contributions to the documentation of the MULTIPLY prior engine are always welcome. The current version can be found at http://multiply.readthedocs.io/.

After forking the repo, please find the documentation files inside the /doc folder in the root path of the repository. Adjust and file a pull request like you would do with code updates.

Testing

We use PyTest in the MULTIPLY software. The test files are located in the test folder in the source directory.

They can be run e.g. via:

pytest -vs

Note

This section will describe testing routines used in the prior engine necessary for development.

Module documentation

prior_engine module

Prior Engine for MULTIPLY.

Copyright (C) 2019 Thomas Ramsauer

class multiply_prior_engine.prior_engine.PriorEngine(**kwargs)

Bases: object

Prior Engine for MULTIPLY.

holds prior initialization methods (e.g. config loading). calls specific submodules (soilmoisture_prior, vegetation_prior, ..)

_check()

initial check for passed values of - config - datestr - variables

Returns

Return type

_concat_priors(prior_dict)

Concatenate individual state vectors and covariance matrices for sm, veg, ..

Returns

dictionary with keys beeing superordinate prior name (sm, ..)

Return type

dictionary

_get_prior(var)

Called by get_priors for all variables to be inferred. For specific variable/prior (e.g. sm climatology) get prior info and calculate/provide prior.

Parameters

var – prior name (e.g. sm, lai, ..)

Returns

Return type

default_config = '/home/docs/checkouts/readthedocs.org/user_builds/multiply-prior-engine/checkouts/latest/docs/../multiply_prior_engine/sample_config_prior.yml'

get_priors()

Get prior data. calls _get_prior for all variables (e.g. sm, lai, ..) passed on to get_mean_state_vector method.

Returns

dictionary with prior names/prior types/filenames as {key/{key/values}}.

Return type

dictionary of dictionary

multiply_prior_engine.prior_engine._get_config(configfile)

Load config from self.configfile. writes to self.config.

Returns

multiply_prior_engine.prior_engine.get_mean_state_vector(datestr: str, variables: list, config: str = './sample_config_prior.yml') → dict

Return dictionary with variable dependent sub dictionary with prior type (key) and filenames of prior files (values).

Parameters

• datestr – The date (time?) for which the prior needs to be derived

• variables – A list of variables (sm, lai, roughness, ..)

for which priors need to be available

Returns

dictionary with keys being the variables and

values being a dictionary of prior type and filename of prior file.

prior module

Prior Class for MULTIPLY.

Copyright (C) 2018 Thomas Ramsauer

class multiply_prior_engine.prior_creator.PriorCreator(**kwargs)

Bases: object

_abc_cache = <_weakrefset.WeakSet object>

_abc_negative_cache = <_weakrefset.WeakSet object>

_abc_negative_cache_version = 211

_abc_registry = <_weakrefset.WeakSet object>

_check()

_create_datetime()

_create_time_vector()

Creates a time vector dependent on start & end time and time interval from config file. A vector containing datetime objects is written to self.time_vector. A vector containing months ids (1-12) for each timestep is written to self.time_vector_months.

Returns

Return type

abstract compute_prior_file() → str

Might perform some computation, then retrieves the path to a file containing the prior info :return:

abstract classmethod get_variable_names() → List[str]

Returns

A list of the variables that this prior creator is able to create priors for

soilmoisture_prior module

Soil Priors for Prior Engine in MULTIPLY.

