Global spatiotemporally continuous MODIS land surface temperature dataset

Yu, Pei; Zhao, Tianjie; Shi, Jiancheng; Ran, Youhua; Li, Jia; Ji, Dabin; Xue, Huazhu

doi:10.1038/s41597-022-01214-8

Cited by 62 publications

(20 citation statements)

References 59 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We employed the clear-sky days from the MOD13C2 Version 6.1 product to obtain a cloudiness measure (the proportion of cloudy to clear-sky days) (https://lpdaac.usgs.gov/products/mod13c2v061/) (Urban et al, 2012). The temperature was also acquired from MOD13C2 (Yu et al, 2022). Hydrometeorological data.…”

Section: Datasetsmentioning

confidence: 99%

Increasing sensitivity of the terrestrial ecosystem to climate change in China

Wei¹,

Zhang³

et al. 2023

Preprint

View full text Add to dashboard Cite

Changes in heat and moisture significantly co-alter ecosystem functioning. However, knowledge on the dynamics of ecosystem responses to climate change is limited. Here, we quantify long-term ecosystem sensitivity (ES) based on weighted ratios of vegetation productivity variability and multiple climate variables from satellite observations, greater values of which indicate more yields per hydrothermal condition change. Our results show that ES exhibits large spatial variability and increases with the aridity index. A positive temporal trend of ES is found in 61.28% of the study area from 2001 to 2021, which is largely attributed to a declining vapor pressure deficit and constrained by solar radiation. Moreover, carbon dioxide plays a dual role; in moderation it promotes the fertilization effect, whereas in excess, it suppresses vegetation growth by triggering droughts. Our findings highlight that moisture stress between land and atmosphere is one of the key prerequisites for ecosystem stability, offsetting part of the negative effects of heat.

show abstract

Section: Datasetsmentioning

confidence: 99%

Increasing sensitivity of the terrestrial ecosystem to climate change in China

Wei¹,

Zhang³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Various all-weather LST datasets using the aforementioned three typical methods have been released in recent years (Duan et al, 2017;Hong et al, 2022b;Jia et al, 2022b;B. Li et al, 2021;Metz et al, 2017b;Muñoz-Sabater et al, 2021;Yao et al, 2023;Yu et al, 2022). However, all-weather LST datasets with both high temporal resolution (four observations per day or higher) and high spatial resolution (1 km or higher) since 2000 for China's landmass and the surrounding areas are still lacking.…”

Section: Introductionmentioning

confidence: 99%

TRIMS LST: a daily 1 km all-weather land surface temperature dataset for China's landmass and surrounding areas (2000–2022)

Tang,

Zhou,

et al. 2024

Earth Syst. Sci. Data

View full text Add to dashboard Cite

Abstract. Land surface temperature (LST) is a key variable within Earth's climate system and a necessary input parameter required by numerous land–atmosphere models. It can be directly retrieved from satellite thermal infrared (TIR) observations, which contain many invalid pixels mainly caused by cloud contamination. To investigate the spatial and temporal variations in LST in China, long-term, high-quality, and spatiotemporally continuous LST datasets (i.e., all-weather LST) are urgently needed. Fusing satellite TIR LST and reanalysis datasets is a viable route to obtain long time-series all-weather LSTs. Among satellite TIR LSTs, the MODIS LST is the most commonly used, and a few corresponding all-weather LST products have been reported recently. However, the publicly reported all-weather LSTs were not available during the temporal gaps of MODIS between 2000 and 2002. In this study, we generated a daily (four observations per day) 1 km all-weather LST dataset for China's landmass and surrounding areas, the Thermal and Reanalysis Integrating Moderate-resolution Spatial-seamless (TRIMS) LST, which begins on the first day of the new millennium (1 January 2000). We used the enhanced reanalysis and thermal infrared remote sensing merging (E-RTM) method to generate the TRIMS LST dataset with the temporal gaps being filled, which had not been achieved by the original RTM method. Specifically, we developed two novel approaches, i.e., the random-forest-based spatiotemporal merging (RFSTM) approach and the time-sequential LST-based reconstruction (TSETR) approach, respectively, to produce Terra/MODIS-based and Aqua/MODIS-based TRIMS LSTs during the temporal gaps. We also conducted a thorough evaluation of the TRIMS LST. A comparison with the Global Land Data Assimilation System (GLDAS) and ERA5-Land LST demonstrates that the TRIMS LST has similar spatial patterns but a higher image quality, more spatial details, and no evident spatial discontinuities. The results outside the temporal gap show consistent comparisons of the TRIMS LST with the MODIS LST and the Advanced Along-Track Scanning Radiometer (AATSR) LST, with a mean bias deviation (MBD) of 0.09/0.37 K and a standard deviation of bias (SD) of 1.45/1.55 K. Validation based on the in situ LST at 19 ground sites indicates that the TRIMS LST has a mean bias error (MBE) ranging from −2.26 to 1.73 K and a root mean square error (RMSE) ranging from 0.80 to 3.68 K. There is no significant difference between the clear-sky and cloudy conditions. For the temporal gap, it is observed that RFSTM and TSETR perform similarly to the original RTM method. Additionally, the differences between Aqua and Terra remain stable throughout the temporal gap. The TRIMS LST has already been used by scientific communities in various applications such as soil moisture downscaling, evapotranspiration estimation, and urban heat island modeling. The TRIMS LST is freely and conveniently available at https://doi.org/10.11888/Meteoro.tpdc.271252 (Zhou et al., 2021).

show abstract

“…In light of the limitations associated with clear‐sky LST observations, many research efforts have been dedicated to developing algorithms for reconstructing all‐sky (or, in other words, all‐weather) LST data sets (Mo et al., 2021). Existing studies have facilitated the production of all‐sky LST products across a variety of spatial scales—from local to national to global (Yao et al., 2023; Yu et al., 2022; X. Zhang et al., 2021).…”

Section: Introductionmentioning

confidence: 99%

Satellite Clear‐Sky Observations Overestimate Surface Urban Heat Islands in Humid Cities

Yang,

Xu,

Wen

et al. 2024

Geophysical Research Letters

View full text Add to dashboard Cite

Satellite‐based thermal infrared (TIR) land surface temperature (LST) is hindered by cloud cover and is applicable solely under clear‐sky conditions for estimating surface urban heat island intensity (SUHII). Clear‐sky SUHII may not accurately represent all‐sky conditions, potentially introducing quantitative biases in assessing urban heat islands. However, the differences between clear‐sky and all‐sky SUHIIs and their spatiotemporal variations are still poorly understood. Our analysis of over 600 global cities demonstrates that clear‐sky SUHII is mostly higher than all‐sky SUHII, particularly in summer, daytime, and precipitation‐rich regions. Besides, clear‐sky SUHII typically exhibits stronger seasonal and diurnal contrasts than all‐sky SUHII, especially for cities located in humid regions. These discrepancies can be attributed mainly to the increased missing LST data caused by cloud enhancement in urban areas. Our findings highlight the tendency for clear‐sky observations to overestimate SUHII, providing valuable insights for standardizing the quantification of surface urban heat islands.

show abstract

Global spatiotemporally continuous MODIS land surface temperature dataset

Cited by 62 publications

References 59 publications

Increasing sensitivity of the terrestrial ecosystem to climate change in China

Increasing sensitivity of the terrestrial ecosystem to climate change in China

TRIMS LST: a daily 1 km all-weather land surface temperature dataset for China's landmass and surrounding areas (2000–2022)

Satellite Clear‐Sky Observations Overestimate Surface Urban Heat Islands in Humid Cities

Contact Info

Product

Resources

About