## 5 Discussion
### Remaining Limitations of Open Access Satellite Data  
Despite the numerous benefits of open-access satellite datasets such as Sentinel and Landsat, several limitations persist. Cloud cover remains a major obstacle for optical sensors, especially in tropical or monsoon-affected zones. Although Sentinel-1 (SAR) can partially overcome this, its spatial resolution and signal noise can limit shoreline discrimination. Additionally, the bandwidth and computing infrastructure required for large-scale image processing often exceeds what is available in developing regions. Limitations also exist in the archives, indicating spatial gaps such as for Landsat in low-income countries. The pre-1984 Landsat archive also lacks consistent data, constraining historical analyses in many parts of the world. Finally, while platforms like Google Earth Engine have improved access, steep learning curves and limited customization still pose barriers for non-expert users. Satellite data in an open-access form has changed different research areas, such as climate monitoring, environmental studies, and sustainable development. Even so, in addition to its advantages, drawbacks remain, some of which include regional gaps in data, temporal resolution, data quality and precision, and data usability and accessibility. Here, this section introduces an examination of those drawbacks based on previous research studies. One of its biggest obstacles is the existence of data gaps at the regional level. These gaps are caused due to uneven coverage, often presented in low-income countries and remote areas. Landsat satellite data, for example, while extensive at the global level, frequently struggles to maintain consistent data in specific areas, resulting in incomplete and skewed interpretations of environmental changes [97]. In this regard, another example is the fact that weather station networks in Africa are also inadequate and uneven in distribution, with most of them located in urban centers while rural and remote locations remain poorly served [98; 99]. Regional differences also become large due to the scarcity of high-resolution satellite data in some regions. For instance, satellite-based precipitation estimates, although useful, tend to have poor precision at finer spatial and temporal resolutions, in areas of intricate terrain, and even in areas of poor ground data validation [99]. Such limitations in data are hindering climate variables from being monitored and analyzed properly, negatively affecting those areas that have intensive climate-dependent sectors like agriculture and water resource management. Temporal resolution problems represent another key constraint for open-access satellite data. Time series gaps in satellite data archives can arise due to launching delays, sensor malfunctions, or cessation of data acquisition. Such gaps reduce the reliability of climate data records and can produce incorrect analyses, especially when identifying long-term climate trends [100]. In addition, the temporal resolution of satellite data is not always sufficient for use by end-users. Applications focused on monitoring extreme weather phenomena or crop productivity demand high-temporal-resolution data, which are not always attainable or accessible [101]. In addition, satellite data integration from other data sources, e.g., ground observations, can create inconsistencies, making temporal climate variable analysis difficult [99]. The introduction of quality and accuracy problems also becomes paramount in open-access satellite data. While satellite data provide useful indicators, its precision is susceptible to problems such as sensor flaws, atmospheric interference, and cloud cover or other obstacles [99; 100]. For instance, [PERSON] et al., (2019) [99] addressed satellite-based rainfall estimation errors and bias in remote locations with poor availability of ground validation data. Moreover, satellite data quality can significantly vary in accordance with the sensor and algorithms utilized.