Assessment of single-channel algorithms for land surface temperature retrieval at two southern Brazil sites

Käfer, Pâmela Suélen; Rolim, Silvia Beatriz Alves; Heinz, Luíza Vargas de Oliveira; Iglesias, María Luján; Rocha, Nájila Souza da; Diaz, Lucas Ribeiro

doi:10.1117/1.jrs.14.016507

Cited by 5 publications

(4 citation statements)

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“…The accuracies of most Ts retrieval algorithms are between 1 and 2 K [26,36,69]. In this sense, this work demonstrates that for the S-SEBI model, although these differences in temperature affect the choice of hot and cold pixels of images and to estimate LE, the possible errors generated by retrieve Ts and emissivity do not affect the results in daily evapotranspiration so significantly (0.5 mm/day).…”

Section: Discussionmentioning

confidence: 73%

The Influence of Land Surface Temperature in Evapotranspiration Estimated by the S-SEBI Model

et al. 2020

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Evapotranspiration (ET) is one of the least understood components of the hydrological cycle. Its applications are varied, from agricultural, ecological and hydrological monitoring, to control of the evolution of climate change. The goal of this work was to analyze the influence that uncertainties in the estimate of land surface temperature (Ts) can cause on ET estimates by S-SEBI model in the Pampa biome area. Also, the specificities of native grassland of Pampa biome related to energy balance were analyzed. The results indicate that the daily evapotranspiration is higher when the pixel Ts is lower, which also shows the influence of land use on the variability of ET. The results demonstrated that the S-SEBI is less dependent on Ts estimation than other models reported in the literature, such as the SEBS, which not exceed 0.5 mm/day in grasslands. The evapotranspiration variability between forest and grassland were lower than expected, demonstrating that the Pampa biome have in Rio Grande do Sul the same importance that forests regarding to the processes of the hydrological cycle, since it covers 63% of the State.

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Section: Discussionmentioning

confidence: 73%

The Influence of Land Surface Temperature in Evapotranspiration Estimated by the S-SEBI Model

et al. 2020

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“…This algorithm requires additional atmospheric information to accurately correct for atmospheric effects. This method has been applied in various studies and improved (Käfer et al, 2020;Sahani, 2021). In the present study, SCA is applied to estimate LST from of band 6 (10.40-12.50µm) of Landsat 5 and Landsat7 datasets in ERDAS Imagine v2015.…”

Section: Databasementioning

confidence: 99%

Spatial Time Series Analysis of Warming Patterns in the Kumaun Himalayas: A Future Perspective

Khan,

Ahmad,

Shamim

2024

Preprint

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In this groundbreaking study, we introduce a novel approach to forecasting Land Surface Temperature (LST) in the Kumaun Himalayas, an area critical for understanding regional impacts of global warming. The novelty of our research lies in the utilization of spatial time series analysis, a method not previously applied for future LST prediction. Combined adoption of remote sensing and advanced statistical techniques such as the Simple Moving Average (SMA), Sen’s Slope, and z-statistics with excellent statistical power, our study analyses LST trends from 1990 to 2030 using comprehensive Landsat data. Notably, the application of z-statistics provides a robust framework for assessing temperature changes, with significant findings such as a z-statistics value of -15.04 for spring, indicating a marked shift in temperature patterns. Similarly, for autumn, the z-statistics value of -21.41 underscores a drastic deviation from historical norms i.e., from 1990 to 2020. These values highlight a future that is significantly warmer than the present, bringing into sharp focus the urgency of climate change mitigation and adaptation strategies in this ecologically sensitive region. The study also suggests differential rate of seasonal warming. The study is not only pivotal for local climate policy but also contribute significantly to the broader understanding of climate dynamics in mountainous terrains is seasonal variation in warming rates. Despite challenges like rugged terrain and variable cloud cover affecting data accuracy, our approach offers a scalable model for similar climatic studies in other regions, marking a significant advancement in the field of climate change.

