scholarly journals Evaluation of Spatio-Temporal Soil Moisture Variability in Semi-Arid Rangeland Ecosystem, Maasai Mara National Reserve, Kenya

2021 ◽  
pp. 64-90
Author(s):  
Charles C. Kapkwang ◽  
Japheth O. Onyando ◽  
Peter M. Kundu ◽  
Joost Hoedjes
Keyword(s):  
Soil Moisture ◽  
Water Content ◽  
Cosmic Ray ◽  
Tourism Industry ◽  
Soil Layers ◽  
Sandy Clay ◽  
Spatially Distributed ◽  
Spatio Temporal ◽  
Semi Arid ◽  
Arid Rangeland

Aim: To evaluate the spatio-temporal soil moisture storage and retention capacities in semi-arid rangeland ecosystem, Maasai Mara National Reserve (MMNR), Kenya Study Design: Randomized complete block design (RCBD) of reference Cosmic Ray Neutron Sensor (CRNS) station, ten-(10) spatially distributed (soil moisture and temperature capacitance) probes (5TM-ECH20) sites. Place and Duration of Study: Kenya, MMNR, the oldest natural semi-arid rangeland ecosystem and globally unique for the great wildebeest migration, between May 2017 and April 2019. Methodology: Soil moisture (SM) variation data was collected using (CRNS) at spatial and point-scale 5TM-ECH2O probes, and gravimetric water content from (10) spatially distributed stations. Both CRNS and 5TM-ECH2O probes were used to monitor near-real time moisture levels at different soil layers ranging between 0-5cm, 5-10cm, 15-20cm, 35-40cm, and 75-80cm. Soil physical and chemical properties were laboratory analyzed. Calibration and validation datasets were obtained from 5TM-ECH2O probe and gravimetric soil samples extracted from respective layers and sites. Results: The pedological characteristics of the investigated ecosystem soil profile indicate decreased bulk density by 2.1% to 11.12% from upper layers (0-5cm) to deeper layers at (75–80 cm). Across the rangeland, 70% of soil textural classes were sandy clay loam (SCL) with higher clay percent and 30% sandy clay (SC) and soil porosity varied between 30.1% and 51% in the ecosystem. Moreover, volumetric 2    water content (VWC) of spatially distributed 5TM-ECH2O probes ranged between 0.11m3m-3 and 0.32m3m-3 during wet season with mean VWC of 0.16m3m-3, however, the VWC ranged between 0.04 m3m-3 and 0.17m3m-3 during the dry season with a mean volume of 0.11m3m-3 across the rangeland ecosystem. Conclusion: In this study, SM exhibited an annual periodicity of seasonal variation of spatial and temporal moisture partitioned as moisture gaining, losing, and a moisture stable period. This probably could be a consequence of increased movement of water to deeper layers caused by high precipitation and less evaporative demand caused by lower temperatures. The calibrated CRNS probe provided good estimates of spatial soil moisture variation when calibrated with 5TM-ECH20 and gravimetric sampling in relation to precipitation events and that deeper soil layers showed higher amount of soil moisture than shallow layers. The findings of the study will provide better formulation of the ecosystem vegetation management policies, conservation and planning for sustainable wildlife tourism industry.

scholarly journals Spatio-temporal soil moisture retrieval at the catchment scale using a dense network of cosmic-ray neutron sensors

2021 ◽  
Vol 25 (9) ◽  
pp. 4807-4824
Author(s):  
Maik Heistermann ◽  
Till Francke ◽  
Martin Schrön ◽  
Sascha E. Oswald
Keyword(s):  
Soil Moisture ◽  
Water Content ◽  
Soil Water ◽  
Soil Water Content ◽  
Root Zone ◽  
Cosmic Ray ◽  
Dense Network ◽  
Constrained Interpolation ◽  
Neutron Count ◽  
Spatio Temporal

