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.
Highlights
The most common techniques used for estimating soil moisture content in situ are gravimetric, neutron probe, time domain reflectometry (TDR), frequency domain reflectometry (FDR), and tensiometer techniques [9]
We found that the most appropriate convolutional neural network (CNN) architecture for Sentinel-1 data are CNN architecture (C), as elaborated later in this paper
This figure illustrates the relationship between in-situ soil moisture and predicted soil moisture, clarifying R2, mean absolute error (MAE), and root mean square error (RMSE) metrics, for (1) each band separately; (2) Sigma0_VH and Sigma0_VV; (3) Gamma0_VH and Gamma0_VV; (4) Beta0_VH and
Summary
Work should be done to increase agricultural production and improve water management, to absorb population growth and its requirements for food and water
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