COsmic-ray Soil Moisture Observing System Research Articles

Study on design, construction and testing of cosmic-ray soil moisture observing system in Vietnam

The newly-developed cosmic-ray method for measuring area-average soil moisture at the hectometer horizontal scale is being implemented in the COsmic-ray Soil Moisture Observing System (or the COSMOS). The stationary cosmic-ray soil moisture probe measures the neutrons that are generated by cosmic rays (or Cosmic-Ray Neutron Sensing-CRNS) within air and soil and other materials, moderated by mainly hydrogen atoms located primarily in soil water, and emitted to the atmosphere where they mix instantaneously at a scale of hundreds of meters and whose density is inversely correlated with soil moisture. The CRNS technique has been known for more than 10 years and validated by many scientific publications in the world. In Vietnam, a country greatly affected by global climate change, this is the first time this technique has been implemented and applied. Together with other traditional methods for soil moisture determination such as point measurement and remote sensing, CRNS technique will effectively contribute to the real-time monitoring of drought, prediction of flash floods and landslides, or planning irrigation in smart agriculture. This paper is written to introduce the results of the COSMOS-VINATOM project of the Ministry of Science and Technology. We describe the cosmic-ray soil moisture measurement method and the design of a specific COSMOS using advanced technologies in neutron and muon detector, signal processing and data transmission.This system has also been tested in the field in Xuan Canh commune, Dong Anh district, Hanoi, Vietnam with initial positive results.

Examining the variation of soil moisture from cosmic-ray neutron probes footprint: experimental results from a COSMOS-UK site

Utilising cosmic-ray neutron probes is a relatively new approach in obtaining larger area soil moisture and various operational monitoring networks have been established worldwide utilising this technology to measure operationally this parameter. One such network located in the United Kingdom (UK) is the Cosmic-ray Soil Moisture Observing System, so-called COSMOS-UK, established in 2013. The present study aims at investigating the true footprint and the variations within the footprint detectable area at the COSMOS-UK sites using as a case study one such site located in Riseholme, UK. At the selected experimental site extensive fieldwork was conducted in July 2017 that allowed examining the agreement among the soil moisture data retrieved by the Time Domain Transmissometer (TDT) sensors and the corresponding estimates from the COSMOS-UK network station probe. The COSMOS-UK site footprint was compared using GPS-aided information from ground instrumentation, assisted by drone imagery acquisition and the implementation of geospatial interpolation methods in a Geographical Information System (GIS) environment. Altogether, this information was used for assessing the soil moisture footprint extent from the COSMOS-UK site. The COSMOS-UK station footprint was representative for an area shorter in size than the alleged footprint of 600 m diameter, as generally proposed in various relevant investigations. The COSMOS network slightly overestimated soil moisture content measured by the TDT sensor probes installed in the area. Our study findings although concern specifically the studied experimental site contribute towards efforts aiming at assessing the COSMOS-UK soil moisture measurement footprint showcasing the added value of geospatial analysis in this direction.

A study of field-scale soil moisture variability using the COsmic-ray Soil Moisture Observing System (COSMOS) at IITM Pune site

This study presents an analysis of daily field-scale soil-moisture (SM) variations, measured using the COsmic-ray Soil Moisture Observing System (COSMOS), over a tropical monsoon site (IITM, Pune) in India, for the period 2017–2020. Being located in the core zone of the Indian summer monsoon, the daily field-scale SM observations at COSMOS-IITM provide an unique opportunity to understand the SM response to monsoon precipitation variations on sub-seasonal, seasonal and interannual time-scales. In addition to the COSMOS-IITM observations, we also evaluated SM variations over this location using satellite, reanalysis and model products for the same period. An important result from our analysis reveals the presence of biweekly (time-scale ~ 10–20 days) and low-frequency intra-seasonal (time-scale ~ 30–60 days) variations in the field-scale SM, which are linked to the dominant modes of Indian summer monsoon subseasonal variability. In particular, we find a pronounced enhancement of the low-frequency signal of SM variations during the 2019 monsoon which was characterized by abnormally excess precipitation and prolongation of rains well beyond the summer monsoon season, in contrast to 2018 monsoon. Moreover, this study highlights a longer persistence of SM memory time-scale (about 60 days) during 2019 as compared to 2017, 2018 and 2020. The validation of coarser resolution data sets revealed that GLDAS and ERA5 reasonably capture a range of observed field-scale SM variabilities over COSMOS-IITM site.

