11 Jan 2018
11 Jan 2018
The Raam regional soil moisture monitoring network in the Netherlands
Harm-Jan F. Benninga et al.
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Cited
11 citations as recorded by crossref.
- Applying transfer function-noise modelling to characterize soil moisture dynamics: a data-driven approach using remote sensing data M. Pezij et al. 10.1016/j.envsoft.2020.104756
- Modified monitoring system of soil temperature based on ARM M. Liu 10.1016/j.eti.2020.101346
- State updating of root zone soil moisture estimates of an unsaturated zone metamodel for operational water resources management M. Pezij et al. 10.1016/j.hydroa.2019.100040
- Anatomy of the 2018 agricultural drought in the Netherlands using in situ soil moisture and satellite vegetation indices J. Buitink et al. 10.5194/hess-24-6021-2020
- Root zone soil moisture estimation with Random Forest C. Carranza et al. 10.1016/j.jhydrol.2020.125840
- Monitoring agricultural field trafficability using Sentinel-1 C. Carranza et al. 10.1016/j.agwat.2019.105698
- Sentinel-1 soil moisture content and its uncertainty over sparsely vegetated fields H. Benninga et al. 10.1016/j.hydroa.2020.100066
- Initial Machine Learning Framework Development of Agriculture Cyber Physical Systems G. Airlangga & A. Liu 10.1088/1742-6596/1196/1/012065
- Towards Estimating Land Evaporation at Field Scales Using GLEAM B. Martens et al. 10.3390/rs10111720
- The Texas Soil Observation Network:A Comprehensive Soil Moisture Dataset for Remote Sensing and Land Surface Model Validation T. Caldwell et al. 10.2136/vzj2019.04.0034
- Machine Learning to Estimate Surface Soil Moisture from Remote Sensing Data H. Adab et al. 10.3390/w12113223
10 citations as recorded by crossref.
- Applying transfer function-noise modelling to characterize soil moisture dynamics: a data-driven approach using remote sensing data M. Pezij et al. 10.1016/j.envsoft.2020.104756
- Modified monitoring system of soil temperature based on ARM M. Liu 10.1016/j.eti.2020.101346
- State updating of root zone soil moisture estimates of an unsaturated zone metamodel for operational water resources management M. Pezij et al. 10.1016/j.hydroa.2019.100040
- Anatomy of the 2018 agricultural drought in the Netherlands using in situ soil moisture and satellite vegetation indices J. Buitink et al. 10.5194/hess-24-6021-2020
- Root zone soil moisture estimation with Random Forest C. Carranza et al. 10.1016/j.jhydrol.2020.125840
- Monitoring agricultural field trafficability using Sentinel-1 C. Carranza et al. 10.1016/j.agwat.2019.105698
- Sentinel-1 soil moisture content and its uncertainty over sparsely vegetated fields H. Benninga et al. 10.1016/j.hydroa.2020.100066
- Initial Machine Learning Framework Development of Agriculture Cyber Physical Systems G. Airlangga & A. Liu 10.1088/1742-6596/1196/1/012065
- Towards Estimating Land Evaporation at Field Scales Using GLEAM B. Martens et al. 10.3390/rs10111720
- The Texas Soil Observation Network:A Comprehensive Soil Moisture Dataset for Remote Sensing and Land Surface Model Validation T. Caldwell et al. 10.2136/vzj2019.04.0034
1 citations as recorded by crossref.
Latest update: 20 Jan 2021
Short summary
Soil moisture is a central hydrological state variable. We set up a soil moisture and soil temperature profile monitoring network of 15 stations, distributed over the 495 km2 Raam region. The Raam catchment faces dry and wet periods, which both have implications for agricultural and regional water management. The measurements at 5 cm depth provide a reference for soil moisture retrievals from earth observations, while the measurements at deeper layers enable investigation of root zone processes.
Soil moisture is a central hydrological state variable. We set up a soil moisture and soil...