scholarly journals A dense network of cosmic-ray neutron sensors for soil moisture observation in a pre-alpine headwater catchment in Germany

2020 ◽  
Author(s):  
Benjamin Fersch ◽  
Till Francke ◽  
Maik Heistermann ◽  
Martin Schrön ◽  
Veronika Döpper ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Hydrological Modeling ◽  
Root Zone ◽  
Spatial Scales ◽  
Cosmic Ray ◽  
Dense Network ◽  
Data Set ◽  
Soil Moisture Dynamics ◽  
Moisture Dynamics ◽  
Headwater Catchment

Abstract. Monitoring soil moisture is still a challenge: it varies strongly in space and time and at various scales while well established sensors typically suffer from a small spatial support. With a sensor footprint up to several hectares, Cosmic-Ray Neutron Sensing (CRNS) is an emerging technology to address that challenge. So far, the CRNS method has typically been applied with single sensors or in sparse national scale networks. This study presents, for the first time, a dense network of 24 CRNS stations that covered, from May to July 2019, an area of just 1 km2: the pre-alpine Rott headwater catchment in Southern Germany which is characterized by strong soil moisture gradients in a heterogeneous landscape with forests and grasslands. With substantially overlapping sensor footprints, that network was designed to study root zone soil moisture dynamics at the catchment-scale. The observations of the dense CRNS network were complemented by extensive measurements that allow to study soil moisture variability at various spatial scales: roving (mobile) CRNS units, remotely sensed thermal images from Unmanned Areal Systems (UAS), permanent and temporary wireless sensor networks, profile probes as well as comprehensive manual soil sampling. Since neutron counts are also affected by hydrogen pools other than soil moisture, vegetation biomass was monitored in forest and grassland patches, as well as meteorological variables; discharge and groundwater tables were recorded to support hydrological modeling experiments. As a result, we provide a unique and comprehensive dataset to several research communities: to those who investigate the retrieval of soil moisture from cosmic-ray neutron sensing, to those who study the variability of soil moisture at different spatio-temporal scales, and to those who intend to better understand the role of root-zone soil moisture dynamics in the context of catchment and groundwater hydrology, as well as land – atmosphere exchange processes. The data set is available through EUDAT, splitted into the two subsets https://doi.org/10.23728/b2share.85fe0f9dac0f48df9215c17e65d1f1e1 (Fersch et al., 2020a) and https://doi.org/10.23728/b2share.93ed99e486904d48a8a6a68083066198 (Fersch et al., 2020b).

scholarly journals A dense network of cosmic-ray neutron sensors for soil moisture observation in a highly instrumented pre-Alpine headwater catchment in Germany

2020 ◽  
Vol 12 (3) ◽  
pp. 2289-2309
Author(s):  
Benjamin Fersch ◽  
Till Francke ◽  
Maik Heistermann ◽  
Martin Schrön ◽  
Veronika Döpper ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Hydrological Modeling ◽  
Root Zone ◽  
Spatial Scales ◽  
Cosmic Ray ◽  
Dense Network ◽  
Data Set ◽  
Soil Moisture Dynamics ◽  
Moisture Dynamics ◽  
Headwater Catchment

Abstract. Monitoring soil moisture is still a challenge: it varies strongly in space and time and at various scales while conventional sensors typically suffer from small spatial support. With a sensor footprint up to several hectares, cosmic-ray neutron sensing (CRNS) is a modern technology to address that challenge. So far, the CRNS method has typically been applied with single sensors or in sparse national-scale networks. This study presents, for the first time, a dense network of 24 CRNS stations that covered, from May to July 2019, an area of just 1 km2: the pre-Alpine Rott headwater catchment in Southern Germany, which is characterized by strong soil moisture gradients in a heterogeneous landscape with forests and grasslands. With substantially overlapping sensor footprints, this network was designed to study root-zone soil moisture dynamics at the catchment scale. The observations of the dense CRNS network were complemented by extensive measurements that allow users to study soil moisture variability at various spatial scales: roving (mobile) CRNS units, remotely sensed thermal images from unmanned areal systems (UASs), permanent and temporary wireless sensor networks, profile probes, and comprehensive manual soil sampling. Since neutron counts are also affected by hydrogen pools other than soil moisture, vegetation biomass was monitored in forest and grassland patches, as well as meteorological variables; discharge and groundwater tables were recorded to support hydrological modeling experiments. As a result, we provide a unique and comprehensive data set to several research communities: to those who investigate the retrieval of soil moisture from cosmic-ray neutron sensing, to those who study the variability of soil moisture at different spatiotemporal scales, and to those who intend to better understand the role of root-zone soil moisture dynamics in the context of catchment and groundwater hydrology, as well as land–atmosphere exchange processes. The data set is available through the EUDAT Collaborative Data Infrastructure and is split into two subsets: https://doi.org/10.23728/b2share.282675586fb94f44ab2fd09da0856883 (Fersch et al., 2020a) and https://doi.org/10.23728/b2share.bd89f066c26a4507ad654e994153358b (Fersch et al., 2020b).

