Evaluation of MODIS land surface temperature products for daily air surface temperature estimation in northwest Vietnam

Phan, Thanh Noi; Kappas, Martin; Nguyen, Khac Thoi; Tran, Trong Phuong; Tran, Quoc Vinh; Emam, Ammar Rafiei

doi:10.1080/01431161.2019.1580789

Cited by 18 publications

(15 citation statements)

References 49 publications

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“…For the monitoring of LST change, this study used Landsat images, which have a higher spatial resolution than MODIS images, which was used in similar previous research on regions in Vietnam [70]. To ensure the LST value representativeness, our study used multiple Landsat scenes for each dry season (2 to 4 scenes) to obtain LST values instead of using only one scene for the whole season as it has been done in other studies [71].…”

Section: Discussionmentioning

confidence: 99%

Monitoring Land Surface Temperature Change with Landsat Images during Dry Seasons in Bac Binh, Vietnam

et al. 2020

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Global warming-induced climate change evolved to be one of the most important research topics in Earth System Sciences, where remote sensing-based methods have shown great potential for detecting spatial temperature changes. This study utilized a time series of Landsat images to investigate the Land Surface Temperature (LST) of dry seasons between 1989 and 2019 in the Bac Binh district, Binh Thuan province, Vietnam. Our study aims to monitor LST change, and its relationship to land-cover change during the last 30 years. The results for the study area show that the share of Green Vegetation coverage has decreased rapidly for the dry season in recent years. The area covered by vegetation shrank between 1989 and 2019 by 29.44%. Our findings show that the LST increase and decrease trend is clearly related to the change of the main land-cover classes, namely Bare Land and Green Vegetation. For the same period, we find an average increase of absolute mean LST of 0.03 °C per year for over thirty years across all land-cover classes. For the dry season in 2005, the LST was extraordinarily high and the area with a LST exceeding 40 °C covered 64.10% of the total area. We expect that methodological approach and the findings can be applied to study change in LST, land-cover, and can contribute to climate change monitoring and forecasting of impacts in comparable regions.

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Section: Discussionmentioning

confidence: 99%

Monitoring Land Surface Temperature Change with Landsat Images during Dry Seasons in Bac Binh, Vietnam

et al. 2020

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“…Water, in terms of precipitation minus evapotranspiration, varies with seasons, elevation, and distance from the ocean and inland water bodies.Since energy and water constraints vary at regional to global scales, there are notable advantages of Earth observations for quantifying these two major VGLFs spatially and temporally via satellite-derived biophysical indicators. In this regard, Earth-observed land surface temperature (LST) has been widely recognized as capable of mimicking near-surface air temperature [7][8][9]. Similarly, the normalized difference vegetation index (NDVI), calculated from spacecraft data, has been found to correlate with ground-measured precipitation at different spatial and temporal scales, and for various Earth observation systems [10][11][12].…”

