Impact of Soil Heterogeneity on Deep Learning Performance Metrics for Surface Soil Moisture Prediction

Although there is a growth of interest in academia in applying remote sensing and particularly Deep Learning (DL) model development techniques, in exploring and computing surface soil moisture over a large area, most of the state-of-the-art works in this field have mainly centered on improving model accuracy. Few works have focused on revealing the influence of soil heterogeneity. This study has clearly focused on evaluating the consequences of soil type heterogeneity on the performance of Deep Learning (DL) models. The study developed the DL models first enhanced by Residual Learning (RL) and Squeeze and Excitation (SE) mechanisms. The models developed also used a data fusion mechanism where Synthetic Aperture Radar (SAR), Normalized Difference Vegetation Index (NDVI), and Red, Green, Blue (RGB) imagery datasets were stacked together. Upon model completion, the study tested the effect of sand, clay, vegetated, and bare soilson the performance of the developed models. The Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) were the performance measurement metrics that were used to assess the effects of soil variation on the model’s performance. The Results indicate that predictive accuracy varies considerably across soil types, with vegetated soils achieving the lowest RMSE (0.0099 m³/m³) and bare soils the highest (0.014 m³/m³). Comparative analysis with standard models, including LightGBM, LSTM, and SMAP filters, proved that the enhanced model with SE and RL models outperformed the majority of state-of-the-art models, achieving lower error rates and improved performance metrics. The results in this study have stressed the influence of soil heterogeneity on deep learning model design and performance assessments. The results suggest that soil heterogeneity affects the conclusion and generalization of the performance of most DL models. Model’s performancemay vary depending on which soil type whose datasets were considered under similar experiments. This outcome provides a milestone and an overview toward the development and implementation of soil moisture models, which are to be developed under different soil types.