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At least 991 records · Page 55

USDC based rapid penetrator of packed soil

Environment protection requires more testing and analysis tools. To detect buried chemical containers or other objects embedded in soil and avoid possible damages of them, a penetrator of packed soil operated using low pushing force was developed. The design was based on a novel driving mechanism of the ultrasonic/sonic driller/corer (USDC) device developed in the NDEAA lab at JPL [Bar-Cohen et al 2001, Bao et al 2003]. In the penetrator, a small free-flying mass is energized by a piezoelectric transducer and impacts a rod probe on its shoulder at frequencies of hundreds times per second. The impacts help the probe to penetrate the packed soil rapidly. A great reduction of the needed pushing force for penetration was achieved. The details of the design of the prototype penetrator and the results of performance tests are presented.

sampling↗

Impact of Rainfall on the Retrieval of Soil Moisture Using AMSR-E Data

Rainfall leads to errors and limitations on the soil moisture retrieval using satellite radiometry. To understand the impact of rainfall, we examined the temporal and spatial correlations between rainfall and soil moisture using AMSR-E (Advanced Microwave Scanning Radiometer-EOS) data. Scan by scan (swath basis) analyses were conducted to find the short time scale relationship between the two physical parameters. The retention of soil moisture after rainfall in different climatic regimes (e.g. humid and arid regions) was also examined.

rainfall↗

A Parameterized Inversion Model for Soil Moisture and Biomass from Polarimetric Backscattering Coefficients

A semi-empirical algorithm for the retrieval of soil moisture, root mean square (RMS) height and biomass from polarimetric SAR data is explained and analyzed in this paper. The algorithm is a simplification of the distorted Born model. It takes into account the physical scattering phenomenon and has three major components: volume, double-bounce and surface. This simplified model uses the three backscattering coefficients ( sigma HH, sigma HV and sigma vv) at low-frequency (P-band). The inversion process uses the Levenberg-Marquardt non-linear least-squares method to estimate the structural parameters. The estimation process is entirely explained in this paper, from initialization of the unknowns to retrievals. A sensitivity analysis is also done where the initial values in the inversion process are varying randomly. The results show that the inversion process is not really sensitive to initial values and a major part of the retrievals has a root-mean-square error lower than 5% for soil moisture, 24 Mg/ha for biomass and 0.49 cm for roughness, considering a soil moisture of 40%, roughness equal to 3cm and biomass varying from 0 to 500 Mg/ha with a mean of 161 Mg/ha

soil moisture↗

Comparison of SMOS and SMAP Soil Moisture Retrieval Approaches Using Tower-based Radiometer Data over a Vineyard Field

The objective of this study was to compare several approaches to soil moisture (SM) retrieval using L-band microwave radiometry. The comparison was based on a brightness temperature (TB) data set acquired since 2010 by the L-band radiometer ELBARA-II over a vineyard field at the Valencia Anchor Station (VAS) site. ELBARA-II, provided by the European Space Agency (ESA) within the scientific program of the SMOS (Soil Moisture and Ocean Salinity) mission, measures multiangular TB data at horizontal and vertical polarization for a range of incidence angles (30-60). Based on a three year data set (2010-2012), several SM retrieval approaches developed for spaceborne missions including AMSR-E (Advanced Microwave Scanning Radiometer for EOS), SMAP (Soil Moisture Active Passive) and SMOS were compared. The approaches include: the Single Channel Algorithm (SCA) for horizontal (SCA-H) and vertical (SCA-V) polarizations, the Dual Channel Algorithm (DCA), the Land Parameter Retrieval Model (LPRM) and two simplified approaches based on statistical regressions (referred to as 'Mattar' and 'Saleh'). Time series of vegetation indices required for three of the algorithms (SCA-H, SCA-V and Mattar) were obtained from MODIS observations. The SM retrievals were evaluated against reference SM values estimated from a multiangular 2-Parameter inversion approach. The results obtained with the current base line algorithms developed for SMAP (SCA-H and -V) are in very good agreement with the reference SM data set derived from the multi-angular observations (R2 around 0.90, RMSE varying between 0.035 and 0.056 m3m3 for several retrieval configurations). This result showed that, provided the relationship between vegetation optical depth and a remotely-sensed vegetation index can be calibrated, the SCA algorithms can provide results very close to those obtained from multi-angular observations in this study area. The approaches based on statistical regressions provided similar results and the best accuracy was obtained with the Saleh methods based on either bi-angular or bipolarization observations (R2 around 0.93, RMSE around 0.035 m3m3). The LPRM and DCA algorithms were found to be slightly less successful in retrieving the 'reference' SM time series (R2 around 0.75, RMSE around 0.055 m3m3). However, the two above approaches have the great advantage of not requiring any model calibrations previous to the SM retrievals.

