Estimating field scale root zone soil moisture using the cosmic-ray neutron probe

Abstract. Many practical hydrological, meteorological and agricultural management problems require estimates of soil moisture with an areal footprint equivalent to "field scale", integrated over the entire root zone. The cosmic-ray neutron probe is a promising instrument to provide field scale areal coverage, but these observations are shallow and require depth scaling in order to be considered representative of the entire root zone. A study to identify appropriate depth-scaling techniques was conducted at a grazing pasture site in central Saskatchewan, Canada over a two year period. Area-averaged soil moisture was assessed using a cosmic-ray neutron probe. Root zone soil moisture was measured at 21 locations within the 5002 m2 area, using a down-hole neutron probe. The cosmic-ray neutron probe was found to provide accurate estimates of field scale surface soil moisture, but accounted for less than 40 % of the seasonal change in root zone storage due to its shallow measurement depth. The root zone estimation methods evaluated were: (1) the coupling of the cosmic-ray neutron probe with a time stable neutron probe monitoring location, (2) coupling the cosmic-ray neutron probe with a representative landscape unit monitoring approach, and (3) convolution of the cosmic-ray neutron probe measurements with the exponential filter. The time stability method provided the best estimate of root zone soil moisture (RMSE = 0.004 cm3 cm−3), followed by the exponential filter (RMSE = 0.012 cm3 cm−3). The landscape unit approach, which required no calibration, had a negative bias but estimated the cumulative change in storage reasonably. The feasibility of applying these methods to field sites without existing instrumentation is discussed. It is concluded that the exponential filter method has the most potential for estimating root zone soil moisture from cosmic-ray neutron probe data.

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Estimating field-scale root zone soil moisture using the cosmic-ray neutron probe

Hydrology and Earth System Sciences ◽

10.5194/hess-20-1373-2016 ◽

2016 ◽

Vol 20 (4) ◽

pp. 1373-1385 ◽

Cited By ~ 21

Author(s):

Amber M. Peterson ◽

Warren D. Helgason ◽

Andrew M. Ireson

Keyword(s):

Soil Moisture ◽

Root Zone ◽

Cosmic Ray ◽

Estimation Methods ◽

Filter Method ◽

Field Scale ◽

Landscape Unit ◽

Neutron Probe ◽

Cumulative Change ◽

Minimal Data

Abstract. Many practical hydrological, meteorological, and agricultural management problems require estimates of soil moisture with an areal footprint equivalent to field scale, integrated over the entire root zone. The cosmic-ray neutron probe is a promising instrument to provide field-scale areal coverage, but these observations are shallow and require depth-scaling in order to be considered representative of the entire root zone. A study to identify appropriate depth-scaling techniques was conducted at a grazing pasture site in central Saskatchewan, Canada over a 2-year period. Area-averaged soil moisture was assessed using a cosmic-ray neutron probe. Root zone soil moisture was measured at 21 locations within the 500 m  ×  500 m study area, using a down-hole neutron probe. The cosmic-ray neutron probe was found to provide accurate estimates of field-scale surface soil moisture, but measurements represented less than 40 % of the seasonal change in root zone storage due to its shallow measurement depth. The root zone estimation methods evaluated were: (a) the coupling of the cosmic-ray neutron probe with a time-stable neutron probe monitoring location, (b) coupling the cosmic-ray neutron probe with a representative landscape unit monitoring approach, and (c) convolution of the cosmic-ray neutron probe measurements with the exponential filter. The time stability method provided the best estimate of root zone soil moisture (RMSE  =  0.005 cm3 cm−3), followed by the exponential filter (RMSE  =  0.014 cm3 cm−3). The landscape unit approach, which required no calibration, had a negative bias but estimated the cumulative change in storage reasonably. The feasibility of applying these methods to field sites without existing instrumentation is discussed. Based upon its observed performance and its minimal data requirements, it is concluded that the exponential filter method has the most potential for estimating root zone soil moisture from cosmic-ray neutron probe data.

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Extension of cosmic-ray neutron probe measurement depth for improving field scale root-zone soil moisture estimation by coupling with representative in-situ sensors

Journal of Hydrology ◽

10.1016/j.jhydrol.2019.02.018 ◽

2019 ◽

Vol 571 ◽

pp. 679-696 ◽

Cited By ~ 4

Author(s):

Hoang Hai Nguyen ◽

Jaehwan Jeong ◽

Minha Choi

Keyword(s):

Soil Moisture ◽

Root Zone ◽

Cosmic Ray ◽

Probe Measurement ◽

Field Scale ◽

Neutron Probe ◽

In Situ Sensors ◽

Moisture Estimation ◽

Measurement Depth

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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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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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High-resolution near-surface soil moisture through the combination of Sentinel-1 and Cosmic-Ray Neutron Probe in a Mediterranean agroforestry

10.5194/egusphere-egu21-7582 ◽

2021 ◽

Author(s):

Aida Taghavi Bayat ◽

Sarah Schönbrodt-Stitt ◽

Paolo Nasta ◽

Nima Ahmadian ◽

Christopher Conrad ◽

...

