New Scheme for Validating Remote-Sensing Land Surface Temperature Products with Station Observations

Yu, Wei; Ma, Ming; Li, Zhao-Liang; Tan, Junlei; Wu, Adan

doi:10.3390/rs9121210

Cited by 30 publications

(15 citation statements)

References 57 publications

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“…Nevertheless, because of the strong heterogeneity of land surface pattern, the accuracy of LST must be retrieved at 1K or better [7]. Thus, a large number of studies have focused on retrieval algorithms [7,12,13] and new validation schemes of LST [14][15][16]. Some studies indicated that location, land cover, vegetation fraction, and elevation had important influences on LST [17,18].…”

Section: Introductionmentioning

confidence: 99%

Global Land Surface Temperature Influenced by Vegetation Cover and PM2.5 from 2001 to 2016

Song

Qiu

et al. 2018

Remote Sensing

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Land surface temperature (LST) is an important parameter to evaluate environmental changes. In this paper, time series analysis was conducted to estimate the interannual variations in global LST from 2001 to 2016 based on moderate resolution imaging spectroradiometer (MODIS) LST, and normalized difference vegetation index (NDVI) products and fine particulate matter (PM2.5) data from the Atmospheric Composition Analysis Group. The results showed that LST, seasonally integrated normalized difference vegetation index (SINDVI), and PM2.5 increased by 0.17 K, 0.04, and 1.02 μg/m3 in the period of 2001–2016, respectively. During the past 16 years, LST showed an increasing trend in most areas, with two peaks of 1.58 K and 1.85 K at 72°N and 48°S, respectively. Marked warming also appeared in the Arctic. On the contrary, remarkable decrease in LST occurred in Antarctic. In most parts of the world, LST was affected by the variation in vegetation cover and air pollutant, which can be detected by the satellite. In the Northern Hemisphere, positive relations between SINDVI and LST were found; however, in the Southern Hemisphere, negative correlations were detected. The impact of PM2.5 on LST was more complex. On the whole, LST increased with a small increase in PM2.5 concentrations but decreased with a marked increase in PM2.5. The study provides insights on the complex relationship between vegetation cover, air pollution, and land surface temperature.

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Section: Introductionmentioning

confidence: 99%

Global Land Surface Temperature Influenced by Vegetation Cover and PM2.5 from 2001 to 2016

Song

Qiu

et al. 2018

Remote Sensing

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“…LST products were validated with field campaigns and radiance-based validation studies and errors are within ± 0.6°C in most cases in the global (Wan 2014). Besides, the mean bias of the MODIS LST product was less than 1°C in Northwest China (Li et al 2014;Yu et al 2017;Duan et al 2018). The MODIS LST product has applicability in comparison of LST over different LCTs in our study area.…”

Section: Data Collection and Methodologymentioning

confidence: 69%

Understanding the attributes of the dual oasis effect in an arid region using remote sensing and observational data

Bie

Xie

Wang

et al. 2019

Ecosyst Health Sustain

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Investigating the attributes of the dual oasis effect and oasis-desert interactions is crucial for understanding the climatic and ecological effects of oases. In this study, oasis effect intensity (OEI) was used as an indicator to investigate the factors that contribute to the dual oasis effect. The oasis effect has two attributes: the surface oasis heating effect (OHE) occurred in winter, while the oasis cooling effect (OCE) occurred in the other three seasons, especially in summer. During the day, the OEI of the whole oasis was −12.8°C, −5.2°C, and −4.5°C in summer, spring, and autumn, respectively, which indicated a strong OCE; and OEI was 0.5°C in winter, which indicated a weaker OHE. In arid regions, the OCE dominated most of the time, but the OHE occurred in winter and cannot be ignored. In addition, through statistical analysis and energy balance analysis, high evapotranspiration of the oasis was the main factor leading to the occurrence of OCE in the summer, while low albedo to the occurrence of OHE in winter.

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“…The study area covers 3285 km 2 and ranges in elevation from 2538 to 4615 m. As a climatic and geographical transition zone, the study area provides an ideal testing ground with complex land surface characteristics for verifying the rationality of the proposed LST fusion framework. Many scholars have verified the applicability of satellite-derived LSTs in this region, and the results showed that root mean square error (RMSE) was generally within 5K [ 42 , 58 , 59 , 60 ]. Besides, a large number of aeronautical experiments have been carried out in the HRB [ 61 ], and some ground observations can be provided to validate the fusion result and analyze the uncertainty.…”

Section: Methodsmentioning

confidence: 99%

A Data Fusion Modeling Framework for Retrieval of Land Surface Temperature from Landsat-8 and MODIS Data

Zhao

Zhang

Tan

et al. 2020

Sensors

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Land surface temperature (LST) is a critical state variable of land surface energy equilibrium and a key indicator of environmental change such as climate change, urban heat island, and freezing-thawing hazard. The high spatial and temporal resolution datasets are urgently needed for a variety of environmental change studies, especially in remote areas with few LST observation stations. MODIS and Landsat satellites have complementary characteristics in terms of spatial and temporal resolution for LST retrieval. To make full use of their respective advantages, this paper developed a pixel-based multi-spatial resolution adaptive fusion modeling framework (called pMSRAFM). As an instance of this framework, the data fusion model for joint retrieval of LST from Landsat-8 and MODIS data was implemented to generate the synthetic LST with Landsat-like spatial resolution and MODIS temporal information. The performance of pMSRAFM was tested and validated in the Heihe River Basin located in China. The results of six experiments showed that the fused LST was high similarity to the direct Landsat-derived LST with structural similarity index (SSIM) of 0.83 and the index of agreement (d) of 0.84. The range of SSIM was 0.65–0.88, the root mean square error (RMSE) yielded a range of 1.6–3.4 °C, and the averaged bias was 0.6 °C. Furthermore, the temporal information of MODIS LST was retained and optimized in the synthetic LST. The RMSE ranged from 0.7 °C to 1.5 °C with an average value of 1.1 °C. When compared with in situ LST observations, the mean absolute error and bias were reduced after fusion with the mean absolute bias of 1.3 °C. The validation results that fused LST possesses the spatial pattern of Landsat-derived LSTs and inherits most of the temporal properties of MODIS LSTs at the same time, so it can provide more accurate and credible information. Consequently, pMSRAFM can be served as a promising and practical fusion framework to prepare a high-quality LST spatiotemporal dataset for various applications in environment studies.

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New Scheme for Validating Remote-Sensing Land Surface Temperature Products with Station Observations

Cited by 30 publications

References 57 publications

Global Land Surface Temperature Influenced by Vegetation Cover and PM2.5 from 2001 to 2016

Global Land Surface Temperature Influenced by Vegetation Cover and PM2.5 from 2001 to 2016

Understanding the attributes of the dual oasis effect in an arid region using remote sensing and observational data

A Data Fusion Modeling Framework for Retrieval of Land Surface Temperature from Landsat-8 and MODIS Data

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