Abstract. Cosmic-ray neutron sensing (CRNS) is a powerful technique for retrieving representative estimates of soil water content at a horizontal scale of hectometres (the “field scale”) and depths of tens of centimetres (“the root zone”). This study demonstrates the potential of the CRNS technique to obtain spatio-temporal patterns of soil moisture beyond the integrated volume from isolated CRNS footprints. We use data from an observational campaign carried out between May and July 2019 that featured a dense network of more than 20 neutron detectors with partly overlapping footprints in an area that exhibits pronounced soil moisture gradients within one square kilometre. The present study is the first to combine these observations in order to represent the heterogeneity of soil water content at the sub-footprint scale as well as between the CRNS stations. First, we apply a state-of-the-art procedure to correct the observed neutron count rates for static effects (heterogeneity in space, e.g. soil organic matter) and dynamic effects (heterogeneity in time, e.g. barometric pressure). Based on the homogenized neutron data, we investigate the robustness of a calibration approach that uses a single calibration parameter across all CRNS stations. Finally, we benchmark two different interpolation techniques for obtaining spatio-temporal representations of soil moisture: first, ordinary Kriging with a fixed range; second, spatial interpolation complemented by geophysical inversion (“constrained interpolation”). To that end, we optimize the parameters of a geostatistical interpolation model so that the error in the forward-simulated neutron count rates is minimized, and suggest a heuristic forward operator to make the optimization problem computationally feasible. Comparison with independent measurements from a cluster of soil moisture sensors (SoilNet) shows that the constrained interpolation approach is superior for representing horizontal soil moisture gradients at the hectometre scale. The study demonstrates how a CRNS network can be used to generate coherent, consistent, and continuous soil moisture patterns that could be used to validate hydrological models or remote sensing products.

scholarly journals Spatio-temporal soil moisture retrieval at the catchment-scale using a dense network of cosmic-ray neutron sensors

2021 ◽  
Author(s):  
Maik Heistermann ◽  
Till Francke ◽  
Martin Schrön ◽  
Sascha E. Oswald
Keyword(s):  
Soil Moisture ◽  
Water Content ◽  
Soil Water ◽  
Soil Water Content ◽  
Ordinary Kriging ◽  
Cosmic Ray ◽  
Constrained Interpolation ◽  
Neutron Count ◽  
Moisture Variation ◽  
Spatio Temporal

Abstract. The method of Cosmic-Ray Neutron Sensing (CRNS) is a powerful technique to retrieve representative estimates of soil water content at a horizontal scale of hectometers (the field scale) and depths of tens of centimeters (the root zone). This study demonstrates the potential of the CRNS technique to obtain spatio-temporal patterns of soil moisture beyond the integrated volume from isolated CRNS footprints. We use data from an observational campaign between May and July 2019 which featured a network of more than 20 neutron detectors with partly overlapping footprints in an area that exhibits pronounced soil moisture gradients within 1 km2. The present study is the first to combine these observations in order to represent the heterogeneity of soil water content at the sub-footprint scale as well as between the CRNS stations. First, we apply a state-of-the-art procedure to correct the observed neutron count rates for static effects such as sensor sensitivity, vegetation biomass, soil organic carbon and lattice water, as well as for the influence of the temporally dynamic factors barometric pressure, air humidity, and incoming flux of neutrons. Based on the homogenised neutron data, we investigate the robustness of a uniform calibration approach using one calibration parameter value across all CRNS stations. Finally, we benchmark two different interpolation techniques in order to obtain space-time representations of soil moisture: first, Ordinary Kriging with a fixed range; second, a heuristic approach that complements the concept of spatial interpolation by the idea of a geophysical inversion (constrained interpolation). For the latter, we define a geostatistical model of the spatial soil moisture variation in the study area, and then optimize the parameters of that model so that the error of the forward-simulated neutron count rates is minimized. In order to make the optimization problem computationally feasible, we use a heuristic forward operator that is based on the physics of horizontal sensitivity of the neutron detector. The comparison with independent measurements from a cluster of soil moisture sensors (SoilNet) shows that the constrained interpolation approach outperforms Ordinary Kriging by putting a stronger emphasis on horizontal soil moisture gradients at the hectometer scale. The study demonstrates how a CRNS network can be used to generate consistent interpolated soil moisture patterns that could be used to validate hydrological models or remote sensing products.

scholarly journals A Novel Lithium Foil Cosmic-Ray Neutron Detector for Measuring Field-Scale Soil Moisture

