Uncovering spatiotemporal pattern of floods with Sentinel-1 synthetic aperture radar in major rice-growing river basins of Tanzania
In Tanzania, 71% of rice is grown in a rainfed lowland rice production ecosystem, primarily in river basins where extreme weather events like floods are frequent. For a six-year period (2017–2022), flood mapping was conducted using Sentinel-1 data in the Google Earth Engine (GEE) platform, utilizing...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-07-01
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| Series: | Frontiers in Earth Science |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/feart.2023.1183834/full |
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| author | Paulo Sulle Michael Hilda G. Sanga Mawazo J. Shitindi Max Herzog Joel L. Meliyo Boniface H. J. Massawe |
| author_facet | Paulo Sulle Michael Hilda G. Sanga Mawazo J. Shitindi Max Herzog Joel L. Meliyo Boniface H. J. Massawe |
| author_sort | Paulo Sulle Michael |
| collection | DOAJ |
| description | In Tanzania, 71% of rice is grown in a rainfed lowland rice production ecosystem, primarily in river basins where extreme weather events like floods are frequent. For a six-year period (2017–2022), flood mapping was conducted using Sentinel-1 data in the Google Earth Engine (GEE) platform, utilizing change detection and thresholding methodology. In addition to flood mapping, land use and land cover (LULC) were also analyzed using Sentinel-2 data in GEE, employing the Random Forest (RF) algorithm for classification. The aim was to understand the spatiotemporal extent of floods in two study locations. The resulting flood maps achieved an overall accuracy (OA) greater than 90% for all sites and study years. The findings revealed that agricultural land was the predominant land use/cover in both sub-basins, and floods were widespread in both regions. The study highlighted the interannual variability in flood extent, both spatially and temporally. Specifically, at the Ikwiriri site, floods were more extensive in 2020, covering 54.95% of the cultivated area, while in 2017, the minimum flood extent occurred, affecting 14% of the cultivated area. Similarly, at the Mngeta site, extensive floods were observed in 2020, with floods impacting 5.53% of the cultivated areas, while lower flood extents were observed in 2017, affecting 1.49% of the cultivated areas. Furthermore, the study demonstrated distinct spatiotemporal patterns of floods in both locations, with areas in proximity to rivers and wetlands experiencing more frequent floods. The research showcased the capabilities of the GEE cloud computation platform for flood inundation mapping, emphasizing its potential for enhancing our understanding of rice-producing environments. The generated flood maps can be utilized to guide the selection of areas for trials of flood-tolerant rice varieties and the dissemination of technologies such as flood-tolerant rice varieties, contributing to the resilience of rice farmers in these two floodplains. |
| first_indexed | 2024-03-12T21:47:04Z |
| format | Article |
| id | doaj.art-3995339479ae45678723f93a836c19ca |
| institution | Directory Open Access Journal |
| issn | 2296-6463 |
| language | English |
| last_indexed | 2024-03-12T21:47:04Z |
| publishDate | 2023-07-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Earth Science |
| spelling | doaj.art-3995339479ae45678723f93a836c19ca2023-07-26T09:16:59ZengFrontiers Media S.A.Frontiers in Earth Science2296-64632023-07-011110.3389/feart.2023.11838341183834Uncovering spatiotemporal pattern of floods with Sentinel-1 synthetic aperture radar in major rice-growing river basins of TanzaniaPaulo Sulle Michael0Hilda G. Sanga1Mawazo J. Shitindi2Max Herzog3Joel L. Meliyo4Boniface H. J. Massawe5Department of Soil and Geological Sciences, College of Agriculture, Sokoine University of Agriculture, Morogoro, TanzaniaDepartment of Soil and Geological Sciences, College of Agriculture, Sokoine University of Agriculture, Morogoro, TanzaniaDepartment of Soil and Geological Sciences, College of Agriculture, Sokoine University of Agriculture, Morogoro, TanzaniaDepartment of Biology, University of Copenhagen, Copenhagen, DenmarkTanzania Agricultural Research Institute (TARI), Dodoma, TanzaniaDepartment of Soil and Geological Sciences, College of Agriculture, Sokoine University of Agriculture, Morogoro, TanzaniaIn Tanzania, 71% of rice is grown in a rainfed lowland rice production ecosystem, primarily in river basins where extreme weather events like floods are frequent. For a six-year period (2017–2022), flood mapping was conducted using Sentinel-1 data in the Google Earth Engine (GEE) platform, utilizing change detection and thresholding methodology. In addition to flood mapping, land use and land cover (LULC) were also analyzed using Sentinel-2 data in GEE, employing the Random Forest (RF) algorithm for classification. The aim was to understand the spatiotemporal extent of floods in two study locations. The resulting flood maps achieved an overall accuracy (OA) greater than 90% for all sites and study years. The findings revealed that agricultural land was the predominant land use/cover in both sub-basins, and floods were widespread in both regions. The study highlighted the interannual variability in flood extent, both spatially and temporally. Specifically, at the Ikwiriri site, floods were more extensive in 2020, covering 54.95% of the cultivated area, while in 2017, the minimum flood extent occurred, affecting 14% of the cultivated area. Similarly, at the Mngeta site, extensive floods were observed in 2020, with floods impacting 5.53% of the cultivated areas, while lower flood extents were observed in 2017, affecting 1.49% of the cultivated areas. Furthermore, the study demonstrated distinct spatiotemporal patterns of floods in both locations, with areas in proximity to rivers and wetlands experiencing more frequent floods. The research showcased the capabilities of the GEE cloud computation platform for flood inundation mapping, emphasizing its potential for enhancing our understanding of rice-producing environments. The generated flood maps can be utilized to guide the selection of areas for trials of flood-tolerant rice varieties and the dissemination of technologies such as flood-tolerant rice varieties, contributing to the resilience of rice farmers in these two floodplains.https://www.frontiersin.org/articles/10.3389/feart.2023.1183834/fullrice submergencefloodplainssynthetic aperture radarGoogle earth engineflood maps |
| spellingShingle | Paulo Sulle Michael Hilda G. Sanga Mawazo J. Shitindi Max Herzog Joel L. Meliyo Boniface H. J. Massawe Uncovering spatiotemporal pattern of floods with Sentinel-1 synthetic aperture radar in major rice-growing river basins of Tanzania Frontiers in Earth Science rice submergence floodplains synthetic aperture radar Google earth engine flood maps |
| title | Uncovering spatiotemporal pattern of floods with Sentinel-1 synthetic aperture radar in major rice-growing river basins of Tanzania |
| title_full | Uncovering spatiotemporal pattern of floods with Sentinel-1 synthetic aperture radar in major rice-growing river basins of Tanzania |
| title_fullStr | Uncovering spatiotemporal pattern of floods with Sentinel-1 synthetic aperture radar in major rice-growing river basins of Tanzania |
| title_full_unstemmed | Uncovering spatiotemporal pattern of floods with Sentinel-1 synthetic aperture radar in major rice-growing river basins of Tanzania |
| title_short | Uncovering spatiotemporal pattern of floods with Sentinel-1 synthetic aperture radar in major rice-growing river basins of Tanzania |
| title_sort | uncovering spatiotemporal pattern of floods with sentinel 1 synthetic aperture radar in major rice growing river basins of tanzania |
| topic | rice submergence floodplains synthetic aperture radar Google earth engine flood maps |
| url | https://www.frontiersin.org/articles/10.3389/feart.2023.1183834/full |
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