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landsatToa.js_Doc

Module: landsatToa.js

Comprehensive Analysis of landsatToa.js

Module Name

  • Module Name: landsatToa.js

Primary Objectives

  • Purpose: This module is designed to process Landsat Top of Atmosphere (TOA) imagery using Google Earth Engine (GEE). It focuses on cloud masking, image acquisition based on specific dates, and generating cloud maps for given geometries.

Critical Functions

  1. addGridPosition(element): Adds grid position metadata to an image.
  2. sliceByRevisit(collection, startingDate, days): Filters an image collection by a specific date range.
  3. maskTOAClouds(image, bandName): Masks clouds in a TOA image using bitwise operations on quality assessment bands.
  4. acquireFromDate(date, mission, geometry): Acquires a mosaicked image for a given date, mission, and geometry, and calculates the cloud ratio.
  5. processCollection(mission, geometry): Processes an image collection to find valid dates for image acquisition based on the mission's revisit cycle.
  6. generateCloudMap(dates, mission, geometry): Generates a list of cloud ratios for a series of dates.
  7. queryAvailable(mission): Queries available dates and cloud ratios for a given mission and geometry.
  8. getAvailable(mission): Placeholder function for getting available data.
  9. acquire(mission): Acquires an image for a specific date and geometry.
  10. format(properties): Formats image properties for display.

Key Variables

  • Metadata.GRID_POSITION: Key for storing grid position metadata.
  • cloudMask, cloudConf, shadowConf, cirrusConf: Bitwise masks for different cloud and shadow conditions.
  • global, enhanced, northeasternPosition, filtered, northeastern, difference, remainder, step, earliestDate, completeCycles, additions, carriers, valid: Variables used in processCollection to manage image collection processing and filtering.

Interdependencies

  • Dependencies on Other Modules:
    • ee from ../../services/earth-engine: Google Earth Engine API.
    • mergeProperties, retrieveExtremes, getDate from ../utils: Utility functions for merging properties, retrieving date extremes, and formatting dates.
    • scoreCloudRatio from ../imagery: Function to score cloud ratios.
    • Metadata from ../../common/metadata: Metadata constants.

Core vs. Auxiliary Operations

  • Core Operations:
    • acquireFromDate
    • processCollection
    • generateCloudMap
    • queryAvailable
    • acquire
  • Auxiliary Operations:
    • addGridPosition
    • sliceByRevisit
    • maskTOAClouds
    • format

Operational Sequence

  1. Image Acquisition:
    • Use acquireFromDate to get a mosaicked image for a specific date.
  2. Cloud Masking:
    • Apply maskTOAClouds to mask clouds in the image.
  3. Collection Processing:
    • Use processCollection to find valid dates for image acquisition.
  4. Cloud Map Generation:
    • Generate cloud maps using generateCloudMap.
  5. Querying:
    • Use queryAvailable to get available dates and cloud ratios.

Performance Aspects

  • Performance Considerations:
    • Efficient filtering and processing of large image collections.
    • Use of bitwise operations for cloud masking to enhance performance.
    • Leveraging GEE's parallel processing capabilities.

Reusability

  • Adaptability for Reuse:
    • Functions are modular and can be reused for different missions and geometries.
    • Utility functions (mergeProperties, retrieveExtremes, getDate) enhance reusability.
    • The module can be extended to support additional missions or different types of imagery.

Usage

  • How It Is Used:
    • Import the module and call its functions to process Landsat TOA imagery.
    • Use queryAvailable to get available dates and cloud ratios for a specific mission and geometry.
    • Use acquire to acquire images for specific dates.
    • Format image properties using format.

Assumptions

  • Assumptions Made:
    • The input geometry is valid and within the bounds of the mission's coverage.
    • The mission object contains necessary metadata such as name, cycle, and bands.
    • The GEE environment is properly set up and accessible.
    • The utility functions (mergeProperties, retrieveExtremes, getDate) and metadata constants are correctly defined and imported.