scholarly journals Analysis of surface and root-zone soil moisture dynamics with ERS scatterometer and the hydrometeorological model SAFRAN-ISBA-MODCOU at Grand Morin watershed (France)

2008 ◽  
Vol 5 (4) ◽  
pp. 1903-1926 ◽  
Author(s):  
T. Paris Anguela ◽  
M. Zribi ◽  
S. Hasenauer ◽  
F. Habets ◽  
C. Loumagne
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Root Zone ◽  
Spatial Scales ◽  
Temporal Variations ◽  
Model Verification ◽  
Atmospheric Conditions ◽  
Soil Moisture Dynamics ◽  
Moisture Dynamics ◽  
Rms Errors

Abstract. Spatial and temporal variations of soil moisture strongly affect flooding, erosion, solute transport and vegetation productivity. Its characterization, offers an avenue to improve our understanding of complex land surface–atmosphere interactions. In this paper, soil moisture dynamics at soil surface (first centimeters) and root-zone (up to 1.5 m depth) are investigated at three spatial scales: local scale (field measurements), 8×8 km2 (hydrological model) and 25×25 km2 scale (ERS scatterometer) in a French watershed. This study points out the quality of surface and root-zone soil moisture data for SIM model and ERS scatterometer for a three year period. Surface soil moisture is highly variable because is more influenced by atmospheric conditions (rain, wind and solar radiation), and presents RMS errors up to 0.08 m3 m−3. On the other hand, root-zone moisture presents lower variability with small RMS errors (between 0.02 and 0.06 m3 m-3). These results will contribute to satellite and model verification of moisture, but also to better application of radar data for data assimilation in future.

scholarly journals Analysis of surface and root-zone soil moisture dynamics with ERS scatterometer and the hydrometeorological model SAFRAN-ISBA-MODCOU at Grand Morin watershed (France)

2008 ◽  
Vol 12 (6) ◽  
pp. 1415-1424 ◽  
Author(s):  
T. Paris Anguela ◽  
M. Zribi ◽  
S. Hasenauer ◽  
F. Habets ◽  
C. Loumagne
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Root Zone ◽  
Spatial Scales ◽  
Soil Surface ◽  
Temporal Variations ◽  
Model Verification ◽  
Atmospheric Conditions ◽  
Soil Moisture Dynamics ◽  
Moisture Dynamics

Abstract. Spatial and temporal variations of soil moisture strongly affect flooding, erosion, solute transport and vegetation productivity. Its characterization, offers an avenue to improve our understanding of complex land surface-atmosphere interactions. In this paper, soil moisture dynamics at soil surface (first centimeters) and root-zone (up to 1.5 m depth) are investigated at three spatial scales: local scale (field measurements), 8×8 km2 (hydrological model) and 25×25 km2 scale (ERS scatterometer) in a French watershed. This study points out the quality of surface and root-zone soil moisture data for SIM model and ERS scatterometer for a three year period. Surface soil moisture is highly variable because is more influenced by atmospheric conditions (rain, wind and solar radiation), and presents RMSE up to 0.08 m3 m−3. On the other hand, root-zone moisture presents lower variability with small RMSE (between 0.02 and 0.06 m3 m−3). These results will contribute to satellite and model verification of moisture, but also to better application of radar data for data assimilation in future.

Area-representative validation of remotely sensed high resolution soil moisture using a cosmic-ray neutron sensor

2020 ◽  
Author(s):  
Dragana Panic ◽  
Isabella Pfeil ◽  
Andreas Salentinig ◽  
Mariette Vreugdenhil ◽  
Wolfgang Wagner ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Soil Water ◽  
Root Zone ◽  
Spatial Scales ◽  
Temporal Frequency ◽  
Cosmic Ray ◽  
Irrigation Scheduling ◽  
Water Index ◽  
The Impact

