An All-Weather Land Surface Temperature Product Based on MSG/SEVIRI Observations

Martins, João Paulo; Trigo, Isabel F.; Ghilain, Nicolas; Jiménez, Carlos; Göttsche, Frank-M.; Ermida, Sofia L.; Olesen, F.; Gellens-Meulenberghs, Françoise; Arboleda, Alirio

doi:10.3390/rs11243044

Cited by 74 publications

(46 citation statements)

References 97 publications

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“…Such an approach, however, would entail disadvantages related to the dependence of LST observations on cloudiness; it would then produce many gaps in the sub-daily ET estimates. The iterative estimation of T sk in an energy balance setting has proven effective to generate estimates equivalent to RS LST [21].…”

Section: The Et Algorithmmentioning

confidence: 99%

“…The ET model in this study was based on the algorithm driving the operational ET product developed in the framework of the Land Surface Analysis Satellite Application Facility (LSA-SAF) [14][15][16]. The LSA-SAF ET algorithm has been in operation for about 10 years now and has proven its robustness in a number of applications [6,[17][18][19][20] and the ability to operate in all-weather conditions [21].…”

Section: Introductionmentioning

confidence: 99%

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Continuous Daily Evapotranspiration with Optical Spaceborne Observations at Sub-Kilometre Spatial Resolution

et al. 2020

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Evapotranspiration (ET) is a key parameter in the description of the energy and water fluxes over land. Continuous and spatially detailed ET simulations are thus required for a number of scientific and management-related purposes. These conditions are determined by the modelling approach and the composition of the forcing dataset. This study aimed at simulating daily ET in a diversity of climate and land cover conditions at a spatial resolution of ∼1 km and higher. The modelling approach was based on the algorithm driving the ET product developed and set in operations in the framework of the Satellite Application Facility on Land Surface Analysis programme (LSA-SAF). The implemented algorithm allowed the ingestion of biophysical parameters derived from SPOT-V and PROBA-V observations developed by the Copernicus Global Land Programme, as well as other model parameters at a similar spatial resolution. The model was tested at an ∼1 km spatial resolution in over 40 sites located in different climate and land cover contexts. The implementation at ∼300 m was tested in the upper Biebrza basin, in Poland. The simulations correlated well with the validation dataset (r2 > 0.75 in 80% of sites) and exhibited root mean squared values lower than 1 mm/day in 80% of the cases. The results also pointed to the need for refining the accuracy of soil moisture data sources, especially in dry areas. The results showed the ability of the modelling approach and the SPOT-V/PROBA-V missions to support the generation of long ET time series. They also opened the gate to incorporate Sentinel-3 in ET continuous modelling.

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Section: The Et Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Continuous Daily Evapotranspiration with Optical Spaceborne Observations at Sub-Kilometre Spatial Resolution

et al. 2020

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“…However, IR cannot penetrate clouds, and consequently, T estimation from IR observations suffers from a clear-sky bias [6]. To overcome this limitation, some products use T derived from a land-surface energy balance model to fill the cloud gaps in the IR-derived T estimates (e.g., [7,8]). Alternative methods to provide 'all-weather' landsurface temperature rely on sensor fusion method [9][10][11], using a combination of model or IR measurements.…”

