Deep learning and data fusion to estimate surface soil moisture from multi-sensor satellite images

Abhilash Singh; Kumar Gaurav

doi:10.1038/s41598-023-28939-9

Deep learning and data fusion to estimate surface soil moisture from multi-sensor satellite images

Sci Rep. 2023 Feb 8;13(1):2251. doi: 10.1038/s41598-023-28939-9.

Authors

Abhilash Singh¹, Kumar Gaurav²

Affiliations

¹ Fluvial Geomorphology and Remote Sensing Laboratory, Department of Earth and Environmental Sciences, Indian Institute of Science Education and Research, Bhopal, 462066, Madhya Pradesh, India.
² Fluvial Geomorphology and Remote Sensing Laboratory, Department of Earth and Environmental Sciences, Indian Institute of Science Education and Research, Bhopal, 462066, Madhya Pradesh, India. kgaurav@iiserb.ac.in.

Abstract

We propose a new architecture based on a fully connected feed-forward Artificial Neural Network (ANN) model to estimate surface soil moisture from satellite images on a large alluvial fan of the Kosi River in the Himalayan Foreland. We have extracted nine different features from Sentinel-1 (dual-polarised radar backscatter), Sentinel-2 (red and near-infrared bands), and Shuttle Radar Topographic Mission (digital elevation model) satellite products by leveraging the linear data fusion and graphical indicators. We performed a feature importance analysis by using the regression ensemble tree approach and also feature sensitivity to evaluate the impact of each feature on the response variable. For training and assessing the model performance, we conducted two field campaigns on the Kosi Fan in December 11-19, 2019 and March 01-06, 2022. We used a calibrated TDR probe to measure surface soil moisture at 224 different locations distributed throughout the fan surface. We used input features to train, validate, and test the performance of the feed-forward ANN model in a 60:10:30 ratio, respectively. We compared the performance of ANN model with ten different machine learning algorithms [i.e., Generalised Regression Neural Network (GRNN), Radial Basis Network (RBN), Exact RBN (ERBN), Gaussian Process Regression (GPR), Support Vector Regression (SVR), Random Forest (RF), Boosting Ensemble Learning (Boosting EL), Recurrent Neural Network (RNN), Binary Decision Tree (BDT), and Automated Machine Learning (AutoML)]. We observed that the ANN model accurately predicts the soil moisture and outperforms all the benchmark algorithms with correlation coefficient (R = 0.80), Root Mean Square Error (RMSE = 0.040 [Formula: see text]), and bias = 0.004 [Formula: see text]. Finally, for an unbiased and robust conclusion, we performed spatial distribution analysis by creating thirty different sets of training-validation-testing datasets. We observed that the performance remains consistent in all thirty scenarios. The outcomes of this study will foster new and existing applications of soil moisture.