An algorithm to estimate soil moisture over vegetated areas based on in situ and remote sensing information

• Published in: International journal of remote sensing Vol. 31; no. 10; pp. 2655 - 2679
• Main Authors: Ramírez-Beltran, N. D., Calderón-Arteaga, C., Harmsen, E., Vasquez, R., Gonzalez, J.
• Format: Journal Article
• Language: English
• Published: Abingdon Taylor & Francis 20.05.2010
• Subjects: Algorithms; Animal, plant and microbial ecology; Applied geophysics; Biological and medical sciences; Earth sciences; Earth, ocean, space; Estimates; Estimating; Exact sciences and technology; Fundamental and applied biological sciences. Psychology; General aspects. Techniques; Internal geophysics; Learning theory; Neural networks; Remote sensing; Soil moisture; Teledetection and vegetation maps; Texture; Puerto Rico; Antilles; Greater Antilles; West Indies; Caribbean region; ASW, Caribbean Sea, Greater Antilles, Puerto Rico; Short term; algorithms; Spatial variability; time variations; Vegetation index; neural networks; vegetation; exhibits; Information; Modeling; Stochastic system; artificial intelligence; spatial resolution; soil moisture; Average; remote sensing; Representation; utilization; Long term; Empirical model; in situ; depth; Nonlinearity; transfer functions; statistical analysis
• ISSN: 0143-1161; 1366-5901
• DOI: 10.1080/01431160903085628

An algorithm is proposed for estimating soil moisture over vegetated areas. The algorithm uses in situ and remote sensing information and statistical tools to estimate soil moisture at 1 km spatial resolution and at 20 cm depth over Puerto Rico. Soil moisture within the study region is characterized by spatial and temporal variability. The temporal variability for a given area exhibits long- and short-term variations that can be expressed by two empirical models. The average monthly soil moisture exhibits the long-term variability and is modelled by an artificial neural network (ANN), whereas the short-term variability is determined by hourly variation and is represented by a nonlinear stochastic transfer function model. Monthly vegetation index, land surface temperature, accumulated rainfall and soil texture are the major drivers of the ANN to estimate the monthly soil moisture. Radar, satellite and in situ observations are the major sources of information of the soil moisture empirical models. A self-organized ANN was also used to identify spatial variability to be able to determine a similar transfer function that best resembles the properties of a particular grid point and estimate the hourly soil moisture across the island. Validation techniques reveal an average absolute error of 3.34% of volumetric water content and this result shows that the proposed algorithm is a potential tool for estimating soil moisture over vegetated areas.