This analysis provides a detailed overview of the landsatToa.js module, covering its primary objectives, critical functions, key variables, interdependencies, and more.

Flow Diagram [via mermaid]

graph TD
    %% External Entities
    User((User))
    EarthEngine((Earth Engine))

    %% Processes
    P1[queryAvailable]
    P2[getAvailable]
    P3[acquire]
    P4[format]
    P5[processCollection]
    P6[generateCloudMap]
    P7[acquireFromDate]
    P8[maskTOAClouds]
    P9[sliceByRevisit]
    P10[addGridPosition]
    P11[mergeProperties]
    P12[retrieveExtremes]
    P13[getDate]
    P14[scoreCloudRatio]

    %% Data Stores
    DS1[Mission Data]
    DS2[Geometry Data]
    DS3[Image Collection]
    DS4[Cloud Data]

    %% Data Flows
    User -->|Request| P1
    P1 -->|Mission, Geometry| P5
    P5 -->|Processed Collection| DS3
    DS3 -->|Image Collection| P6
    P6 -->|Dates, Mission, Geometry| P7
    P7 -->|Image| P8
    P8 -->|Masked Image| P14
    P14 -->|Cloud Ratio| P7
    P7 -->|Image with Clouds| DS4
    DS4 -->|Cloud Data| P1
    P1 -->|Available Data| User

    User -->|Request| P2
    P2 -->|Mission, Geometry| P5
    P5 -->|Processed Collection| DS3
    DS3 -->|Image Collection| P6
    P6 -->|Dates, Mission, Geometry| P7
    P7 -->|Image| P8
    P8 -->|Masked Image| P14
    P14 -->|Cloud Ratio| P7
    P7 -->|Image with Clouds| DS4
    DS4 -->|Cloud Data| P2
    P2 -->|Available Data| User

    User -->|Request| P3
    P3 -->|Mission, Date, Geometry| P7
    P7 -->|Image| P8
    P8 -->|Masked Image| P14
    P14 -->|Cloud Ratio| P7
    P7 -->|Image with Clouds| P3
    P3 -->|Acquired Data| User

    User -->|Request| P4
    P4 -->|Properties| P11
    P11 -->|Formatted Data| P4
    P4 -->|Formatted Data| User

    %% Internal Data Flows
    P5 -->|Mission, Geometry| P12
    P12 -->|Extremes| P5
    P5 -->|Mission, Geometry| P10
    P10 -->|Grid Position| P5
    P5 -->|Mission, Geometry| P9
    P9 -->|Sliced Collection| P5
    P5 -->|Processed Collection| DS3

    P6 -->|Dates, Mission, Geometry| P7
    P7 -->|Image| P8
    P8 -->|Masked Image| P14
    P14 -->|Cloud Ratio| P7
    P7 -->|Image with Clouds| DS4
    DS4 -->|Cloud Data| P6

    P7 -->|Image| P11
    P11 -->|Merged Properties| P7
    P7 -->|Image with Clouds| DS4

    P7 -->|Image| P13
    P13 -->|Date| P7

Module: landsatToa.js

Comprehensive Analysis of landsatToa.js

Module Name

  • Module Name: landsatToa.js

Primary Objectives

  • Purpose: This module is designed to process Landsat Top of Atmosphere (TOA) imagery using Google Earth Engine (GEE). It focuses on cloud masking, image acquisition based on specific dates, and generating cloud maps for given geometries.

Critical Functions

  1. addGridPosition(element): Adds grid position metadata to an image.
  2. sliceByRevisit(collection, startingDate, days): Filters an image collection by a date range.
  3. maskTOAClouds(image, bandName): Masks clouds in a TOA image based on specific bitwise operations.
  4. acquireFromDate(date, mission, geometry): Acquires a mosaicked image for a given date, mission, and geometry, and calculates the cloud ratio.
  5. processCollection(mission, geometry): Processes an image collection to find valid dates for image acquisition.
  6. generateCloudMap(dates, mission, geometry): Generates a list of cloud ratios for given dates.
  7. queryAvailable(mission): Queries available images and their cloud ratios for a given mission and geometry.
  8. getAvailable(mission): Placeholder function for getting available images.
  9. acquire(mission): Acquires an image for a given date and geometry.
  10. format(properties): Formats image properties for display.

