Remote sensing (RS) plays an important role gathering data in many critical domains (eg,
global climate change, risk assessment and vulnerability reduction of natural hazards …

The continuous increase in the volume of geospatial data has led to the creation of storage
tools and the cloud to process data. Google Earth Engine (GEE) is a cloud-based platform …

The hydrologic community has experienced a surge in interest in machine learning in recent
years. This interest is primarily driven by rapidly growing hydrologic data repositories, as …

The spatial information of rockhead is crucial for the design and construction of tunneling or
underground excavation. Although the conventional site investigation methods (ie borehole …

Incomplete representations of physical processes often lead to structural errors in process-
based (PB) hydrologic models. Machine learning (ML) algorithms can reduce streamflow …

Tile drainage is one of the dominant agricultural management practices in the United States
and has greatly expanded since the late 1990s. It has proven effects on land surface water …

Exposure to arsenic through private drinking water wells causes serious human health risks
throughout the globe. Water testing data indicates there is arsenic contamination in private …

Over the past four decades, satellite systems and land surface models have been used to
estimate global-scale surface soil moisture (SSM). However, in areas such as densely …

As climate change shifts crop exposure to dry and wet extremes, a better understanding of
factors governing crop response is needed. Recent studies identified shallow groundwater …

Due to its relation to the Earth's climate and weather and phenomena like drought, flooding,
or landslides, knowledge of the soil moisture content is valuable to many scientific and …