Copyright (C) 2019 Thomas Ramsauer

class multiply_prior_engine.soilmoisture_prior_creator.MapPriorCreator(**kwargs)

Bases: multiply_prior_engine.prior_creator.PriorCreator

Not Implemented Prior which is based on a LC map and a LUT

_abc_cache = <_weakrefset.WeakSet object>

_abc_negative_cache = <_weakrefset.WeakSet object>

_abc_negative_cache_version = 211

_abc_registry = <_weakrefset.WeakSet object>

classmethod get_variable_names()

Returns

A list of the variables that this prior creator is able to create priors for

class multiply_prior_engine.soilmoisture_prior_creator.RoughnessPriorCreator(**kwargs)

Bases: multiply_prior_engine.soilmoisture_prior_creator.MapPriorCreator

Not Implemented Roughness Prior Creator which is based on a LC map and a LUT

_abc_cache = <_weakrefset.WeakSet object>

_abc_negative_cache = <_weakrefset.WeakSet object>

_abc_negative_cache_version = 211

_abc_registry = <_weakrefset.WeakSet object>

_map_lut()

should do the mapping of s, l, ACL type

_read_lc()

_read_lut()

calc()

compute_prior_file()

Might perform some computation, then retrieves the path to a file containing the prior info :return:

classmethod get_variable_names()

Returns

A list of the variables that this prior creator is able to create priors for

save()

save mapped roughness data to file

class multiply_prior_engine.soilmoisture_prior_creator.SoilMoisturePriorCreator(**kwargs)

Bases: multiply_prior_engine.prior_creator.PriorCreator

Soil moisture prior class. Calculation of climatological prior.

_abc_cache = <_weakrefset.WeakSet object>

_abc_negative_cache = <_weakrefset.WeakSet object>

_abc_negative_cache_version = 211

_abc_registry = <_weakrefset.WeakSet object>

_calc_climatological_prior()

Calculate climatological prior. Reads climatological file and extracts proper values for given timespan and -interval. Then converts the means and stds to state vector and covariance matrices.

Returns

state vector and covariance matrix

Return type

tuple

_check_gdal_compliance(fn)

_create_global_vrt(fn, local=True)

Create VRT file for file.

By default, the .vrt-file will be written to a local temporary directory. If local is set to False, the file is written to the directory the input file (fn) currently lives in.

Parameters

• fn – file name

• local – create temporary local vrt.

Returns

file name of created vrt, or initial file name if no success.

Return type

string

_extract_climatology()

Extract climatology values for ROI. Part of _clac_climatological_prior().

_get_climatology_file()

Load pre-processed climatology into self.clim_data. Part of prior._calc_climatological_prior().

_get_prior_file_from_dir(directory, return_vrt=True)

Get filename(s) of prior file(s) from directory. If multiple files are found self._merge_multiple_prior_files is called.

Currently, the following prior types are supported: - climatology (calculated from ESA CCI data, standard) - coarse (daily aggregated SMAP L4 data, standard) - soil_map (gloabal soil texture map data from soilgrids.org) - user prior, provided through user_prior_creator

Parameters

directory – directory containing the files (from config)

Returns

filename

Return type

string

_get_recent_sm_proxy()

_merge_multiple_prior_files(fn_list)

Merge files if more than one is available for current time step. should be obsolete.

Parameters

fn_list – file list to process

Returns

file name of merged file

Return type

string

_provide_prior_file()

Provide variable and prior type specific prior file name to Prior Engine.

Returns

absolute path to prior file for requested prior.

The file is gdal-compatible to be used in inference engine - either GeoTiff or VRT format. It includes 2 bands:

1. mean value raster

2. uncertainty raster

Return type

string

compute_prior_file()

Initialize prior specific (climatological, …) calculation.

Returns

filename of prior file

Return type

string

classmethod get_variable_names()

Returns

A list of the variables that this prior creator is able to create priors for

vegetation_prior module

class multiply_prior_engine.vegetation_prior_creator.VegetationPriorCreator(**kwargs)

Bases: multiply_prior_engine.prior_creator.PriorCreator

Description

AssignPFTTraits2Map(PFT, PFT_ids, varnames)

Create Vegetation trait Prior map, using the Trait-database and PFT distribution maps This function sets up a parallel processing chain around - processespercore: here the actual assignment of traits to PFT distributions is performed

Parameters

• PFT – arrays containing global Maps of PFT distributions

• PFT_ids – a list containing PFT ids

• varnames – list of variables to be converted into global file

Returns

map of vegetation trait-averages per PFT id, map of vegetation trait-uncertainties per PFT id

