Coupled estimation of surface heat fluxes and vegetation dynamics from remotely sensed land surface temperature and fraction of photosynthetically active radiation

AbstractSurface heat fluxes are vital to hydrological and environmental studies, but mapping them accurately over a large area remains a problem. In this study, brightness temperature (TB) observations or soil moisture retrievals from the NASA Soil Moisture Active Passive (SMAP) mission and land surface temperature (LST) product from the Geostationary Operational Environmental Satellite (GOES) are assimilated together into a coupled water and heat transfer model to improve surface heat flux estimates. A particle filter is used to assimilate SMAP data, while a particle smoothing method is adopted to assimilate GOES LST time series, correcting for both systematic biases via parameter updating and for short-term error via state updating. One experiment assimilates SMAP TB at horizontal polarization and GOES LST, a second experiment assimilates SMAP TB at vertical polarization and GOES LST, and a third experiment assimilates SMAP soil moisture retrievals along with GOES LST. The aim is to examine if the assimilation of physically consistent TB and LST observations could yield improved surface heat flux estimates. It is demonstrated that all three assimilation experiments improved flux estimates compared to a no-assimilation case. Assimilating TB data tends to produce smaller bias in soil moisture estimates compared to assimilating soil moisture retrievals, but the estimates are influenced by the respective bias correction approaches. Despite the differences in soil moisture estimates, the flux estimates from different assimilation experiments are in general very similar.

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A Review of Reconstructing Remotely Sensed Land Surface Temperature under Cloudy Conditions

Remote Sensing ◽

10.3390/rs13142838 ◽

2021 ◽

Vol 13 (14) ◽

pp. 2838

Author(s):

Yaping Mo ◽

Yongming Xu ◽

Huijuan Chen ◽

Shanyou Zhu

Keyword(s):

Surface Temperature ◽

Land Surface Temperature ◽

Land Surface ◽

Spatial Scales ◽

Remotely Sensed ◽

Urban Heat Islands ◽

Reconstruction Algorithms ◽

Gap Filling ◽

Practical Applications ◽

Data Gaps

Land surface temperature (LST) is an important environmental parameter in climate change, urban heat islands, drought, public health, and other fields. Thermal infrared (TIR) remote sensing is the main method used to obtain LST information over large spatial scales. However, cloud cover results in many data gaps in remotely sensed LST datasets, greatly limiting their practical applications. Many studies have sought to fill these data gaps and reconstruct cloud-free LST datasets over the last few decades. This paper reviews the progress of LST reconstruction research. A bibliometric analysis is conducted to provide a brief overview of the papers published in this field. The existing reconstruction algorithms can be grouped into five categories: spatial gap-filling methods, temporal gap-filling methods, spatiotemporal gap-filling methods, multi-source fusion-based gap-filling methods, and surface energy balance-based gap-filling methods. The principles, advantages, and limitations of these methods are described and discussed. The applications of these methods are also outlined. In addition, the validation of filled LST values’ cloudy pixels is an important concern in LST reconstruction. The different validation methods applied for reconstructed LST datasets are also reviewed herein. Finally, prospects for future developments in LST reconstruction are provided.

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Trends of land surface heat fluxes on the Tibetan Plateau from 2001 to 2012

International Journal of Climatology ◽

10.1002/joc.5119 ◽

2017 ◽

Vol 37 (14) ◽

pp. 4757-4767 ◽

Cited By ~ 16

Author(s):

Cunbo Han ◽

Yaoming Ma ◽

Xuelong Chen ◽

Zhongbo Su

Keyword(s):

Tibetan Plateau ◽

Land Surface ◽

Surface Heat ◽

Heat Fluxes ◽

The Tibetan Plateau ◽

Surface Heat Fluxes ◽

Land Surface Heat Fluxes

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Temporal Variations and Associated Remotely Sensed Environmental Variables of Dengue Fever in Chitwan District, Nepal

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi7070275 ◽

2018 ◽

Vol 7 (7) ◽

pp. 275 ◽

Cited By ~ 1

Author(s):

Bipin Acharya ◽

Chunxiang Cao ◽

Min Xu ◽

Laxman Khanal ◽

Shahid Naeem ◽

...

Keyword(s):

Surface Temperature ◽

Dengue Fever ◽

Land Surface Temperature ◽

Land Surface ◽

Environmental Variables ◽

Vegetation Index ◽

Generalized Additive Model ◽

Additive Model ◽

Remotely Sensed ◽

Cross Correlation Analysis

Dengue fever is one of the leading public health problems of tropical and subtropical countries across the world. Transmission dynamics of dengue fever is largely affected by meteorological and environmental factors, and its temporal pattern generally peaks in hot-wet periods of the year. Despite this continuously growing problem, the temporal dynamics of dengue fever and associated potential environmental risk factors are not documented in Nepal. The aim of this study was to fill this research gap by utilizing epidemiological and earth observation data in Chitwan district, one of the frequent dengue outbreak areas of Nepal. We used laboratory confirmed monthly dengue cases as a dependent variable and a set of remotely sensed meteorological and environmental variables as explanatory factors to describe their temporal relationship. Descriptive statistics, cross correlation analysis, and the Poisson generalized additive model were used for this purpose. Results revealed that dengue fever is significantly associated with satellite estimated precipitation, normalized difference vegetation index (NDVI), and enhanced vegetation index (EVI) synchronously and with different lag periods. However, the associations were weak and insignificant with immediate daytime land surface temperature (dLST) and nighttime land surface temperature (nLST), but were significant after 4–5 months. Conclusively, the selected Poisson generalized additive model based on the precipitation, dLST, and NDVI explained the largest variation in monthly distribution of dengue fever with minimum Akaike’s Information Criterion (AIC) and maximum R-squared. The best fit model further significantly improved after including delayed effects in the model. The predicted cases were reasonably accurate based on the comparison of 10-fold cross validation and observed cases. The lagged association found in this study could be useful for the development of remote sensing-based early warning forecasts of dengue fever.

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