Land surface temperature from multiple geostationary satellites

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.

<strong class="journal-contentHeaderColor">Abstract.</strong> 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&ndash;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&ndash;7 km) but high temporal (30 minute) resolution, gap-free data from MSG, with the low temporal (2&ndash;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.

An improved atmospheric profile retrieval system for the current Geostationary Operational Environmental Satellite (GOES) Sounder data process has been developed. This algorithm can also be applied to process Advanced Baseline Imager (ABI) on the next generation GOES-R to continue the current GOES class Sounder legacy products. The Spinning Enhanced Visible and Infrared Imager (SEVIRI) data from the Meteosat Second Generation (MSG) satellite is employed as proxy to test and evaluate the algorithm for ABI legacy product. Since there is only a few sounding spectral bands in SEVIRI, a first guess from forecast is needed in legacy profile retrieval. The results show that if a set of temperature/humidity profiles from weather forecast is applied as first guess, the accuracy of temperature/humidity profiles can be achieved with that from the current GOES Sounder. Considering that there is only one temperaturesensitive spectral band in SEVIRI, temperature information is limited; however, the improvement on humidity profile retrieval over forecast is noticeable because there are two water vapor absorption spectral bands in SEVIRI. The results of total precitable water (TPW) and lift index (LI) from combined SEVIRI and forecast are presented as well.

[1] The time continuity of measurements from the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) on board the Meteosat Second Generation (MSG) Meteosat-8/9 and from the Advanced Baseline Imager (ABI) on board the next generation of Geostationary Operational Environmental Satellite (GOES-R) can be uniquely taken into account for infrared (IR) land surface emissivity (LSE) retrievals. The algorithm is based on the assumption that land surface temperature (LST) is temporally variable while the LSE is temporally invariable within a short period of time, i.e., a few hours. SEVIRI/ABI radiances from multiple time steps can be used to retrieve temporally invariable IR LSE and variable LST. The algorithm theoretical basis is described. Sensitivity studies with simulations show that (1) the algorithm is less sensitive to the first guesses of LST and the 8.7 μm LSE but quite sensitive to the first guesses of the 10.8 and 12 μm LSE, (2) the algorithm is weakly sensitive to the observational noise and radiative transfer calculation uncertainty (in the form of random noise), and (3) except for the 8.7 μm LSE and LST, the algorithm is weakly sensitive to the radiance biases from dust contamination but sensitive to the radiance biases in the 12 μm channel from the radiative transfer calculation. It is emphasized that the radiance biases from dust contamination are very difficult if not impossible to estimate due to the high temporal and spatial variations of the spatial distribution and optical properties of dust aerosol. It is also found that the algorithm is sensitive to the LST weighting functions rather than the sensor's local zenith angle; as long as the LST weighting functions are large enough, the retrieval precision is good.

Currently,application-oriented researches on the data of Medium Resolution Spectral Imager(MERSI),which is on board China's new generation polar orbit meteorological satellite FY-3,are very insufficient,due to the reason that the data as a new source have been delivered only since 2008.With the normal operation of FY-3 satellite system,it is necessary to develop an operational module for FY-3/MERSI regional land surface temperature(LST) retrieval and its post-processing,since LST is required for a wide variety of scientific studies but FY-3/MERSI's operational LST products have not yet been provided by National Satellite Meteorological Center(NSMC).Based on an analysis of FY-3/MERSI L1 data's HDF5 format and its channel characteristics,the authors selected the generalized single-channel algorithm developed by Jimenez-Mu n～oz Sobrino to directly realize the LST retrieval at 250 m spatial resolution with MATLAB programming and the thematic mapping of LST derivative products.This paper describes the parametric processes of LST retrieval algorithm in detail,which include radiometric calibration,cloud detection,estimation of two intermediate parameters-surface emissivity and atmospheric water vapor,and calculation of thermal indexes from LST.On these bases,an automatic flowchart for FY-3/MERSI LST retrieval and thematic mapping was established.Experimental results of this flowchart applied in Shanghai thermal environmental monitoring show that it can process FY-3/MERSI L1 data in a fast,real-time and automatic way,thus suitable for operational products producing and sharing,with the saving of human resources.It is also proved that FY-3/MERSI data and various forms of LST products can reveal the spatial pattern of Shanghai thermal field and the urban heat island effect more finely and intuitively.