Return type

Combine2PFT(LCC_map, CLM_map_i)

Create PFT maps using CCI Landcover and Koppen Climate zone information :param LCC_map: CCI Landcover map :param CLM_map_i: Regridded Koppen Climate Zone map :returns: PFT occurrence map, PFT classes, Number of PFTs, PFT ids :rtype:

CombineTiles2Virtualfile(variable, doystr, directory_data)

Combine all geotiff files into a virtual global file

Parameters

• variable – variable to be converted into global file

• doystr – string containing date&time ‘2007-12-31 04:23’ for which global file needs to be created

Returns

the filename of the global VRT file

Return type

CreateDummyDatabase()

create netcdf Database files to hold database values

Returns

Return type

CreateRealDatabase()

DownloadCrossWalkingTable()

Download Crosswalking table Here the Cross walking table is downloaded to create the CCI landcover map. At the moment this is simply a placeholder for future functionality.

Returns

Return type

DynamicProcessing(varnames, LCC_lon, LCC_lat, Prior_pbm_avg, Prior_pbm_unc, doystr, write_output=True)

Extending Peak Biomass (PBM) traits to seasonal Priors At this moment, this function is only a placeholder for the later implementations. The final implementation will be modelled using - covariances between traits and (seasonal) meteorological variables - phenological evolution (trained using plant growth models)

Parameters

• varnames – list of variables to be converted into global file

• LCC_lon – array with longitude values of (subsetted tile of) study area

• LCC_lat – array with latitude values of (subsetted tile of) study area

• Prior_pbm_avg – Vegetation Traits mean value at PBM

• doystr – string containing date&time ‘2007-12-31 04:23’ for processing needs to be performed

• Prior_pbm_unc – Vegetation Traits uncertainty value at PBM

• write_output – Binary Value (TRUE/FALSE) controlling the writing of outputfiles

Returns

Return type

ExtractPFT4TryDatabaseEntries(Lat_, Lon_, Plantgroup_, Crop_, LeafType_, C3C4_, LeafPhen_)

OfflineProcessing()

Creation of LCC landcover map This :returns: :rtype:

PhenologicalEvolution(Prior_pbm_avg, Prior_pbm_unc, doystr, Meteo_map_i=None)

Model the Phenological Evolution of Vegetation traits This function is a placeholder to be used when the dynamic functionality is created.

Parameters

• Prior_pbm_avg – Vegetation trait-averages at Peak Biomass

• Prior_pbm_unc – Vegetation trait-uncertainty (@PBM)

• doystr – string containing date&time ‘2007-12-31 04:23’ for processing needs to be performed

• Meteo_map_i – Place_holder for meteorological data-files

Returns

Temporal Prior-averages, Temporal Prior-uncertainties

Return type

ProcessData(variables=None, state_mask=None, timestr='2005-05-05 05:55', logger=None, file_prior=None, file_lcc=None, file_biome=None, file_meteo=None)

Process Data Apriori Calculation of prior using Databases of Vegetation Traits. This function is split into two parts (which are run for all Tiles over the study are) - OfflineProcessing: This only has to be performed once (to make sure all the input data is available) - StaticProcessing: Creating Peak Biomass (PBM) Priors - DynamicProcessing: Extending PBM traits to seasonal priors (a placeholder for the later implementations)

Parameters

• variables – list of variables to be converted into global file

• state_mask – place holder for spatial mask (not implemented)

• timestr – string containing date&time ‘2007-12-31 04:23’ for which global file needs to be created

• logger – log-file for capturing message from the scripts

• file_prior – place-holder for prior (TRY) database - filename (at the moment hardcoded)

• file_lcc – place-holder for landcover data - filename (at the moment hardcoded)

• file_biome – place-holder for biome data - filename (at the moment hardcoded)

• file_meteo – place-holder for meteorological data - filename (at the moment hardcoded)

Returns

filenames to global VRT prior files

Return type

ReadClimate()

Read Climate Zone information A Climate Zone map (created on basis of the Koppen Climatic Zone classification)is read.