2021 ◽  
Vol 3 ◽  
Author(s):  
Andres Patrignani ◽  
Tyson E. Ochsner ◽  
Benjamin Montag ◽  
Steven Bellinger
Keyword(s):  
Soil Moisture ◽  
Water Content ◽  
Atmospheric Pressure ◽  
Lower Cost ◽  
Cosmic Ray ◽  
Absolute Difference ◽  
Atmospheric Conditions ◽  
Volumetric Soil Water Content ◽  
The Mean

During the past decade, cosmic-ray neutron sensing technology has enabled researchers to reveal soil moisture spatial patterns and to estimate landscape-average soil moisture for hydrological and agricultural applications. However, reliance on rare materials such as helium-3 increases the cost of cosmic-ray neutron probes (CRNPs) and limits the adoption of this unique technology beyond the realm of academic research. In this study, we evaluated a novel lower cost CRNP based on moderated ultra-thin lithium-6 foil (Li foil system) technology against a commercially-available CRNP based on BF3 (boron trifluoride, BF-3 system). The study was conducted in a cropped field located in the Konza Prairie Biological Station near Manhattan, Kansas, USA (325 m a.s.l.) from 10 April 2020 to 18 June 2020. During this period the mean atmospheric pressure was 977 kPa, the mean air relative humidity was 70%, and the average volumetric soil water content was 0.277 m3 m−3. Raw fast neutron counts were corrected for atmospheric pressure, atmospheric water vapor, and incoming neutron flux. Calibration of the CRNPs was conducted using four intensive field surveys (n > 120), in combination with continuous observations from an existing array of in situ soil moisture sensors. The time series of uncorrected neutron counts of the Li foil system was highly correlated (r2 = 0.91) to that of the BF-3 system. The Li foil system had an average of 2,250 corrected neutron counts per hour with an uncertainty of 2.25%, values that are specific to the instrument size, detector configuration, and atmospheric conditions. The estimated volumetric water content from the Li foil system had a mean absolute difference of 0.022 m3 m−3 compared to the value from the array of in situ sensors. The new Li foil detector offers a promising lower cost alternative to existing cosmic-ray neutron detection devices used for hectometer-scale soil moisture monitoring.

scholarly journals Relating surface backscatter response from TRMM Precipitation Radar to soil moisture: results over a semi-arid region

2009 ◽  
Vol 6 (5) ◽  
pp. 6425-6454
Author(s):  
H. Stephen ◽  
S. Ahmad ◽  
T. C. Piechota ◽  
C. Tang
Keyword(s):  
Soil Moisture ◽  
Water Content ◽  
Soil Water ◽  
Soil Water Content ◽  
Arid Regions ◽  
Vegetation Index ◽  
Temporal Trends ◽  
Model Parameters ◽  
Precipitation Radar ◽  
Semi Arid

Abstract. The Tropical Rainfall Measuring Mission (TRMM) carries aboard the Precipitation Radar (TRMMPR) that measures the backscatter (σ°) of the surface. σ° is sensitive to surface soil moisture and vegetation conditions. Due to sparse vegetation in arid and semi-arid regions, TRMMPR σ° primarily depends on the soil water content. In this study we relate TRMMPR σ° measurements to soil water content (ms) in Lower Colorado River Basin (LCRB). σ° dependence on ms is studied for different vegetation greenness values determined through Normalized Difference Vegetation Index (NDVI). A new model of σ° that couples incidence angle, ms, and NDVI is used to derive parameters and retrieve soil water content. The calibration and validation of this model are performed using simulated and measured ms data. Simulated ms is estimated using Variable Infiltration Capacity (VIC) model whereas measured ms is acquired from ground measuring stations in Walnut Gulch Experimental Watershed (WGEW). σ° model is calibrated using VIC and WGEW ms data during 1998 and the calibrated model is used to derive ms during later years. The temporal trends of derived ms are consistent with VIC and WGEW ms data with correlation coefficient (R) of 0.89 and 0.74, respectively. Derived ms is also consistent with the measured precipitation data with R=0.76. The gridded VIC data is used to calibrate the model at each grid point in LCRB and spatial maps of the model parameters are prepared. The model parameters are spatially coherent with the general regional topography in LCRB. TRMMPR σ° derived soil moisture maps during May (dry) and August (wet) 1999 are spatially similar to VIC estimates with correlation 0.67 and 0.76, respectively. This research provides new insights into Ku-band σ° dependence on soil water content in the arid regions.

