## 1 Introduction  
Puerto Rico (PR) is a tropical island in the Caribbean that commonly experiences atmospheric events that impact its ecosystems and hydrological cycle; they are designated as 10-, 50-, and 100-year events. The timeframe between Hurricanes Irma and Maria in 2017 and Hurricane Fiona in 2022 indicates that these 10-, 50-, and 100-year events are happening more frequently than normally estimated. Therefore, tools that can help us understand ecosystem resilience, using remotely sensed temporal and spatial data, are needed to manage these challenges caused by extreme weather events. Although PR has a vast amount of meteorological or atmospheric data, other insular territories (e.g., Haiti, Dominican Republic) have limited or null meteorological data measured in situ. Therefore, the use of remote sensing meteorological and atmospheric data can be an alternative method to compensate for a lack of readily available datasets. Remote sensing data can help determine ecosystem health through metabolism or productivity assessment and by observing interactions between other system components [1; 2]. Products such as Landsat 8-9 and Moderate Resolution Image Spectroradiometer (MODIS) are good alternative tools that can produce high-resolution images at a regional and national scale. For example, MOD16A is based on the Penman-Monteith equation, which contains daily meteorological data along with MODIS data such as vegetation dynamics, albedo (fraction of light that a surface reflects), and land cover. Most MODIS bands have a spatial resolution of 1 km, where each pixel would represent an area of \(1\times 1\) km\({}^{2}\). Moreover, these products usually have a radiometer resolution of 16-bit depth. Precipitation, evapotranspiration (ET), and runoff are the major water fluxes considered in the hydrologic cycle, which comprises atmospheric water and land water (rivers, lakes, and groundwater) [3]. ET values consider the water that is released into the atmosphere via evaporation and transpiration, and it is a function of solar radiation, temperature, wind, and soil moisture. Climate change has affected the water cycle due to increased temperatures [4; 5]. Thus, ET values are a sensitive measure to help us better understand the water cycle and the resilience of ecosystems [6; 7]. Surface temperature is also a measure that can provide us with valuable information regarding the influence of the exchange of water between the atmosphere and the soil surface under extreme hydrological events [8]. ET represents vegetation's water loss via its vascular system and evaporation from Earth's surface. Examination of ET helps to determine the amount of water lost that needs to be replaced either by irrigation or rainfall. ET can also be used to estimate the water requirement by crops and plants in an ecosystem, thus making irrigation practices more efficient for precision agriculture [9]. Due to the impact of global climate change and extensive human activities, the study of ET and its dynamics is important in elucidating regional water cycle processes, optimizing water resource allocation, and assessing the water resources necessary for ecological development and sustainable socioeconomic progress [6; 7; 9]. Direct measurements to estimate ET are feasible through various instruments, such as evapotranspiration meters, vorticity correlators, and large-aperture scintillators, spanning scales from meters to kilometers [10]. However, capturing the spatiotemporal dynamics of ET at the regional scale remains challenging due to limited field observations. The integration of remote sensing techniques, climate models, and land surface models into ET numerical simulations has led to the development of diverse global and regional ET products [11; 12].