ABSTRACT:

This dataset contains observations made by smartphone and an additional handheld sensor in order to estimate evapotranspiration. These include luminence, surface temperature, air temperature, air pressure, relative humidity, and wind speed. The dataset also contains hourly routine meteorological observations, as well as evapotranspiration observations by a set of 6 weighing lysimeters and eddy covariance, all collected at the Rollesbroich TERENO site, which is operated by the Institute of Bio- and Geosciences of FZ Julich. The smartphone observations were used to train a simple machine learning algorithm on the observed fluxes.

ABSTRACT:

This collection contains observations made by smartphone and an additional handheld sensor in order to estimate evapotranspiration. These include luminence, surface temperature, air temperature, air pressure, relative humidity, and wind speed. The dataset also contains hourly routine meteorological observations, as well as evapotranspiration observations by weighing lysimeters and eddy covariance, collected at various sites in The Netherlands, Germany, and Switzerland. The smartphone observations were used to train a simple machine learning algorithm on the observed fluxes.

ABSTRACT:

This dataset contains observations made by smartphone and an additional handheld sensor in order to estimate evapotranspiration. These include luminence, surface temperature, air temperature, air pressure, relative humidity, and wind speed. The dataset also contains hourly routine meteorological observations, as well as evapotranspiration observations by a large weighing lysimeter and eddy covariance, all collected at the Büel meteorological station in the Rietholzbach catchment, which is operated by the Land-Climate Dynamics group at the Institute for Atmospheric and Climate Science (IAC), ETH Zurich. The smartphone observations were used to train a simple machine learning algorithm on the observed fluxes.