This is a catalog record. The data is held outside UD Dataverse. Original dataset: https://doi.org/10.5281/zenodo.15866496. Please cite the original dataset rather than this record.
Coastal agricultural lands in the eastern USA are increasingly plagued by escalating soil salinity, rendering them unsuitable for profitable farming. Increasing soil salinization can lead to both land cover modification and conversion. Two notable instances of such transformations include the conversion of farmland to marshland or to barren salt patches devoid of vegetation. However, quantifying these land cover changes across large geographic regions poses a significant challenge due to their varying spatial granularity. To tackle this issue, we first developed a machine-learning based method using Sentinel-2 imagery for 2022 where we used a non-linear spectral unmixing approach utilizing a Random Forest (RF) algorithm (Sarupria et al., 2025). The RF models were constructed using 100 trees and 27,437 reference data points, resulting in two sets of ten models: one for salt patches and another for marshland. Validation metrics for sub-pixel fractional abundances revealed a moderate R-squared value of 0.50 for the salt model ensemble and a high R-squared value of 0.90 for the marsh model ensemble. Building upon this methodology, we then generated annual gridded datasets of fractional abundance for salt patch and marshland across the Delmarva Peninsula (14 coastal counties in Delaware, Maryland and Virginia, USA) for 2019-2023. In these datasets, we only report mean fractional abundance values ranging from 0.4 to 1 for salt patches and 0.25 to 1 for marshland, along with the standard deviation associated with each value.
Time period: 2019-2023
Location: 14 counties on Delmarva peninsula (Delaware, Maryland, Virginia)
Formats: .tif
Version: V2
Access (per the DSI catalog): Public (Dataset is or could be made publicly available to all without restrictions)
Project: Non-linear spectral unmixing for monitoring rapidly salinizing coastal landscapes. Coastal farmlands in the eastern United States of America (USA) are increasingly suffering from rising soil salinity, rendering them unsuitable for economically productive agriculture. Saltwater intrusion (SWI) into the groundwater reservoir or soil salinization can result in land cover modification (e.g. reduced plant growth) or land cover conversion. Two primary examples of such land cover conversion are farmland to marsh or farmland to salt patches with no vegetation growth. However, due to varying spatial granularity of these conversions, it is challenging to quantify these land covers over a large geographic scale. To address this challenge, we evaluated a non-linear spectral unmixing approach with a Random Forest (RF) algorithm to quantify fractional abundance of salt patch and marshes. Using Sentinel-2 imagery from 2022, we generated gridded datasets for salt patches and marshes across the Delmarva Peninsula, and the associated uncertainty. Moreover, we developed two new spectral indices to enhance the spectral unmixing accuracy: the Normalized Difference Salt Patch Index (NDSPI) and the Modified Salt Patch Index (MSPI). We constructed two sets of ten RF models: one for salt patches and the other for marshes, achieving high (>99 %) training and testing accuracies for classification. The consistently high accuracy and low error values across different model runs demonstrate the method's reliability for classifying spectrally similar land cover classes in the mid-Atlantic region and beyond. Validation metrics for sub-pixel fractional abundances in the salt model revealed a moderate R-squared value of 0.50, and a high R-squared value of 0.90 for the marsh model. Our method complements labor-intensive field-based salinity measurements by offering a reproducible method that can be repeated annually and scaled up to cover large geographic regions. Funding: NASA EPSCoR Grant DE-80NSSC20M0220, EPA (Assistance Agreement no. CB96358101), USDA NRCS (Assistance Agreement no. NR193A750007C005) and the National Fish and Wildlife Foundation’s Chesapeake Bay Stewardship Fund (grant 0603.20.071142), Additionally: State of Maryland, Harry R. Hughes Center for Agro-Ecology.
Source: UD Dataset Catalog, UD Data Science Institute: https://research.dsi.udel.edu/dataset/metadata.html#Fractional-Abundance-Datasets-for-Salt-Patch-and-Marshland-Across-the-Delmarva-Peninsula-v2