Soil Moisture↗

Dynamic Modeling and Soil Mechanics for Path Planning of the Mars Exploration Rovers

To help minimize risk of high sinkage and slippage during drives and to better understand soil properties and rover terramechanics from drive data, a multidisciplinary team was formed under the Mars Exploration Rover project to develop and utilize dynamic computer-based models for rover drives over realistic terrains. The resulting system, named ARTEMIS (Adams-based Rover Terramechanics and Mobility Interaction System), consists of the dynamic model, a library of terramechanics subroutines, and the high-resolution digital elevation maps of the Mars surface. A 200-element model of the rovers was developed and validated for drop tests before launch, using Adams dynamic modeling software. The external library was built in Fortran and called by Adams to model the wheel-soil interactions include the rut-formation effect of deformable soils, lateral and longitudinal forces, bull-dozing effects, and applied wheel torque. The paper presents the details and implementation of the system. To validate the developed system, one study case is presented from a realistic drive on Mars of the Opportunity rover. The simulation results match well from the measurement of on-board telemetry data. In its final form, ARTEMIS will be used in a predictive manner to assess terrain navigability and will become part of the overall effort in path planning and navigation for both Martian and lunar rovers.

rover↗

Interactive Vegetation Phenology, Soil Moisture, and Monthly Temperature Forecasts

The time scales that characterize the variations of vegetation phenology are generally much longer than those that characterize atmospheric processes. The explicit modeling of phenological processes in an atmospheric forecast system thus has the potential to provide skill to subseasonal or seasonal forecasts. We examine this possibility here using a forecast system fitted with a dynamic vegetation phenology model. We perform three experiments, each consisting of 128 independent warm-season monthly forecasts: 1) an experiment in which both soil moisture states and carbon states (e.g., those determining leaf area index) are initialized realistically, 2) an experiment in which the carbon states are prescribed to climatology throughout the forecasts, and 3) an experiment in which both the carbon and soil moisture states are prescribed to climatology throughout the forecasts. Evaluating the monthly forecasts of air temperature in each ensemble against observations, as well as quantifying the inherent predictability of temperature within each ensemble, shows that dynamic phenology can indeed contribute positively to subseasonal forecasts, though only to a small extent, with an impact dwarfed by that of soil moisture.

Seasonal Forecasting↗

Xeropreservation of Functionalized Lipid Biomarkers in Hyperarid Soils in the Atacama Desert

