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More accurate, spatio-temporally, and physically consistent LST estimationhas been a main interest in Earth system research. Developing physics-drivenmechanism models and data-driven machine learning (ML) models are two majorparadigms for gapless LST estimation, which have their respective advantagesand disadvantages. In this paper, a physics-constrained ML model, whichcombines the strengths in the mechanism model and ML model, is proposed togenerate gapless LST with physical meanings and high accuracy. The hybrid modelemploys ML as the primary architecture, under which the input variable physicalconstraints are incorporated to enhance the interpretability and extrapolationability of the model. Specifically, the light gradient-boosting machine (LGBM)model, which uses only remote sensing data as input, serves as the pure MLmodel. Physical constraints (PCs) are coupled by further incorporating keyCommunity Land Model (CLM) forcing data (cause) and CLM simulation data(effect) as inputs into the LGBM model. This integration forms the PC-LGBMmodel, which incorporates surface energy balance (SEB) constraints underlyingthe data in CLM-LST modeling within a biophysical framework. Compared with apure physical method and pure ML methods, the PC-LGBM model improves theprediction accuracy and physical interpretability of LST. It also demonstratesa good extrapolation ability for the responses to extreme weather cases,suggesting that the PC-LGBM model enables not only empirical learning from databut also rationally derived from theory. The proposed method represents aninnovative way to map accurate and physically interpretable gapless LST, andcould provide insights to accelerate knowledge discovery in land surfaceprocesses and data mining in geographical parameter estimation.

A physics-constrained machine learning method for mapping gapless land surface temperature