Continental Scale Monitoring of Subdaily and Daily Evapotranspiration Enhanced by the Assimilation of Surface Soil Moisture Derived from Thermal Infrared Geostationary Data

Ghilain, Nicolas

doi:10.1016/b978-0-12-803388-3.00016-4

Cited by 6 publications

(6 citation statements)

References 53 publications

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“…This result is consistent with the findings of past studies [51] that the amplitude of the land surface temperature diurnal cycle was related to soil moisture in regions controlled by thermal inertia, not in transpiration-driven regions [52]. The validation study presented in Ghilain et al [53] exhibits the same conclusions: results from the comparison show an overall good performance over semiarid regions and degradation toward more wet and vegetated areas. Proceeding from a diurnal cycle [37] to long-term seasonal consideration of correlation between soil moisture and land surface temperature, the results of our study confirm this effect.…”

Section: Spatial Variability Of Correlation Between Smai and Lst Anomsupporting

confidence: 90%

Spatial-Temporal Variability of Land Surface Dry Anomalies in Climatic Aspect: Biogeophysical Insight by Meteosat Observations and SVAT Modeling

et al. 2019

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The spatial-temporal variability of drought occurrence over Bulgaria is characterized based on long-term records (2007–2018) of Meteosat information and the SVAT model-derived soil moisture availability index (referred to root zone depth, SMAI). Land surface temperature according to the satellite-derived Land Surface Analysis Satellite Application Facility Land Surface Temperature (LSASAF LST) product and SMAI were used to designate land surface state dry anomalies. The utility of LST for drought assessment is tested by statistical comparative analyses, applying two approaches, site-scale quantitative comparison, and evaluation of spatial-temporal consistency between SMAI and LST variability. Pearson correlation and regression modeling techniques were applied. The main results indicate for a synchronized behavior between SMAI and LST during dry spells, as follows: opposite mean seasonal course (March–October); high to strong negative monthly correlation for different microclimate regimes. Negative linear regressions between the anomalies of SMAI and LST (monthly mean), with a strong correlation in their spatial-temporal variability. Qualitative evaluation of spatial-temporal variability dynamics is analyzed using color maps. Drought-prone areas were identified on the bases of LST maps (monthly mean), and it is illustrated they are more vulnerable to vegetation burning as detected by the Meteosat FRP-PIXEL product. The current study provides an advanced framework for using LST retrievals based on IR satellite observations from the geostationary MSG satellite as an alternative tool to SMAI, whose calculation requires the input of many parameters that are not always available.

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Section: Spatial Variability Of Correlation Between Smai and Lst Anomsupporting

confidence: 90%

Spatial-Temporal Variability of Land Surface Dry Anomalies in Climatic Aspect: Biogeophysical Insight by Meteosat Observations and SVAT Modeling

et al. 2019

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“…This result is consistent with the findings of past studies [74] that the amplitude of the land surface temperature diurnal cycle was related to soil moisture in regions controlled by thermal inertia, not in transpiration-driven regions [75]. The validation study presented in Ghilain et al [76] exhibits the same conclusions: results from the comparison show an overall good performance over semiarid regions and degradation toward more wet and vegetated areas. Proceeding from a diurnal cycle [59] to longterm seasonal consideration of correlation between soil moisture and land surface temperature, the results of our study confirm this effect.…”

Section: Spatial Variability Of Correlation Between Smai and Lst Anomaliessupporting

confidence: 90%

Dry Anomalies in Land-Atmosphere Interactions: Land Surface Temperature from Geostationary Satellites and Soil Moisture Availability from SVAT Modeling as Biogeophysical Indexes

Stoyanova¹,

Georgiev²,

Neytchev³

et al. 2021

Earth and Its Atmosphere

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“…The LSAF all-sky LST product based on the SEVIRI instrument onboard the geostationary Meteosat Second Generation (MSG, Martins et al (2019)) is a combination of the clear-sky MSG level 2 product, MSLT (LSA-001), based on a Generalised Split-Window (GSW) algorithm (Trigo et al, 2008a), and output from an energy balance algorithm which is also used for the estimation of MSG 30-minute evaporation (MET-v2, LSA-311) dataset (Ghilain, 2016). The energy balance algorithm incorporates other LSAF SEVIRI-based products such as shortwave and longwave radiation fluxes, land surface albedo or vegetation, soil moisture based on the assimilation of scatterometer observations provided by the Hydrology SAF (H-SAF), and near surface meteorological information obtained from the European Centre for Medium-Range Weather Forecasts (ECMWF) operational forecasts (Ghilain et al, 2020).…”

Section: Lstmentioning

confidence: 99%

High-resolution all-sky land surface temperature and net radiation over Europe

Rains

Trigo

Dutra

et al. 2022

Preprint

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Abstract. Land Surface Temperature (LST) and Surface Net Radiation (SNR) are vital inputs for many land surface and hydrological models. However, current remote sensing datasets of these variables come mostly at coarse resolutions. Although high-resolution LST and SNR retrievals are available, they have large gaps due to cloud-cover that hinder their use as input in models. Here, we present a downscaled and continuous daily LST and SNR product across Europe for 2018–2019. The LST product is based on all-sky LST retrievals from the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) onboard the geostationary Meteosat Second Generation (MSG) satellite, and clear-sky LST retrievals from the Sea and Land Surface Temperature Radiometer (SLSTR) onboard the polar-orbiting Sentinel 3 satellites. The product combines the medium spatial (approx. 5–7 km) but high temporal (30 minute) resolution, gap-free data from MSG, with the low temporal (2–3 days) but high spatial (1 km) resolution of the Sentinel 3 LST retrievals. The resulting 1 km and daily LST dataset is based on an hourly merging of both datasets through bias-correction and Kalman Filter assimilation. Longwave outgoing radiation is computed from the merged LST product in combination with MSG-based emissivity data. Shortwave outgoing radiation is computed from the incoming shortwave radiation from MSG and downscaled albedos using 1 km PROBA-V data. MSG incoming shortwave and longwave radiation and the outgoing radiation components at 1 km spatial resolution are used together to compute the final daily SNR dataset in a consistent manner. Validation results indicate an improvement of the root mean squared error by ca. 8 % with a substantial increase in spatial detail compared to the original MSG product. The resulting pan-European LST and SNR dataset can be used for hydrological modelling and as input to models dedicated to estimating evaporation and surface turbulent heat fluxes and will be regularly updated in the future.

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Continental Scale Monitoring of Subdaily and Daily Evapotranspiration Enhanced by the Assimilation of Surface Soil Moisture Derived from Thermal Infrared Geostationary Data

Cited by 6 publications

References 53 publications

Spatial-Temporal Variability of Land Surface Dry Anomalies in Climatic Aspect: Biogeophysical Insight by Meteosat Observations and SVAT Modeling

Spatial-Temporal Variability of Land Surface Dry Anomalies in Climatic Aspect: Biogeophysical Insight by Meteosat Observations and SVAT Modeling

Dry Anomalies in Land-Atmosphere Interactions: Land Surface Temperature from Geostationary Satellites and Soil Moisture Availability from SVAT Modeling as Biogeophysical Indexes

High-resolution all-sky land surface temperature and net radiation over Europe

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