Soil moisture observation in a forested headwater catchment: combining a dense cosmic-ray neutron sensor network with roving and hydrogravimetry at the TERENO site Wüstebach

Abstract. Cosmic Ray Neutron Sensing (CRNS) has become an effective method to measure soil moisture at a horizontal scale of hundreds of meters and a depth of decimeters. Recent studies proposed to operate CRNS in a network with overlapping footprints in order to cover root-zone water dynamics at the small catchment scale, and, at the same time, to represent spatial heterogeneity. In a joint field campaign from September to November 2020 (JFC-2020), five German research institutions deployed 15 CRNS sensors in the 0.4 km2 Wüstebach catchment (Eifel mountains, Germany). The catchment is dominantly forested (but includes a substantial fraction of open vegetation), and features a topographically distinct watershed. In addition to the dense CRNS coverage, the campaign featured a unique combination of additional instruments and techniques: hydro-gravimetry (to detect water storage dynamics also below the root zone); ground-based and, for the first time, airborne CRNS roving; an extensive wireless soil sensor network, supplemented by manual measurements; and six weighable lysimeters. Together with comprehensive data from the long-term local research infrastructure, the published dataset (available at https://doi.org/10.23728/b2share.afb20a34a6ac429ca6b759238d842765) will be a valuable asset in various research contexts: to advance the retrieval of landscape water storage from CRNS, wireless soil sensor networks, or hydrogravimetry; to identify scale-specific combinations of sensors and methods to represent soil moisture variability; to improve the understanding and simulation of land-atmosphere exchange as well as hydrological and hydrogeological processes at the hill-slope and the catchment scale; and to support the retrieval soil water content from airborne and spaceborne remote sensing platforms.

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Catchment-scale connection between vegetation accessible storage and satellite-derived Soil Water Index

10.5194/egusphere-egu2020-6917 ◽

2020 ◽

Cited By ~ 1

Author(s):

Laurène Bouaziz ◽

Susan Steele-Dunne ◽

Jaap Schellekens ◽

Albrecht Weerts ◽

Jasper Stam ◽

...

Keyword(s):

Soil Moisture ◽

Soil Water ◽

Surface Soil ◽

Root Zone ◽

Water Storage ◽

Surface Soil Moisture ◽

Catchment Scale ◽

Near Surface ◽

Water Index ◽

Optimal Values

Estimates of water volumes stored in the root-zone of vegetation are a key element controlling the hydrological response of a catchment. Remotely-sensed soil moisture products are available globally. However, they are representative of the upper-most few centimeters of the soil. For reliable runoff predictions, we are interested in root-zone soil moisture estimates as they regulate the partitioning of precipitation to drainage and evaporation. The Soil Water Index approximates root-zone soil moisture from near-surface soil moisture and requires a single parameter representing the characteristic time length T of temporal soil moisture variability. Climate and soil properties are typically assumed to influence estimates of T, however, no clear quantitative link has yet been established and often a standard value of 20 days is assumed. In this study, we hypothesize that optimal T values are linked to the accumulated difference between precipitation (water supply) and evaporation (atmospheric water demand) during dry periods with return periods of 20 years, and, thus, to catchment-scale vegetation-accessible water storage capacities. We identify the optimal values of T that provide an adequate match between estimated SWI from several satellite-based near-surface soil moisture products (derived from AMSR2, SMAP and Sentinel-1) and modeled time series of root-zone soil moisture from a calibrated process-based model in 16 contrasting catchments of the Meuse river basin. We found that optimal values of T vary between 1 and 98 days with a median of 17 days across the studied catchments and soil moisture products. We furthermore show that T, which was previously known to increase with increasing depth of the soil layer, is positively and strongly related with catchment-scale root-zone water storage capacity, estimated based on long-term water balance data. &#160;This is useful to generate estimates of root-zone soil moisture from satellite-based surface soil moisture, as they are a key control of the response of hydrological systems.

