It should be noted that the basis of this project is forked from a repo developed by Quinn Brencher ([email protected]) and Eric Gagliano ([email protected]) that documents some of their incredible contributions to snow research. More can be found below.
Understanding snow depth is crucial in hydrological risk assessment, water resource management, climate change modeling, and more. Remote sensing technologies such as light detection and ranging (LiDAR), synthetic aperture radar (SAR), and optical imagery allow for measurements of snow depth, land cover, and topography across a spatial scale unachievable by traditional manual measurements and models. Current machine learning models that use remote sensing data to measure snow depth are making great strides, but struggle in terms of accuracy at a large spatial scale. The incorporation of spatially-sparse, highly-precise snow depth stations into these models to improve this accuracy is a challenge many snow scientists are facing today. We hope to develop methodology that efficiently encodes point and raster data into machine learning architectures, using Quinn and Eric's "deep-snow" data and models (CNNs).
$ conda install mamba -n base -c conda-forge
Clone the repo and set up the environment
$ git clone https://github.com/Jack-Hayes/mlgeo-2024-deep-snow.git
$ cd ./mlgeo-2024-deep-snow
$ mamba env create -f environment.yml
$ conda activate deep-snow
Install the package locally
$ pip install -e .
*The below is copied from Quinn and Eric's repo
- Sentinel-1 RTC backscatter data (snow on and snow off)
- Sentinel-2 imagery (snow on)
- Fractional forest cover
- COP30 digital elevation model
- Airborne Snow Observatory (ASO) lidar snow depth maps
- SNOTEL snowpack monitoring stations
Snow-on Sentinel-1 and 2 data were collected nearby in time to corresponding ASO acquistions. All products were reprojected to the appropriate UTM zone and resampled to a matching 50 m grid. Products were divided up spatially into training, testing, and validation tiles and subset to produce a machine-learning ready dataset. Our training dataset includes ~37,000 image stacks, each of which includes all of the above listed inputs.
It should be noted that we unfortunately cannot share our cleaned ASO LiDAR snow depth data due to ASO's data sharing policy. The data downloading notebooks do rely on this data so this is not entirely replicable (though accessing the other data sources through the API endpoints with any geometry is still possible, you just won't have the 'ground truth' data to test against). Furthermore, we have too much data to host on our repo so the notebooks below (particularly notebooks/visualizations/ai-ready_viz.ipynb will showcase the data). The AI-ready dataset is split into tiles corresponding to ASO LiDAR flight extents. Each tile (32x32km or 1024km2) will have multiple subdivided rasters of size 128x128 pixels where each pixel is 50m (! 6.4x6.4km). Each of these pixels will contain data listed from the various remote sensing sources below. Tiles do not all have the same amount of rasters within due to missing data. In total, there are 451 testing rasters, 3366 training rasters, and 589 validation rasters. There are 16 testing tiles, 125 training tiles, and 16 validation tiles. The data covers the Sierra Nevada, Rocky Mountain, and Olympic mountain ranges from 2016-2023.
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data
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Airborne Snow Observatory (ASO) lidar snow depth maps
- ASO lidar snow depth (target dataset)
- gaps in ASO data
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- snow on Sentinel-1 VV polarization backscatter (snowon_vv)
- snow on Sentinel-1 VH polarization backscatter (snowon_vh)
- snow off Sentinel-1 VV polarization backscatter (snowoff_vv)
- snow off Sentinel-1 VH polarization backscatter (snowoff_vh)
- snow on Sentinel-1 VV polarization backscatter, 4-week mean (snowon_vv_mean)
- snow on Sentinel-1 VH polarization backscatter, 4-week mean (snowon_vh_mean)
- snow off Sentinel-1 VV polarization backscatter, 4-week mean (snowoff_vv_mean)
- snow off Sentinel-1 VH polarization backscatter, 4-week mean (snowoff_vh_mean)
- snow on Sentinel-1 cross ratio (VH - VV) (snowon_cr)
- snow off Sentinel-1 cross ratio (VH - VV) (snowoff_cr)
- change in cross ratio, snow on vs snow off (delta_cr)
- gaps in Sentinel-1 data (rtc_gap_map)
- gaps in Sentinel-1 mean data (rtc_mean_gap_map)
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Sentinel-2 multispectral imagery
- aerosol optical thickness (snow on aerosol_optical_thickness)
- coastal aerosol band (snow on coastal_aerosol)
- blue band (snow on blue)
- green band (snow on green)
- red band (snow on red)
- red edge 1 band (snow on red_edge1)
- red edge 2 band (snow on red_edge2)
- red edge 3 band (snow on red_edge3)
- near infrared (NIR) band (snow on nir)
- water vapor band (snow on water_vapor)
- shortwave infrared 1 band (snow on swir1)
- shortwave infrared 2 band (snow on swir2)
- scene classification map (snow on scene_class_map)
- water vapor product (snow on water_vapor_product)
- Normalized Difference Vegetation Index (NDVI)
- Normalized Difference Snow Index (NDSI)
- Normalized Difference Water Index (NDWI)
- gaps in Sentinel-2 data (s2_gap_map)
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PROBA-V Global Land Cover dataset
- fractional forest cover (fcf)
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- elevation
- slope
- aspect
- curvature
- topographic position index (tpi)
- terrain ruggedness index (tri)
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Geospatial Information
- latitude
- longitude
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Derived Variables
- day of water year (dowy)
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Data Download and Raw Data Organization
- The assignment-required script is at scripts/clean_data.py but please just look through the notebooks/dataset_prep notebooks as they're much better organized and easier to understand
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Basic Data Cleaning and Manipulation
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Organizing Data into AI-Ready Format
- notebooks/Prepare_AI_Ready_Data.ipynb
- note that the subdirectories in data/ai_ready/ are empty due to the ASO data sharing policies described above. the data are stored in .nc files so all of the training, testing, and validation files contain this sensitive data
- See notebooks/visualizations/ai-ready_viz.ipynb for data visualizations which will serve as our data/ai_ready/ replacement
- Note the the tile boundary polygons were generated in QGIS using the "Create grid" tool.
