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Module: sentinel.js

Comprehensive Analysis of sentinel.js

Module Name

  • Module Name: sentinel.js

Primary Objectives

  • Purpose: This module is designed to interact with satellite imagery data, specifically Sentinel and Landsat missions, to process and filter images based on cloud coverage and other criteria. It provides functionalities to acquire images from specific dates, process collections of images, and generate cloud maps.

Critical Functions

  1. addGridPosition(element): Adds grid position metadata to an image.
  2. sliceByRevisit(collection, startingDate, days): Filters a collection of images by a date range.
  3. getImageDate(image): Extracts the date from an image's ID.
  4. maskS2Clouds(img, geometry, bandName, scale, imgDateParam): Masks clouds in Sentinel-2 images and calculates the cloud coverage ratio.
  5. acquireFromDate(date, mission, geometry): Acquires an image from a specific date and mission, applying cloud masking.
  6. processCollection(mission, geometry): Processes a collection of images to find valid dates for image acquisition.
  7. generateCloudMap(dates, mission, geometry): Generates a list of cloud coverage values for a series of dates.
  8. queryAvailable(mission): Returns a function that queries available images for a mission and geometry.
  9. getAvailable(mission): Similar to queryAvailable, but returns the results directly.
  10. acquire(mission): Returns a function to acquire an image for a specific date and geometry.
  11. format(properties): Formats image properties for display.

Key Variables

  • Metadata.GRID_POSITION: Metadata key for grid position.
  • bandName: Name of the band used for cloud masking.
  • scale: Scale used for cloud masking.
  • geometry: Geometric area of interest.
  • mission: Object containing mission details like name and cycle.

Interdependencies

  • External Services:
    • ee from ../../services/earth-engine: Earth Engine API for image processing.
  • Utility Functions:
    • mergeProperties, retrieveExtremes, getDate from ../utils.
  • Algorithms:
    • acquireFromDateLandSat from ../../algorithms/satellite/landsat.
  • Common Metadata:
    • Metadata from ../../common/metadata.

Core vs. Auxiliary Operations

  • Core Operations:
    • acquireFromDate
    • processCollection
    • generateCloudMap
    • maskS2Clouds
  • Auxiliary Operations:
    • addGridPosition
    • sliceByRevisit
    • getImageDate
    • queryAvailable
    • getAvailable
    • acquire
    • format

Operational Sequence

  1. Image Acquisition: acquireFromDate is used to acquire images from a specific date and mission.
  2. Cloud Masking: maskS2Clouds is applied to filter out cloud-covered areas.
  3. Collection Processing: processCollection processes image collections to find valid dates.
  4. Cloud Map Generation: generateCloudMap generates cloud coverage data for a series of dates.
  5. Querying: queryAvailable and getAvailable are used to query available images and their cloud coverage.

Performance Aspects

  • Efficiency: The module uses Earth Engine's efficient image processing capabilities.
  • Scalability: Designed to handle large collections of satellite images.
  • Optimization: Cloud masking and filtering are optimized for performance.

Reusability

  • Adaptability: Functions are modular and can be reused for different satellite missions and geometries.
  • Extensibility: New missions or additional processing steps can be easily integrated.

Usage

  • Image Processing: Used to process and filter satellite images based on cloud coverage.
  • Data Analysis: Generates cloud maps and valid image acquisition dates for further analysis.
  • Integration: Can be integrated into larger systems for environmental monitoring, agriculture, and other applications.

Assumptions

  • Image Metadata: Assumes images have specific metadata like system:id and CLOUDY_PIXEL_PERCENTAGE.
  • Date Format: Assumes image IDs contain date information in a specific format.
  • Geometry: Assumes valid geometric areas are provided for image processing.
  • Mission Details: Assumes mission objects contain necessary details like name and cycle.

This analysis provides a comprehensive overview of the sentinel.js module, detailing its structure, functionality, and usage within a larger system.

Flow Diagram [via mermaid]

graph TD
    A[User] -->|Requests data| B[queryAvailable]
    A[User] -->|Requests data| C[getAvailable]
    A[User] -->|Requests data| D[acquire]
    
    B -->|Calls| E[processCollection]
    C -->|Calls| E[processCollection]
    D -->|Calls| F[acquireFromDate]
    
    E -->|Calls| G[retrieveExtremes]
    E -->|Calls| H[addGridPosition]
    E -->|Calls| I[sliceByRevisit]
    E -->|Calls| J[getDate]
    
    F -->|Calls| I[sliceByRevisit]
    F -->|Calls| K[maskS2Clouds]
    F -->|Calls| L[mergeProperties]
    
    B -->|Returns data| A
    C -->|Returns data| A
    D -->|Returns data| A

Module: sentinel.js

Comprehensive Analysis of sentinel.js

Module Name

  • Module Name: sentinel.js

Primary Objectives

  • Purpose: The module is designed to interact with satellite imagery data, specifically Sentinel and Landsat missions, to process and filter images based on cloud coverage and other criteria. It provides functionalities to acquire images from specific dates, process collections of images, and generate cloud maps.