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Calibration approaches of cosmic-ray neutron sensing for soil moisture measurement in cropped fields

Hydrology and Earth System Sciences Discussions ◽

10.5194/hessd-10-4237-2013 ◽

2013 ◽

Vol 10 (4) ◽

pp. 4237-4274 ◽

Cited By ~ 5

Author(s):

C. A. Rivera Villarreyes ◽

G. Baroni ◽

S. E. Oswald

Keyword(s):

Soil Moisture ◽

Cosmic Ray ◽

Fast Neutrons ◽

Winter Rye ◽

Moisture Profile ◽

Hill Slope ◽

Vadose Zone Hydrology ◽

Classical Point ◽

Soil Moisture Measurement ◽

Single Set

Abstract. Measurement of soil moisture at the plot or hill-slope scale is an important link between local vadose-zone hydrology and catchment hydrology. This study evaluates the applicability of the cosmic-ray neutron sensing for soil moisture in cropped fields. Measurements of cosmic-ray neutrons (fast neutrons) were performed at a lowland farmland in Bornim (Brandenburg, Germany) cropped with sunflower and winter rye. Three field calibration approaches and four different ways of integration the soil moisture profile to an integral value for cosmic-ray neutron sensing were evaluated in this study. The cosmic-ray sensing (CRS) probe was calibrated against a network of classical point-scale soil moisture measurements. A large CRS parameter variability was observed by choosing calibration periods within the different growing stages of sunflower and winter rye. Therefore, it was not possible to identify a single set of parameters perfectly estimating soil moisture for both sunflower and winter rye periods. On the other hand, CRS signal and its parameter variability could be understood by some crop characteristics and by predicting the attenuated neutrons by crop presence. This study proves the potentiality of the cosmic-ray neutron sensing at the field scale; however, its calibration needs to be adapted for seasonal vegetation in cropped fields.

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Cosmic Ray Neutron Sensing: Integration with land surface modelling using data assimilation for improved field-scale soil moisture estimates

10.5194/egusphere-egu2020-10419 ◽

2020 ◽

Author(s):

Amol Patil ◽

Benjamin Fersch ◽

Harrie-Jan Hendricks-Franssen ◽

Harald Kunstmann

Keyword(s):

Soil Moisture ◽

Data Assimilation ◽

Spatial Resolution ◽

Land Surface ◽

Root Zone ◽

Cosmic Ray ◽

Soil Parameters ◽

Surface Model ◽

Field Scale ◽

Spatio Temporal

Soil moisture is a key variable in atmospheric modelling to resolve the partitioning of net radiation into sensible and latent heat fluxes. Therefore, high resolution spatio-temporal soil moisture estimation is getting growing attention in this decade. The recent developments to observe soil moisture at field scale (170 to 250 m spatial resolution) using Cosmic Ray Neutron Sensing (CRNS) technique has created new opportunities to better resolve land surface atmospheric interactions; however, many challenges remain such as spatial resolution mismatch and estimation uncertainties. Our study couples the Noah-MP land surface model to the Data Assimilation Research Testbed (DART) for assimilating CRN intensities to update model soil moisture. For evaluation, the spatially distributed Noah-MP was set up to simulate the land surface variables at 1 km horizontal resolution for the Rott and Ammer catchments in southern Germany. The study site comprises the TERENO-preAlpine observatory with five CRNS stations and additional CRNS measurements for summer 2019 operated by our Cosmic Sense research group. We adjusted the soil parametrization in Noah-MP to allow the usage of EU soil data along with Mualem-van Genuchten soil hydraulic parameters. We use independent observations from extensive soil moisture sensor network (SoilNet) within the vicinity of CRNS sensors for validation. Our detailed synthetic and real data experiments are evaluated for the analysis of the spatio-temporal changes in updated root zone soil moisture and for implications on the energy balance component of Noah-MP. Furthermore, we present possibilities to estimate root zone soil parameters within the data assimilation framework to enhance standalone model performance.

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Analysis of water dynamics in the soil-plant-atmosphere continuum using a multi-sensor approach

10.5194/egusphere-egu2020-18686 ◽

2020 ◽

Author(s):

David Chaparro ◽

Thomas Jagdhuber ◽

Dara Entekhabi ◽

María Piles ◽

Anke Fluhrer ◽

...

Keyword(s):