Retrieval of High-Resolution Soil Moisture through Combination of Sentinel-1 and Sentinel-2 Data

Estimating soil moisture based on synthetic aperture radar (SAR) data remains challenging due to the influences of vegetation and surface roughness. Here we present an algorithm that simultaneously retrieves soil moisture, surface roughness and vegetation water content by jointly using high-resolution Sentinel-1 SAR and Sentinel-2 multispectral imagery, with an application directed towards the provision of information at the precision agricultural scale. Sentinel-2-derived vegetation water indices are investigated and used to quantify the backscatter resulting from the vegetation canopy. The proposed algorithm then inverts the water cloud model to simultaneously estimate soil moisture and surface roughness by minimizing a cost function constructed by model simulations and SAR observations. To examine the performance of VV- and VH-polarized backscatters on soil moisture retrievals, three retrieval schemes are explored: a single channel algorithm using VV (SCA-VV) and VH (SCA-VH) polarizations and a dual channel algorithm using both VV and VH polarizations (DCA-VVVH). An evaluation of the approach using a combination of a cosmic-ray soil moisture observing system (COSMOS) and Soil Climate Analysis Network measurements over Nebraska shows that the SCA-VV scheme yields good agreement at both the COSMOS footprint and single-site scales. The features of the algorithms that have the most impact on the retrieval accuracy include the vegetation water content estimation scheme, parameters of the water cloud model and the specification of initial ranges of soil moisture and roughness, all of which are comprehensively analyzed and discussed. Through careful consideration and selection of these factors, we demonstrate that the proposed SCA-VV approach can provide reasonable soil moisture retrievals, with RMSE ranging from 0.039 to 0.078 m3/m3 and R2 ranging from 0.472 to 0.665, highlighting the utility of SAR for application at the precision agricultural scale.

SMAP soil moisture improves global evapotranspiration

Accurate estimation of global evapotranspiration (ET) is essential to understand water cycle and land-atmosphere feedbacks in the Earth system. Satellite-driven ET models provide global estimates, but many of the ET algorithms have been designed independently of soil moisture observations. As water for ET is sourced from the soil, incorporating soil moisture into global remote sensing algorithms of ET should, in theory, improve performance, especially in water-limited regions. This paper presents an update to the widely-used Priestley Taylor-Jet Propulsion Laboratory (PT-JPL) ET algorithm to incorporate spatially explicit daily surface soil moisture control on soil evaporation and canopy transpiration. The updated algorithm is evaluated using 14 AmeriFlux eddy covariance towers co-located with COsmic-ray Soil Moisture Observing System (COSMOS) soil moisture observations. The new PT-JPLSM model shows reduced errors and increased explanation of variance, with the greatest improvements in water-limited regions. Soil moisture incorporation into soil evaporation improves ET estimates by reducing bias and RMSE by 29.9% and 22.7% respectively, while soil moisture incorporation into transpiration improves ET estimates by reducing bias by 30.2%, RMSE by 16.9%. We apply the algorithm globally using soil moisture observations from the Soil Moisture Active Passive Mission (SMAP). These new global estimates of ET show reduced error at finer spatial resolutions and provide a rich dataset to evaluate land surface and climate models, vegetation response to changes in water availability and environmental conditions, and anthropogenic perturbations to the water cycle.

Mapping soil moisture with the OPtical TRApezoid Model (OPTRAM) based on long-term MODIS observations

