## 1 Introduction  
Over the past few decades, remote sensing has emerged as a powerful tool with great potential in numerous areas of land monitoring. It plays a crucial role in supporting global efforts in ecology and forest management [1]. The availability of approximately 40 years of free satellite data has transformed the approach to ecological monitoring practices [2]. Remote sensing has a wide range of applications in the field of ecology, utilizing information from satellite constellations such as Landsat, Sentinel, and MODIS. The diverse applications include land cover change/use analysis [3], estimation of fractional vegetation cover [4], crop monitoring [5, 6], analysis of forest vegetation health [7, 8, 9], biodiversity [10] and soil erosion [11] assessment and monitoring. The analysis of historical time series of vegetation indices and indicators offers huge potential for investigating temporal dynamics such as land cover change and understanding the impacts of climate change. By studying the changing patterns over time with a hight frequency of data, we can gain valuable insights into the effects of climate change on vegetation and its ecosystems. Therefore, there is a widespread acknowledgement regarding the significance of investigating prolonged changes in the Earth's land surface and establishing a reliable, uninterrupted series of vegetation index data that accurately captures the fluctuations in vegetation patterns; this is crucial for extracting valuable information about seasonal vegetation characteristics such as green vegetation cover and leaf area index, as well as comprehending interannual variations related to land degradation, forest disturbances, climate changes, and vegetation classification [12; 13; 14]. The effects of global climate change on terrestrial ecosystems have been significant over the last century [15; 16; 17; 18]; therefore, exploring the spatial and temporal patterns of ecosystem responses to climate change is fundamental [19]. Given the vast amount of data covering a wide historical range, the use of satellite products is an important opportunity in the field of environmental monitoring; among the most utilized products in this field are those derived from NASA's Landsat-7 and Landsat-8 satellites, launched in 1999 and 2013, respectively, and the Sentinel-2 satellite, part of the European Space Agency's Copernicus program launched in 2015; all satellites are equipped with advanced sensors providing valuable multispectral imagery, making them indispensable tools for various environmental applications. A combination of Landsat-7, Landsat-8, Sentinel-2A, and Sentinel-2B satellite data can yield a worldwide median average revisit interval of 2.9 days and, over one year, this combination can also provide a median minimum revisit interval of 14 min (\(\pm\)1 min) and a maximum revisit interval of 7.0 days [20]. The potential of these tools becomes particularly relevant when they are compared and integrated. Numerous studies in the literature have documented various instances of data integration between Sentinel and Landsat datasets, addressing diverse issues such as geological exploration [21], forest and environmental applications [22; 23], and land cover change assessments [24]. However, it is worth noting that most of these studies do not delve into the aspect of spectral feature consistency between the datasets and simply assume its validity. Despite this, it is well known that significant differences can exist between the sensors of different satellites [25; 26; 27]. The standardization of the spatial resolution of spectral bands across satellite platforms allows us to obtain a greater quantity of data available for those areas or periods of the year in which the image quality is disrupted by atmospheric phenomena (e.g., cloud cover) [28]. Furthermore, the analysis of the effects of climate change is sometimes localized and linked to specific breakpoints, making the greater frequency of data important to detect ecosystem responses to climate change effects [29].