# Assessing Landsat Images Availability and Its Effects on Phenological Metrics
## 1 Introduction  
In the context of global change, information on the magnitude and spatio-temporal patterns of land changes is needed to assess the relationships between changes, their causes and consequences. Remote sensing is a cheap and efficient tool to obtain consistent information about the spatio-temporal land cover dynamics. Change detection techniques were usually based on the comparison between images acquired in different years to depict land cover changes as deforestation [1, 2, 3]. With the advent of Sentinel-2 and Landsat constellations, dense optical satellite image time series, with a high spatial resolution, are available nowadays. Time series of remotely sensed data, particularly vegetation indices, are valuable data sets in many Earth science fields. [PERSON] et al. [4] reported nine essential applications based on time series of vegetation indices including vegetation dynamics, environmental monitoring and climate change, mapping, biodiversity, yield production estimates, drought monitoring, land change detection and vegetation's biophysical variables retrieval. Recently, studies that analyze dynamic phenomena such as vegetation-phenotype use a large number of intra-annual observations [5; 6; 7]. The seasonal pattern of the variation in reflectance of vegetated land surfaces observed from remote sensing is called land surface phenology and reflects vegetation's response to changes in the climate and hydrologic cycle [8; 9]. For instance, the seasonal variation in vegetation greenness described by vegetation indices' temporal dynamics proved to be useful for vegetation types and tree species mapping [10; 11; 12; 13]. Moreover, the greenness curve analysis allows computing phenological metrics that describe annual dynamics of the vegetation and can be related to plants productivity and aboveground carbon stored in vegetation [14]. Phenology is also essential for understanding ecosystems' responses and feedback to the climate system and is a reliable bioindicator of climate change [15; 16; 17]. Landsat and Copernicus (Sentinel) are two critical remote sensing programs because they freely provide multi-spectral images with a relatively high spatial resolution (10 to 60 m). The Landsat program started in 1972 and allowed the acquisition of millions of images. The data acquired from the Landsat 4 satellite (1982) have improved spatial resolution (30 m) and a 16-day temporal resolution. Landsat imagery has been widely used in land change assessment [18; 19]. Currently, two satellites, Landsat 7 and 8, are in operation. The Sentinel-2 mission consists of a constellation of two identical satellites (Sentinel-2A and Sentinel-2B) launched, respectively, in 2015 and 2017 [20]. Although the temporal resolution from Landsat 4 onwards is sixteen days and Sentinel-2 constellation is five days at the equator, the spatio-temporal availability of these images is not homogeneous due to acquisition policies and atmospheric conditions (presence of clouds). It is worth noting that in the range 0\({}^{\circ}\)-10\({}^{\circ}\) south latitude, especially in the summer and autumn, there is a high rainfall and high clouds presence [21]. In a previous study, we assessed the availability of Landsat images during the period 1984-2019 and found that the number of valid data was low in some Brazilian biomes such Amazonia (tropical rain forests) and Caatinga (mosaic deciduous woody formations) due to clouds presence [22]. This study aims to evaluate the availability of Landsat 7 and 8 images over the Brazilian territory, seeking to verify whether there is sufficient availability to support studies of the analysis of phenodynamics of vegetation. We hypothesise that it will be challenging to characterise vegetation phenology in regions where the number of valid (cloud-free) remotely-sensed observation is low or when the observations are unevenly distributed over the year. This paper is organised as follows.