Discussing the state-of-the-art in the remote sensing of surface turbulent heat fluxes and soil surface moisture content, this book offers the most up-to-date understanding of the natural processes of earth systems and their interactions with man-made activities. Identifying effective, accurate, and practical methods, it allows researchers to obtain much needed data on the soilscape at decreased cost: both reducing the amount of field data collection and increasing coverage area. An all-inclusive overview of methods and modeling techniques, it provides case studies and considers future trends, prospects, and scientific challenges.
Physics, Conventional Measurement Methods, Remote Sensing Principles and Sensors: Energy Fluxes and Soil Moisture at the Earth’s Surface: Importance, Physics and Theory. Conventional Approaches and Ground Observational Networks in the Estimation of Surface Heat Fluxes and Soil Moisture Content. Remote Sensing Technology in the Estimation of Energy Fluxes and Soil Moisture Content: Principles, Sensors, Operational Products Available. Surface Energy Fluxes Estimation by Remote Sensing: Algorithms, Techniques, Applications: Surface Energy Fluxes Estimation by Remote Sensing: State-of-the-Art Overview. One-Layer Models in the Estimation of Turbulent Heat Fluxes (i.e.: SEBS, SEBAL, METRIC): A Case-Study. Two-Source Modelling Approaches in the Estimation of Turbulent Heat Fluxes (i.e. Norman et al., 1995, ALEXI/DISALEXI, TSEB): A Case Study. Methods Based on the Use of a Surface Temperature and Fractional Vegetation Cover Scatterplot Domain: A Case Study. Data Assimilation Approaches in the Estimation of Turbulent Heat Fluxes: A Case Study. Soil Surface Moisture Content Estimation by Remote Sensing: Algorithms, Techniques, Applications: Soil Surface Moisture Content Estimation by Remote Sensing: State-of-the-Art Overview. Optical and Thermal Remote Sensing of Surface Soil Moisture: A Case Study. Microwave Remote Sensing Methods of Surface Soil Moisture Content: A Case Study. Data Fusion Methods for the Estimation of Soil Moisture by Remote Sensing: Case Studies.