An Approach To Developing A Neural Network For Determining The Moisture Index Of Plants

The paper presents an approach to soil moisture prediction by integrating remote sensing data from Sentinel-2 and in-situ measurements from the International Soil Moisture Network (ISMN). The proposed methodology involves preprocessing and aligning spectral satellite imagery with ground-based soil moisture readings to create a dataset suitable for deep learning models. Various Sentinel-2 bands, including Red Edge 3, Near-Infrared (NIR), and Short-Wave Infrared 1 (SWIR 1), are analyzed for their effectiveness in soil moisture estimation. A convolutional neural network (CNN) model is developed to identify patterns in satellite imagery that correlate with soil moisture variations. Experimental results demonstrate that the proposed approach improves soil moisture prediction accuracy, offering potential applications in precision agriculture and water resource management.