Índices biofísicos da bacia hidrográfica do Rio Sucuru-PB utilizando o Google Earth Engine.

Abstract

The Sucuru River Basin covers an area of 1,652.5 km² and requires efficient monitoring of its natural resources. Remote Sensing (RS) has emerged as a crucial tool for this purpose. This study aimed to analyze the dynamics of the Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST) in relation to variations in rainfall during the period from 2001 to 2019. To carry out this analysis, the Google Earth Engine (GEE) platform was used, along with data from the Moderate Resolution Imaging Spectroradiometer (MODIS) and TerraClimate sensors, using JavaScript programming. Pearson's correlation coefficient (r) in the R® software was applied to assess the relationships between NDVI time series data and rainfall, as well as between LST data and rainfall. This study highlighted the effectiveness of the GEE platform in processing the images, allowing for a comprehensive analysis of the dynamics of the Sucuru River basin and its relationship with key climatic factors, providing crucial information for managing water resources in the region, highlighting the importance of RS as an effective tool for monitoring the natural resources of river basins. Pearson's correlation coefficients revealed a moderate correlation between NDVI and rainfall, as well as between LST and rainfall, indicating a strong association between vegetation dynamics and LST with rainfall patterns.