30 Years of simultaneous crop & land cover land use maps for Middle Rio Grande from 1994 to 2024
Abstract This study introduces the crop and land cover land use (CLCLU) dataset, a 30 m resolution product providing annual maps of CLCLU across the transnational Middle Rio Grande (MRG) region, spanning both the U.S. and Mexico from 1994 to 2024. The model was trained using the Cropland Data Layer...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-08-01
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| Series: | Scientific Data |
| Online Access: | https://doi.org/10.1038/s41597-025-05771-6 |
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| author | Saman Ebrahimi Mahdis Khorram Raquel Neri Barranco Rosario Sanchez Rocky Talchabhadel Santosh S. Palmate Marisol Dominguez-Tuda Elizabeth F. Racine Saurav Kumar |
| author_facet | Saman Ebrahimi Mahdis Khorram Raquel Neri Barranco Rosario Sanchez Rocky Talchabhadel Santosh S. Palmate Marisol Dominguez-Tuda Elizabeth F. Racine Saurav Kumar |
| author_sort | Saman Ebrahimi |
| collection | DOAJ |
| description | Abstract This study introduces the crop and land cover land use (CLCLU) dataset, a 30 m resolution product providing annual maps of CLCLU across the transnational Middle Rio Grande (MRG) region, spanning both the U.S. and Mexico from 1994 to 2024. The model was trained using the Cropland Data Layer (CDL) on the US side. Dual-month (July and December) Landsat composites and a semantic segmentation model, MANet with ResNeXt-101 encoder, under four strategies were used to address sensor and temporal variability. This model architecture was chosen for its intrinsic ability to capture detailed spatial patterns and contextual dependencies through its attention-based design and ResNeXt-101 encoder, which demonstrated strong performance, particularly in generalizing across data-scarce regions in Mexico. The dataset achieved 97.10% overall accuracy and 78.85% mean Intersection over Union (mIoU), over validation process using a held-out CDL subset. Validation against NLCD and MCD12Q1-UMD confirmed high agreement. Data availability differences, minimal ground truth on the Mexican side, and cloud-related artifacts in early years led to some misclassification. |
| format | Article |
| id | doaj-art-819bee610e63474c8807af574ef5d960 |
| institution | Kabale University |
| issn | 2052-4463 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Data |
| spelling | doaj-art-819bee610e63474c8807af574ef5d9602025-08-24T11:07:32ZengNature PortfolioScientific Data2052-44632025-08-0112111510.1038/s41597-025-05771-630 Years of simultaneous crop & land cover land use maps for Middle Rio Grande from 1994 to 2024Saman Ebrahimi0Mahdis Khorram1Raquel Neri Barranco2Rosario Sanchez3Rocky Talchabhadel4Santosh S. Palmate5Marisol Dominguez-Tuda6Elizabeth F. Racine7Saurav Kumar8School of Sustainable Engineering and the Built Environment, Arizona State UniversitySchool of Sustainable Engineering and the Built Environment, Arizona State UniversitySchool of Sustainable Engineering and the Built Environment, Arizona State UniversityTexas Water Resources Institute, Texas A&M UniversityDepartment of Civil and Environmental Engineering, Jackson State UniversityTexas A&M AgriLife ResearchTexas A&M AgriLife ResearchTexas A&M AgriLife ResearchSchool of Sustainable Engineering and the Built Environment, Arizona State UniversityAbstract This study introduces the crop and land cover land use (CLCLU) dataset, a 30 m resolution product providing annual maps of CLCLU across the transnational Middle Rio Grande (MRG) region, spanning both the U.S. and Mexico from 1994 to 2024. The model was trained using the Cropland Data Layer (CDL) on the US side. Dual-month (July and December) Landsat composites and a semantic segmentation model, MANet with ResNeXt-101 encoder, under four strategies were used to address sensor and temporal variability. This model architecture was chosen for its intrinsic ability to capture detailed spatial patterns and contextual dependencies through its attention-based design and ResNeXt-101 encoder, which demonstrated strong performance, particularly in generalizing across data-scarce regions in Mexico. The dataset achieved 97.10% overall accuracy and 78.85% mean Intersection over Union (mIoU), over validation process using a held-out CDL subset. Validation against NLCD and MCD12Q1-UMD confirmed high agreement. Data availability differences, minimal ground truth on the Mexican side, and cloud-related artifacts in early years led to some misclassification.https://doi.org/10.1038/s41597-025-05771-6 |
| spellingShingle | Saman Ebrahimi Mahdis Khorram Raquel Neri Barranco Rosario Sanchez Rocky Talchabhadel Santosh S. Palmate Marisol Dominguez-Tuda Elizabeth F. Racine Saurav Kumar 30 Years of simultaneous crop & land cover land use maps for Middle Rio Grande from 1994 to 2024 Scientific Data |
| title | 30 Years of simultaneous crop & land cover land use maps for Middle Rio Grande from 1994 to 2024 |
| title_full | 30 Years of simultaneous crop & land cover land use maps for Middle Rio Grande from 1994 to 2024 |
| title_fullStr | 30 Years of simultaneous crop & land cover land use maps for Middle Rio Grande from 1994 to 2024 |
| title_full_unstemmed | 30 Years of simultaneous crop & land cover land use maps for Middle Rio Grande from 1994 to 2024 |
| title_short | 30 Years of simultaneous crop & land cover land use maps for Middle Rio Grande from 1994 to 2024 |
| title_sort | 30 years of simultaneous crop land cover land use maps for middle rio grande from 1994 to 2024 |
| url | https://doi.org/10.1038/s41597-025-05771-6 |
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