The OPtical TRApezoid Model (OPTRAM) has recently been proposed for estimation of soil moisture using only optical remote sensing data. The model relies on a physical linear relationship between the soil moisture content and shortwave infrared transformed reflectance (STR) and can be parameterized universally (i.e., a single calibration for a given area) based on the pixel distribution within the STR-Normalized Difference Vegetation Index (NDVI) trapezoidal space. The main motivation for this study was to evaluate how the universal parameterization of OPTRAM works for long periods of time (e.g., several decades). This is especially relevant for uncovering the soil moisture and agricultural drought history in response to climate change in different regions. In this study, MODIS satellite observations from 2001 to 2017 were acquired and used for the analysis. Cosmic-ray neutron (CRN) soil moisture data, collected with the COsmic-ray Soil Moisture Observing System (COSMOS) at five different sites in the U.S. covering diverse climates, soil types, and land covers, were applied for evaluation of the MODIS-OPTRAM-based soil moisture estimates. The OPTRAM soil moisture estimates were further compared to the Soil Moisture Active and Passive (SMAP) (L-band), the Soil Moisture Ocean Salinity (SMOS) (L-band), and the Advanced AScatterometer (ASCAT) (C-band) soil moisture retrievals. OPTRAM soil moisture data were also analyzed for potential monitoring of agricultural drought through comparison of the OPTRAM-based Soil Water Deficit Index (OPTRAM-SWDI) with the widely-applied Crop Moisture Index (CMI). Evaluation results indicate that OPTRAM-based soil moisture estimates provide overall unbiased RMSE and R between 0.050 and 0.085 cm3 cm−3 and 0.10 to 0.70, respectively, for all investigated sites. The performance of OPTRAM is comparable with the ASCAT retrievals, but slightly less accurate than SMAP and SMOS. OPTRAM and the three microvave satellites captured CRN soil moisture temporal dynamics very well for all five investigated sites. A close agreement was observed between the OPTRAM-SWDI and CMI drought indices for most selected sites. In conclusion, OPTRAM can estimate temporal soil moisture dynamics with reasonable accuracy for a range of climatic conditions (semi-arid to humid), soil types, and land covers, and can potentially be applied for agricultural drought monitoring.

Thermal neutrons of the terrestrial origin in the Brazilian region

Dynamics of near ground concentration of ambient thermal neutrons (TNs) was studied using data of neutron detectors of the COsmic-ray Soil Moisture Observing System (COSMOS) network and those of the Laboratory of environmental radiation of the Instituto Tecnologico de Aeronautica (Brazil). A significant excess of TN count rate over that of rapid ones was found in two COSMOS probes located in Brazil and other sites in North America and Europe. The effect was explained in terms of neutron production in nuclear (α, n) reactions initiated by decay of radon gas comprised in the ground rather than in the atmosphere. A revealed strongly marked diurnal variation of the TN concentration proved to be in antiphase with air temperature that is characteristic for particles in thermal equilibrium with air. A previously unknown effect of saturation of nighttime TN concentration was observed. It is explained in terms of a dynamic equilibrium state, which sets in the absence of vertical convection between the neutrons escaping from the ground and their losses in nuclear reaction in the air. The results described can be used for the determination of ground concentration of ambient radon and its dynamics in boundary layer of atmosphere as well as in other geophysical studies.

Comparative study of soil moisture estimations from SMOS satellite mission, GLDAS database, and cosmic-ray neutrons measurements at COSMOS station in Eastern Poland

The paper presents a detailed comparative study of three surface soil moisture datasets retrieved from Cosmic-Ray Soil Moisture Observing System (COSMOS) in situ neutron measurement, CATDS Soil Moisture Ocean Salinity (SMOS) satellite microwave observations, and modelled data of the Global Land Data Assimilation System (GLDAS). Subsurface datasets were also calculated from the in situ and satellite measurements by using an exponential filter and were compared with the GLDAS estimates. For these comparisons, the Triple Collocation (TC) method, Nash–Sutcliffe Efficiency (NSE) coefficients, trend charts, and scatterplots were used.The main goal of this work was to verify the concordance of cosmic-ray neutron measurements with low-resolution soil moisture data from the CATDS SMOS and GLDAS products. The second objective was to determine the possibility of obtaining comparable subsurface soil moisture products from the aforementioned soil moisture data source.The obtained results show that data from the COSMOS sensor, which assesses soil moisture in an area 600m in diameter, agree reasonably well with the CATDS SMOS and GLDAS data having spatial resolution of about 25km. These conclusions suggest that COSMOS Derlo measurements can be particularly useful for validation of low-resolution satellite soil moisture observations as well as modelled values.The results obtained by using the TC method also revealed satisfactory agreement among all studied surface soil moisture data.However, the performed analysis shows some preponderance of SMOS and COSMOS data over GLDAS products. Although GLDAS data show a noticeable smoothing effect, COSMOS and CATDS SMOS more effectively reveal temporal soil moisture changes. Thus, for some applications, the use of CATDS SMOS estimates rather than GLDAS products may be more appropriate.Results retrieved by using an exponential filter are significant and encouraging. In particular, subsurface soil moisture values calculated from CATDS SMOS show stronger correlation with COSMOS Derlo data than those of GLDAS. Furthermore, COSMOS Derlo data also respond more intensively to surface soil moisture changes.

