## 2 Materials and Methods
### A New Multi-Source Remote Sensing Platform DA
utilized a high spatio-temporal resolution UAV-based point spectrometer to significantly improve the accuracy of Sun-Induced Chlorophyll Fluorescence (SIF) measurements at the field scale, opening up a new technological pathway for refined monitoring in agriculture [82]. Concurrently, [PERSON] et al. leveraged UAV multispectral technology to accurately estimate SPAD values across multiple growth stages of bychees, providing a powerful tool for rapid assessment of crop nutritional status [83]. New DA research reveals that the assimilation application value of UAVs is especially pronounced in small-scale, refined agricultural management scenarios. [PERSON] et al. integrated DA with UAV monitoring and plant parameter estimation techniques to enable real-time monitoring of vegetation health status, providing a scientific basis for precision agricultural management [84]. Additionally, [PERSON] et al. proposed partial least square method and random forest method to estimate winter wheat grain filling rate (GFR) by using leaf chlorophyll content (LCC) and LAI extracted from UAV images, providing a new perspective for dynamic monitoring of crop growth [85]. Numerous studies have also relied on key parameters related to crop growth status and health obtained via UAVs to provide timely and effective guidance for crop management, propelling DA towards increased intelligence and refinement [86; 87; 88]. It is worth noting that UAVs possess the capability to rapidly cover vast areas of surface information and can operate in complex terrains and hazardous areas, greatly enhancing the efficiency and accuracy of RS data acquisition [34; 89; 90]. In summary, the application prospects of UAVs in the precise agriculture DA domain are set to become increasingly broad, and their role in promoting agricultural modernization and boosting agricultural productivity will be even more pronounced. It is worth noting that the latest technological innovation of the RS platform has injected a strong impetus into the research of DA. The emergence of diversified devices such as ground observation stations, vehicle mobile platforms, and handheld spectrometers greatly expands the breadth and depth of RS data sources, which can effectively make up for the lack of satellite and UAV data and provide near-surface information closer to the actual state of crops, thus significantly improving the accuracy of model assimilation. For example, some researchers have creatively proposed a method of combining multi-sensor ground mapping and representation, which skillfully integrates a variety of heterogeneous but complementary sensor data carried on unmanned rovers to generate a multi-level three-dimensional map of the environment [91]. This technique not only significantly enhances the resolution of mapping texture details, but also opens up a new research approach for DA research. At the same time, [PERSON] et al. successfully achieved accurate estimation of outdoor vineyard yield by using a consumer-grade RGB-D camera mounted on a mobile robot platform. In addition, they discussed the potential of handheld mid-infrared spectrometers in field fresh soil sampling and real-time analysis, providing strong technical support for soil science research and agricultural precision management [92]. Looking ahead, we can expect greater results from these methods, with the emergence of more high-quality ground observation station data, such as the high-resolution satellite-driven automatic soil observation network SONTE-China, and the multi-spectral sensor system DIEGO deployed on the International Space Station [93]. [PERSON] et al. 's ground-based RS platform will play an increasingly important role in the field of DA research, starting a new upsurge of scientific research and helping to achieve comprehensive and accurate monitoring and management of agro-ecosystems [94].