Preprints
https://doi.org/10.5194/essd-2025-353
https://doi.org/10.5194/essd-2025-353
04 Aug 2025
 | 04 Aug 2025
Status: this preprint is currently under review for the journal ESSD.

Spatiotemporal mapping of invasive yellow sweetclover blooms using Sentinel-2 and high-resolution drone imagery

Sakshi Saraf, Ranjeet John, Venkatesh Kolluru, Khushboo Jain, Geoffrey Henebry, Jiquan Chen, and Raffaele Lafortezza

Abstract. Yellow sweetclover (Melilotus officinalis (L.) Lam.; MEOF) is an invasive forb pervasive across the Northern Great Plains, often linked to traits such as wide adaptability, strong stress tolerance, and high productivity. Despite MEOF's prevalent ecological-economic impacts and importance, knowledge of its spatial distribution and temporal evolution is extremely limited. Here, we aim to develop a spatial database of annual MEOF abundance (2016–2023) across western South Dakota (SD) at 10 m spatial resolution by applying a generalized prediction model on Sentinel-2 imagery. We collected in situ quadrat-based total vegetation cover with MEOF percent cover estimates across western SD from 2021 through 2023 and synthesized with other available percent cover estimates (2016–2022) of several federal, state, and non-governmental sources. We conducted drone overflights at 14 sites across Butte County, SD in 2023 to develop very high spatial resolution (4–6 cm) and accurate MEOF cover maps by applying a random forest (RF) classification model. The field-measured and uncrewed aerial system (UAS) derived MEOF percent cover estimates were used to train, test, and validate a RF regression model. The predicted MEOF percent cover dataset was validated with UAS-derived percent cover in 2023 across four sites (out of 14 sites). We found that the variation in the Tasseled Cap Greenness and Normalized Difference Yellowness Index were among the top predicting variables in predicting MEOF abundance. Our predictive model yielded greater accuracies with an R2 of 0.76, RMSE of 15.11 %, MAE of 10.95 %, and MAPE of 1.06 %. We validated our 2023 predicted maps using the 3-m resolution PlanetScope imagery for regions where field samples could not be collected in 2023. The database of MEOF abundance showed consecutive years of average or above-average precipitation yielded a higher MEOF abundance across the study region. The database could assist local land managers and government officials pinpoint locations requiring timely land management to control the rapid spread of MEOF in the Northern Great Plains. The developed invasive MEOF percent cover datasets are freely available at the figshare repository (https://doi.org/10.6084/m9.figshare.29270759.v1).

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this preprint. The responsibility to include appropriate place names lies with the authors.
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Sakshi Saraf, Ranjeet John, Venkatesh Kolluru, Khushboo Jain, Geoffrey Henebry, Jiquan Chen, and Raffaele Lafortezza

Status: open (until 10 Sep 2025)

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Sakshi Saraf, Ranjeet John, Venkatesh Kolluru, Khushboo Jain, Geoffrey Henebry, Jiquan Chen, and Raffaele Lafortezza

Data sets

Spatiotemporal mapping of invasive yellow sweetclover blooms using Sentinel-2 and high-resolution drone imagery Sakshi Saraf, Ranjeet John, Venkatesh Kolluru, Khushboo Jain, Geoffrey Henebry, Jiquan Chen, Raffaele Lafortezza https://doi.org/10.6084/m9.figshare.29270759.v1

Model code and software

Spatiotemporal mapping of invasive yellow sweetclover blooms using Sentinel-2 and high-resolution drone imagery Sakshi Saraf, Ranjeet John, Venkatesh Kolluru, Khushboo Jain, Geoffrey Henebry, Jiquan Chen, Raffaele Lafortezza https://doi.org/10.6084/m9.figshare.29270759.v1

Sakshi Saraf, Ranjeet John, Venkatesh Kolluru, Khushboo Jain, Geoffrey Henebry, Jiquan Chen, and Raffaele Lafortezza
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Latest update: 04 Aug 2025
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Short summary
We developed the maps to identify the spread of an invasive plant, yellow sweetclover, in the western South Dakota from 2016 to 2023 using satellite and drone imagery. Our study shows that the plant blooms widely during wet years and is often found near roads and moist areas. The developed percent cover maps using field data, drone images, and machine learning models would help land managers find and control this invasive species, protecting Northern Great Plains grasslands.
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