Remote sensing applied for the estimation of crop coefficient and detection of forest cover changes

With the objective of applying remote sensing techniques for crop coefficient estimation and detection of changes in forest cover, in order to generate information that contributes to the sustainable management of agricultural and forestry resources, a study was conducted based on the theoretical foundations of agriculture 4.0, through the implementation of advanced technologies and intelligent data integration to optimize the entire agricultural production cycle. The methodology adopted includes the capture and processing of multispectral images from satellite platforms and unmanned aerial vehicles (UAVs), in order to obtain geometric and spectral information on various crops. Calculations of spectral indices (NDVI, NDMI, NDWI, Kc) and analysis of forest stand losses were performed using advanced software tools in GIS environment and the Google Earth Engine platform. The drone images made it possible to calculate the NDWI to classify soil moisture in high, moderate and low levels. Satellite images facilitated the identification of relationships between crop evaporation coefficient (Kc) and climatic parameters, as well as the detection of areas with forest losses in the Carrizal river basin. The results suggest strategies for the development of precision agriculture activities, promoting the substitution of conventional practices for sustainable development mechanisms based on geospatial technologies. This study contributes to the literature by demonstrating the application of advanced geospatial technologies to optimize agricultural production and sustainability.