Returns

climate zone map, longitude, latitude, climate zone classes

Return type

ReadLCC()

Read Landcover information The Landcover map from the Climate Change Initiaive (CCI) is read.

Returns

landcover map, longitude, latitude, landcover class names

Return type

ReadMeteorologicalData(doystr)

Read Meteorological Variables This function is a placeholder to be used when the dynamic functionality is created.

Parameters

doystr – string containing date&time ‘2007-12-31 04:23’ for processing needs to be performed

Returns

Meteorological data (to be used for upscaling Peak Biomass traits to seasonal priors)

Return type

ReadTraitDatabase(varnames, pft_id=1)

Read Traits from Database A local (modified) version of the Try Database (containing vegetation traits) is read.

Parameters

• varnames – list of variables to be converted into global file

• pft_id – list of pft id numbers for which the traits needs to be read.

Returns

an array of Traits per PFT group

Returns

an array of Traits per PFT group

Return type

ReadTryDatabase()

ReadTryFile()

RescaleCLM(CLM_lon, CLM_lat, CLM_map, LCC_lon, LCC_lat)

Collocate Climate Zone map with landcover coordinates The Climate Zone map has a different resolution/grid than the Landcover map. This preprocessing is performed to collocate both (in order to facilitate the merging downstream.)

Parameters

• CLM_lon – array containing the longitude values of the Climate Zone map

• CLM_lat – array containing the latitude values of the Climate Zone map

• CLM_map – array containing the Climate Zone map

• LCC_lon – array containing the longitude values of the CCI Landcover map

• LCC_lat – array containing the latitude values of the CCI Landcover map

Returns

array containing the Regridded Climate Zone map

Return type

RunCrossWalkingTable(Path2CWT_tool=None, Path2LC=None)

Creating CCI landcover maps (using crosswalking table).

please note that to run the crosswalking tool, the specific requirements for BEAM need to be met (java64bit + …)

Parameters

• Path2CWT_tool –

• Path2LC –

Returns

Return type

StaticProcessing(varnames, write_output=False)

Creating Peak Biomass (PBM) Priors Priors are created by upscaling vegetation traits obtained through the TRY database. Within the TRY database vegetation traits are provided per PFT group. In order to upscale these values, a global PFT map is required. This is created by merging a global Landcover map (from Climate Change Initiative, CCI) with a climate zone map (using the Koppen classification). This is accomplished by -ReadLCC: Reading the CCI Landcover map -ReadClimate: Reading the Koppen Climate zone map -RescaleCLM: Rescaling Climate zone map to collocate with Landcover CCI. -Combine2PFT: Combining Climate zone + Landcover maps into PFTs Using this global PFT map, the values from the TRY database are afterwards spatially distributed by -AssignPFTTraits2Map: assigning and aggregating traits to PFT maps.

Parameters

• varnames – list of variables to be converted into global file

• write_output – Binary value (TRUE/FALSE) controlling the writing of outputfiles

Returns

longitude, latitude, Prior_avg, Prior_unc

Return type

WriteGeoTiff(LCC_lon, LCC_lat, Prior_avg, Prior_unc, doystr='static')

Write Vegetation Prior data (mean/unc) to GEOTIFF outputfiles. :param LCC_lon: longitude of the Prior data (same as used Landcover map) :param LCC_lat: latitude of the Prior data (same as used Landcover map) :param Prior_avg: Vegetation prior average values :param Prior_unc: Vegetation prior uncertainty values :param doystr: string containing date&time ‘2007-12-31 04:23’ for data to be written :returns: -

WriteOutput(LCC_lon, LCC_lat, Prior_avg, Prior_unc, doystr='static')

Write Vegetation Prior data (mean/unc) to NETCDF outputfiles. This functionality is obsolete as all outputs are written to GeoTiff files