<p>Reliable measurements of soil moisture (SM) are required for many applications worldwide, e.g., for flood and drought forecasting, and for improving the agricultural water use efficiency (e.g., irrigation scheduling). For the retrieval of large-scale SM datasets with a high temporal frequency, remote sensing methods have proven to be a valuable data source. (Sub-)daily SM is derived, for example, from observations of the Advanced Scatterometer (ASCAT) since 2007. These measurements are available on spatial scales of several square kilometers and are in particular useful for applications that do not require fine spatial resolutions but long and continuous time series. Since the launch of the first Sentinel-1 satellite in 2015, the derivation of SM at a spatial scale of 1 km has become possible for every 1.5-4 days over Europe (SSM1km) [1]. Recently, efforts have been made to combine ASCAT and Sentinel-1 to a Soil Water Index (SWI) product, in order to obtain a SM dataset with daily 1 km resolution (SWI1km) [2]. Both datasets are available over Europe from the Copernicus Global Land Service (CGLS, https://land.copernicus.eu/global/). As the quality of such a dataset is typically best over grassland and agricultural areas, and degrades with increasing vegetation density, validation is of high importance for the further development of the dataset and for its subsequent use by stakeholders.</p><p>Traditionally, validation studies have been carried out using in situ SM sensors from ground networks. Those are however often not representative of the area-wide satellite footprints. In this context, cosmic-ray neutron sensors (CRNS) have been found to be valuable, as they provide integrated SM estimates over a much larger area (about 20 hectares), which comes close to the spatial support area of the satellite SM product. In a previous study, we used CRNS measurements to validate ASCAT and S1 SM over an agricultural catchment, the Hydrological Open Air Laboratory (HOAL), in Petzenkirchen, Austria. The datasets were found to agree, but uncertainties regarding the impact of vegetation were identified.</p><p>In this study, we validated the SSM1km, SWI1km and a new S1-ASCAT SM product, which is currently developed at TU Wien, using CRNS. The new S1-ASCAT-combined dataset includes an improved vegetation parameterization, trend correction and snow masking. The validation has been carried out in the HOAL and on a second site in Marchfeld, Austria’s main crop producing area. As microwaves only penetrate the upper few centimeters of the soil, we applied the soil water index concept [3] to obtain soil moisture estimates of the root zone (approximately 0-40 cm) and thus roughly corresponding to the depth of the CRNS measurements. In the HOAL, we also incorporated in-situ SM from a network of point-scale time-domain-transmissivity sensors distributed within the CRNS footprint. The datasets were compared to each other by calculating correlation metrics. Furthermore, we investigated the effect of vegetation on both the satellite and the CRNS data by analyzing detailed information on crop type distribution and crop water content.</p><p>[1] Bauer-Marschallinger et al., 2018a: https://doi.org/10.1109/TGRS.2018.2858004<br>[2] Bauer-Marschallinger et al., 2018b: https://doi.org/10.3390/rs10071030<br>[3] Wagner et al., 1999: https://doi.org/10.1016/S0034-4257(99)00036-X</p>

scholarly journals Potential and limitations of multidecadal satellite soil moisture observations for climate model evaluation studies

2013 ◽  
Vol 10 (3) ◽  
pp. 3541-3594 ◽  
Author(s):  
A. Loew ◽  
T. Stacke ◽  
W. Dorigo ◽  
R. de Jeu ◽  
S. Hagemann
Keyword(s):  
Soil Moisture ◽  
Model Evaluation ◽  
Climate Model ◽  
Global Climate Models ◽  
Satellite Observations ◽  
Data Set ◽  
Soil Moisture Dynamics ◽  
Soil Moisture Data ◽  
Climate Model Evaluation ◽  
Moisture Dynamics

Abstract. Soil moisture is an essential climate variable of major importance for land-atmosphere interactions and global hydrology. An appropriate representation of soil moisture dynamics in global climate models is therefore important. Recently, a first multidecadal, observational based soil moisture data set has become available that provides information on soil moisture dynamics from satellite observations (ECVSM). The present study investigates the potential and limitations of this new dataset for several applications for climate model evaluation. We compare soil moisture data from satellite observations, reanalysis data and simulation results from a state-of-the-art climate model and analyze relationships between soil moisture and precipitation anomalies in the different datasets. In a detailed regional study, we show that ECVSM is capable to capture well interannual and intraannual soil moisture and precipitation dynamics in the Sahelian region. Current deficits of the new dataset are critically discussed and summarized at the end of the paper to provide guidance for an appropriate usage of the ECVSM dataset for climate studies.