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confidence: 99%

Spatial and Seasonal Patterns in Vegetation Growth-Limiting Factors over Europe

et al. 2019

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Water and energy are recognized as the most influential climatic vegetation growth-limiting factors. These factors are usually measured from ground meteorological stations. However, since both vary in space, time, and scale, they can be assessed by satellite-derived biophysical indicators. Energy, represented by land surface temperature (LST), is assumed to resemble air temperature; and water availability, related to precipitation, is represented by the normalized difference vegetation index (NDVI). It is hypothesized that positive correlations between LST and NDVI indicate energy-limited conditions, while negative correlations indicate water-limited conditions. The current project aimed to quantify the spatial and seasonal (spring and summer) distributions of LST-NDVI relations over Europe, using long-term (2000Europe, using long-term ( -2017 MODIS images. Overlaying the LST-NDVI relations on the European biome map revealed that relations between LST and NDVI were highly diverse among the various biomes and throughout the entire study period (March-August). During the spring season (March-May), 80% of the European domain, across all biomes, showed the dominance of significant positive relations. However, during the summer season (June-August), most of the biomes-except the northern ones-turned to negative correlation. This study demonstrates that the drought/vegetation/stress spectral indices, based on the prevalent hypothesis of an inverse LST-NDVI correlation, are spatially and temporally dependent. These negative correlations are not valid in regions where energy is the limiting factor (e.g., in the drier regions in the southern and eastern extents of the domain) or during specific periods of the year (e.g., the spring season). Consequently, it is essential to re-examine this assumption and restrict applications of such an approach only to areas and periods in which negative correlations are observed. Predicted climate change will lead to an increase in temperature in the coming decades (i.e., increased LST), as well as a complex pattern of precipitation changes (i.e., changes of NDVI). Thus shifts in plant species locations are expected to cause a redistribution of biomes. IntroductionIn terrestrial ecosystems, vegetation growth-limiting factors (VGLFs) are species-specific values attributed to a cause that constrains the biological response to appropriate environmental conditions [1]. The primary VGLFs are either climatic or terrestrial. The climatic constraints include water, energy, light, relative humidity, wind, and atmospheric elements (e.g., gases). Climatic variables directly and indirectly affect all levels of biodiversity, from the individual species through to population, community, ecosystem, and up to biome scales [2]. Terrestrial VGLFs include soil characteristics (e.g., texture, depth, and nutrients) and terrain properties (e.g., elevation, slope, and aspect). Different VGLFs should be in balance in order to allow optimal growth and development. When one or more of these factors exceed a low o...

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“…Hence, all the collected data (Tables 1 to 3), that might be relevant to the LST-retrieval calculations, were selected as the possible input parameters for the neural network training in the proposed MBR-LST model. To analyse the correlation between the output (viz.…”

Section: Methodsmentioning

confidence: 99%

“…Land surface temperature (LST) is of fundamental importance to many aspects in geographic and environmental sciences, such as surface energy, water balance, net radiation, budget at the Earth surface, as well as an important indicator of both the greenhouse effect and the physics of land-surface processes at local or global scales. 1–5 With the rapid development of the remote sensing technology and the successful launching of many relevant satellite sensors, various algorithms and methodologies to retrieve LST from space-based thermal infrared (TIR) data have been widely investigated in the past decades. 6,7…”

Section: Introductionmentioning

confidence: 99%

Land surface temperature retrieval from Landsat 8 OLI/TIRS images based on back-propagation neural network

Zhang

Hong

2019

Indoor and Built Environment

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Land surface temperature (LST) is an important parameter related to the environmental assessment concerning the surface energy, water balance, greenhouse effect, etc. at local and global scales. With the rapid development of the remote sensing technology, various methodologies have been developed to retrieve LST from space-based remote sensing images. Due to the ill-posed problem, the LST retrieval is still a challenge. In this research, a so-called multiple band reflectance (MBR)-LST model has been proposed based on the back-propagation neural (BPN) network, which can be employed to retrieve the LSTs from Landsat 8 Operational Land Imager (OLI)/TIRS images as well as produce continuous spatial LST distributions with a spatial resolution of 30 m. Experiments conducted in two randomly selected areas in mainland China proved that the proposed MBR-LST model has yielded a much better performance than the traditional radiative transfer equation (RTE) method with respect to both the accuracy and stability for the LST retrievals. Moreover, another significant advantage of the proposed MBR-LST is the generic nature – once trained by the sample data in the whole region of mainland China, the proposed MBR-LST model can be utilized for the accurate LST-retrievals in any area of mainland China.

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Evaluation of MODIS land surface temperature products for daily air surface temperature estimation in northwest Vietnam

Cited by 18 publications

References 49 publications

Monitoring Land Surface Temperature Change with Landsat Images during Dry Seasons in Bac Binh, Vietnam

Monitoring Land Surface Temperature Change with Landsat Images during Dry Seasons in Bac Binh, Vietnam

Spatial and Seasonal Patterns in Vegetation Growth-Limiting Factors over Europe

Land surface temperature retrieval from Landsat 8 OLI/TIRS images based on back-propagation neural network

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