Our understanding of long-term organic matter preservation comes mostly from studies in aquatic systems. In contrast, taphonomic processes in extremely dry environments are relatively understudied and are poorly understood. We investigated the accumulation and preservation of lipid biomarkers in hyperarid soils in the Yungay region of the Atacama Desert. Lipids from seven soil horizons in a 2.5 meter vertical profile were extracted and analyzed using GC-MS ( Gas Chromatography-Mass Spectrometry) and LC-MS (Liquid Chromatography-Mass Spectrometry). Diagnostic functionalized lipids and geolipids were detected and increased in abundance and diversity with depth. Deeper clay units contain fossil organic matter (radiocarbon dead) that has been protected from rainwater since the onset of hyperaridity. We show that these clay units contain lipids in an excellent state of structural preservation with functional groups and unsaturated bonds in carbon chains. This indicates that minimal degradation of lipids has occurred in these soils since the time of their deposition between more than 40,000 and up to 2 million years ago. The exceptional structural preservation of biomarkers is likely due to the long-term hyperaridity that has minimized microbial and enzymatic activity, a taphonomic process we term xeropreservation (i.e. preservation by drying). The degree of biomarker preservation allowed us to reconstruct major changes in ecology in the Yungay region that reflect a shift in hydrological regime from wet to dry since the early Quaternary. Our results suggest that hyperarid environments, which comprise 7.5 percent of the continental landmass, could represent a rich and relatively unexplored source of paleobiological information on Earth.

Xeropreservation↗

Expanding the application of soil moisture monitoring systems through regression-based transformation

Relative to other geophysical variables, soil moisture (SM) estimates derived from land surface models (LSMs) and land data assimilation systems (LDAS) are difficult to transfer between platforms and applications. This difficulty stems from the highly model-dependent nature of LSM SM estimates and differences in the vertical support of discretized SM values. As a result, operational SM estimates generated by one LSM (or LDAS) cannot generally be directly applied to a hydrologic monitoring or forecast system designed around a second LSM. This lack of transferability is particularly problematic for LDAS applications, where the time, expertise, and computational resources required to generate an operational LDAS analysis cannot be practically duplicated for every LSM-specific application. Here, we develop a set of simple regression tools for translating SM estimates between LSMs and multiple LDAS analyses. Results demonstrate that simple multivariate linear regression - utilizing independent variables based on multi-layer and temporally lagged SM estimates - can significantly improve upon baseline transformation approaches using direct percentile matching. The proposed regression approaches are effective for both the LSM-to-LSM and LDAS-to-LDAS transformation of multi-layer SM percentiles. Application of this approach will expand the utility of existing, high-quality (but LSM-specific) operational sources of SM information like the NASA Soil Moisture Active Passive Level-4 Soil Moisture product.

Soil Moisture↗

Validation of Soil Moisture Data Products from the NASA SMAP Mission

The National Aeronautics and Space Administration (NASA)Soil Moisture Active Passive(SMAP) mission has been validatingits soil moisture (SM) products since the start of data production onMarch 31, 2015. Prior to launch, the mission defined a set of criteria for core validation sites (CVS) that enable the testing of the key mission SM accuracy requirement(unbiased root-mean-square error <0.04 m3/m3). Thevalidation approach also includes other (“sparse network”) in situSM measurements, satellite SM products, model-based SM products, and field experiments. Over the past six years, the SMAP SM products have been analyzed with respect to thesereference data,and the analysis approaches themselves have been scrutinizedin an effort to best understand the products’ performance. Validation of themost recent SMAP Level 2 and 3 SMretrievalproducts (R17000) shows that the L-band (1.4 GHz) radiometer-based SM record continues to meet mission requirements. The products aregenerallyconsistentwith SM retrievals from the European Space Agency (ESA)Soil Moisture Ocean Salinity mission, althoughthere aredifferencesin some regions. The high-resolution (3-km) SM retrieval product,generated by combining Copernicus Sentinel-1 data with SMAP observations,performswithin expectations. Currently, however,there is limited availability of3-kmCVSdatato support extensive validation at this spatial scale. The most recent (version 5)SMAP Level4 SMdata assimilation productprovidingsurface and root-zone SM with complete spatio-temporal coverageat 9-km resolution also meets performance requirements. The SMAP SM validation program will continue throughout the mission life; futureplans include expanding ittoforestedand high-latituderegions

SMAP↗

Evaluation of Extreme Soil Moisture Conditions During the 2020 Sahel Floods and Implications for Disease Outbreaks