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“…1). The dune field covers an area of approximately 30 km² and is composed of 99.53% quartz (Käfer et al, 2019(Käfer et al, , 2020 -mineral that presents characteristic spectral features (Reststrahlen band) in the TIR region (Salisbury & D'Aria, 1992). We are particularly interested on future applications of refined atmospheric profiles for atmospheric correction of TIR remote sensing data purposes.…”

Section: Study Areamentioning

confidence: 99%

Using the WRF Model to Refine NCEP CFSv2 Reanalysis Atmospheric Profile: A Southern Brazil Test Case

et al. 2020

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ABSTRACT. The vertical distribution of atmospheric parameters is pivotal to understand atmospheric dynamic processes. This study analyzes the use of the Weather Research and Forecasting (WRF) model to generate atmospheric profiles as an alternative for a local radiosonde. We performed simulations in the WRF model for two dates with distinct weather conditions (01/09/2018 – cloudy and 03/14/2018 – clear-sky). The simulated data were compared with radiosondes launched in the study area, both (i) along the entire profile and (ii) with focus on the Planetary Boundary Layer (PBL) – for the variables water vapor mixing ratio (q), potential temperature (θ) and wind speed (Speed). The results showed a high correlation between the simulated and observed profiles, with most R values higher than 0.9. Bias and RMSE, respectively, ranged between -1.29 – 0.66 g/kg and 0.48 – 2.01 g/kg for q; -0.52 – 0.25 K and 0.44 – 3.00 K for θ; and -0.20 – 1.31 m/s and 1.61 – 2.77 m/s for Speed. The difference between the profiles from different WRF nested grids was minimal, suggesting that a resolution of approximately 12 km is enough to a good trade-off between detailing and computational cost. WRF model seems suitable to simulate fine atmospheric profiles. Keywords: vertical profiles, numerical weather prediction, meteorology, remote sensing, planetary boundary layer. Uso do Modelo WRF para refinar perfis atmosféricos de reanálise NCEP CFSv2: um estudo de caso no Sul do BrasilRESUMO. A distribuição vertical dos parâmetros atmosféricos é fundamental para entender os processos dinâmicos da atmosfera. Este estudo analisa o uso do modelo Weather Research and Forecasting (WRF) na geração de perfis atmosféricos, como uma alternativa à radiossondagens locais. Foram realizadas simulações com o WRF para duas datas em condições de tempo distintas (09/01/2018 – alta nebulosidade e 14/03/2018 – céu claro). Os dados simulados foram comparados com radiossondagens lançadas na área de estudo, (i) ao longo de todo o perfil e (ii) com foco na camada limite planetária (PBL) – para as variáveis razão de mistura do vapor de água (q), temperatura potencial (θ) e velocidade do vento (Speed). Os resultados mostraram uma alta correlação entre os perfis simulados e observados, com a maioria dos valores de R superiores a 0,9. Viés e RMSE, respectivamente, variaram entre -1,29 – 0,66 g/kg e 0,48 – 2,01 g/kg para q; -0,52 – 0,25 K e 0,44 – 3,00 K para θ; e -0,20 – 1,31 m/s e 1,61 – 2,77 m/s para Speed. Mínimas diferenças entre os perfis das diferentes grades aninhadas sugerem que uma resolução horizontal de aproximadamente 12 km é um bom balanço entre detalhamento e custo computacional. O WRF mostrou potencial na simulação de perfis atmosféricos refinados. Palavras-chave: perfis verticais, previsão numérica do tempo, meteorologia, sensoriamento remoto, camada limite planetária.

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Assessment of single-channel algorithms for land surface temperature retrieval at two southern Brazil sites

Cited by 5 publications

References 58 publications

The Influence of Land Surface Temperature in Evapotranspiration Estimated by the S-SEBI Model

The Influence of Land Surface Temperature in Evapotranspiration Estimated by the S-SEBI Model

Spatial Time Series Analysis of Warming Patterns in the Kumaun Himalayas: A Future Perspective

Using the WRF Model to Refine NCEP CFSv2 Reanalysis Atmospheric Profile: A Southern Brazil Test Case

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