A Multi-Year Dataset of Annual Inundation Dynamics in Nebraska (2017–2025) Derived from AlphaEarth Foundation Embeddings

# Annual Surface Water Inundation Dataset — Nebraska, USA (2017–2025) **Author:** Aditya Kapoor **Affiliations:** University of Nebraska-Lincoln; Indian Institute of Technology Roorkee **DOI:** https://doi.org/10.5281/zenodo.18741436 **Contact:** Aditya Kapoor --- ## Overview This Zenodo record bundles two complementary data products from the same study area: 1. **Annual surface water inundation classification rasters and inundation-frequency layers** for 901 HydroLakes surface water bodies across the US state of Nebraska, covering a 9-year period from 2017 to 2025. Generated using AlphaEarth Foundations Embeddings (AFE) — a precomputed, 10-metre-resolution, 64-dimensional annual embedding dataset from Google DeepMind — classified within Google Earth Engine (GEE). 2. **(New in v2)** A **validation-data package** containing the in-situ daily water-surface elevation records, elevation-area curves, satellite zonal statistics, and per-reservoir validation tables that underpin an IEEE conference-paper validation of the AFE-derived annual inundation against gauge-derived reference area at five Nebraska reservoirs (Enders, Hugh Butler, Swanson, Harlan County, and Lake McConaughy). The record accompanies two manuscripts: > Kapoor, A. *Assessment of Inundation Dynamics in Nebraska Using AlphaEarth Foundations Embeddings.* (Under review) > Kapoor, A. *Evaluating AlphaEarth Foundation Model Embeddings for Annual Inundation Mapping: A Case Study of Nebraska.* IEEE conference paper. --- ## Dataset Contents | File | Size | Description | |---|---|---| | `AEF_RF_2017_2025.zip` | 26.2 MB | Annual inundation rasters from supervised Random Forest classification (2017–2025) | | `AEF_K_MEANS_2017_2025.zip` | 346.7 MB | Annual inundation rasters from unsupervised K-means classification (2017–2025) | | `DYNAMIC_WORLD_DATA_2017_2025.zip` | 40.0 MB | Annual inundation data derived from the Dynamic World product for comparison (2017–2025) | | `INUNDATION_FREQUENCY_RASTERS_RF_2017_2025.rar` | 6.7 MB | Inundation frequency rasters derived from Random Forest classification | | **`AlphaEarth_Nebraska_Validation_Data_v1.zip`** | **0.5 MB** | **(v2 addition)** In-situ daily elevation records, elevation-area curves, satellite zonal statistics, and the per-reservoir validation table for the five reservoirs used in the IEEE conference paper. Contains 14 CSV files and an internal `README.md` data dictionary. | | `ZENODO_README.md` | — | This documentation file | --- ## Data Specifications ### Raster products | Property | Value | |---|---| | Spatial resolution | 10 metres | | Temporal coverage | 2017–2025 (annual composites) | | Spatial coverage | State of Nebraska, USA | | Coordinate reference system | EPSG:4326 (WGS 84) | | File format | GeoTIFF | | Pixel values | 1 = Inundated, 0 = Non-inundated, 255 = No data | ### Validation data (inside `AlphaEarth_Nebraska_Validation_Data_v1.zip`) | Property | Value | |---|---| | Spatial coverage | Five reservoirs in the Nebraska Great Plains | | Temporal coverage | 2017-01-01 through 2025-12-31 (daily for in-situ; annual for satellite) | | Sample size | 45 reservoir-year pairs in the headline `annual_area_comparison_2017_2025.csv` | | File format | CSV (UTF-8) plus an internal Markdown README | | Reservoirs | Enders, Hugh Butler, Swanson, Harlan County, Lake McConaughy | Refer to the internal `README.md` inside the validation ZIP for the column-by-column data dictionary. --- ## Methodology ### Input Data Annual inundation extents were derived by classifying AFE — a precomputed, analysis-ready embedding dataset that encodes spectral, temporal, and spatial signatures from multiple Earth observation sources at 10 m resolution. ### Classification **1. Random Forest (Supervised)** A Random Forest classifier (Breiman, 2001) was trained on 30,000 labelled points across three classes: inundation, vegetation, and built-up area. An independent validation dataset of 7,500 points was used for accuracy assessment. Training was performed on the 2017 AFE dataset and applied to all years (2017–2025). Training accuracy, validation accuracy, and validation Kappa were approximately 99.9%. **2. K-means (Unsupervised)** K-means clustering (MacQueen, 1967) with four clusters was applied to the 64-dimensional AFE without labelled training data. Classification is