The Soil Moisture Active Passive Marena, Oklahoma, In Situ Sensor Testbed (SMAP‐MOISST): Testbed Design and Evaluation of In Situ Sensors

Core Ideas Soil moisture sensors have varying accuracies that can be improved with calibration. In situ sensors require scaling to improve their representativeness of large areas. Soil moisture sensors in profile have decreasing ability to accurately represent the surface soil moisture. In situ soil moisture monitoring networks are critical to the development of soil moisture remote sensing missions as well as agricultural and environmental management, weather forecasting, and many other endeavors. These in situ networks utilize a variety of sensors and installation practices, which confounds the development of a unified reference database for satellite calibration and validation programs. As part of the Soil Moisture Active Passive Mission, the Marena, Oklahoma, In Situ Sensor Testbed (SMAP‐MOISST) was initiated to perform inter‐comparisons and study sensor limitations. Soil moisture sensors that are deployed in major monitoring networks were included in the study, along with new and emerging technologies, such as the Cosmic Ray Soil Moisture Observing System (COSMOS), passive/active distributed temperature sensing (DTS), and global positioning system reflectometers (GPSR). Four profile stations were installed in May of 2010, and soil moisture was monitored to a depth of 1 m on an hourly basis. The four stations were distributed within a circular domain of approximately 600 m diameter, adequate to encompass the sensing range of COSMOS. The sensors included in the base station configuration included the Stevens Water Hydra Probe, Campbell Scientific 616 and 229, Decagon EC‐TM, Delta‐T Theta Probe, Acclima, and Sentek EnviroSMART capacitance system. In addition, the Pico TRIME system and additional time‐domain reflectometry (TDR) systems were deployed when available. It was necessary to apply site‐specific calibration to most sensors to reach an RMSE below 0.04 m3 m−3. For most sensor types, a single near surface sensor could be scaled to represent the areal‐average of a field domain by simple linear regression, resulting in RMSE values around 0.03 m3 m−3.

A global comparison of alternate AMSR2 soil moisture products: Why do they differ?

This study assesses two remotely sensed soil moisture products from the Advanced Microwave Scanning Radiometer 2 (AMSR2), a sensor onboard the Global Change Observation Mission 1 — Water (GCOM-W1) that was launched in May 2012. The soil moisture products were retrieved by the Japan Aerospace Exploration Agency (JAXA) algorithm and the Land Parameter Retrieval Model (LPRM) developed by the VU University Amsterdam, in collaboration with the National Aeronautics and Space Administration (NASA). The two products are compared at the global scale. In addition, the products are evaluated against field measurements from 47 stations from the COsmic-ray Soil Moisture Observing System (COSMOS) network which are located in the United States (36 stations), Australia (7 stations), Europe (2 stations) and Africa (2 stations).After examining the retrieval algorithms, it is hypothesized that four factors, namely, physical surface temperatures, surface roughness, vegetation and ground soil wetness conditions, may affect the quality of soil moisture retrievals. From the inter-comparisons at the global scale, the correlations of the two products highlight differences in the representation of the seasonal cycle of soil moisture, with negative correlations found for several regions. Correlations of the anomaly time series were generally strong (R>0.6) as a result of soil moisture sensitivity to external meteorological forcing and possibly also random noise in the satellite observations. Due to the inherent differences in spatial coverage and measurement scale of the COSMOS and satellite data, the comparisons in terms of correlation coefficients are the most reliable. It was found that both products show rapid decreases in correlation coefficients under low mean temperature (<290K), high mean EVI (>0.3) and highly wetted conditions. These findings are further supported by the bias and RMSE estimates which show that JAXA has relatively better performance under dry conditions while the bias and RMSE of LPRM are generally smaller than JAXA, when considered against the four variables. These results provide information on appropriate parameterizations and model selection for the retrieval algorithms and a future research direction to improve the quality by leveraging the strengths of the JAXA and LPRM algorithms. With these, when a multi-year dataset is available, there will be more confidence in defining the seasonal cycle and the data can be decomposed to identify the anomalies where the bias is not relevant.