Key Variables

  • Metadata.GRID_POSITION: Key for storing grid position metadata.
  • cloudMask, cloudConf, shadowConf, cirrusConf: Bitwise masks for cloud, cloud confidence, shadow confidence, and cirrus confidence.

Interdependencies

  • External Modules:
    • ee from ../../services/earth-engine: Google Earth Engine API.
    • Utility functions from ../utils: mergeProperties, retrieveExtremes, getDate.
    • scoreCloudRatio from ../imagery: Function to score cloud ratio.
    • Metadata from ../../common/metadata: Common metadata keys.

Core vs. Auxiliary Operations

  • Core Operations:
    • acquireFromDate
    • processCollection
    • generateCloudMap
    • queryAvailable
    • acquire
  • Auxiliary Operations:
    • addGridPosition
    • sliceByRevisit
    • maskTOAClouds
    • format

Operational Sequence

  1. Image Acquisition:
    • Use processCollection to find valid dates.
    • Use acquireFromDate to acquire images for those dates.
  2. Cloud Masking:
    • Apply maskTOAClouds to mask clouds in the images.
  3. Cloud Map Generation:
    • Use generateCloudMap to create a list of cloud ratios for the images.
  4. Querying:
    • Use queryAvailable to get available images and their cloud ratios.

Performance Aspects

  • Efficiency: The module uses GEE's efficient filtering and mapping functions to handle large datasets.
  • Scalability: Designed to work with large image collections and extensive geographical areas.

Reusability

  • Adaptability: Functions are modular and can be reused for different missions and geometries. The use of GEE makes it adaptable to various remote sensing tasks.

Usage

  • How it is used:
    • Import the module and call its functions to process Landsat TOA imagery.
    • Use queryAvailable to get available images and cloud ratios.
    • Use acquire to acquire specific images.
    • Use format to format image properties for display.

Assumptions

  • Assumptions Made:
    • The input geometries are valid and within the bounds of the image collection.
    • The mission object contains necessary metadata like name, cycle, and bands.
    • The GEE environment is properly set up and authenticated.

This analysis provides a detailed overview of the landsatToa.js module, covering its primary objectives, critical functions, key variables, interdependencies, and more.

Flow Diagram [via mermaid]

graph TD
    A[User] -->|queryAvailable| B[landsatToa.js]
    A[User] -->|getAvailable| B[landsatToa.js]
    A[User] -->|acquire| B[landsatToa.js]
    A[User] -->|format| B[landsatToa.js]
    
    B[landsatToa.js] -->|import| C[ee]
    B[landsatToa.js] -->|import| D[mergeProperties, retrieveExtremes, getDate]
    B[landsatToa.js] -->|import| E[scoreCloudRatio]
    B[landsatToa.js] -->|import| F[Metadata]
    
    B[landsatToa.js] -->|addGridPosition| G[ee.Image]
    B[landsatToa.js] -->|sliceByRevisit| H[ee.ImageCollection]
    B[landsatToa.js] -->|maskTOAClouds| I[ee.Image]
    B[landsatToa.js] -->|acquireFromDate| J[ee.Image]
    B[landsatToa.js] -->|processCollection| K[ee.ImageCollection]
    B[landsatToa.js] -->|generateCloudMap| L[ee.List]
    B[landsatToa.js] -->|queryAvailable| M[ee.Dictionary]
    B[landsatToa.js] -->|getAvailable| N[ee.List]
    B[landsatToa.js] -->|acquire| O[ee.Image]
    B[landsatToa.js] -->|format| P[String]