期刊
INTERNATIONAL JOURNAL OF REMOTE SENSING
卷 37, 期 23, 页码 5605-5631出版社
TAYLOR & FRANCIS LTD
DOI: 10.1080/01431161.2016.1244366
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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 unmixing 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, Pt-1, Pt-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.
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