About this Research Topic
Remote sensing and mechanistic models are two methods for monitoring terrestrial water cycle components and studying natural changes. The key components of terrestrial water cycle have been monitored by remote sensing missions, such as soil moisture (i.e., AMSR, SMAP, SMOS), total water storage change (i.e., GRACE), precipitation (i.e., TRMM, GPM), water level (i.e., TOPEX), vegetation phenology (i.e., MODIS, OCO-2), and land surface temperature (i.e., MODIS, Landsat). Mechanistic models (hydrologic models and land surface models) have been developed to simulate key variables in terrestrial water cycle over all the local, regional, continental, and global scales. Data assimilation, machine learning, and deep learning techniques can be used to integrate remote sensing, in-situ observations, and mechanistic models to monitor water cycle components, such as soil moisture, precipitation, evapotranspiration, surface runoff, terrestrial water storage, groundwater, streamflow, snow, ice, and glaciers.
This Research Topic targets to call for original work using multi-variable, multi-scale, multi-sensor approaches for studying and monitoring the important variables of water cycle. We welcome collaborative research that focus on (1) use of available satellite remote sensing data and airborne remote sensing techniques to address hydrologic questions and enrich knowledge in water cycles, (2) deep learning-mechanistic model integration method development and machine learning applications for monitoring the terrestrial water cycle components, (3) application of mechanistic modeling and data assimilation in terrestrial water cycles.
Keywords: Terrestrial Water Cycle, Remote Sensing, Hydrologic and Land Surface Modeling, Machine Learning, Data Assimilation
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