## 2 Materials and Methods
### A New Multi-Source Remote Sensing Platform DA  
As a new type of RS with the most potential, satellite constellation plays an indispensable role in key fields such as fine water resources management, disaster early warning, and crop yield prediction because of its excellent environmental adaptability, strong comprehensive resolution of multi-parameters, and dual characteristics of wide area coverage and fine  
Figure 3: The development trends of different satellite sensors in detail, feature recognition, and scale requirements are compared. The red square represents the spatial resolution of adjacent RS images, modified from [70]. monitoring, and provides unprecedented spatial information support for DA work [71]. The satellite constellation consists of a set of carefully planned satellite units that orbit the Earth in a specific orbital layout and configuration, ensuring seamless global coverage and efficient revisit capabilities [72]. It is particularly worth mentioning that the constellation integrates a wide range of sensor devices, including optical cameras (such as panchromatic, multispectral, and hyperspectral cameras), thermal infrared sensors, and microwave radar (such as synthetic Aperture Radar, SAR), etc., which can capture land surface information in an omni-directional and multi-band manner, laying a rich and accurate data foundation for various DA applications [71; 73]. Assimilation research in recent years has shown great potential in improving the accuracy and refinement of data analysis. For example, [PERSON] et al. effectively assimilated Sentinel-2 satellite LAI data into the SAFY model by using ensemble Kalman filtering method, which significantly improved the estimation accuracy of spatial variability of winter wheat yield by more than 70% [74]. Meanwhile, [PERSON] et al. innovatively combined PlanetScope and Sentinel-2 imagery to create a high-density time-series LAI dataset, accurately simulating the dynamic changes in crop yields within fields with notable research outcomes. Additionally, the sub-meter to tens-of-meters high spatial resolution provided by satellite constellations meets the demanding requirements for precise identification of subtle agricultural features [75]. [PERSON] et al. adeptly used remote sensing LAI data to realize fine-grained simulation of rice yields at the sub-field scale [76]. Moreover, [PERSON] et al. leveraged the high-resolution datasets from PlanetScope and Sentinel-2, along with advanced machine learning (ML) algorithms such as Random Forest, Support Vector Machines, and Regression Trees, to conduct large-scale Land Use and Land Cover (LULC) mapping with striking classification accuracies, reaching up to 93% and 91%, respectively [77]. Looking ahead, with the successful deployment of Landsat-9 and the anticipated efficient operation of its successor Landsat-Next, coupled with the extensive integration of Dove small satellites from Planet Labs into the DA field, we can reasonably anticipate near-real-time, daily global coverage of RS monitoring, offering timely insights into crop growth conditions [78]. Such advancements will not only elevate the timeliness and accuracy of DA but also substantially bolster the capacity of agriculture and environmental sectors to tackle global challenges, providing robust data support for informed decisions toward sustainable development. As an important part of modern remote sensing technology, unmanned aerial vehicles (UAVs) have significant advantages compared with remote sensing satellite sensors in terms of mobility, flexibility, safety, cost, ease of operation, response speed, and high-resolution image acquisition ability, and can efficiently capture high-precision details of farmland microtopography, vegetation structure, and water status. UAVs have brought significant innovation and development to the field of data analysis [79; 80]. For instance, [PERSON] et al. demonstrated the enormous potential of UAV imaging technology in rapid and non-destructive field phenotyping [81]. [PERSON] et al.