TY - DATA
T1 - Data for the article: "Root zone soil moisture estimation with Random Forests"
PY - 2020/12/18
AU - WUR Data Librarian
AU - Corjan Nolet
AU - Michiel Pezij
AU - M.J. (Martine) van der Ploeg
UR - https://data.4tu.nl/articles/dataset/Data_for_the_article_Root_zone_soil_moisture_estimation_with_Random_Forests_/13148231/1
DO - 10.4121/13148231.v1
KW - Random Forests modeling approach
KW - soil moisture contents
N2 - The
dataset contains information on soil moisture, meteorological conditions, land
cover, and soil hydrological groups for the soil moisture stations
installed within the Raam soil moisture network. The network has a
total of 15 stations within the Raam catchment, located in
the southeastern portion of the Netherlands. The datasets covers the periods from April 2016 to December 2018. It was used for predicting root zone soil moisture using a Random
Forest model and a 1-dimensional process-based model.<p><br>

</p>
<p>For
each station, daily in situ measurements of surface soil moisture
(SSM) at 5 cm and zone weighted depth-averaged root zone soil
moisture (RZSM) are given. The meteorological conditions are obtained
from daily datasets available from KNMI. First, the measurements from KNMI meteorological stations are interpolated in order to get
spatially distributed values covering the study sites. The values
from the interpolated maps were extracted for each point in the Raam
network. <br></p><p><br></p><p>The vegetation characteristics are represented by leaf area
index (LAI) obtained from MODIS. The crops at each station for each
year are obtained from fieldwork data. The BOdemFysische Eenheden
Kaart (BOFEK2012, Wosten et al., 2013), which is a map of soil
hydro-physical properties for the Netherlands, was the basis for the
information on the soil groups at the study sites.</p>
<p><br>
<br>

</p>
ER -