This repository presents a comprehensive analytical framework for examining glacier albedo temporal dynamics utilizing Moderate Resolution Imaging Spectroradiometer (MODIS) MOD10A1 snow cover products. The implementation employs Google Earth Engine computational infrastructure to process multi-decadal datasets (2010-2024) with rigorous quality assurance protocols and advanced statistical methodologies for climate change research applications.
Glacier surface albedo represents a critical parameter in climate system dynamics, governing energy balance processes and influencing ablation rates. The MOD10A1 Collection 6.1 snow cover product provides systematic observations of snow and ice albedo derived from Terra satellite measurements at 500-meter spatial resolution with daily temporal coverage.
This analytical framework addresses the need for standardized, reproducible glacier albedo trend analysis through implementation of conservative quality filtering, robust statistical methods, and comprehensive metadata preservation suitable for peer-reviewed research applications.
The analysis utilizes the MODIS/Terra Snow Cover Daily Global 500m Grid (MOD10A1) Collection 6.1 dataset accessed through Google Earth Engine. Primary data products include:
- Normalized Difference Snow Index (NDSI) snow cover fraction
- Snow albedo daily measurements
- Quality assessment flags and algorithm confidence indicators
- Cloud state and geolocation accuracy metadata
Data filtering employs a multi-stage quality control framework:
Stage 1: Basic Quality Assessment
- Exclusion of night observations (flag value 211)
- Removal of ocean pixels (flag value 239)
- Elimination of poor quality observations (flags 2-3)
Stage 2: Algorithm-Specific Filtering
- Cloud detection screening using Collection 6.1 protocols
- Algorithm confidence thresholds
- Sensor viewing geometry constraints
- Atmospheric correction quality indicators
Stage 3: Spatial-Temporal Constraints
- Minimum glacier fraction coverage thresholds (default: 75%)
- Temporal aggregation reliability minimums (≥10 valid pixels)
- Seasonal analysis windows (default: June-September)
Sen's slope estimator provides robust, non-parametric trend quantification resistant to outliers and non-normal distributions. The method calculates median slope values across all data point pairs, providing confidence intervals through bootstrap resampling.
Structural break identification employs piecewise linear regression with breakpoint optimization through least squares minimization. Multiple change points are detected using dynamic programming algorithms with Bayesian Information Criterion selection.
Standardized anomalies are calculated using z-score transformations based on long-term climatological means and standard deviations. Extreme events are identified using threshold criteria (|z| > 2.0) with temporal persistence requirements.
Temporal persistence analysis employs autocorrelation functions to quantify serial correlation patterns and identify characteristic time scales of albedo variability.
| Parameter | Default Value | Description | Units |
|---|---|---|---|
| NDSI_threshold | 0 | Minimum NDSI for snow classification | Index (0-100) |
| glacier_fraction | 75 | Minimum glacier coverage requirement | Percentage |
| season_start | 152 | Analysis period start (June 1) | Day of year |
| season_end | 273 | Analysis period end (September 30) | Day of year |
| quality_threshold | 1 | Maximum acceptable quality flag | Categorical |
- Asset Integration: Glacier mask geometries imported from user-specified Earth Engine assets
- Temporal Filtering: Date range specification and seasonal subsetting
- Spatial Masking: Application of glacier boundaries and fraction thresholds
- Quality Control: Multi-stage filtering protocol implementation
- Statistical Aggregation: Pixel-wise and regional summary calculations
- Export Generation: CSV format outputs with comprehensive metadata
- Google Earth Engine account with asset upload capabilities
- Glacier boundary datasets (GeoTIFF or Shapefile format)
- Minimum 1GB Google Drive storage for export files
- Web browser with JavaScript support for interactive interface
- Access the Google Earth Engine Code Editor at https://code.earthengine.google.com/
- Copy the complete script from
MOD10A1_albedo_analysis.js - Paste into a new script file within the Code Editor environment