based on the spectral similarity of pixel signatures. **3. Dynamic World (Benchmark)** Dynamic World (Brown et al., 2022) is a Sentinel-2-based near-real-time land use/land cover product used here as a benchmark for comparison against AFE-based classifications. ### Inundation Frequency Inundation frequency (IF) was computed by summing annual binary inundation rasters and dividing by the number of years, yielding the proportion of years a pixel was inundated. IF values range from 0% (never inundated) to 100% (permanently inundated). ### Reservoir-scale validation (v2 addition) For each of the five validated reservoirs, daily water-surface elevation records from the U.S. Bureau of Reclamation HydroMet system (Enders, Hugh Butler, Swanson, Harlan County) and the Central Nebraska Public Power and Irrigation District (Lake McConaughy) were averaged to an annual mean elevation. The mean annual elevation was mapped to an in-situ reference surface area by piecewise-linear interpolation on the reservoir-specific elevation-area curve from ACAP92 sedimentation surveys (Ferrari, 1985–2001) and USGS SIR 2005-5040 (Kress et al., 2005). The AFE Random Forest annual inundation rasters were summarised within the HydroLAKES reference polygon for each reservoir using zonal statistics in EPSG:5070. The resulting 45 reservoir-year pairs are tabulated in `01_validation_summary/annual_area_comparison_2017_2025.csv` inside the validation ZIP. Full per-reservoir validation statistics (r, R², RMSE, MAE, bias) are reported in the IEEE conference paper. --- ## Google Earth Engine Script The full GEE analysis script is publicly accessible at: https://code.earthengine.google.com/bfaf1067b39ea6b3ef8ad628b673921e Two interactive web applications accompany this dataset: - **Annual classified inundation extents** — https://adityain2003.users.earthengine.app/view/comparison - **Inundation frequency layer** — https://adityain2003.users.earthengine.app/view/inundation-frequency-webapp --- ## Key References - Breiman, L. (2001). Random Forests. *Machine Learning*, 45(1), 5–32. - Brown, C. F., et al. (2022). Dynamic World, near real-time global 10 m land use land cover mapping. *Scientific Data*, 9(1), 251. - Brown, C. F., et al. (2025). AlphaEarth Foundations: An embedding field model for accurate and efficient global mapping from sparse label data. *arXiv:2507.22291*. - Ferrari, R. L. (1985–2001). Bureau of Reclamation ACAP92 sedimentation survey reports for Enders, Hugh Butler, Swanson, and Harlan County reservoirs, Nebraska. - Kress, W. H., Sebree, S. K., Littin, G. R., Drain, M. A., & Kling, M. E. (2005). Comparison of preconstruction and 2003 bathymetric and topographic surveys of Lake McConaughy, Nebraska. U.S. Geological Survey Scientific Investigations Report 2005-5040. - MacQueen, J. (1967). Some methods for classification and analysis of multivariate observations. *Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability*, 1(14), 281–297. - Messager, M. L., et al. (2016). Estimating the volume and age of water stored in global lakes using a geo-statistical approach. *Nature Communications*, 7, 13603. --- ## License and Use This dataset is published under the **Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)** licence. You are free to share and adapt the material for **non-commercial purposes**, provided appropriate credit is given. **Commercial use of the dataset — including use of the data to train commercial machine-learning models or to develop commercial products or services — requires prior written permission from the author.** Please contact Aditya Kapoor. In addition to the formal license terms, researchers planning to use this dataset in a publication, dissertation, or technical report are kindly requested to notify the author prior to manuscript submission. This enables coordination with related ongoing work and avoids inadvertent duplication of analyses. This request supplements, but does not modify, the CC BY-NC 4.0 license under which the data is released. --- ## Citation If you use this dataset, please cite the accompanying manuscript(s) and this Zenodo record: > Kapoor, A. *Assessment of Inundation Dynamics in Nebraska Using AlphaEarth Foundations Embeddings.* (Under review) > Kapoor, A. *Evaluating AlphaEarth Foundation Model Embeddings for Annual Inundation Mapping: A Case Study of Nebraska.* IEEE conference paper. > Dataset: https://doi.org/10.5281/zenodo.18741436