The June-October 2020 growing season was characterized by sustained and extreme flooding across the African Sahel. One consequence of flooding events such as this is outbreaks of vector borne diseases (VBDs) which are often associated with climate anomalies. In this study, data from the soil moisture active passive (SMAP) mission is used with other soil moisture and precipitation data to show that the 2020 Sahelian growing season was the most extreme over the past four decades, ranking first in seasonally accumulated precipitation, which on average exceeded the climatology by around 300 mm. VBD outbreaks of Rift Valley fever and Chikungunya followed in Mauritania/Senegal and Chad, respectively. In some cases, soil moisture is a better indicator of VBD outbreak risk than precipitation, which has so far been more commonly used in studies of VBD outbreaks. It is expected that this finding will inform future monitoring and prediction efforts for VBD risk.

Afrocam Saje;↗

Global Land Surface Soil Moisture Database Derived from IASI Measurements on Board MetOp Satellites

Global land surface soil moisture has been derived from MetOp IASI ultraspectral infrared measurements over the past 16 years. Monthly and spatially gridded soil moisture is produced to show some phenomena associated with its natural variability. This study has aimed to demonstrate that thermal infrared remote sensing data can be used for monitoring global surface environmental characteristics and associated change through the continuity observations provided by the MetOp series. The time-series of soil moisture can be used to estimate its associated trend.

Remote sensing↗

New York Ecological Conservation: Evaluating Agricultural Conservation Easement Impact Using Earth Observations to Examine Avoided Soil Carbon Loss to Development

Farmland provides ecosystems and communities with services ranging from habitat conservation to food security. As total U.S. farmland continues to decline, agricultural lands near urban areas are especially vulnerable. Our project partners—Finger Lakes Land Trust, Genesee Land Trust, and Saratoga Preserving Land and Nature (PLAN)—can use study results to better profile farmland vulnerability, issuing conservation easements to protect maximum acreage in Saratoga County and the Finger Lakes Region of New York. Multiple existing studies effectively use remote sensing imagery to analyze historical land cover and forecast future change. This study examined soil carbon stocks and land cover change to estimate avoided soil carbon loss. We also predicted farmland vulnerability. We completed these analyses using European Space Agency (ESA) and NASA Earth observations that include Landsat 5 Thematic Mapper (TM), Landsat 8 Operational Land Imager (OLI), Landsat 9 OLI-2, Sentinel-2 Multispectral Instrument (MSI), and Suomi National Polar-Orbiting Partnership (NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) for nighttime lights data that aided in the land change model analysis. We determined that the conversion of agriculture to development from 1990 to 2022 occurred at rates of 0.91% (Finger Lakes) and 7% (Saratoga). Urban development is predicted to increase surrounding urban centers through 2030 and 2050. We also estimate that between 26.5 and 348,101 kilotons (Finger Lakes) and 3.7 and 58,006 kilotons (Saratoga) of soil carbon losses have been avoided through agricultural easements. Findings from this study will support our partners in determining agricultural conservation easement benefits and prioritizing the acquisition of future easement sites.

Agriculture↗

Impact of Design Factors for ESA CCI Satellite Soil Moisture Data Assimilation over Europe

In this study, soil moisture retrievals of the combined active–passive ESA Climate Change Initiative (CCI) soil moisture product are assimilated into the Noah-MP land surface model over Europe using a one-dimensional ensemble Kalman filter and an 18-yr study period. The performance of the data assimilation (DA) system is evaluated by comparing it with a model-only experiment (at in situ sites) and by assessing statistics of innovations and increments as DA diagnostics (over the entire domain). For both assessments, we explore the impact of three design choices, resulting in the following insights. 1) The magnitude of the assumed observation errors strongly affects the skill improvements evaluated against in situ stations and internal diagnostics. 2) Choosing between climatological or monthly cumulative distribution function matching as the observation bias correction method only has a marginal effect on the in situ skill of the DA system. However, the internal diagnostics suggest a more robust system parameterization if the observations are rescaled monthly. 3) The choice of atmospheric reanalysis dataset to force the land surface model affects the model-only skill and the DA skill improvements. The model-only skill is higher with input from the MERRA-2 than with input from the ERA5 reanalysis, resulting in larger DA skill improvements for the latter. Additionally, we show that the added value of the DA strongly depends on the quality of the satellite retrievals and land cover, with the most substantial soil moisture skill improvements occurring over croplands and skill degradation occurring over densely forested areas.