Drought assessment is a complex undertaking, requiring monitoring of deficiencies in multiple components of the hydrologic budget. Precipitation anomalies reflect variability in water supply to the land surface, while soil moisture, groundwater and surface water anomalies reflect deficiencies in moisture storage. In contrast, evapotranspiration (ET) anomalies provide unique yet complementary information, reflecting variations in actual water use by crops – a useful diagnostic of vegetation health. Here we describe a remotely sensed Evaporative Stress Index (ESI) based on anomalies in actual-to-reference ET ratio. Actual ET is retrieved from thermal remote sensing data using a diagnostic soil-plant-atmosphere modeling system forced by measurements of morning land-surface temperature (LST) rise from geostationary satellites. In comparison with vegetation indices, LST is a relatively fast-response variable, with the potential for providing early warning of crop stress reflected in increasing canopy temperatures. Spatiotemporal patterns in ESI have been compared with patterns in the U.S. Drought Monitor and in standard precipitation-based indices, demonstrating reasonable agreement. However, because ESI does not use precipitation as an input, it provides an independent assessment of evolving drought conditions, and is more portable to data-sparse parts of the world lacking dense rain-gauge and Doppler radar networks. Integrating LST information from geostationary and polar orbiting systems through data fusion, the ESI has unique potential for sensing moisture stress at field scale, with potential benefits to yield estimation and loss compensation efforts. The ESI is routinely produced over the continental U.S. using data from the Geostationary Operational Environmental Satellites, with expansion to North and South America underway. In addition drought and ET monitoring applications are being developed over Africa and Europe using land-surface products from the Meteosat Second Generation (MSG) platform.

Near real-time estimation of Sea and Land surface temperature for MSG SEVIRI sensors

This work presents Land Surface Temperature (LST) retrieval from Landsat-8 data using the Generalized Split-Window (GSW) algorithm. First, radiative transfer modeling experiments were conducted using the moderate spectral resolution atmospheric transmittance algorithm and computer model (MODTRAN) 4.0 fed with SeeBor V5.0 atmospheric profile database to simulate the brightness temperatures in Thermal InfraRed Sensor (TIRS) bands 10 (centered at 10.9 μm) and 11 (centered at 12.0 μm) related to Land Surface Emissivities (LSEs) and Total Precipitable Water (TPW). Then, the unknown coefficients of the GSW algorithm were obtained through multi-variable regressions, in which the simulated data were grouped into several subranges to improve fitting accuracy. Next, LSTs were derived from the clear-sky TIRS/Landsat-8 data in 2015, where LSEs were estimated from Operational Land Imager (OLI) measurements using the Normalized Difference Vegetation Index (NDVI) Based Emissivity Method (NBEM), and TPWs were extracted from the European Centre for Medium-range Weather Forecasts (ECMWF) reanalysis data. Finally, the derived LSTs were validated with MOD11_L2 V5 product. The results show that the GSW algorithm developed in this work can accurately retrieve LST from the Landsat-8 data, and the relative error is 0.18±0.68 K against the MOD11_L2 V5 product.