- The SNOTEL snow depth station data we want is easily queryable as seen at notebooks/dataset_prep/snotel_exploration.ipynb (note that the interactive maps aren't viewable on the repo so see this notebook instead). This data is not technically AI-ready, but a main goal of this project is to how best make this data AI-ready and how that ties in with encoding the point data most effectively.
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Exploratory Data Analysis (EDA)
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Dimensionality Discussion and Reduction
Important
Below is the readme of the repo this was forked from
- Quinn Brencher, [email protected]
- Eric Gagliano, [email protected]
2023 GeoSMART Hackweek team:
- Bareera Mirza
- Ibrahim Alabi
- Dawn URycki
- Taylor Ganz
- Abner Bogan
- Mansa Krishna
- Taryn Black
- Will Rosenbluth
- Yen-Yi Wu
- Fadji Maina
- Hui Gao
- Jacky Chen Xu
- Nicki Shobert
- Kathrine Udell-Lopez
2024 NASA Earth Sciences and UW Hackweek team:
- Ekaterina (Katya) Bashkova
- Manda Chasteen
- Sarah Kilpatrick
- Isabella Chittumuri
- Kavita Mitkari
- Shashank Bhushan (Helper)
- Adrian Marziliano (Helper)
Seasonal snow provides drinking water for billions, but current global measurements of snow depth lack adequate spatial and temporal resolution for effective resource management--especially in mountainous terrain. Recent work has demonstrated the potential to retrieve snow-depth measurements from Sentinel-1 synthetic aperture radar (SAR) backscatter data. However, comparisons with airborne lidar data suggest that existing snow depth retrieval algorithms fail to capture the full complexity of relationships between snow depth, terrain, vegetation, and SAR backscatter, the physics of which are poorly understood. We suggest that a machine learning model may be able to effectively learn these relationships and retrieve snow depth from SAR backscatter with improved accuracy.
During the 2023 GeoSMART Hackweek, the deep-snow team trained a convolutional neural network to predict snow depth (see results here). Initial results are promising! But this model needs to be improved, validated, and applied.
The overarching goal for this hackweek is to improve our snow depth prediction model such that it outperforms the initial model implemented last year. This is an ongoing machine learning project with opportunities to contribute at various stages in the project lifecycle!
- improve hyperparameters, model architecture, and input selection
- perform model validation and testing
- apply model to predict snow depth in new areas
- improve visualizations
- Create a snow depth map at peak snow water equivalent 2023 for the entire western U.S (or some large area within it)
- Compare snow depth results to SNOTEL/spicy-snow measurements
- Develop a tool that takes a date range and a bounding box and produces a snow depth time series using our model
- implement some way to track experiments
- perform sensitivity analysis to quantify the importance of each input
Our dataset includes:
- Sentinel-1 RTC backscatter data (snow on and snow off)
- Sentinel-2 imagery (snow on)
- Fractional forest cover
- COP30 digital elevation model
- Airborne Snow Observatory (ASO) lidar snow depth maps
Snow-on Sentinel-1 and 2 data were collected nearby in time to corresponding ASO acquistions. All products were reprojected to the appropriate UTM zone and resampled to a matching 50 m grid. Products were divided up spatially into training, testing, and validation tiles and subset to produce a machine-learning ready dataset. Our training dataset includes ~37,000 image stacks, each of which includes all of the above listed inputs.
Download and install Miniconda Set up Mamba
$ conda install mamba -n base -c conda-forge
Clone the repo and set up the environment
$ git clone https://github.com/geo-smart/deep-snow.git
$ cd ./deep-snow
$ mamba env create -f environment.yml
$ conda activate deep-snow
Install the package locally
$ pip install -e .