the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
High-resolution all-sky land surface temperature and net radiation over Europe
Isabel Trigo
Emanuel Dutra
Sofia Ermida
Darren Ghent
Petra Hulsman
Jose Gómez-Dans
Diego Gonzales Miralles
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.
Dominik Rains et al.
Status: final response (author comments only)
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RC1: 'Comment on essd-2022-302', Anonymous Referee #1, 23 Oct 2022
This manuscript introduced a downscaled and continuous daily LST and SNR product across Europe for 2018–2019. The validations of radiation against BSRN in-situ measured are also presented in the paper. And it is said that 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 paper indicates that 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. The LST and SNR product is important to describe Earth surface energy balance. Overall, this manuscript is clear. And the study is of great significance to improve the new understanding of energy balance in Europe. However, there are several issues that need to be taken care of before this paper becomes acceptable for publication.
- the high resolution LST product is merged from LEAF (all sky) and Sentinel 3 LST (clear sky). The two LSTs have different spatial and temporal resolutions. While doing the merging, if any cloud effect is considered? If any cloud product is involved? If yes, please indicated it.
- while downscaling the LST product, if any edge effects (coast lines, cloud edges) are considered?
- Line 220, it is said “Extensive validation of the LSAF and Sentinel 3 LST products has already been performed (see below). Both have an average accuracy below 1.5 K, although it varies across space and time. Our goal is to combine their individual strengths in terms of spatial and temporal resolution to obtain an enhanced representation of landscape heterogeneity”. Although there are extensive validations of the LSAF and Sentinel 3 LST products, the validations are based on different spatial and temporal resolutions. It does not mean that the merged product could also has a good performance. It is good to give the statistics.
- The paper is lack of statistics. e.g. figure 1, any overall statistics could be summarized in a table? And the absolute RMSEs are given in Figure 1. The percentage-wise is worth known. And so does the validations of outgoing raditaions and SNR. Please summarize the overall statistics (R, bias, RMSE (including percentages)), degree of freedom) in tables.
- More detailed information of in-situ sites could be given or summarized.
- Figure 2, 3, 4 and A1, A2, A3 could also give the bar chart distribution.
- Please explain the reasons for case selections. e.g. 30 June 2018 in Figure 4 and 30 Sep 2018 in Figure A2.
- If the LST and SNR products are compared with any other reanalysis or satellite products?
Citation: https://doi.org/10.5194/essd-2022-302-RC1 -
AC1: 'Reply on RC1', Dominik Rains, 25 Jan 2023
The comment was uploaded in the form of a supplement: https://essd.copernicus.org/preprints/essd-2022-302/essd-2022-302-AC1-supplement.pdf
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RC2: 'Comment on essd-2022-302', Anonymous Referee #2, 31 Oct 2022
The manuscript “High-resolution all-sky land surface temperature and net radiation over Europe” has been reviewed. The authors presented a methodology to combine the advantages of geostationary observations at high temporal resolution with observations from polar-orbiting satellites at high spatial resolution, resulting in a gap-free all-sky LST and net radiation dataset at 1-km spatial resolution and daily frequencies for 2018-2019 across Europe. This dataset is important for hydrological modelling and as input to models dedicated to estimating evaporation and surface turbulent heat fluxes. However, more comprehensive analysis on this dataset is required before further consideration.
- Lines 223-225. As the dataset includes all-sky land surface temperature, I think it is necessary to implement accuracy assessment to tell us the uncertainties of the produced LST data.
- A discussion section is required to explain the results and to compare against existing datasets. For example, lines 217-218, why there are worse accuracy in Belgium for SWin and around the Alps for LWin?
- Pearson’s correlation coefficient and RMSE are not enough for validation. Examples of comparison of temporal patterns between estimated values and in-situ observations at typical stations are suggested. Meanwhile, the impact factors on the estimated variables can also be analyzed. For example, how does the RMSE change across seasons? Do land cover types significantly affect the accuracy of estimated variables? How about the accuracies in areas with and without missing satellite observations?
- Lines 42-57. A comprehensive summary of existing studies/datasets (including advantages and drawbacks) may help to emphasize the novelty of this study.
- Lines 58-61. What research gaps have the authors solved? It is better to describe it here.
- Lines 108-111. What is the overpass time for clear-sky LST estimates from Sentinel 3A and 3B, respectively? Why do the authors only use the data from Sentinel 3A.
- Section 3.3. The performance of the merging method needs to be evaluated.
- Line 199. More details on the Kalman Filter can be added to make an easier understanding by readers.
Citation: https://doi.org/10.5194/essd-2022-302-RC2 -
AC2: 'Reply on RC2', Dominik Rains, 25 Jan 2023
The comment was uploaded in the form of a supplement: https://essd.copernicus.org/preprints/essd-2022-302/essd-2022-302-AC2-supplement.pdf
Dominik Rains et al.
Data sets
LSTRAD (0.31) Rains, Dominik https://doi.org/10.5281/zenodo.7026612
LSTRAD (0.3) Rains, Dominik https://doi.org/10.5281/zenodo.7008066
Dominik Rains et al.
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