scholarly journals Investigating temporal field sampling strategies for site-specific calibration of three soil moisture–neutron intensity parameterisation methods

2015 ◽  
Vol 19 (7) ◽  
pp. 3203-3216 ◽  
Author(s):  
J. Iwema ◽  
R. Rosolem ◽  
R. Baatz ◽  
T. Wagener ◽  
H. R. Bogena
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Satellite Remote Sensing ◽  
Cosmic Ray ◽  
Sampling Strategies ◽  
Site Specific ◽  
Neutron Intensity ◽  
Soil Wetness ◽  
Semi Arid

Abstract. The Cosmic-Ray Neutron Sensor (CRNS) can provide soil moisture information at scales relevant to hydrometeorological modelling applications. Site-specific calibration is needed to translate CRNS neutron intensities into sensor footprint average soil moisture contents. We investigated temporal sampling strategies for calibration of three CRNS parameterisations (modified N0, HMF, and COSMIC) by assessing the effects of the number of sampling days and soil wetness conditions on the performance of the calibration results while investigating actual neutron intensity measurements, for three sites with distinct climate and land use: a semi-arid site, a temperate grassland, and a temperate forest. When calibrated with 1 year of data, both COSMIC and the modified N0 method performed better than HMF. The performance of COSMIC was remarkably good at the semi-arid site in the USA, while the N0mod performed best at the two temperate sites in Germany. The successful performance of COSMIC at all three sites can be attributed to the benefits of explicitly resolving individual soil layers (which is not accounted for in the other two parameterisations). To better calibrate these parameterisations, we recommend in situ soil sampled to be collected on more than a single day. However, little improvement is observed for sampling on more than 6 days. At the semi-arid site, the N0mod method was calibrated better under site-specific average wetness conditions, whereas HMF and COSMIC were calibrated better under drier conditions. Average soil wetness condition gave better calibration results at the two humid sites. The calibration results for the HMF method were better when calibrated with combinations of days with similar soil wetness conditions, opposed to N0mod and COSMIC, which profited from using days with distinct wetness conditions. Errors in actual neutron intensities were translated to average errors specifically to each site. At the semi-arid site, these errors were below the typical measurement uncertainties from in situ point-scale sensors and satellite remote sensing products. Nevertheless, at the two humid sites, reduction in uncertainty with increasing sampling days only reached typical errors associated with satellite remote sensing products. The outcomes of this study can be used by researchers as a CRNS calibration strategy guideline.

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

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

<p>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.</p>

scholarly journals Use of cosmic ray neutron sensors for soil moisture monitoring in forests

2015 ◽  
Vol 12 (9) ◽  
pp. 9813-9864 ◽  
Author(s):  
I. Heidbüchel ◽  
A. Güntner ◽  
T. Blume
Keyword(s):  
Time Series ◽  
Soil Moisture ◽  
Water Content ◽  
Soil Water ◽  
Soil Water Content ◽  
Cosmic Ray ◽  
Soil Samples ◽  
Calibration Function ◽  
Neutron Sensors ◽  
Hydrogen Mass

Abstract. Cosmic ray neutron sensors (CRS) are a promising technique to measure soil moisture at intermediate scales. To convert neutron counts to average volumetric soil water content a simple calibration function can be used (the N0-calibration of Desilets et al., 2010). This calibration function is based on soil water content derived directly from soil samples taken within the footprint of the sensor. We installed a CRS in a mixed forest in the lowlands of north-eastern Germany and calibrated it 10 times throughout one calendar year. Each calibration with the N0-calibration function resulted in a different CRS soil moisture time series, with deviations of up to 0.12 m3 m-3 for individual values of soil water content. Also, many of the calibration efforts resulted in time series that could not be matched with independent in situ measurements of soil water content. We therefore suggest a new calibration function with a different shape that can vary from one location to another. A two-point calibration proved to be adequate to correctly define the shape of the new calibration function if the calibration points were taken during both dry and wet conditions covering at least 50 % of the total range of soil moisture. The best results were obtained when the soil samples used for calibration were linearly weighted as a function of depth in the soil profile and non-linearly weighted as a function of distance from the CRS, and when the depth-specific amount of soil organic matter and lattice water content was explicitly considered. The annual cycle of tree foliation was found to be a negligible factor for calibration because the variable hydrogen mass in the leaves was small compared to the hydrogen mass changes by soil moisture variations. Finally, we provide a best practice calibration guide for CRS in forested environments.