- Modify the glacier asset path (line 85) to reference your uploaded glacier mask:
var glacier = ee.Image('projects/your-project/assets/your-glacier-mask');
- Parameter Configuration: Adjust analysis parameters in the configuration section
- Script Execution: Run the complete script using the Code Editor interface
- Console Monitoring: Review filtering statistics and processing summaries
- Interactive Visualization: Utilize the generated user interface for parameter testing
- Export Tasks: Initiate CSV export tasks to Google Drive
Annual Statistical Summary (2010-2024):
Columns: year, glacier_fraction_class, mean_albedo, pixel_count,
standard_deviation, quality_flag, processing_date
Daily Time Series Dataset:
Columns: date, year, day_of_year, decimal_year, mean_albedo,
median_albedo, glacier_fraction_class, pixel_count,
quality_metrics, processing_metadata
Statistical reliability indicators include:
- Temporal coverage completeness ratios
- Spatial sampling density assessments
- Quality flag distribution analyses
- Cloud contamination frequency evaluations
Primary uncertainty components:
- Sensor calibration stability over multi-decadal periods
- Atmospheric correction algorithm limitations
- Sub-pixel heterogeneity effects in mixed glacier-rock surfaces
- Temporal sampling irregularities due to cloud cover
Cross-validation employs:
- Comparison with in-situ albedo measurements where available
- Inter-sensor consistency assessments using Aqua MODIS data
- Spatial coherence analysis across glacier boundaries
- Temporal stability evaluation during snow-free periods
The generated user interface provides real-time parameter adjustment capabilities:
- Threshold sliders for NDSI and glacier fraction parameters
- Temporal range selectors for seasonal analysis windows
- Visual feedback through dynamic map overlays
- Statistical summary updates with parameter modifications
Processing statistics include:
- Total pixel counts before and after filtering stages
- Quality flag distribution summaries
- Temporal coverage assessments by year and season
- Export task status and completion confirmations
Interactive map functionality enables:
- Individual pixel quality assessment through click events
- Detailed metadata display for selected locations
- Time series visualization for specific coordinates
- Quality flag interpretation and filtering impact assessment
Typical analysis results demonstrate:
- Multi-decadal albedo trend coefficients with confidence intervals
- Seasonal variability patterns and amplitude changes
- Extreme event frequency and magnitude assessments
- Spatial heterogeneity quantification across glacier surfaces
This framework supports:
- Climate change impact assessments on glacier energy balance
- Validation of regional climate model albedo parameterizations
- Long-term monitoring program data standardization
- Comparative studies across different glacier systems
- MODIS MOD10A1 Collection 6.1: NASA/USGS Land Processes Distributed Active Archive Center
- Glacier boundary datasets: User-provided through Earth Engine Assets
- Reference coordinate systems: WGS84 geographic projection
Generated datasets include comprehensive provenance metadata:
- Processing timestamp and software version identifiers
- Complete parameter configuration documentation
- Quality control statistics and filtering summaries
- Spatial and temporal coverage assessments
MODIS MOD10A1 Glacier Albedo Analysis Framework (2024)
GitHub Repository: https://github.com/tofunori/MODIS-MOD10A1-Albedo-Glacier
Accessed: [Date]
Hall, D.K., Riggs, G.A., Salomonson, V.V., DiGirolamo, N.E., Bayr, K.J. (2002). MODIS snow-cover products. Remote Sensing of Environment, 83(1-2), 181-194.
Riggs, G.A., Hall, D.K., Román, M.O. (2017). Overview of NASA's MODIS and Visible Infrared Imaging Radiometer Suite (VIIRS) snow-cover Earth System Data Records. Earth System Science Data, 9(2), 765-777.
- Google Earth Engine JavaScript API
- Earth Engine Code Editor development environment
- Modern web browser with HTML5 and JavaScript support
MIT License
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files, to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.
For technical inquiries, methodological questions, or collaboration proposals, please submit issues through the GitHub repository issue tracking system. Include detailed descriptions of analysis requirements, dataset specifications, and expected outcomes for optimal support response.