Journal Article

A Convolutional Neural Network Algorithm for Soil Moisture Prediction from Sentinel-1 SAR Images

Yang, Lingbo, Hegazi, Ehab H., Huang, Jingfeng

2021

Overview

Achieving the rational, optimal, and sustainable use of resources (water and soil) is vital to drink and feed 9.725 billion by 2050. Agriculture is the first source of food production, the biggest consumer of freshwater, and the natural filter of air purification. Hence, smart agriculture is a “ray of hope” in regard to food, water, and environmental security. Satellites and artificial intelligence have the potential to help agriculture flourish. This research is an essential step towards achieving smart agriculture. Prediction of soil moisture is important for determining when to irrigate and how much water to apply, to avoid problems associated with over- and under-watering. This also contributes to an increase in the number of areas being cultivated and, hence, agricultural productivity and air purification. Soil moisture measurement techniques, in situ, are point measurements, tedious, time-consuming, expensive, and labor-intensive. Therefore, we aim to provide a new approach to detect moisture content in soil without actually being in contact with it. In this paper, we propose a convolutional neural network (CNN) architecture that can predict soil moisture content over agricultural areas from Sentinel-1 images. The dual-pol (VV–VH) Sentinel-1 SAR data have being utilized (V = vertical, H = horizontal). The CNN model is composed of six convolutional layers, one max-pooling layer, one flatten layer, and one fully connected layer. The total number of Sentinel-1 images used for running CNN is 17,325 images. The best values of the performance metrics (coefficient of determination (R2=0.8664), mean absolute error (MAE=0.0144), and root mean square error (RMSE=0.0274)) have been achieved due to the use of Sigma naught VH and Sigma naught VV as input data to the CNN architecture (C). Results show that VV polarization is better than VH polarization for soil moisture retrieval, and that Sigma naught, Gamma naught, and Beta naught have the same influence on soil moisture estimation.

• Publisher: MDPI AG
• Subject: Agricultural production / Agricultural research / Agriculture / air / Air purification / Algorithms / Artificial intelligence / Artificial neural networks / convolutional neural network (CNN) / Deep learning / Digital agriculture / Environmental security / Food / Food production / Food sources / freshwater / Humidity / Irrigation / Machine learning / Moisture content / Neural networks / Performance measurement / Polarization / Precipitation / prediction / Remote sensing / Root-mean-square errors / Satellites / Sentinel-1 / Soil moisture / Soil water / soil water content / Sustainable use / Synthetic aperture radar / Water content