Soil moisture↗

The Impact of Decarbonization on Particulate Soiling of Solar Panels

Climate researchers have examined many impacts of climate change on energy supply and demand under various scenarios. However, the effect of changing particulate deposition onto solar panel surfaces on solar power production efficiency (i.e., soiling) has not been studied. We therefore characterize probabilistic outcomes across multiple climate models and scenarios. We find large current regional losses (up to 40% without manual cleaning, up to 20% with monthly cleaning and rain removal) in generation that grow slightly under a high-emission scenario, largely due to regional increases in windblown dust. In contrast, under a low-emissions scenario, potential production increases significantly (2–8% interquartile range with only rain removal) due to reduced soiling, especially in regions of Asia and Africa where anthropogenic aerosols are major contributors to soiling. Projected changes vary widely across models in many dusty areas outside of the Sahara and Arabia. Differences can also be large in regions dominated by anthropogenic aerosols, such as Nigeria, eastern China, and northern India, where the full range across modeled potential power production changes extends from −1 to +11% for the end of the century (without manual cleaning), underscoring the need to consider multiple climate models. With large increases in projected solar power deployment, the relatively small potential production increases reported here could nevertheless represent a large dividend in additional energy production. Hence, reductions in air pollution attributable to decarbonization could provide positive feedback under which a greater deployment of solar power (or other renewables) increases the production of solar power, facilitating the transition to renewable energy.

renewable energy↗

Extendable mast used in one shot soil penetrometer

Penetrometer to test soil characteristics has a piercing head with soil instrumentation equipment attached to an expandable mast actuated by compressed air. The penetrometer gives continuous measurements as the mast pushes the piercing head through the soil.

Hotz, G. M.↗

Sampling and handling of desert soils

Report on sampling and handling desert soils includes sections on selection, characterization, and photography of area, site, and soil, sterilization of sampling equipment and containers, and soil sample collection, transport, storage, and dispersal.

Blank, G. B.↗

Lunar surface engineering properties experiment definition. Volume 2: Mechanics of rolling sphere-soil slope interaction

The soil deformation mode under the action of a rolling sphere (boulder) was determined, and a theory based on actual soil failure mechanism was developed which provides a remote reconnaissance technique for study of soil conditions using boulder track observations. The failure mechanism was investigated by using models and by testing an instrumented spherical wheel. The wheel was specifically designed to measure contact pressure, but it also provided information on the failure mechanism. Further tests included rolling some 200 spheres down sand slopes. Films were taken of the rolling spheres, and the tracks were measured. Implications of the results and reevaluation of the lunar boulder tracks are discussed.

Hovland, H. J.↗

Spectral reflectance measurements of plant soil combinations

Field and laboratory observations of plant and soil reflectance spectra were made to develop an understanding of the reflectance of solar energy by plants and soils. A related objective is the isolation of factors contributing to the image formed by multispectral scanners and return beam vidicons carried by ERTS or film-filter combinations used in the field or on aircraft. A set of objective criteria are to be developed for identifying plant and soil types and their changing condition through the seasons for application of space imagery to resource management. This is because the global scale of earth observations satellites requires objective rather than subjective techniques, particularly where ground truth is either not available or too costly to acquire. As the acquiring of ground truth for training sets may be impractical in many cases, attempts have been made to identify objectively standard responses which could be used for image interpretation.

Macleod, N. H.↗