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

Institutions

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

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Biome-Specific Estimation of Maximum Air Temperature Using MODIS LST in the São Francisco River Basin

Mahelvson Bazilio Chaves, Fábio Farias Pereira, Mayara Camila Santos Silva, Claudia Rivera Escorcia et al.
Meteorology
Species Distribution and Climate Change
article

Biome-Specific Estimation of Maximum Air Temperature Using MODIS LST in the São Francisco River Basin

Mahelvson Bazilio Chaves, Fábio Farias Pereira, Mayara Camila Santos Silva, Claudia Rivera Escorcia, José Anderson Farias da Silva Bomfim
article en

Abstract

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.

MeteorologyVol. 4(3)
Universidade Federal do Rio Grande do Norte (BR), Instituto Federal de Educação, Ciência e Tecnologia de Alagoas (BR), Universidade Federal de Alagoas (BR)
Fundação de Amparo à Pesquisa do Estado de Alagoas
Openalex Percentile: Top 92%
Species Distribution and Climate Change
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.