Accurate near-surface soil moisture (θ; ~ 5 cm) estimation is one of the most crucial challenges in agricultural management and hydrological studies. This study aims to
map θ at high spatiotemporal resolution (17 m grid size, satellite overpass of 6 days) in a small-scale agroforestry experimental site (~ 30 ha) in southern Italy. The observation period is from November 2018 until March 2019. We employed an ensemble machine-learning method based on Random Forest (RF) to map θ. This RF method is based on three input data types: i) Sentinel-1 (S1) Synthetic Aperture
Radar (SAR) measurements, ii) terrain features, and iii) supporting values of sparse point-scale θ simulated in HYDRUS-1D. We propose two different approaches to obtain supporting θ simulations via inverse modeling in HYDRUS1D. The first approach is based on θ simulated in HYDRUS1D, which was calibrated on soil moisture data monitored at two soil depths of 15 cm and 30 cm over 20 positions belonging to the SoilNet wireless sensor network installed in the experimental site. The second approach is based on the downscaling of field-scale θ simulated in HYDRUS-1D which was calibrated on Cosmic-Ray Neutron Probe (CRNP) data.
The field-scale θ was downscaled in order to obtain sparse point-scale supporting θ over the same 20 positions by using the physical-empirical Equilibrium Moisture from
Topography (EMT) model. The CRNP-based approach performed similarly to the one based on SoilNet data. Therefore, this study highlights the enormous potential for
modeling reliable θ maps by integrating soft data such as S1 SAR-based measurements, topographic information, and CRNP data, having the advantage of being non-invasive and easy to maintain.
%0 Journal Article
%1 display:block).nova-c-button--color-blue.nova-c-button--theme-solid:focus-visible-webkit-box-shadow:0
%A Taghavi Bayat, Aida
%A Schönbrodt-Stitt, Sarah
%A Nasta, Paolo
%A Ahmadian, Nima
%A Conrad, Christopher
%A Bogena, Heye R.
%A Vereecken, Harry
%A Jakobi, Jannis
%A Baatz, Roland
%A Romano, Nunzio
%D 2020
%I IEEE Xplore Digital Library
%K Schoenbrodt article conference lsfe myown oral proceedings
%R 10.1109/MetroAgriFor50201.2020.9277557
%T Mapping near-surface soil moisture in a Mediterranean agroforestry ecosystem using Cosmic-Ray Neutron Probe and Sentinel-1 Data
%X Accurate near-surface soil moisture (θ; ~ 5 cm) estimation is one of the most crucial challenges in agricultural management and hydrological studies. This study aims to
map θ at high spatiotemporal resolution (17 m grid size, satellite overpass of 6 days) in a small-scale agroforestry experimental site (~ 30 ha) in southern Italy. The observation period is from November 2018 until March 2019. We employed an ensemble machine-learning method based on Random Forest (RF) to map θ. This RF method is based on three input data types: i) Sentinel-1 (S1) Synthetic Aperture
Radar (SAR) measurements, ii) terrain features, and iii) supporting values of sparse point-scale θ simulated in HYDRUS-1D. We propose two different approaches to obtain supporting θ simulations via inverse modeling in HYDRUS1D. The first approach is based on θ simulated in HYDRUS1D, which was calibrated on soil moisture data monitored at two soil depths of 15 cm and 30 cm over 20 positions belonging to the SoilNet wireless sensor network installed in the experimental site. The second approach is based on the downscaling of field-scale θ simulated in HYDRUS-1D which was calibrated on Cosmic-Ray Neutron Probe (CRNP) data.
The field-scale θ was downscaled in order to obtain sparse point-scale supporting θ over the same 20 positions by using the physical-empirical Equilibrium Moisture from
Topography (EMT) model. The CRNP-based approach performed similarly to the one based on SoilNet data. Therefore, this study highlights the enormous potential for
modeling reliable θ maps by integrating soft data such as S1 SAR-based measurements, topographic information, and CRNP data, having the advantage of being non-invasive and easy to maintain.
%@ 978-1-7281-8783-9
@article{display:block){.nova-c-button--color-blue.nova-c-button--theme-solid:focus-visible{-webkit-box-shadow:0,
abstract = {Accurate near-surface soil moisture (θ; ~ 5 cm) estimation is one of the most crucial challenges in agricultural management and hydrological studies. This study aims to
map θ at high spatiotemporal resolution (17 m grid size, satellite overpass of 6 days) in a small-scale agroforestry experimental site (~ 30 ha) in southern Italy. The observation period is from November 2018 until March 2019. We employed an ensemble machine-learning method based on Random Forest (RF) to map θ. This RF method is based on three input data types: i) Sentinel-1 (S1) Synthetic Aperture
Radar (SAR) measurements, ii) terrain features, and iii) supporting values of sparse point-scale θ simulated in HYDRUS-1D. We propose two different approaches to obtain supporting θ simulations via inverse modeling in HYDRUS1D. The first approach is based on θ simulated in HYDRUS1D, which was calibrated on soil moisture data monitored at two soil depths of 15 cm and 30 cm over 20 positions belonging to the SoilNet wireless sensor network installed in the experimental site. The second approach is based on the downscaling of field-scale θ simulated in HYDRUS-1D which was calibrated on Cosmic-Ray Neutron Probe (CRNP) data.
The field-scale θ was downscaled in order to obtain sparse point-scale supporting θ over the same 20 positions by using the physical-empirical Equilibrium Moisture from
Topography (EMT) model. The CRNP-based approach performed similarly to the one based on SoilNet data. Therefore, this study highlights the enormous potential for
modeling reliable θ maps by integrating soft data such as S1 SAR-based measurements, topographic information, and CRNP data, having the advantage of being non-invasive and easy to maintain. },
added-at = {2020-11-26T20:02:07.000+0100},
author = {Taghavi Bayat, Aida and Schönbrodt-Stitt, Sarah and Nasta, Paolo and Ahmadian, Nima and Conrad, Christopher and Bogena, Heye R. and Vereecken, Harry and Jakobi, Jannis and Baatz, Roland and Romano, Nunzio},
biburl = {https://www.bibsonomy.org/bibtex/2143c21857af666c6e2e8a8c236e737a0/earthobs_uniwue},
doi = {10.1109/MetroAgriFor50201.2020.9277557},
eventdate = {2020 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor)},
eventtitle = {2020 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor)},
interhash = {010de95def323ce4bbc5e9a563ea02ee},
intrahash = {143c21857af666c6e2e8a8c236e737a0},
isbn = {978-1-7281-8783-9},
keywords = {Schoenbrodt article conference lsfe myown oral proceedings},
language = {English},
month = {November},
organization = {2020 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor)},
publisher = {IEEE Xplore Digital Library},
timestamp = {2020-12-10T10:10:08.000+0100},
title = {Mapping near-surface soil moisture in a Mediterranean agroforestry ecosystem using Cosmic-Ray Neutron Probe and Sentinel-1 Data},
venue = {Trente, Italy},
year = 2020
}