Soil Moisture ◽

Leaf Area Index ◽

Water Content ◽

Leaf Area ◽

Optical Depth ◽

Time Lag ◽

Water Storage ◽

Water Dynamics ◽

Area Index ◽

L Band

Changing climate patterns have increased hydrological extremes in many regions [1]. This impacts water and carbon cycles, potentially modifying vegetation processes and thus terrestrial carbon uptake. It is therefore crucial to understand the relationship between the main water pools linked to vegetation (i.e., soil moisture, plant water storage, and atmospheric water deficit), and how vegetation responds to changes of these pools. Hence, the goal of this research is to understand the water pools and fluxes in the soil-plant-atmosphere continuum (SPAC) and their relationship with vegetation responses.Our study spans from April 2015 to March 2019 and is structured in two parts:Firstly, relative water content (RWC) is estimated using a multi-sensor approach to monitor water storage in plants. This is at the core of our research approach towards water pool monitoring within SPAC. Here, we will present a RWC dataset derived from gravimetric moisture content (mg) estimates using the method first proposed in [2], and further validated in [3]. This allows retrieving RWC and mg independently from biomass influences. Here, we apply this method using a sensor synergy including (i) vegetation optical depth from SMAP L-band radiometer (L-VOD), (ii) vegetation height (VH) from ICESat-2 Lidar and (iii) vegetation volume fraction (d) from AQUARIUS L-band radar. RWC status and temporal dynamics will be discussed.Secondly, water dynamics in the SPAC and their impact on leaf changes are analyzed. We will present a global, time-lag correlation analysis among: (i) the developed RWC maps, (ii) surface soil moisture from SMAP (SM), (iii) vapor pressure deficit (VPD; from MERRA reanalysis [4]), and (iv) leaf area index (LAI; from MODIS [5]). Resulting time-lag and correlation maps, as well as analyses of LAI dynamics as a function of SPAC, will be presented at the conference.&#160;References[1] IPCC. (2013). Annex I: Atlas of global and regional climate projections. In: van Oldenborgh, et al. (Eds.) Climate Change 2013: The Physical Science Basis (pp. 1311-1393). Cambridge University Press.[2] Fink, A., et al. (2018). Estimating Gravimetric Moisture of Vegetation Using an Attenuation-Based Multi-Sensor Approach. In IGARSS 2018 (pp. 353-356). IEEE.[3] Meyer, T., et al. Estimating Gravimetric Water Content of a Winter Wheat Field from L-Band Vegetation Optical Depth, Remote Sens.&#160;2019,&#160;11(20), 2353[4] NASA (2019). Modern-Era Retrospective analysis for Research and Applications, Version 2. Accessed 2020-01-14 from https://gmao.gsfc.nasa.gov/reanalysis/MERRA-2/.[5] Myneni, R., et al. (2015). MOD15A2H MODIS/Terra Leaf Area Index/FPAR 8-Day L4 Global 500m SIN Grid V006. Accessed 2020-01-14 from https://doi.org/10.5067/MODIS/MOD15A2H.006.

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

10.5194/egusphere-egu2020-16222 ◽

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

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.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.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&#8217;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.[1] Bauer-Marschallinger et al., 2018a: https://doi.org/10.1109/TGRS.2018.2858004 [2] Bauer-Marschallinger et al., 2018b: https://doi.org/10.3390/rs10071030 [3] Wagner et al., 1999: https://doi.org/10.1016/S0034-4257(99)00036-X

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Global joint assimilation of GRACE and SMOS for improved estimation of root-zone soil moisture and vegetation response

10.5194/hess-2018-442 ◽

2018 ◽

Cited By ~ 1

Author(s):

Siyuan Tian ◽

Luigi J. Renzullo ◽

Albert I. J. M. van Dijk ◽

Paul Tregoning ◽

Jeffrey P. Walker

Keyword(s):

Soil Moisture ◽

Water Content ◽

Soil Water ◽

Root Zone ◽

Water Storage ◽

Microwave Remote Sensing ◽

Open Loop ◽

Total Water ◽

Vegetation Response ◽

Vegetation Greenness

Abstract. The lack of direct measurement of root-zone soil moisture poses a challenge to the large-scale prediction of ecosystem response to variation in soil water. Microwave remote sensing capability is limited to measuring moisture content in the uppermost few centimetres of soil. In contrast, GRACE (Gravity Recovery and Climate Experiment) mission detected the variability in storage within the total water column, which is often dominated by groundwater variation. However, not all vegetation communities can access groundwater. In this study, satellite-derived water content from GRACE and SMOS were jointly assimilated into an ecohydrological model to better predict the impact of changes in root-zone soil moisture on vegetation vigour. Overall, the accuracy of root-zone soil moisture prediction though the joint assimilation of surface soil moisture and total water storage retrievals showed improved consistency with ground-based soil moisture measurements and satellite-observed greenness when compared to open-loop estimates (i.e. without assimilation). For example, the correlation between modelled and in-situ measurements of root-zone moisture increased by 0.1 on average over grasslands and croplands. Improved correlations were found between vegetation greenness and soil water storage derived from the joint assimilation with an increase up to 0.47 over grassland compared to open-loop estimates. Joint assimilation results show a more severe deficit in soil water in eastern Australia, western North America and eastern Brazil over the period of 2010 to 2015 than the open-loop, consistent with the satellite-observed vegetation greenness. The assimilation of satellite-observed water content contributes to more accurate knowledge of soil water availability, providing new insights for monitoring hidden water stress and vegetation response.

