Vegetation classification has always been the focus of remote sensing applications, especially for tropical regions with fragmented terrain, cloudy and rainy climates, and dense vegetation. How to effectively classify vegetation in tropical regions by using multi-spectral remote sensing with high resolution and red-edge spectrum needs to be further verified. Based on the experiment in Wenchang, Hainan, China, which is located in the tropical monsoon region, and combined with the ZY-1 02D 2.5 m fused images in January, March, July, and August, this paper discusses whether NDVI and four red-edge vegetation indices (VIs), CIre, NDVIre, MCARI, and TCARI, can promote vegetation classification and reduce the saturation. The results show that the schemes with the highest classification accuracies in all phases are those in which the red-edge VIs are involved, which suggests that the red-edge VIs can effectively contribute to the classification of vegetation. The maximum accuracy of the single phase is 86%, and the combined accuracy of the four phases can be improved to 92%. It has also been found that CIre and NDVIre do not reach saturation as easily as NDVI and MCARI in July and August, and their ability to enhance the separability between different vegetation types is superior to that of TCARI. In general, red-edge VIs can effectively promote vegetation classification in tropical monsoon regions, and red-edge VIs, such as CIre and NDVIre, have an anti-saturation performance, which can slow down the confusion between different vegetation types due to saturation.

Due to budget and sensor technology constraints, a single sensor cannot simultaneously provide observational images with both a high spatial and temporal resolution. To solve the above problem, the spatiotemporal fusion (STF) method was proposed and proved to be an indispensable tool for monitoring land surface dynamics. There are relatively few systematic reviews of the STF method. Bibliometrics is a valuable method for analyzing the scientific literature, but it has not yet been applied to the comprehensive analysis of the STF method. Therefore, in this paper, we use bibliometrics and scientific mapping to analyze the 2967 citation data from the Web of Science from 1991 to 2023 in a metrological manner, covering the themes of STF, data fusion, multi-temporal analysis, and spatial analysis. The results of the literature analysis reveal that the number of articles displays a slow to rapid increase during the study period, but decreases significantly in 2023. Research institutions in China (1059 papers) and the United States (432 papers) are the top two contributors in the field. The keywords “Sentinel”, “deep learning” (DL), and “LSTM” (Long Short-Term Memory) appeared most frequently in the past three years. In the future, remote sensing spatiotemporal fusion research can address more of the limitations of heterogeneous landscapes and climatic conditions to improve fused images’ accuracy.

As the resolution of remote sensing quantitative inversion for albedo, clumping index, canopy height, and other biophysical parameter data is gradually refined, there is an increasing demand for higher spatial resolution bidirectional reflectance distribution function and angular reflectance data. In this study, we applied and evaluated Flexible Spatiotemporal DAta Fusion(FSDAF) to downscale angular reflectance using low-resolution bidirectional reflectance distribution function data (MCD43A1) and high-resolution Sentinel-2 data as inputs. We selected Sentinel-2 orbital overlap regions with various anisotropic flat index types and overpassing times as the study area as to obtain angular reflectance data under different anisotropic and solar-viewing geometry conditions. We found that the fusion method can downscale the angular reflectance with high accuracy (root mean square error <0.04, bias <0.02). The downscaling accuracy was higher in the cross-principal plane than in the principal plane, whereas there was no significant variation in accuracy under different anisotropic flat index conditions. Owing to the lack of angular surface reflectance at larger viewing angles, validation was performed only within a view zenith angle of 10.3°. Further validation using satellites with larger viewing angles, such as Gaofen-6 or unmanned aerial vehicles, can be used to evaluate the accuracy of this method. This study presents a strategy for downscaling angular information, preliminary validation results, and satellite-based evidence of scale effects between the 500–10 m scales of the bidirectional reflectance distribution function.

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.