Bodemvocht uit satellietdata: wat kan de Nederlandse waterbeheerder ermee?

Het onderzoeksproject ‘Optimizing Water Availability with Sentinel-1 Satellites’ heeft als doel te onderzoeken hoe satellietdata gebruikt kan worden in het Nederlandse waterbeheer. Het onderzoek laat zien dat de satelliet Sentinel-1 buiten het groeiseizoen om al een vrij goed beeld geeft van het bodemvochtgehalte. Hiermee kan bijvoorbeeld de berijdbaarheid van landbouwpercelen in kaart gebracht kan worden. Ook is met Deltares en HKV een data-assimilatietool ontwikkeld die ingezet kan worden om simulaties met het Landelijk Hydrologisch Model te verbeteren

Data underlying the publication: Applying transfer function-noise modelling to characterize soil moisture dynamics: a data-driven approach using remote sensing data

This dataset includes the input data, Python scripts, and Pastas model output for the scientific manuscript "Applying transfer function-noise modelling to characterize soil moisture dynamics: a data-driven approach using remote sensing data". The manuscript is currently under review. The data covers the years 2016, 2017, and 2018. We refer to the readme file included in the dataset for further details

Bodemvocht uit satellietdata: wat kan de Nederlandse waterbeheerder ermee?

Het onderzoeksproject ‘Optimizing Water Availability with Sentinel-1 Satellites’ heeft als doel te onderzoeken hoe satellietdata gebruikt kan worden in het Nederlandse waterbeheer. Het onderzoek laat zien dat de satelliet Sentinel-1 buiten het groeiseizoen om al een vrij goed beeld geeft van het bodemvochtgehalte. Hiermee kan bijvoorbeeld de berijdbaarheid van landbouwpercelen in kaart gebracht kan worden. Ook is met Deltares en HKV een data-assimilatietool ontwikkeld die ingezet kan worden om simulaties met het Landelijk Hydrologisch Model te verbeteren

Reconstructing Environmental Variables with Missing Field Data via End-to-End Machine Learning

Real-world time series often present missing values due to sensor malfunctions or human errors. Traditionally, missing values are simply omitted or reconstructed through imputation or interpolation methods. Omitting missing values may cause temporal discontinuity. Reconstruction methods, on the other hand, alter in some way the original time series. In this paper, we consider an application in the field of meteorological variables that exploits end-to-end machine learning. The idea is to entrust the task of dealing with missing values to a suitably trained recurrent neural network that completely by-passes the phase of reconstruction of missing values. A difficult case of reproduction of a rainfall field from five rain gauges in Northern Italy is used as an example, and the results are compared to those computed by more traditional methods. The proposed methodology is general-purpose and can be easily applied to every kind of spatial time series prediction problem, quite common in many environmental studies

The Raam regional soil moisture monitoring network in the Netherlands

We have established a soil moisture profile monitoring network in the Raam region in the Netherlands. This region faces water shortages during summers and excess of water during winters and after extreme precipitation events. Water management can benefit from reliable information on the soil water availability and water storing capacity in the unsaturated zone. In situ measurements provide a direct source of information on which water managers can base their decisions. Moreover, these measurements are commonly used as a reference for the calibration and validation of soil moisture content products derived from earth observations or obtained by model simulations. Distributed over the Raam region, we have equipped 14 agricultural fields and 1 natural grass field with soil moisture and soil temperature monitoring instrumentation, consisting of Decagon 5TM sensors installed at depths of 5, 10, 20, 40 and 80 cm. In total, 12 stations are located within the Raam catchment (catchment area of 223 km2), and 5 of these stations are located within the closed sub-catchment Hooge Raam (catchment area of 41 km2). Soil-specific calibration functions that have been developed for the 5TM sensors under laboratory conditions lead to an accuracy of 0.02 m3 m−3. The first set of measurements has been retrieved for the period 5 April 2016–4 April 2017. In this paper, we describe the Raam monitoring network and instrumentation, the soil-specific calibration of the sensors, the first year of measurements, and additional measurements (soil temperature, phreatic groundwater levels and meteorological data) and information (elevation, soil physical characteristics, land cover and a geohydrological model) available for performing scientific research. The data are available at https://doi.org/10.4121/uuid:dc364e97-d44a-403f-82a7-121902deeb56

Regional soil moisture monitoring network in the Raam catchment in the Netherlands - 2016-04 / 2017-04 [version 1]

Measurements from 2016-04 to 2017-04

Regional soil moisture monitoring network in the Raam catchment in the Netherlands - 2018-04 / 2019-04

The Raam soil moisture measurement network dataset contains soil moisture and soil temperature measurements for 15 locations in the Raam, which is a 223-km2 river catchment in the southeast of the Netherlands. The network monitors soil moisture in the unsaturated zone for different soil textures and land covers present in the area, and it covers the topographic gradient of the region. At each location we installed Decagon 5TM sensors at depths of 5 cm, 10 cm, 20 cm, 40 cm and 80 cm. The logging time interval is set on 15 minutes. The Raam network is operational since April 2016 and is measurements are on-going. In ‘additional_datasets.txt’ we describe additional datasets which are freely available for the Raam catchment (elevation, soil physical, land use, groundwater level and meteorological data)

Regional soil moisture monitoring network in the Raam catchment in the Netherlands - 2016-04 / 2017-04 (corrected)

Note: The original dataset (https://data.4tu.nl/repository/uuid:dc364e97-d44a-403f-82a7-121902deeb56) contains errors. Some loggers were not correctly set to cope with daylight saving time differences. Therefore, some data are incorrectly shifted by an hour. This shift is corrected in this dataset. Original text: The Raam soil moisture measurement network dataset contains soil moisture and soil temperature measurements for 15 locations in the Raam, which is a 223-km2 river catchment in the southeast of the Netherlands. The network monitors soil moisture in the unsaturated zone for different soil textures and land covers present in the area, and it covers the topographic gradient of the region. At each location we installed Decagon 5TM sensors at depths of 5 cm, 10 cm, 20 cm, 40 cm and 80 cm. The logging time interval is set on 15 minutes. The Raam network is operational since April 2016 and the measurements are on-going