Modeling MODIS LST at European scale

Bussalleu, Alonso; Hoogh, Kees de; Hoek, Gerard; Kloog, Itai

doi:10.1289/isee.2022.p-0304

Cited by 2 publications

(1 citation statement)

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“…Furthermore, sensors in the visible region are only utilized during the day as reflectance is the principal technique used to detect snow and ice. It is therefore limited in its ability to observe snow and sea ice due to the nature of its optical sensors (Olsson et al, 2016;Bussalleu et al, 2022).…”

Section: Modis Datamentioning

confidence: 99%

A case study about the forest fire occurred on 05 July 2021 over Khenchela province, Algeria, using space-borne remote sensing

Guehaz,

Sivakumar

2023

Front. Remote Sens.

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In this study, space-borne remote sensing (Landsat-8, MODIS) was employed to evaluate the effects of forest fires occurring on 05 July 2021, over Khenchela province, Algeria. Our objective is to understand the severity of damage caused by the fire and its implications for vegetation and land cover. Utilizing the Normalized Difference Vegetation Index (NDVI) from MODIS data and Landsat-8 imagery, we report changes in vegetation health and land cover. To identify areas affected by forest fires and evaluate the severity of damage, the Normalized Burn Ratio (NBR) and Differenced Normalized Burn Ratio (dNBR) were calculated. Analysis showed that −1825.11 ha (1.21%) of the total area experienced severe burns, 3843.54 ha (2.54%) moderate to high severity burns, 3927.97 ha (2.59%) moderate to low severity burns and 9864.45 ha (6.51%) low severity burns. The area covered by vegetation decreased from 2014 to 2021, indicating a negative trend in vegetation cover over the study period.

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Section: Modis Datamentioning

confidence: 99%

A case study about the forest fire occurred on 05 July 2021 over Khenchela province, Algeria, using space-borne remote sensing

Guehaz,

Sivakumar

2023

Front. Remote Sens.

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Seasonal and Temporal Ensemble Models for Accurate Near-Surface Air Temperature Estimation

Jalbuena,

Yee

2024

Sensors

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The near-surface air temperature (NSAT) is crucial for understanding thermal and urban environments. Traditional estimation methods using general remote sensing images often focus on the types of spatial data or machine learning models used, neglecting the importance of seasonal and temporal variations, limiting their accuracy. This study introduces a novel ensemble model that incorporates both seasonal and temporal information integrated with satellite-derived land surface temperature (LST) data to enhance NSAT estimation, along with a rigorous feature importance analysis to identify the most impactful parameters. Data from 2022, collected from 147 South Korean weather stations, were used to develop and evaluate the models. Thirteen initial variables, including the LST and other auxiliary data, were considered. Random forest regression was employed to build separate models for each season. This novel approach of separating data by season allowed optimized feature selection tailored to each season, improving the model efficiency and capturing finer seasonal and daily temperature variations. These seasonal models were then combined to form an ensemble model. The seasonal models demonstrated varying accuracy, with the R2 values indicating a strong correlation between the predicted and actual NSAT, particularly high in spring and fall and lower in summer and winter. The ensemble model showed improved performance, achieving an MAE of 0.534, an RMSE of 0.391, an R2 of 0.996, and a cross-validated R2 of 0.968. These findings highlight the effectiveness of incorporating seasonal and temporal information into NSAT estimation models, offering significant improvements over traditional approaches. The developed models support precise temperature monitoring and forecasting, aiding environmental and urban management.

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Modeling MODIS LST at European scale

Cited by 2 publications

References 0 publications

A case study about the forest fire occurred on 05 July 2021 over Khenchela province, Algeria, using space-borne remote sensing

A case study about the forest fire occurred on 05 July 2021 over Khenchela province, Algeria, using space-borne remote sensing

Seasonal and Temporal Ensemble Models for Accurate Near-Surface Air Temperature Estimation

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