Biome-Specific Estimation of Maximum Air Temperature Using MODIS LST in the São Francisco River Basin
The São Francisco River provides water for agriculture, urban areas, and hydroelectric power generation, benefiting millions of people in Brazil. Its Basin supports various species, some of which are endemic and rely on its unique habitats for survival. Currently, monitoring maximum air temperature in the São Francisco River Basin is limited due to sparse weather stations. This study proposes three linear regression models to estimate maximum air temperature using satellite-derived land surface temperature from the Aqua’s moderate resolution imaging spectroradiometer across the Basin’s three main biomes: Caatinga, Cerrado, and Mata Atlântica. With over 94,000 paired observations of ground and satellite data, the models showed good performance, accounting for 46% to 54% of temperature variation. Cross-validation confirmed reliable estimates with errors below 2.7 °C. The findings demonstrate that satellite data can improve air temperature monitoring in areas with limited ground observations and suggest that the proposed biome-specific models could assist in environmental management and water resource planning in the São Francisco River Basin. This includes providing more informed policies for climate adaptation and sustainable development or analyzing variations in maximum air temperature in arid and semi-arid regions to contribute to desertification mitigation strategies in the São Francisco River Basin.
Authors
- Mahelvson Bazilio Chaves (ORCID: https://orcid.org/0000-0001-5099-3600)
- Fábio Farias Pereira (ORCID: https://orcid.org/0000-0002-5678-6730)
- Mayara Camila Santos Silva (ORCID: https://orcid.org/0000-0002-7675-4314)
- Claudia Rivera Escorcia
- José Anderson Farias da Silva Bomfim (ORCID: https://orcid.org/0000-0002-0830-2658)
Institutions
- Universidade Federal do Rio Grande do Norte (BR)
- Instituto Federal de Educação, Ciência e Tecnologia de Alagoas (BR)
- Universidade Federal de Alagoas (BR)
Publication Details
- Journal
- Meteorology
- Published
- 2025-06-30
- DOI
- https://doi.org/10.3390/meteorology4030017
- Primary Topic
- Species Distribution and Climate Change
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Fundação de Amparo à Pesquisa do Estado de Alagoas