Diego Fernando Angrino Chiran, Oscar Javier Munar Vivas and Sebastián Anderson Guerrero
Centro Colombiano de Investigaciones de la Caña de Azúcar (Cenicaña), Florida, Valle del Cauca, Colombia; pracagro2@cenicana.org
The capability of Synthetic Aperture Radar (SAR) technology to estimate soil moisture in sugarcane crops in the Cauca River Valley was evaluated. Given its ability to provide a detailed view of the land surface, SAR emerges as a promising tool for monitoring soil moisture, a critical factor for agriculture. Soil moisture is critical for efficient irrigation management and the optimization of agricultural production, aspects that have a direct impact on crop yields and the operating costs of sugar mills.The analysis performed uses a machine learning model to process the data obtained by SAR, thereby improving the accuracy of soil moisture estimation. This approach can offer significant practical applications for agricultural operations, as it facilitates better decision-making regarding irrigation management and harvest scheduling. Furthermore, optimizing the logistics of agricultural machinery transport, a costly process for sugar mills in the Cauca Valley, could be possible thanks to the accurate information provided by soil moisture estimates obtained by SAR. Consequently, the results of this study could contribute to more efficient and sustainable agriculture, promoting cost reduction and increased productivity in the region’s sugar industry.This innovative approach not only reinforces the use of advanced technology in agriculture but also paves the way for improving resource management in a key industry for the regional economy.