the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 25 Mar 2025
Generation of global 1 km daily land surface – air temperature difference and sensible heat flux products from 2000 to 2020
Abstract. Accurate estimation of land surface sensible heat flux (H) is crucial for comprehending the dynamics of surface energy transfer and the cycles of water and carbon. Yet, existing H products mainly are meteorological reanalysis datasets with coarse spatial resolutions and high uncertainties. FLUXCOM is the sole remotely sensed product with its 0.0833° spatial and 8-day temporal resolution spanning from 2001 to 2015, so there is still a need for accurate and high spatial resolution global product based on satellite data. To address these issues, we generated the first global high resolution (1 km) daily H product from 2000 to 2020 using long short-term memory (LSTM) deep learning models, incorporating data from the Global LAnd Surface Satellite (GLASS) product suite. Furthermore, considering that the difference between land surface temperature and air temperature (Tsa) is a key driver of H, we introduce the first global accurate satellite-based Tsa product. This product refines the uncertainty compared with obtaining Tsa directly from existing products by subtracting air temperature from land surface temperature. Our model, distinct from previous models that estimate H per pixel through physically-based models requiring parameters that are not readily accessible, can conveniently derive global values and efficiently capture nonlinear interactions. Additionally, it accounts for the temporal variation of H. Validation against independent in-situ measurements yielded a root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R2) of 25.54 Wm⁻2, 18.649 Wm⁻2, and 0.54 for H, and 1.459 K, 1.071 K, and 0.53 for Tsa, respectively. The estimated H and Tsa values are more accurate than current products such as MERRA2, ERA5-Land, ERA5, and FLUXCOM under most conditions. Additionally, the new H product offers more detailed spatial information in diverse landscapes. The estimated global average land surface H from 2000 to 2020 is 35.29±0.71 Wm⁻2. These high-resolution H and Tsa products are invaluable for climatic researches and numerous other applications. The daily mean values for the first three days of each year can be freely downloaded from https://doi.org/10.5281/zenodo.14986255 (Liang et al., 2025), and the complete product will be available at www.glass.hku.hk (last access: 7 March 2025).
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RC1: 'Comment on essd-2025-136', Anonymous Referee #1, 23 Apr 2025
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Liang et al. describe the development of a global 1km data product for land surface – air temperature differences and sensible heat fluxes. In principle such data-driven products are very valuable for the community. However, I have several methodological concerns, primarily related to the validation approach and with respect to variable selection as input to the models.
Major points:
- The cross-validation strategy chosen by the authors is not adequate and yields overoptimistic results. It is absolutely compulsory to stratify train and test data by sites and not (only) by time. This is simply because data from one site are not independent and the objective of the study is to estimate at unmeasured locations. This needs to be done correctly.
- The authors chose Rn and ET as input to the model to predict H. In my opinion this is hard to justify as H=Rn-LE-G and predicting ET is a similar problem as predicting H. I would find it conceptually more appealing if input variables are close to observations and not already derived products with additional layers of uncertainty.
- The authors chose slope and aspect as predictors. While it is clear that these variables are very relevant in principle, the footprint of flux towers is supposed to be restricted to reasonably flat terrain. Therefore, I cannot imagine that robust patterns wrt these terrain variables can be learned.
- The authors also chose day of year as predictor, which has no direct environmental meaning. I suggest to drop this or replace by e.g. potential radiation or sun angle.
- The authors mentioned a ‘circular’ approach between training and testing for hyper-parameter tuning (line 300). It is absolutely forbidden to use test data for any kind of model tuning. Perhaps this is a misunderstanding. Please clarify.
- The authors use the Twine et al approach to correct flux tower based sensible heat fluxes by forcing energy balance closure. This is a critical assumption, which needs through discussion because the uncertainty related to energy balance correction is very large, esp. for H (see Mauder et al 2024, AFM)
Minor points:
- I find the uncertainty estimates listed in Table 1 and referenced in the text a bit misleading as they are not comparable among the products because they were not calculated consistently
- Model tree ensembles for FLUXCOM mentioned in table 1 is likely wrong as I suppose the authors used the ensemble product
- Line 113: sentence starting with “Therefore” seems incomplete
- The choice of LSTM for estimating H is unclear – have not seen a clear comparison to RF and the other methods (did I miss this?)
- Are the comparisons of global H values in section 5 based on exactly the same spatial domain. This matters as e.g. FLUXCOM does not cover deserts where H is particularly large.
Citation: https://doi.org/10.5194/essd-2025-136-RC1 -
RC2: 'Comment on essd-2025-136', Anonymous Referee #2, 15 May 2025
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It is a good idea to directly estimate daily land surface-air temperature difference (Tsa) and sensible heat flux (H) using machine learning method.
The manuscript explores the linkage between Tsa and its predictors and demonstrates the feasibility of establishing this linkage using machine learning.
The performance of the developed product is comprehensively evaluated and superior to that of corresponding reanalysis products and satellite product.
I expected to see the complete product soon.
Detailed comments,
1. How to remove the effects of spatial resolution mismatch between multi-source predictors on the estimated Tsa and H?
2. If the estimated Tsa is used to derive the physically based model, what is the accuracy of H.
Citation: https://doi.org/10.5194/essd-2025-136-RC2 -
RC3: 'Comment on essd-2025-136', Anonymous Referee #3, 27 May 2025
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This study presents two valuable global datasets spanning 2000-2020: land surface sensible heat flux (H) and land surface-air temperature difference (Tsa), generated using data-driven approaches. Recognizing that Tsa is the primary driver of H, the authors first refine the estimation of Tsa and subsequently employ it to predict H. Overall, the article is well-structured, with detailed explanations of the algorithm design, variable selection, and comparative analyses against existing products across multiple scales. The generated datasets hold significant value for global energy balance studies.
A notable limitation is the absence of data for 2021–2024. Additionally, a few minor issues should be addressed:
- Table 3: the last row, there is a spelling mistake on “Leaf area index”;keep the initial letter case consistent for variable names in the first column of the table.
- InSection 2.2.2, the GLASS product provides ET and FVC data at an 8-day temporal resolution, whereas the model requires daily input. Please clarify how this temporal discrepancy was addressed (e.g., through interpolation,or another method). A detailed explanation should be included for reproducibility.
Citation: https://doi.org/10.5194/essd-2025-136-RC3
Data sets
Global 1 km daily land surface - air temperature difference and sensible heat flux products from 2000 to 2020 Hui Liang, Shunlin Liang, Bo Jiang, Tao He, Feng Tian, Jianglei Xu, Wenyuan Li, Fengjiao Zhang, and Husheng Fang https://doi.org/10.5281/zenodo.14986254
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