Remote sensing images captured by satellites play a critical role in Earth observation (EO). With the advancement of satellite technology, the number and variety of remote sensing satellites have increased, which provide abundant data for precise environmental monitoring and effective resource management. However, existing satellite imagery often faces a trade-off between spatial and temporal resolutions. It is challenging for a single satellite to simultaneously capture images with high spatial and temporal resolutions. Consequently, spatiotemporal fusion techniques, which integrate images from different sensors, have garnered significant attention. Over the past decade, research on spatiotemporal fusion has achieved remarkable progress. Nevertheless, traditional fusion methods often encounter difficulties when dealing with complicated fusion scenarios. With the development of computer science, deep learning models, such as convolutional neural networks (CNNs), generative adversarial networks (GANs), Transformers, and diffusion models, have recently been introduced into the field of spatiotemporal fusion, resulting in efficient and accurate algorithms. These algorithms exhibit various strengths and limitations, which require further analysis and comparison. Therefore, this paper reviews the literature on deep learning-based spatiotemporal fusion methods, analyzes and compares existing deep learning-based fusion algorithms, summarizes current challenges in this field, and proposes possible directions for future studies.

In physical model inversion of leaf area index (LAI) over complex vegetation canopies, variations in canopy structure, background moisture, and background brightness can cause spectrally similar lookup table (LUT) candidates to represent different combinations of canopy and background parameters, reducing retrieval stability. This study proposes a PROSAIL lookup table inversion method for GaoFen-5A (GF-5A) hyperspectral imagery based on physically informed parameter range refinement. A small set of key GF-5A feature bands was selected to preserve information sensitive to canopy structure and background conditions while reducing spectral redundancy. Vegetation type information was used to determine the LIDFa range, while the normalized difference infrared index (NDII) and brightness index (BI) were used to guide the refinement of psoil and rsoil ranges according to moisture related and brightness related spectral responses, respectively. Combining compact spectral inputs with refined PROSAIL parameter ranges improved LUT candidate selection under variable canopy and background conditions. Validation using 62 field LAI values aggregated at the GF-5A pixel scale yielded a coefficient of determination (R²) of 0.6000, a root mean square error (RMSE) of 1.1461, a mean absolute error (MAE) of 0.8712, and a Bias of −0.0092, outperforming the comparison schemes. These results indicate improved physical consistency and stability in LAI inversion under complex canopy and background conditions.

Solar-induced chlorophyll fluorescence (SIF) provides a new pathway for the accurate estimation of gross primary productivity (GPP) by directly probing vegetation photosynthetic functioning, but mechanistic estimation is still constrained by the difficulty of obtaining the seasonal dynamics of key physiological parameters. Using maize, a representative C4 crop, as an experimental case, this study integrated long-term tower-based SIF observations with the SCOPE model to propose a cross-variable constraint strategy, in which key vegetation parameters were optimized against in situ SIF observations and independently validated against eddy covariance-derived GPP. Specifically, key sensitive parameters were first identified via Sobol global sensitivity analysis. A double-baseline asymmetric Gaussian phenology model was then constructed to characterize the seasonal dynamics of V_cmax, while C_ab was derived from V_cmax using a smoothed piecewise-linear empirical relationship. A two-stage Bayesian optimization scheme was applied to optimize the parameters, which were subsequently used to run the SCOPE model and reconstruct continuous SIF and GPP time series. The results showed that the estimated daily SIF and GPP agreed well with ground observations, yielding R² values of 0.86 and 0.74, respectively. Furthermore, the upscaled results correlated strongly with satellite products (MODIS: R = 0.94, R² = −2.69; PML: R = 0.95, R² = 0.81). This study demonstrates the feasibility of using SIF observations to constrain SCOPE vegetation parameters for improved GPP estimation, providing a parameterization basis and methodological support for mechanistic remote sensing monitoring of vegetation productivity.