Section: Introductionmentioning

confidence: 99%

A Method to Downscale Satellite Microwave Land-Surface Temperature

2021

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High-spatial-resolution land-surface temperature is required for several applications such as hydrological or climate studies. Global estimates of surface temperature are available from sensors observing in the infrared (IR), but without ‘all-weather’ observing capability. Passive microwave (MW) instruments can also be used to provide surface-temperature measurements but suffer from coarser spatial resolutions. To increase their resolution, a downscaling methodology applicable over different land environments and at any time of the day is proposed. The method uses a statistical relationship between clear sky-predicting variables and clear-sky temperatures to estimate temperature patterns that can be used in conjunction with coarse measurements to create high-resolution products. Different predicting variables are tested showing the need to use IR-derived information on vegetation, temperature diurnal evolution, and a temporal information. To build a true ‘all-weather’ methodology, the effect of clouds on surface temperatures is accounted for by correcting the clear-sky diurnal cycle amplitude, using cloud parameters from meteorological reanalysis. Testing the method on a coarse IR synthetic data at ∼25 km resolution yields a Root Mean Square Deviations (RMSD) between the ∼5 km high-resolution and downscaled temperatures smaller than 1 ∘C. When applied to observations by the Special Sensor Microwave Imager Sounder (SSMIS) at ∼25 km resolution, the downscaling to ∼5 km yields a smaller RMSD compared to IR observations. These results demonstrate the relevance of the methodology to downscale MW land-surface temperature and its potential to spatially enhanced the current ‘all-weather’ satellite monitoring of surface temperatures.

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“…On average, 60% of the land surface is covered by clouds, which limits the practical application of TIR LSTs [9][10][11]. An alternative method is to use satellite passive microwave (MW) measurements to estimate LSTs.…”

Section: Introductionmentioning

confidence: 99%

Estimating Land Surface Temperature from Satellite Passive Microwave Observations with the Traditional Neural Network, Deep Belief Network, and Convolutional Neural Network

et al. 2020

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Neural networks, especially the latest deep learning, have exhibited good ability in estimating surface parameters from satellite remote sensing. However, thorough examinations of neural networks in the estimation of land surface temperature (LST) from satellite passive microwave (MW) observations are still lacking. Here, we examined the performances of the traditional neural network (NN), deep belief network (DBN), and convolutional neural network (CNN) in estimating LST from the AMSR-E and AMSR2 data over the Chinese landmass. The examinations were based on the same training set, validation set, and test set extracted from 2003, 2004, and 2009, respectively, for AMSR-E with a spatial resolution of 0.25°. For AMSR2, the three sets were extracted from 2013, 2014, and 2016 with a spatial resolution of 0.1°, respectively. MODIS LST played the role of “ground truth” in the training, validation, and testing. The examination results show that CNN is better than NN and DBN by 0.1–0.4 K. Different combinations of input parameters were examined to get the best combinations for the daytime and nighttime conditions. The best combinations are the brightness temperatures (BTs), NDVI, air temperature, and day of the year (DOY) for the daytime and BTs and air temperature for the nighttime. By adding three and one easily obtained parameters on the basis of BTs, the accuracies of LST estimates can be improved by 0.8 K and 0.3 K for the daytime and nighttime conditions, respectively. Compared with the MODIS LST, the CNN LST estimates yielded root-mean-square differences (RMSDs) of 2.19–3.58 K for the daytime and 1.43–2.14 K for the nighttime for diverse land cover types for AMSR-E. Validation against the in-situ LSTs showed that the CNN LSTs yielded root-mean-square errors of 2.10–4.72 K for forest and cropland sites. Further intercomparison indicated that ~50% of the CNN LSTs were closer to the MODIS LSTs than ESA’s GlobTemperature AMSR-E LSTs, and the average RMSDs of the CNN LSTs were less than 3 K over dense vegetation compared to NASA’s global land parameter data record air temperatures. This study helps better the understanding of the use of neural networks for estimating LST from satellite MW observations.

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An All-Weather Land Surface Temperature Product Based on MSG/SEVIRI Observations

Cited by 74 publications

References 97 publications

Continuous Daily Evapotranspiration with Optical Spaceborne Observations at Sub-Kilometre Spatial Resolution

Continuous Daily Evapotranspiration with Optical Spaceborne Observations at Sub-Kilometre Spatial Resolution

A Method to Downscale Satellite Microwave Land-Surface Temperature

Estimating Land Surface Temperature from Satellite Passive Microwave Observations with the Traditional Neural Network, Deep Belief Network, and Convolutional Neural Network

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