Parameters

• LCC_lon – longitude of the Prior data (same as used Landcover map)

• LCC_lat – latitude of the Prior data (same as used Landcover map)

• Prior_avg – Vegetation prior average values

• Prior_unc – Vegetation prior uncertainty values

• doystr – string containing date&time ‘2007-12-31 04:23’ for data to be written

Returns

Return type

_abc_cache = <_weakrefset.WeakSet object>

_abc_negative_cache = <_weakrefset.WeakSet object>

_abc_negative_cache_version = 212

_abc_registry = <_weakrefset.WeakSet object>

compute_prior_file()

Combine Tiles into single Prior VRT file

Returns

filename of specific VRT file

Return type

classmethod get_variable_names()

Returns

A list of the variables that this prior creator is able to create priors for

multiply_prior_engine.vegetation_prior_creator._get_config(configfile)

Load config from self.configfile. writes to self.config.

Returns

multiply_prior_engine.vegetation_prior_creator.fun(f, q_in, q_out)

multiply_prior_engine.vegetation_prior_creator.parmap(f, X, nprocs=4)

Enable Parallel processing This code is created to enable parallel processing with python

Parameters

• f – function to be called

• X – input to the function

• nprocs – number of cores to be used

Returns

output of function

Return type

multiply_prior_engine.vegetation_prior_creator.processespercore(varname, PFT, PFT_ids, VegetationPriorCreator)

Create Prior values from PFT distributions and Vegetation traits For each PFT the specific trait (according to varname) are read from the Trait-Database. These traits are then statistically analysed to produce the mean and standard deviations. These trait values are then evaluated against the PFT distribution (occurrence) map and joint together to create a single Prior (mean&uncertainty) estimate for each spatial location

Please note that: This function is encapsulated within the parmap method to run in parallel on different cores

Parameters

• varname – variable to be processed

• PFT – arrays containing global Maps of PFT distributions

• PFT_ids – a list containing PFT ids

• VegetationPriorCreator – class containing all the functionality to be run (per core)

Returns

Vegetation Prior average values, Vegetation Prior uncertainty values

Return type

License

GNU GENERAL PUBLIC LICENSE

Version 3, 29 June 2007

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    If you convey a covered work, knowingly relying on a patent license, and the Corresponding Source of the work is not available for anyone to copy, free of charge and under the terms of this License, through a publicly available network server or other readily accessible means, then you must either (1) cause the Corresponding Source to be so available, or (2) arrange to deprive yourself of the benefit of the patent license for this particular work, or (3) arrange, in a manner consistent with the requirements of this License, to extend the patent license to downstream recipients. “Knowingly relying” means you have actual knowledge that, but for the patent license, your conveying the covered work in a country, or your recipient’s use of the covered work in a country, would infringe one or more identifiable patents in that country that you have reason to believe are valid.

    If, pursuant to or in connection with a single transaction or arrangement, you convey, or propagate by procuring conveyance of, a covered work, and grant a patent license to some of the parties receiving the covered work authorizing them to use, propagate, modify or convey a specific copy of the covered work, then the patent license you grant is automatically extended to all recipients of the covered work and works based on it.

    A patent license is “discriminatory” if it does not include within the scope of its coverage, prohibits the exercise of, or is conditioned on the non-exercise of one or more of the rights that are specifically granted under this License. You may not convey a covered work if you are a party to an arrangement with a third party that is in the business of distributing software, under which you make payment to the third party based on the extent of your activity of conveying the work, and under which the third party grants, to any of the parties who would receive the covered work from you, a discriminatory patent license (a) in connection with copies of the covered work conveyed by you (or copies made from those copies), or (b) primarily for and in connection with specific products or compilations that contain the covered work, unless you entered into that arrangement, or that patent license was granted, prior to 28 March 2007.

    Nothing in this License shall be construed as excluding or limiting any implied license or other defenses to infringement that may otherwise be available to you under applicable patent law.