Critical Functions

  1. addGridPosition(element): Adds grid position metadata to an image.
  2. sliceByRevisit(collection, startingDate, days): Filters a collection of images by a date range.
  3. getImageDate(image): Extracts the date from an image's ID.
  4. maskS2Clouds(img, geometry, bandName, scale, imgDateParam): Masks clouds in Sentinel-2 images and calculates the cloud coverage ratio.
  5. acquireFromDate(date, mission, geometry): Acquires an image from a specific date and mission, applying cloud masking.
  6. processCollection(mission, geometry): Processes a collection of images to find valid dates for image acquisition.
  7. generateCloudMap(dates, mission, geometry): Generates a list of cloud coverage values for a series of dates.
  8. queryAvailable(mission): Returns a function that queries available images for a mission and geometry.
  9. getAvailable(mission): Returns a function that gets available images for a mission and geometry.
  10. acquire(mission): Returns a function that acquires an image for a specific date and geometry.
  11. format(properties): Formats image properties for display.

Key Variables

  • Metadata.GRID_POSITION: Metadata key for grid position.
  • bandName: Name of the band used for cloud masking.
  • scale: Scale for cloud masking operations.
  • geometry: Geometric area of interest for image processing.
  • mission: Object containing mission details like name and cycle.

Interdependencies

  • Dependencies on Other Modules:
    • ../../services/earth-engine: Provides Earth Engine services.
    • ../utils: Utility functions like mergeProperties, retrieveExtremes, and getDate.
    • ../../algorithms/satellite/landsat: Specific functions for Landsat satellite data.
    • ../imagery: Functions related to imagery processing.
    • ../../common/metadata: Common metadata definitions.

Core vs. Auxiliary Operations

  • Core Operations:
    • acquireFromDate
    • processCollection
    • generateCloudMap
    • maskS2Clouds
  • Auxiliary Operations:
    • addGridPosition
    • sliceByRevisit
    • getImageDate
    • queryAvailable
    • getAvailable
    • acquire
    • format

Operational Sequence

  1. Image Acquisition: acquireFromDate is used to acquire images from a specific date and mission.
  2. Cloud Masking: maskS2Clouds is applied to filter out cloud-covered areas.
  3. Collection Processing: processCollection processes a collection of images to find valid dates.
  4. Cloud Map Generation: generateCloudMap generates a list of cloud coverage values for a series of dates.
  5. Querying and Formatting: queryAvailable, getAvailable, and format are used for querying and formatting image data.

Performance Aspects

  • Performance Considerations:
    • Efficient filtering and masking of large image collections.
    • Handling of large datasets with Earth Engine's reduceRegion and filterDate methods.
    • Use of ee.Algorithms.If and ee.Number for conditional operations and calculations.

Reusability

  • Adaptability for Reuse:
    • The module is designed with reusable functions that can be adapted for different satellite missions and geometries.
    • Functions like maskS2Clouds and acquireFromDate can be reused with minor modifications for other satellite data.

Usage

  • How It Is Used:
    • The module is used to acquire and process satellite images, specifically for cloud masking and date-based filtering.
    • It can be integrated into larger systems for environmental monitoring, agricultural analysis, and other geospatial applications.

Assumptions

  • Assumptions Made:
    • The input images have specific metadata fields like system:id and CLOUDY_PIXEL_PERCENTAGE.
    • The Earth Engine service is available and accessible.
    • The geometry provided for image processing is valid and correctly formatted.
    • The mission object contains necessary details like name and cycle.

This comprehensive analysis provides a detailed understanding of the sentinel.js module, its functionalities, and its role within a larger system.

Flow Diagram [via mermaid]

graph TD
    A[User] -->|Requests Data| B[queryAvailable]
    A -->|Requests Data| C[getAvailable]
    A -->|Requests Data| D[acquire]
    A -->|Requests Data| E[format]

    B -->|Calls| F[processCollection]
    C -->|Calls| F[processCollection]
    D -->|Calls| G[acquireFromDate]
    E -->|Calls| H[format]

    F -->|Uses| I[ee.ImageCollection]
    F -->|Uses| J[retrieveExtremes]
    F -->|Uses| K[addGridPosition]
    F -->|Uses| L[sliceByRevisit]
    F -->|Uses| M[mergeProperties]
    F -->|Uses| N[getDate]

    G -->|Uses| O[maskS2Clouds]
    G -->|Uses| P[mergeProperties]
    G -->|Uses| Q[sliceByRevisit]
    G -->|Uses| R[acquireFromDateLandSat]

    H -->|Uses| S[ee.Image]
    H -->|Uses| T[ee.Dictionary]

    style A fill:#f9f,stroke:#333,stroke-width:4px
    style B fill:#bbf,stroke:#333,stroke-width:2px
    style C fill:#bbf,stroke:#333,stroke-width:2px
    style D fill:#bbf,stroke:#333,stroke-width:2px
    style E fill:#bbf,stroke:#333,stroke-width:2px
    style F fill:#ccf,stroke:#333,stroke-width:2px
    style G fill:#ccf,stroke:#333,stroke-width:2px
    style H fill:#ccf,stroke:#333,stroke-width:2px
    style I fill:#ddf,stroke:#333,stroke-width:2px
    style J fill:#ddf,stroke:#333,stroke-width:2px
    style K fill:#ddf,stroke:#333,stroke-width:2px
    style L fill:#ddf,stroke:#333,stroke-width:2px
    style M fill:#ddf,stroke:#333,stroke-width:2px
    style N fill:#ddf,stroke:#333,stroke-width:2px
    style O fill:#eef,stroke:#333,stroke-width:2px
    style P fill:#eef,stroke:#333,stroke-width:2px
    style Q fill:#eef,stroke:#333,stroke-width:2px
    style R fill:#eef,stroke:#333,stroke-width:2px
    style S fill:#eef,stroke:#333,stroke-width:2px
    style T fill:#eef,stroke:#333,stroke-width:2px