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The Application of WiFi-based Wireless Sensor Network (WSN) in Hill Slope Condition Monitoring

Jurnal Teknologi ◽

10.11113/jt.v73.4250 ◽

2015 ◽

Vol 73 (3) ◽

Author(s):

Mohd Amri Md Yunus ◽

Sallehuddin Ibrahim ◽

Mohd Taufiq Md Khairi ◽

Mahdi Faramarzi

Keyword(s):

Wireless Sensor Network ◽

Moisture Content ◽

Sensor Network ◽

Safety Factor ◽

Slope Angle ◽

Wireless Sensor ◽

Average Moisture Content ◽

Landslide Monitoring ◽

Hill Slope ◽

The Hill

In this paper, a wireless sensor network for landslide monitoring (WSNLM) system is described. WSNLM utilized a wireless protocol which is 802.11g. The hardware structure of the WSNLM is discussed where the important parts had been discussed in details. In order to assess the susceptibility of a hill slope to landslide, several parameters had been considered for the network. The important factors that affect landslide is the ground status, which is soil moisture, vibration in the land and also soil temperature. Other factors that can relate to landslide is the environment of the surrounding such as air temperature, humidity and atmospheric pressure. The outputs from the ADXL335 accelerometer were used for slope angle measurement. The output ofa vibration transducer was also used to monitor the hill slope. To account for the susceptibility of the hill slope to the land slide, safety factor value is calculated in real time. The outcomes show that the average moisture content in the soil is around 3 % on a sunny day and the safety factor for a sunny day is around 75. The moisture content in the soil on a rainy day increases tremendously to more than 20 %. At the same time, the safety factor drops to around 70. The system in this paper has the potential to be used as a useful tool for the detection of lanslides.

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Closing the water balance with cosmic-ray soil moisture measurements and assessing their relation to evapotranspiration in two semiarid watersheds

Hydrology and Earth System Sciences ◽

10.5194/hess-20-329-2016 ◽

2016 ◽

Vol 20 (1) ◽

pp. 329-345 ◽

Cited By ~ 18

Author(s):

A. P. Schreiner-McGraw ◽

E. R. Vivoni ◽

G. Mascaro ◽

T. E. Franz

Keyword(s):

Soil Moisture ◽

Water Balance ◽

Sensor Network ◽

Cosmic Ray ◽

Semiarid Ecosystems ◽

Moisture Sensor ◽

Distributed Sensor ◽

Experimental Range ◽

Distributed Sensor Network ◽

Good Agreement

Abstract. Soil moisture dynamics reflect the complex interactions of meteorological conditions with soil, vegetation and terrain properties. In this study, intermediate-scale soil moisture estimates from the cosmic-ray neutron sensing (CRNS) method are evaluated for two semiarid ecosystems in the southwestern United States: a mesquite savanna at the Santa Rita Experimental Range (SRER) and a mixed shrubland at the Jornada Experimental Range (JER). Evaluations of the CRNS method are performed for small watersheds instrumented with a distributed sensor network consisting of soil moisture sensor profiles, an eddy covariance tower, and runoff flumes used to close the water balance. We found a very good agreement between the CRNS method and the distributed sensor network (root mean square error (RMSE) of 0.009 and 0.013 m3 m−3 at SRER and JER, respectively) at the hourly timescale over the 19-month study period, primarily due to the inclusion of 5 cm observations of shallow soil moisture. Good agreement was also obtained in soil moisture changes estimated from the CRNS and watershed water balance methods (RMSE of 0.001 and 0.082 m3 m−3 at SRER and JER, respectively), with deviations due to bypassing of the CRNS measurement depth during large rainfall events. Once validated, the CRNS soil moisture estimates were used to investigate hydrological processes at the footprint scale at each site. Through the computation of the water balance, we showed that drier-than-average conditions at SRER promoted plant water uptake from deeper soil layers, while the wetter-than-average period at JER resulted in percolation towards deeper soils. The CRNS measurements were then used to quantify the link between evapotranspiration and soil moisture at a commensurate scale, finding similar predictive relations at both sites that are applicable to other semiarid ecosystems in the southwestern US.

