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Estimation of spatially enhanced soil moisture combining remote sensing and artificial intelligence approaches
- Moosavi, Vahid, Talebi, Ali, Mokhtari, Mohammad Hossein, Hadian, Mohammad Reza
- International journal of remote sensing 2016 v.37 no.23 pp. 5605-5631
- Landsat, algorithms, artificial intelligence, data collection, irrigation, models, moderate resolution imaging spectroradiometer, remote sensing, soil water, temperature, vegetation cover, vegetation index
- The main objective of this study is to combine remote-sensing and artificial intelligence (AI) approaches to estimate surface soil moisture (SM) at 100 m spatial and daily temporal resolution. The two main variables used in the Triangle method, that is, land-surface temperature (LST) and vegetation cover, were downscaled and calculated at 100 m spatial resolution. LSTs were downscaled applying the Wavelet-Artificial Intelligence Fusion Approach (WAIFA) on Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat imageries. Vegetation fractions were also estimated at 100 m spatial resolution using linear spectral un-mixing and Wavelet–AI models. Vegetation indices (VIs) were replaced with the vegetation fractions obtained from sub-pixel classification in the T ₛ–VI triangle space. The downscaled data were then used for calculating the evaporative fraction (EF), temperature-vegetation-dryness index (TVDI), vegetation temperature condition index (VTCI), and temperature-vegetation index (TVX) at 100 m spatial resolution. Thereafter, surface SM modelling was performed using a combination of Particle Swarm Optimization with Adaptive Neuro Fuzzy Inference System (PSO-ANFIS) and Support Vector Regression (PSO-SVR) modelling approaches. Results showed that the best input data set to estimate SM includes EF, TVDI, T ₛ, F ᵥₑgₑₜₐₜᵢₒₙ, F ₛₒᵢₗ, temperature (T), precipitation at time t (P ₜ, P ₜ –₁, P ₜ –₂), and irrigation (I). It was also confirmed that PSO-SVR outperformed the PSO-ANFIS modelling approach and could estimate SM with a coefficient of determination (R ²) of 0.93 and a root mean square error (RMSE) of 1.29 at 100 spatial resolution. Range of error was limited between −2.64% and 2.8%. It was also shown that the method proposed by Tang et al., (2010) improved the final SM estimations.