scholarly journals Use of cosmic-ray neutron sensors for soil moisture monitoring in forests

2016 ◽  
Vol 20 (3) ◽  
pp. 1269-1288 ◽  
Author(s):  
Ingo Heidbüchel ◽  
Andreas Güntner ◽  
Theresa Blume
Keyword(s):  
Time Series ◽  
Soil Moisture ◽  
Water Content ◽  
Soil Water ◽  
Soil Water Content ◽  
Cosmic Ray ◽  
Soil Samples ◽  
Calibration Function ◽  
Total Range ◽  
Hydrogen Mass

Abstract. Measuring soil moisture with cosmic-ray neutrons is a promising technique for intermediate spatial scales. To convert neutron counts to average volumetric soil water content a simple calibration function can be used (the N0-calibration of Desilets et al., 2010). The calibration is based on soil water content derived directly from soil samples taken within the footprint of the sensor. We installed a cosmic-ray neutron sensor (CRS) in a mixed forest in the lowlands of north-eastern Germany and calibrated it 10 times throughout one calendar year. Each calibration with the N0-calibration function resulted in a different CRS soil moisture time series, with deviations of up to 0.1 m3 m−3 (24 % of the total range) for individual values of soil water content. Also, many of the calibration efforts resulted in time series that could not be matched with independent in situ measurements of soil water content. We therefore suggest a modified calibration function with a different shape that can vary from one location to another. A two-point calibration was found to effectively define the shape of the modified calibration function if the calibration points were taken during both dry and wet conditions spanning at least half of the total range of soil moisture. The best results were obtained when the soil samples used for calibration were linearly weighted as a function of depth in the soil profile and nonlinearly weighted as a function of distance from the CRS, and when the depth-specific amount of soil organic matter and lattice water content was explicitly considered. The annual cycle of tree foliation was found to be a negligible factor for calibration because the variable hydrogen mass in the leaves was small compared to the hydrogen mass changes by soil moisture variations. As a final point, we provide a calibration guide for a CRS in forested environments.

scholarly journals Investigating temporal field sampling strategies for site-specific calibration of three soil moisture – neutron intensity parameterisation methods

2015 ◽  
Vol 12 (2) ◽  
pp. 2349-2389 ◽  
Author(s):  
J. Iwema ◽  
R. Rosolem ◽  
R. Baatz ◽  
T. Wagener ◽  
H. R. Bogena
Keyword(s):  
Soil Moisture ◽  
Cosmic Ray ◽  
Strong Increase ◽  
Sampling Strategies ◽  
Work Effort ◽  
Site Specific ◽  
Temporal Sampling ◽  
Soil Wetness ◽  
Temporal Field ◽  
Semi Arid

Abstract. The Cosmic-Ray Neutron Sensor (CRNS) can provide soil moisture information at scales relevant to hydrometeorological modeling applications. Site-specific calibration is needed to translate CRNS neutron intensities into sensor footprint average soil moisture contents. We investigated temporal sampling strategies for calibration of three CRNS parameterisations (modified N0, HMF, and COSMIC) by assessing the effects of the number of sampling days and soil wetness conditions on the performance of the calibration results, for three sites with distinct climate and land use: a semi-arid site, a temperate grassland and a temperate forest. When calibrated with a year of data, COSMIC performed relatively good at all three sites, and the modified N0 method performed best at the two humid sites. It is advisable to collect soil moisture samples on more than a single day regardless of which parameterisation is used. In any case, sampling on more than ten days would, despite the strong increase in work effort, improve calibration results only little. COSMIC needed the least number of days at each site. At the semi-arid site, the N0mod method was calibrated better under average wetness conditions, whereas HMF and COSMIC were calibrated better under drier conditions. Average soil wetness condition gave better calibration results at the two humid sites. The calibration results for the HMF method were better when calibrated with combinations of days with similar soil wetness conditions, opposed to N0mod and COSMIC, which profited from using days with distinct wetness conditions. The outcomes of this study can be used by researchers as a CRNS calibration strategy guideline.

Sign in / Sign up

Export Citation Format

Share Document