Soil Moisture Estimation Using Cosmic-Ray Soil Moisture Sensing at Heterogeneous Farmland

The Cosmic-ray Soil Moisture Observing System is a promising soil moisture measurement network. It can measure soil moisture at an intermediate spatial scale with a single sensor. In this letter, the measured cosmic-ray neutron counts during the Heihe Watershed Allied Telemetry Experimental Research were used to evaluate the capabilities of the cosmic-ray probe in soil moisture retrieval at a heterogeneous farmland. The Cosmic-ray Soil Moisture Interaction Code model was utilized to model the interaction between the measured neutron counts and the soil water content. Soil moisture at the footprint scale of the cosmic-ray probe obtained using a wireless sensor network (SoilNET) was used as the calibration and validation data. The results show that the cosmic-ray probe is capable of monitoring the hourly heterogeneous soil moisture dynamics at the intermediate spatial scale in a noninvasive way. Moreover, the informative measurement depth of the cosmic-ray probe can also be derived and is consistent with the soil moisture results.

Field-scale moisture estimates using COSMOS sensors: A validation study with temporary networks and Leaf-Area-Indices

Summary The COsmic-ray Soil Moisture Observing System (COSMOS) is a new and innovative method for estimating surface and near-surface soil moisture at large (∼700 m) scales. This system accounts for liquid water within its measurement volume. Many of the sites used in the early validation of the system had low and stable vegetation water content. It is necessary to perform validation of COSMOS in a landscape with a significant change in vegetation water content to estimate its impact on the COSMOS estimate, and potentially, correct the estimate to account for the dynamic vegetation cover. A COSMOS station was installed in Beltsville, MD in the spring of 2012, within the domain of a long-term experimental station on USDA property. This station has a large array of soil moisture sensors in profile across the field, which is approximately 700 m in diameter. Frequent estimates of Leaf Area Index (LAI) were made throughout the 2012 and 2013 growing seasons (May through September). COSMOS readings, with a simple linear adjustment, were able to produce estimates of area-weighted averages of the existing in situ network with root-mean-squared-errors (RMSE) of soil-water content well below 0.04 m3/m3 and with additional modeling to account for Leaf Area Index (LAI) values, RMSE values less than 0.03 m3/m3 were achieved.

Calibration and Validation of the COSMOS Rover for Surface Soil Moisture Measurement

The COsmic‐ray Soil Moisture Observing System (COSMOS) rover may be useful for validating satellite‐based estimates of near‐surface soil moisture, but the accuracy with which the rover can measure 0‐ to 5‐cm soil moisture has not been previously determined. Our objectives were to calibrate and validate a COSMOS rover for mapping 0‐ to 5‐cm soil moisture at spatial scales suitable for evaluating satellite‐based soil moisture estimates. Region‐specific calibrations for the COSMOS rover were developed using field‐average soil moisture measured using impedance probes and the oven‐drying method on volumetric samples. The resulting calibrations were applied to map soil moisture on two dates in a 16‐ by 10‐km region around the Marena, Oklahoma, In Situ Sensor Testbed (MOISST) in north central Oklahoma and one date in a 34‐ by 14‐km region in the Little Washita River watershed in southwestern Oklahoma. The mapped soil moisture patterns were consistent with the regional spatial variability in surface soil texture and with soil wetting by an intervening rainfall. The rover measured field‐average soil moisture with a root mean squared difference (RMSD) of 0.03 cm3 cm−3 relative to impedance probes. Likewise, the regional‐average 0‐ to 5‐cm soil moisture determined by the rover was within ±0.03 cm3 cm−3 of the best available independent estimates for each region. These results demonstrate that a COSMOS rover can be used effectively for 0‐ to 5‐cm soil moisture mapping and for determining the average soil moisture at spatial scales suitable for satellite calibration and validation.

