| 02 Mar 2026
Effectively Assimilate Satellite Land Surface Temperature into Offline Land Surface Models within Ensemble-based Assimilation Frameworks
Abstract. Land surface temperature (LST) plays a vital role in controlling the water and energy fluxes at the interface between the land and atmosphere, and the main aim of assimilating LST observations into Land Surface Models (LSMs) is to not only provide better initial conditions for the LSM itself, but also yield more accurate land–atmosphere interactions. While observation systems provide a vast amount of satellite-derived LST observations in recent years, they are not as widely used as soil-moisture observations in land data assimilation (DA), in research or in operations, owing to the fast temporally varying nature of LST. To effectively improve the impact of LST assimilation, this study proposes a new scheme by jointly updating the soil temperature and soil moisture within the upper surface layers. The DA method and LSM used in this study are the Local Ensemble Transform Kalman Filter (LETKF) and the Common Land Model (CoLM), respectively. Moderate Resolution Imaging Spectroradiometer (MODIS) derived LST is assimilated into CoLM every 3 hours using the proposed scheme. The assimilation and open-loop experiments are conducted for one year with a global resolution of 0.5° × 0.5°. The LST shows marginal enhancement after assimilation, owing to its fast-varying nature dominated by atmospheric forcings. However, the BIAS in soil temperature over Northeast Asia is reduced significantly, with a magnitude of 1.0 K, 1.5 K, and 2.0 K for the layers within 0–10 cm, 40–100 cm, and 100–200 cm, respectively. Prominent improvements in snow temperature and snow depth are observed over Northeast Asia, with a reduction in root mean square difference (RMSD) of approximately 4 K and 150 mm, respectively. The improvements in soil water content are also notable, particularly over humid tropical regions. The largest reductions in unbiased RMSD of soil water content over the Amazon Rainforest are approximately 0.06, 0.12, 0.15, and 6.00 kg/m2 for the layers within 0–10 cm, 10–0 cm, 40–100 cm, and 100–200 cm, respectively. These consistent improvements in both the energy and water components of CoLM demonstrate the effectiveness of the proposed scheme and the importance of LST assimilation for land-surface-process modeling.
This manuscript addresses a long-standing challenge in the field of Land Data Assimilation: how to effectively assimilate Land Surface Temperature (LST) into offline Land Surface Models (LSMs), given that LST varies drastically over time and is predominantly driven by downward atmospheric radiation forcing. Based on the Common Land Model (CoLM) and the Local Ensemble Transform Kalman Filter (LETKF) algorithm, the authors propose a novel scheme to implicitly and jointly update surface soil temperature and soil moisture by utilizing the cross-correlations among variables.
Overall, this research topic holds strong scientific value and practical operational significance. The manuscript is logically structured with a rigorous experimental design, effectively overcoming the limitations of single-variable updates in traditional observation operators. It provides valuable insights for researchers in meteorology, hydrology, and Earth system modeling. However, to further enhance the scientific rigor of this study, there is still room for optimization and expansion. Specifically, the authors need to further justify the physical rationality of key parameter settings within the assimilation framework (such as the large-scale localization strategy, parameter perturbation magnitudes, and observation representativeness errors). Additionally, it is essential to demonstrate the comparative advantages of the LST observation operator, supplement the evaluation with independent in-situ validation, and provide an in-depth mechanistic interpretation of the degraded assimilation performance in specific regions. In light of these considerations, I recommend that the manuscript be accepted after Minor Revisions.
Specific Comments
1. Localization Strategy in Land DA
The study adopts a distance-dependent decay localization strategy, with the localization radius D set to decrease linearly from 730 km at the equator to 146 km at the poles. While such a large-scale spatial smoothing strategy is common in atmospheric assimilation systems, the land surface exhibits extreme spatial heterogeneity (strongly influenced by topography, vegetation cover, soil texture, etc.). Forcing covariance propagation across different underlying surfaces (e.g., boundaries between lakes and deserts, or transition zones of different vegetation types) is highly prone to introducing spurious correlations. The authors are advised to thoroughly discuss the physical rationality of employing such a large spatial localization radius (up to 730 km) in land assimilation, and clarify whether matching constraints for underlying surface characteristics were considered during the covariance propagation process.
2. Model Parameter Perturbations
To maintain a reasonable ensemble spread, the authors introduced random perturbations to 14 tunable parameters in the CoLM model (see Table 1). Please provide further explanation regarding the physical justification for selecting these specific parameters and the rationality of setting their maximum perturbed magnitudes. Additionally, it is recommended to briefly discuss which of these 14 parameters contribute most significantly to the simulated variance of LST and the soil hydro-thermal cross-covariance.
3. Lack of Independent In-situ Validation
The authors utilized ERA5-Land, GLDAS, and MERRA2 as evaluation benchmarks. Although these datasets offer the advantage of global coverage, they are inherently products driven by specific model parameterization schemes and atmospheric forcings, inevitably containing their own systematic biases. Please explain why independent in-situ observation data were not utilized for validation. If conditions permit, it is strongly recommended to supplement the study with site-level validation in representative regions to further enhance the objectivity and persuasiveness of the assimilation performance evaluation.
4. Observation Error Representation
Given the extremely high spatial resolution of MODIS LST, the authors used a simple area-weighted processing method and excluded cloud-contaminated pixels when aggregating it to the 0.5-degree model grids. It is recommended to further clarify how the observation error covariance matrix was explicitly specified in the LETKF update. When aggregating high-resolution observational data, did the observation error account not only for the retrieval error of MODIS itself but also reasonably incorporate the "representativeness error" caused by scale mismatch? This is crucial for the appropriate allocation of weights in the final analysis increments.
5. LST Operator
The accuracy of the observation operator itself plays a decisive role in the effectiveness of the assimilation. Since the method for calculating LST varies across different models, please explain the method used to calculate LST in CoLM and elaborate on its advantages or differences compared to observation operators in other models. This will help demonstrate the rationality of the adopted methodology.
6. Degradation in Specific Regions
Figures 8 and 9 show that the simulation performance deteriorated in certain regions after LST assimilation (indicated by the red areas). Please provide an in-depth analysis of the physical mechanisms or algorithmic reasons leading to the degradation in these specific areas. Is this negative impact constrained by special local underlying surface conditions, limitations in the observation error settings, or a failure of the cross-correlations among physical variables? Please discuss whether this phenomenon is reasonable within the current assimilation framework.