Land surface temperature algorithm calibration through meteorological stations

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2021

Autores

Oliveira, M (Autor) Outra; Ana Teodoro (Autor) FCUP; Freitas A (Autor) FMUP; Hernani Goncalves (Autor) FMUP

Ata de Conferência Internacional

Título: EARTH RESOURCES AND ENVIRONMENTAL REMOTE SENSING/GIS APPLICATIONS XII

Conference on Earth Resources and Environmental Remote Sensing/GIS Applications XII / SPIE Remote Sensing Conference

ELECTR NETWORK, SEP 13-18, 2021

Indexação

ISI Web of Knowledge - 0 Citações

Scopus - 0 Citações

Outras Informações

ID Authenticus: P-00V-PE2

DOI: 10.1117/12.2599909

Abstract (EN): This methodologic paper arises from the necessity to gather Land Surface Temperature (LST) data over a relatively large period and territory: 2000-2018, Portugal. The computational power required to complete this task was found to be a major barrier. However, platforms such as Google Earth Engine (GEE) offer a vast data archive freely accessible through a web interactive development environment or an application programming interface, namely, Python's API. Additionally, the computation using GEE is hosted in Google's servers, drastically reducing the processing times. However, computing LST through Landsat-7 satellite imagery resulted on a difference of -8 degrees C +/- 6 degrees C compared to the values from meteorological ground stations. As such, this paper aims to further calibrate computed LST through meteorological stations and make the methodology and corresponding code available, thus encouraging cooperation on the development and integration of local calibration methods. A sensitivity analysis of the representativeness of each station was performed using three methods of temperature extraction: station coordinate's pixel, buffers around the station, and surrounding soil occupation (identifying the area with the same soil occupation as the station's location). Pearson's correlation coefficient was on average significant at 0.81 in the raw data and increased to 0.89 after clearing data from outliers. The best representativeness method for meteorologic stations was the one based on soil occupation, which resulted on a Pearson's r of 0.91. As a result, we advise researchers to complement their remote sensing work with ground data whenever possible through the usage of a method like the one here described.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 19