Predicting soil electro-conductivity using Sentinel-1 images

Abstract

The quality and yield of a soil can be measured by using a wide range of soil indicators. One such indicator is soil’s electro-conductivity which is an excellent indicator of the presence of soil nutrients. This work aims to create a machine learning model to predict the soil’s electro-conductivity (EC) using radar images from the satellite Sentinel-1. Using EC readings from 14 corn field parcels and Sentinel-1 readings over the course of one agriculture year, several regression models were generated. These mod- els were designed using information from the full agriculture year or only 3 months, both or only one of the VV and VH polarisations. The results show that when using a full year data VV and VH polarisations are able to generate models with similar performance (R2 of 0.888 for VH and 0.884 for VV) but when using only 3 months data, only April to June trimester using both polarisations are able to reach similar a performance (R2 of 0.867); moreover VH polarisation seems to carry out more descriptive in- formation when compared with VV (specially when using only 3 months Radar data was collected from two time windows each corresponding data). Finally, performance results seem to be independent of the yearly radar data time-window.