Surface heat fluxes play a crucial role in the surface energy and water balance. In situ measurements are costly and difficult, and large‐scale flux mapping is hindered by surface heterogeneity. Previous studies have demonstrated that surface heat fluxes can be estimated by assimilating land surface temperature (LST) and soil moisture to determine two key parameters: a neutral bulk heat transfer coefficient (CHN) and an evaporative fraction (EF). Here a methodology is proposed to estimate surface heat fluxes by assimilating Soil Moisture Active Passive (SMAP) soil moisture data and Geostationary Operational Environmental Satellite (GOES) LST data into a dual‐source (DS) model using a hybrid particle assimilation strategy. SMAP soil moisture data are assimilated using a particle filter (PF), and GOES LST data are assimilated using an adaptive particle batch smoother (APBS) to account for the large gap in the spatial and temporal resolution. The methodology is implemented in an area in the U.S. Southern Great Plains. Assessment against in situ observations suggests that soil moisture and LST estimates are in better agreement with observations after assimilation. The RMSD for 30 min (daytime) flux estimates is reduced by 6.3% (8.7%) and 31.6% (37%) for H and LE on average. Comparison against a LST‐only and a soil moisture‐only assimilation case suggests that despite the coarse resolution, assimilating SMAP soil moisture data is not only beneficial but also crucial for successful and robust flux estimation, particularly when the uncertainties in the model estimates are large.

Land surface temperature (LST) is fundamental for monitoring surface energy balance and environmental dynamics, with remote sensing providing the primary means of acquisition. However, directional anisotropy (DA) introduces systematic bias in satellite-derived LST products, particularly over complex landscapes. This study examines the impact of angular effects on LST retrievals from three leading East Asian geostationary satellites (FengYun 4A, FengYun 4B, and Himawari 9) across Hunan Province, China, using integrated comparison with in situ measurements and reanalysis data. Results show that all products exhibit a systematic cold bias, with FY4B achieving the highest accuracy. Diurnal retrieval precision increases with higher solar zenith angles (SZA), while no consistent relationship is observed between viewing zenith angle (VZA) and retrieval accuracy. Notably, the retrieval bias of the FY4 series increases significantly when the sun and sensor are aligned in azimuth, particularly when the relative azimuth angle (RAA) is less than or equal to 30°. Parametric modeling reveals that emissivity kernel-induced anisotropy is the principal driver of significant LST deviations in central Hunan, while solar kernel effects result in LST overestimation in mountainous regions and underestimation in plains. Increases in elevation or vegetation density reduce emissivity-induced errors but amplify errors caused by shadowing and sunlit effects. Emissivity anisotropy is thus identified as the primary source of LST DA. These findings deepen the understanding of LST DA in remote sensing and provide essential guidance for refining retrieval algorithms and improving the applicability of LST products in complex terrains.

The presence of two thermal bands in Landsat 8 brings the opportunity to use either one or both of these bands to retrieve Land Surface Temperature (LST). In order to compare the performances of existing algorithms, we used four methods to retrieve LST from Landsat 8 and made an intercomparison among them. Apart from the direct use of the Radiative Transfer Equation (RTE), Single-Channel Algorithm and two Split-Window Algorithms were used taking an agricultural region in Bangladesh as the study area. The LSTs retrieved in the four methods were validated in two ways: first, an indirect validation against reference LST, which was obtained in the Atmospheric and Topographic CORection (ATCOR) software module; second, cross-validation with Terra MODerate Resolution Imaging Spectroradiometer (MODIS) daily LSTs that were obtained from the Application for Extracting and Exploring Analysis Ready Samples (AEEARS) online tool. Due to the absence of LST-monitoring radiosounding instruments surrounding the study area, in situ LSTs were not available; hence, validation of satellite retrieved LSTs against in situ LSTs was not performed. The atmospheric parameters necessary for the RTE-based method, as well as for other methods, were calculated from the National Centers for Environmental Prediction (NCEP) database using an online atmospheric correction calculator with MODerate resolution atmospheric TRANsmission (MODTRAN) codes. Root-mean-squared-error (RMSE) against reference LST, as well as mean bias error against both reference and MODIS daily LSTs, was used to interpret the relative accuracy of LST results. All four methods were found to result in acceptable LST products, leaving atmospheric water vapor content (w) as the important determinant for the precision result. Considering a set of several Landsat 8 images of different dates, Jiménez-Muñoz et al.’s (2014) Split-Window algorithm was found to result in the lowest mean RMSE of . Du et al.’s (2015) Split-Window algorithm resulted in mean RMSE of . The RTE-based direct method and the Single-Channel algorithm provided the mean RMSE of and , respectively. For Du et al.’s algorithm, the w range of to g cm−2 was considered, whereas for the other three methods, w values as retrieved from the NCEP database were considered for corresponding images. Land surface emissivity was retrieved through the Normalized Difference Vegetation Index (NDVI)-threshold method. This intercomparison study provides an LST retrieval methodology for Landsat 8 that involves four algorithms. It proves that (i) better LST results can be obtained using both thermal bands of Landsat 8; (ii) the NCEP database can be used to determine atmospheric parameters using the online calculator; (iii) MODIS daily LSTs from AEEARS can be used efficiently in cross-validation and intercomparison of Landsat 8 LST algorithms; and (iv) when in situ LST data are not available, the ATCOR-derived LSTs can be used for indirect verification and intercomparison of Landsat 8 LST algorithms.