13. No Surrender of Others’ Freedom.

    If conditions are imposed on you (whether by court order, agreement or otherwise) that contradict the conditions of this License, they do not excuse you from the conditions of this License. If you cannot convey a covered work so as to satisfy simultaneously your obligations under this License and any other pertinent obligations, then as a consequence you may not convey it at all. For example, if you agree to terms that obligate you to collect a royalty for further conveying from those to whom you convey the Program, the only way you could satisfy both those terms and this License would be to refrain entirely from conveying the Program.

14. Use with the GNU Affero General Public License.

    Notwithstanding any other provision of this License, you have permission to link or combine any covered work with a work licensed under version 3 of the GNU Affero General Public License into a single combined work, and to convey the resulting work. The terms of this License will continue to apply to the part which is the covered work, but the special requirements of the GNU Affero General Public License, section 13, concerning interaction through a network will apply to the combination as such.

15. Revised Versions of this License.

    The Free Software Foundation may publish revised and/or new versions of the GNU General Public License from time to time. Such new versions will be similar in spirit to the present version, but may differ in detail to address new problems or concerns.

    Each version is given a distinguishing version number. If the Program specifies that a certain numbered version of the GNU General Public License “or any later version” applies to it, you have the option of following the terms and conditions either of that numbered version or of any later version published by the Free Software Foundation. If the Program does not specify a version number of the GNU General Public License, you may choose any version ever published by the Free Software Foundation.

    If the Program specifies that a proxy can decide which future versions of the GNU General Public License can be used, that proxy’s public statement of acceptance of a version permanently authorizes you to choose that version for the Program.

    Later license versions may give you additional or different permissions. However, no additional obligations are imposed on any author or copyright holder as a result of your choosing to follow a later version.

16. Disclaimer of Warranty.

    THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY APPLICABLE LAW. EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM “AS IS” WITHOUT WARRANTY OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE. THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM IS WITH YOU. SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF ALL NECESSARY SERVICING, REPAIR OR CORRECTION.

17. Limitation of Liability.

    IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS), EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF SUCH DAMAGES.

18. Interpretation of Sections 15 and 16.

    If the disclaimer of warranty and limitation of liability provided above cannot be given local legal effect according to their terms, reviewing courts shall apply local law that most closely approximates an absolute waiver of all civil liability in connection with the Program, unless a warranty or assumption of liability accompanies a copy of the Program in return for a fee.

             END OF TERMS AND CONDITIONS
    
    How to Apply These Terms to Your New Programs
    

    If you develop a new program, and you want it to be of the greatest possible use to the public, the best way to achieve this is to make it free software which everyone can redistribute and change under these terms.

    To do so, attach the following notices to the program. It is safest to attach them to the start of each source file to most effectively state the exclusion of warranty; and each file should have at least the “copyright” line and a pointer to where the full notice is found.

    <one line to give the program’s name and a brief idea of what it does.> Copyright (C)

    This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

    This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

    You should have received a copy of the GNU General Public License along with this program. If not, see http://www.gnu.org/licenses/.

Also add information on how to contact you by electronic and paper mail.

If the program does terminal interaction, make it output a short

notice like this when it starts in an interactive mode:

<program>  Copyright (C) <year>  <name of author>
This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
This is free software, and you are welcome to redistribute it
under certain conditions; type `show c' for details.

The hypothetical commands show w' andshow c’ should show the appropriate parts of the General Public License. Of course, your program’s commands might be different; for a GUI interface, you would use an “about box”.

You should also get your employer (if you work as a programmer) or school,

if any, to sign a “copyright disclaimer” for the program, if necessary. For more information on this, and how to apply and follow the GNU GPL, see http://www.gnu.org/licenses/.

The GNU General Public License does not permit incorporating your program

into proprietary programs. If your program is a subroutine library, you may consider it more useful to permit linking proprietary applications with the library. If this is what you want to do, use the GNU Lesser General Public License instead of this License. But first, please read http://www.gnu.org/philosophy/why-not-lgpl.html.

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