Keyword(s):

Soil Moisture ◽

Surface Soil ◽

Cosmic Ray ◽

Small Scale ◽

Surface Soil Moisture ◽

Field Scale ◽

Point Scale ◽

Near Surface ◽

Neutron Probe ◽

Topographic Attributes

The precise estimation and mapping of the near-surface soil moisture (~5cm, SM5cm) is key to supporting sustainable water management plans in Mediterranean agroforestry environments. In the past few years, time series of Synthetic Aperture Radar (SAR) data retrieved from Sentinel-1 (S1) enable the estimation of SM5cm at relatively high spatial and temporal resolutions. The present study focuses on developing a reliable and flexible framework to map SM5cm in a small-scale agroforestry experimental site (~30 ha) in southern Italy over the period from November 2018 to March 2019. Initially, different SAR-based polarimetric parameters from S1 (in total 62 parameters) and hydrologically meaningful topographic attributes from a 5-m Digital Elevation Model (DEM) were derived. These SAR and DEM-based parameters, and two supporting point-scale estimates of SM5cm were used to parametrize a Random Forest (RF) model. The inverse modeling module of the Hydrus-1D model enabled to simulate two &#160;supporting estimates of SM5cm by using i) sparse soil moisture data at the soil depths of 15 cm and 30 cm acquired over 20 locations comprised in a SoilNet wireless sensor network (SoilNet-based approach), and ii) field-scale soil moisture monitored by a Cosmic-Ray Neutron Probe (CRNP-based approach). In the CRNP-based approach, the field-scale SM5cm was further downscaled to obtain point-scale supporting SM5cm data over the same 20 positions by using the physical-empirical Equilibrium Moisture from Topography (EMT) model. Our results show that the CRNP-based approach can provide reasonable SM5cm retrievals with RMSE values ranging from 0.034 to 0.050 cm&#179; cm-3 similar to the ones based on the SoilNet approach ranging from 0.029 to 0.054 cm&#179; cm-3. This study highlights the effectiveness of integrating S1 SAR-based measurements, topographic attributes, and CRNP data for mapping SM5cm at the small agroforestry scale with the advantage of being non-invasive and easy to maintain.&#160;

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Field-scale soil moisture bridges the spatial-scale gap between drought monitoring and agricultural yields

10.5194/hess-2020-364 ◽

2020 ◽

Author(s):

Noemi Vergopolan ◽

Sitian Xiong ◽

Lyndon Estes ◽

Niko Wanders ◽

Nathaniel W. Chaney ◽

...

Keyword(s):

Soil Moisture ◽

Spatial Scale ◽

Root Zone ◽

Maize Yield ◽

Remotely Sensed ◽

Drought Monitoring ◽

Surface Model ◽

Field Scale ◽

Yield Data ◽

Scale Modeling

Abstract. Soil moisture is highly variable in space, and its deficits (i.e. droughts) plays an important role in modulating crop yields and its variability across landscapes. Limited hydroclimate and yield data, however, hampers drought impact monitoring and assessment at the farmer field-scale. This study demonstrates the potential of field-scale soil moisture simulations to advance high-resolution agricultural yield prediction and drought monitoring at the smallholder farm field-scale. We present a multi-scale modeling approach that combines HydroBlocks, a physically-based hyper-resolution Land Surface Model (LSM), and machine learning. We applied HydroBlocks to simulate root zone soil moisture and soil temperature in Zambia at 3-hourly 30-m resolution. These simulations along with remotely sensed vegetation indices, meteorological conditions, and data describing the physical properties of the landscape (topography, land cover, soil properties) were combined with district-level maize data to train a random forest model (RF) to predict maize yields at the district- and field-scale (250-m) levels. Our model predicted yields with a coefficient of variation (R2) of 0.61, Mean Absolute Error (MAE) of 349 kg ha−1, and mean normalized error of 22 %. We captured maize losses due to the 2015/2016 El Niño drought at similar levels to losses reported by the Food and Agriculture Organization (FAO). Our results revealed that soil moisture is the strongest and most reliable predictor of maize yield, driving its spatial and temporal variability. Consequently, soil moisture was also the most effective indicator of drought impacts in crops when compared with precipitation, soil and air temperatures, and remotely-sensed NDVI-based drought indices. By combining field-scale root zone soil moisture estimates with observed maize yield data, this research demonstrates how field-scale modeling can help bridge the spatial scale discontinuity gap between drought monitoring and agricultural impacts.

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Review of :Incorporation of globally available datasets into the cosmic-ray neutron probe method for estimating field scale soil water content

10.5194/hess-2016-92-rc3 ◽

2016 ◽

Author(s):

Anonymous

Keyword(s):

Water Content ◽

Soil Water ◽

Soil Water Content ◽

Cosmic Ray ◽

Probe Method ◽

Field Scale ◽

Neutron Probe

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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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