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Improving calibration and validation of cosmic-ray neutron sensors in the light of spatial sensitivity

Hydrology and Earth System Sciences ◽

10.5194/hess-21-5009-2017 ◽

2017 ◽

Vol 21 (10) ◽

pp. 5009-5030 ◽

Cited By ~ 35

Author(s):

Martin Schrön ◽

Markus Köhli ◽

Lena Scheiffele ◽

Joost Iwema ◽

Heye R. Bogena ◽

...

Keyword(s):

Soil Moisture ◽

Land Surface ◽

Cosmic Ray ◽

Water Dynamics ◽

Hydrological Processes ◽

Spatial Sensitivity ◽

Calibration And Validation ◽

Physically Based ◽

Point Data ◽

Neutron Sensors

Abstract. In the last few years the method of cosmic-ray neutron sensing (CRNS) has gained popularity among hydrologists, physicists, and land-surface modelers. The sensor provides continuous soil moisture data, averaged over several hectares and tens of decimeters in depth. However, the signal still may contain unidentified features of hydrological processes, and many calibration datasets are often required in order to find reliable relations between neutron intensity and water dynamics. Recent insights into environmental neutrons accurately described the spatial sensitivity of the sensor and thus allowed one to quantify the contribution of individual sample locations to the CRNS signal. Consequently, data points of calibration and validation datasets are suggested to be averaged using a more physically based weighting approach. In this work, a revised sensitivity function is used to calculate weighted averages of point data. The function is different from the simple exponential convention by the extraordinary sensitivity to the first few meters around the probe, and by dependencies on air pressure, air humidity, soil moisture, and vegetation. The approach is extensively tested at six distinct monitoring sites: two sites with multiple calibration datasets and four sites with continuous time series datasets. In all cases, the revised averaging method improved the performance of the CRNS products. The revised approach further helped to reveal hidden hydrological processes which otherwise remained unexplained in the data or were lost in the process of overcalibration. The presented weighting approach increases the overall accuracy of CRNS products and will have an impact on all their applications in agriculture, hydrology, and modeling.

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Field scale root zone soil moisture estimation by coupling cosmic-ray neutron sensor with soil moisture sensors

10.5194/egusphere-egu2020-4618 ◽

2020 ◽

Author(s):

Hami Said ◽

Georg Weltin ◽

Lee Kheng Heng ◽

Trenton Franz ◽

Emil Fulajtar ◽

...

Keyword(s):

Water Management ◽

Soil Moisture ◽

Root Zone ◽

Cosmic Ray ◽

Agricultural Water ◽

Field Scale ◽

Agricultural Water Management ◽

Soil Moisture Sensors ◽

Major Disadvantage ◽

Filter Approach

Since it has become clear that climate change is having a major impact on water availability for agriculture and crop productivity, an accurate estimation of field-scale root-zone soil moisture (RZSM) is essential for improved agricultural water management. The Cosmic Ray Neutron Sensor (CRNS) has recently been used for field-scale soil moisture (SM) monitoring in large areas and is a credible and robust technique. Like other remote or proximal sensing techniques, the CRNS provides only SM data in the near surface. One of the challenges and needs is to extend the vertical footprint of the CRNS to the root zone of major crops. This can be achieved by coupling the CRNS measurements with conventional methods for soil moisture measurements, which provide information on soil moisture for whole rooting depth.The objective of this poster presentation is to estimate field-scale RZSM by correlating the CRNS information with that from soil moisture sensors that provide soil moisture data for the whole root depth. In this study, the Drill and Drop probes which provide continuous profile soil moisture were selected. The RZSM estimate was calculated using an exponential filter approach.Winter Wheat cropped fields in Rutzendorf, Marchfeld region (Austria) were instrumented with a CRNS and Drill & Drop probes. An exponential filter approach was applied on the CRNS and Drill and drop sensor data to characterize the RZSM. The preliminary results indicate the ability of the merging framework procedure to improve field-scale RZSM in real-time. This study demonstrated how to combine the advantages of CRNS nuclear technique (especially the large footprint and good representativeness of obtained data) with the advantages of conventional methods (providing data for whole soil profile) and overcome the shortcoming of both methods (the lack of information in the deeper part of soil profile being the major disadvantage of CRNS and the spatial limitation and low representativeness of point data being the major disadvantage of conventional capacitance sensors). This approach can be very helpful for improving agricultural water management.

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