The Effect of Atmospheric Water Vapor on Neutron Count in the Cosmic-Ray Soil Moisture Observing System

Abstract The cosmic-ray method for measuring soil moisture, used in the Cosmic-Ray Soil Moisture Observing System (COSMOS), relies on the exceptional ability of hydrogen to moderate fast neutrons. Sources of hydrogen near the ground, other than soil moisture, affect the neutron measurement and therefore must be quantified. This study investigates the effect of atmospheric water vapor on the cosmic-ray probe signal and evaluates the fast neutron response in realistic atmospheric conditions using the neutron transport code Monte Carlo N-Particle eXtended (MCNPX). The vertical height of influence of the sensor in the atmosphere varies between 412 and 265 m in dry and wet atmospheres, respectively. Model results show that atmospheric water vapor near the surface affects the neutron intensity signal by up to 12%, corresponding to soil moisture differences on the order of 0.10 m3 m−3. A simple correction is defined to identify the true signal associated with integrated soil moisture that rescales the measured neutron intensity to that which would have been observed in the atmospheric conditions prevailing on the day of sensor calibration. Use of this approach is investigated with in situ observations at two sites characterized by strong seasonality in water vapor where standard meteorological measurements are readily available.

The COsmic-ray Soil Moisture Interaction Code (COSMIC) for use in data assimilation

Abstract. Soil moisture status in land surface models (LSMs) can be updated by assimilating cosmic-ray neutron intensity measured in air above the surface. This requires a fast and accurate model to calculate the neutron intensity from the profiles of soil moisture modeled by the LSM. The existing Monte Carlo N-Particle eXtended (MCNPX) model is sufficiently accurate but too slow to be practical in the context of data assimilation. Consequently an alternative and efficient model is needed which can be calibrated accurately to reproduce the calculations made by MCNPX and used to substitute for MCNPX during data assimilation. This paper describes the construction and calibration of such a model, COsmic-ray Soil Moisture Interaction Code (COSMIC), which is simple, physically based and analytic, and which, because it runs at least 50 000 times faster than MCNPX, is appropriate in data assimilation applications. The model includes simple descriptions of (a) degradation of the incoming high-energy neutron flux with soil depth, (b) creation of fast neutrons at each depth in the soil, and (c) scattering of the resulting fast neutrons before they reach the soil surface, all of which processes may have parameterized dependency on the chemistry and moisture content of the soil. The site-to-site variability in the parameters used in COSMIC is explored for 42 sample sites in the COsmic-ray Soil Moisture Observing System (COSMOS), and the comparative performance of COSMIC relative to MCNPX when applied to represent interactions between cosmic-ray neutrons and moist soil is explored. At an example site in Arizona, fast-neutron counts calculated by COSMIC from the average soil moisture profile given by an independent network of point measurements in the COSMOS probe footprint are similar to the fast-neutron intensity measured by the COSMOS probe. It was demonstrated that, when used within a data assimilation framework to assimilate COSMOS probe counts into the Noah land surface model at the Santa Rita Experimental Range field site, the calibrated COSMIC model provided an effective mechanism for translating model-calculated soil moisture profiles into aboveground fast-neutron count when applied with two radically different approaches used to remove the bias between data and model.

COSMOS: the COsmic-ray Soil Moisture Observing System

Abstract. The newly-developed cosmic-ray method for measuring area-average soil moisture at the hectometer horizontal scale is being implemented in the COsmic-ray Soil Moisture Observing System (or the COSMOS). The stationary cosmic-ray soil moisture probe measures the neutrons that are generated by cosmic rays within air and soil and other materials, moderated by mainly hydrogen atoms located primarily in soil water, and emitted to the atmosphere where they mix instantaneously at a scale of hundreds of meters and whose density is inversely correlated with soil moisture. The COSMOS has already deployed more than 50 of the eventual 500 cosmic-ray probes, distributed mainly in the USA, each generating a time series of average soil moisture over its horizontal footprint, with similar networks coming into existence around the world. This paper is written to serve a community need to better understand this novel method and the COSMOS project. We describe the cosmic-ray soil moisture measurement method, the instrument and its calibration, the design, data processing and dissemination used in the COSMOS project, and give example time series of soil moisture obtained from COSMOS probes.