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

Abstract

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.

Potential of Remote Sensing Images for Soil Moisture Retrieving Using Ensemble Learning Methods in Vegetation-Covered Area

“…Studies by many scholars have shown that radar signals VV and VH together give better soil moisture prediction results than VV or VH signals alone [42] .In particular, DEM can be used as reliable data for inversion of soil moisture when topography has some control on soil moisture movement [43,44]. Soil roughness is also an important parameter for soil moisture inversion, but the actual measurement of soil roughness is a difficult task [45,46]. Especially at the time that has passed, we cannot get the soil roughness value at that time.…”

Machine Learning for Smart Irrigation in Agriculture: How Far along Are We?

“…Through the capture of critical features in early layers and intricate patterns in subsequent levels, they are intended to understand the spatial hierarchy of features. They have been exploited for smart irrigation to estimate soil moisture from the in situ field or remotely (from satellite [40], Unmanned Aerial Vehicle [42] or airborne [41] acquired images [43]). Some works make use of CNNs to identify probable plant illnesses associated with irrigation systems [51].…”

Soil Moisture Mapping in Indian Tropical Island Ecosystem using C-band SAR Data and Neural Network Models

“…The model adopted by (Pathe et al 2009) estimated the soil moisture by using the estimated backscatter coefficient of ASAR satellite data. Several other authors have attempted to estimate soil moisture using hybrid or neural networkbased models (Baghdadi et al 2002;Mirsoleimani et al 2019;Hamze et al 2021;Hegazi et al 2021;Nativel et al 2022;Rodolfo 1969).…”