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Traditional methods for aerosol retrieval and atmospheric correction of remote sensing data over water surfaces are based on the assumption of zero water reflectance in the near-infrared.
Two numerical examples demonstrate the accuracy and efficiency of our approach: inversion of an integral equation in atmospheric remote sensing, where the data dimension is very high; and the inference of a heterogeneous transmissivity field in a groundwater system, which involves a partial differential equation forward model with high dimensional state and parameters.
The LPJ-DGVM has been extensively validated using various data from atmospheric measurements, active and passive remote sensing data, and flux measurements.
Identification of inoculum sources of economically important plant pathogens, as well as highly allergenic bioaerosols like Cladosporium species, has not been yet explored with remote sensing data and atmospheric models such as Hybrid Single Particle Lagrangian Integrated Trajectory HYSPLITT).
They used remote sensing data for precipitation, radiation, and temperature to represent the atmosphere.
Remote sensing data can be used to access information which is closely related to an earthquake.
Recently, use of remote sensing data for determining the orientation of stress has been demonstrated.
The model setup and calibration is based almost entirely on globally available remote sensing data.
An increase in satellite and remote sensing data has also contributed to the data explosion.
The state of terrestrial vegetation has been monitored using remote sensing data for decades.
Over my career I've carried out a number of roles, including work on remote sensing data and observation data.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com