Our objective is to develop a framework for deriving long term, consistent Land Surface Temperatures (LSTs) from Geostationary (GEO) satellites that is able to account for satellite sensor updates. Specifically, we use the Radiative Transfer for TOVS (RTTOV) model driven with Modern-Era Retrospective Analysis for Research and Applications (MERRA-2) information and Combined ASTER and MODIS Emissivity over Land (CAMEL) products. We discuss the results from our comparison of the Geostationary Operational Environmental Satellite East (GOES-E) with the MODIS Land Surface Temperature and Emissivity (MOD11) products, as well as several independent sources of ground observations, for daytime and nighttime independently. Based on a six-year record at instantaneous time scale (2004–2009), most LST estimates are within one std from the mean observed value and the bias is under 1% of the mean. It was also shown that at several ground sites, the diurnal cycle of LST, as averaged over six years, is consistent with a similar record generated from satellite observations. Since the evaluation of the GOES-E LST estimates occurred at every hour, day and night, the data are well suited to address outstanding issues related to the temporal variability of LST, specifically, the diurnal cycle and the amplitude of the diurnal cycle, which are not well represented in LST retrievals form Low Earth Orbit (LEO) satellites.

The land surface temperature (LST) drives many terrestrial biophysical processes and varies rapidly in space and time primarily due to the earth’s diurnal and annual cycles. Models of the diurnal and annual LST cycle retrieved from satellite data can be reduced to several gap-free parameters that represent the surface’s thermal characteristics and provide a generalized characterization of the LST temporal dynamics. In this letter, we use such an approach to map Europe’s annual and diurnal LST dynamics. In particular, we reduce a five-year time series (2009–2013) of diurnal LST from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) to 48 sets of half-hourly annual cycle parameters (ACPs), namely, the mean annual LST, the yearly amplitude of LST, and the LST phase shift from the spring equinox. The derived data provide a complete representation of how mainland Europe responds to the heating of the sun and the nighttime LST decay and reveal how Europe’s biogeographic regions differ in that respect. We further argue that the SEVIRI ACP can provide an observation-based spatially consistent background for studying and characterizing the thermal behavior of the surface and also a data set to support climate classification at a finer spatial resolution.

Surface solar radiation forecasts by advecting cloud physical properties derived from Meteosat Second Generation observations

Much achievements have been presented for retrieving land surface temperature(LST) from thermal infrared satellite sensor data.The accuracy of retrieval results can reach 1K.But the thermal infrared remote sensing is greatly influenced much by cloud,atmospheric water content and rainfall,which may cause many difficulties in LST retrieval studies.However,the passive microwave remote sensing can just overcome these disadvantages.Passive microwave emission can penetrate non-precipitating clouds,thereby providing a better representation of LST under nearly all sky conditions.Passive microwave remote sensing holds a unique advantage in retrieving LST.But passive microwave emission can also be influenced by certain surface factors.So more efforts are needed for the algorithms improvement of LST retrieval from passive microwave remote sensing data at present.This paper systematically reviews the empirical LST retrieval models,physical LST retrieval models and semi-empirical LST retrieval models from passive microwave data based on different passive microwave radiometers.The weakness and difficulties for LST inversion at the present study stage are analyzed,which may be very helpful to the future researches related to the kind of microware sensors.