Understanding soil moisture dynamics through cosmic rays: a global analysis

2021 ◽  
Author(s):  
Daniel Power ◽  
Rafael Rosolem ◽  
Miguel Rico-Ramirez ◽  
Darin Desilets ◽  
Sharon Desilets
Keyword(s):  
Soil Moisture ◽  
Cosmic Rays ◽  
Cosmic Ray ◽  
Fast Neutrons ◽  
Field Scale ◽  
Hydrogen Atoms ◽  
Soil Moisture Dynamics ◽  
Large Sample ◽  
Wide Range ◽  
Moisture Dynamics

<p>Despite its importance in many hydrological and environmental applications, direct estimates of soil moisture at the field-scale is still challenging. The spatial gap between point scale sensors and satellite derived products is becoming increasingly important to consider in the push for hyper-resolution (sub)kilometre-hydrometeorological models. Cosmic-Ray Neutron Sensors (CRNS) can help to bridge this spatial gap. CRNS provide estimates of field-scale (sub-kilometre) root-zone integrated soil moisture typically at hourly intervals. They achieve this by counting fast neutrons which are produced in the atmosphere from incoming cosmic rays. Fast neutrons are mitigated primarily by hydrogen atoms, and it is this relationship that allows us to estimate field averaged soil moisture. National networks of CRNS are available in the USA, Australia, the UK, and Germany, along with individual sites across the globe. As these networks have expanded, so has our knowledge on best practices for calibration and correction of the sensor measurements. However, there continues to be a divergence and lack of harmonization in some processing data methods leading to an additional uncertainty when comparing sensors in different networks. This can undermine efforts to employ large-sample hydrological analysis of CRNS across a wide range of climate and biomes. To provide an easily accessible platform for multi-site comparison worldwide, we developed the Cosmic Ray Sensor Python tool (crspy). Crspy is an open-source Python package which is designed to process CRNS data from global networks in a uniform and harmonized way (https://www.github.com/danpower101/crspy). Additionally, crspy has been developed for multi-site ‘big-data’ analysis in hydrology. Our crspy tool produces detailed information in the form of metadata for each site, using both site specific data as well as global data products to give information on soil properties (SoilGridsv2), land cover/aboveground biomass (ESA CCI) and climate data (ERA5-land). Our preliminary analysis and tool development was carried out using data from more than 100 sites globally from the public domain. We will present an analysis of this large sample of data, utilising the harmonized soil moisture readings along with detailed metadata for each site. We aim to increase our understanding of the dominant mechanisms controlling soil moisture dynamics which will undoubtedly be useful in multiple areas of research such as catchment classification, agriculture and irrigation, and hydrological model development.</p>

scholarly journals The Indian COSMOS Network (ICON): Validating L-Band Remote Sensing and Modelled Soil Moisture Data Products

2021 ◽  
Vol 13 (3) ◽  
pp. 537
Author(s):  
Deepti B Upadhyaya ◽  
Jonathan Evans ◽  
Sekhar Muddu ◽  
Sat Kumar Tomer ◽  
Ahmad Al Bitar ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Water Resource Management ◽  
Surface Soil ◽  
Root Zone ◽  
Cosmic Ray ◽  
Surface Soil Moisture ◽  
Data Set ◽  
Scale Difference

Availability of global satellite based Soil Moisture (SM) data has promoted the emergence of many applications in climate studies, agricultural water resource management and hydrology. In this context, validation of the global data set is of substance. Remote sensing measurements which are representative of an area covering 100 m2 to tens of km2 rarely match with in situ SM measurements at point scale due to scale difference. In this paper we present the new Indian Cosmic Ray Network (ICON) and compare it’s data with remotely sensed SM at different depths. ICON is the first network in India of the kind. It is operational since 2016 and consist of seven sites equipped with the COSMOS instrument. This instrument is based on the Cosmic Ray Neutron Probe (CRNP) technique which uses non-invasive neutron counts as a measure of soil moisture. It provides in situ measurements over an area with a radius of 150–250 m. This intermediate scale soil moisture is of interest for the validation of satellite SM. We compare the COSMOS derived soil moisture to surface soil moisture (SSM) and root zone soil moisture (RZSM) derived from SMOS, SMAP and GLDAS_Noah. The comparison with surface soil moisture products yield that the SMAP_L4_SSM showed best performance over all the sites with correlation (R) values ranging from 0.76 to 0.90. RZSM on the other hand from all products showed lesser performances. RZSM for GLDAS and SMAP_L4 products show that the results are better for the top layer R = 0.75 to 0.89 and 0.75 to 0.90 respectively than the deeper layers R = 0.26 to 0.92 and 0.6 to 0.8 respectively in all sites in India. The ICON network will be a useful tool for the calibration and validation activities for future SM missions like the NASA-ISRO Synthetic Aperture Radar (NISAR).

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