Magnetic field coupling with lunar soil simulants
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An algorithm for estimating moisture content of a bare soil from the observed brightness temperature at 1.4 GHz is discussed and applied to a limited data base. The method is based on a radiative transfer model calculation, which has been successfully used in the past to account for many observational results, with some modifications to take into account the effect of surface roughness. Besides the measured brightness temperatures, the three additional inputs required by the method are the effective soil thermodynamic temperature, the precise relation between moisture content and the smooth field brightness temperatures and a pair of parameters related to surface roughness. The procedures of estimating surface roughness parameters and of obtaining moisture content from observed brightness temperature are discussed. The algorithm is applied to observations from truck mounted and airborne radiometers. The estimated moisture contents compare favorably with the observations in the top 2 cm layer.
Ground water table (GWT) depth and soil moisture (SM) have been monitored at several locations at the Point Reyes field site (Californian coastal grassland) from 2021 to 2024. Monitoring is still on-going and data may be added to this archive at later time. The SM data have been acquired using Teros 12 Meter soil moisture sensors placed at 10, 30, 60 and 90 cm depth at 5 locations along a small hillslope. These sensors also collect soil temperature and bulk conductance. In addition, some collocated sensors provide pore pressure and Photochemical Reflectance Index (PRI). The GWT depth has been inferred from various type of Onset pressure transducers. The pressure measurements have been corrected for atmospheric pressure variations and sensor position relative to the ground surface to infer GWT depth, as well as with RTK GPS data to infer GWT elevation. The GWT data have been acquired at 5 distinct locations from 2020 to 2024 with the sensors placed at about 4 m depth. In addition, GWT data has been acquired for the 2023-2024 period with sensors located in 1 m deep shallow wells installed near each deeper well. This data is intended to evaluate possibly different dynamic in shallow (perched) and deep aquifer. The datasets are all provided in csv format. Please note that the interpretation of the GWT data needs to be done with consideration of environmental and well characteristics at the site and uncertainty in various variables. For more information on GWT and SM data, please contact the author.
The tilled row structure in agricultural fields is one of the important factors affecting observations of microwave emission from such fields. Measurements of this effect were performed with L-band and X-band radiometers mounted on a mobile truck on a bare 40 m x 45 m row tilled field; the soil moisture content during measurements ranged from 10 to 30% by dry weight. Results showed that the variations of the antenna temperatures with incident angle changed with the azimuth angle measured from the row direction. It is found that the observed difference between horizontally and vertically polarized antenna temperatures is due to the change in the local angle of field emission within the antenna field of view caused by the large-scale row structure.
An automated, fieldable ion-selective sensor system for frequent detection of nutrients in soils or water is set forth. In one aspect, an integrated small form-factor housing includes a battery, processor, a fluid manipulation unit and reservoirs, and a chemical species detection cell. A flow-through sampling head can be connected to the detection cell and a waste reservoir by flexible tubing sealingly connected at opposite ends of the tube. In one aspect, improved performance of the ion-selective sensor includes adding an ion-to-electron transfer interlayer between an ion-selective membrane and its working electrode. In one aspect, the sensor is solid-state and uses printed polymeric composite of POT-MoS$_{2}$. The use of a porous tube for a sampling head and fluid connection at opposite ends allows not only fluid to be removed from the head to the detection cell for measurement but also flow in an opposite direction and out to a waste reservoir to clean and reset for a next sample measurement.
It is pointed out that in order to develop the full potential of multispectral measurements acquired from satellite or aircraft sensors to monitor, map, and inventory agricultural resources, increased knowledge and understanding of the spectral properties of crops and soils are needed. The present state of knowledge is reviewed, emphasizing current investigations of the multispectral reflectance characteristics of crops and soils as measured from laboratory, field, aerial, and satellite sensor systems. The relationships of important biological and physical characteristics to their spectral properties of crops and soils are discussed. Future research needs are also indicated.
Background and aims Plant-soil feedbacks are the result of multiple abiotic and biotic mechanisms. However, few studies have addressed how feedbacks vary based on abiotic context or attempted to identify microbiota responsible for feedbacks. In this work, we investigated whether plant-soil feedbacks of an ectomycorrhizal tree (Quercus macrocarpa) varied based on soil nutrient status and whether fungal community composition and diversity could explain feedback patterns. Furthermore, we inoculated Q. macrocarpa seedlings with field-sampled soils taken from five soil origins – including heterospecific and conspecific trees and an old field – which were profiled using fungal DNA metabarcoding.Results Despite finding that soils associated with different hosts harbored distinct fungal communities and that fungal communities were predictive of plant growth, we did not find any significant plant-soil feedbacks regardless of fertilization status. We did find that the growth promotive effect associated with ectomycorrhizal OTU diversity was weakened with fertilization, suggesting context-dependent relations between plant growth and a guild of fungal mutualists. Our results demonstrate that the host-specific accumulation of functionally important soil microbes is not always sufficient to drive observable plant-soil feedbacks. Our data provide support for a role for ECM fungal diversity in mediating plant growth responses, though it is unclear whether this effect is direct or indirect.
The atmospheric effect on the upward radiance of sunlight scattered from the earth-atmosphere system is strongly influenced by the contrasts between fields and their sizes. In this paper, the radiances above finite fields are computed to simulate radiances measured by a satellite. A simulation case including 11 agricultural fields and four natural fields (water, soil, savanah, and forest) is used to test the effect of field size, background reflectance, and optical thickness of the atmosphere on the classification accuracy. For a given atmospheric turbidity, the atmospheric effect on classification of surface features may be much stronger for nonuniform surfaces than for uniform surfaces. Therefore, the classification accuracy of agricultural fields and urban areas is dependent not only on the optical characteristics of the atmosphere, but also on the size of the surface elements to be classified and their contrasts. It is concluded that new atmospheric correction methods, which take into account the finite size of the fields, are needed.
The author has identified the following significant results. The soil moisture difference between the flight lines was partly due to water-holding capacity differences of the two soil types. Fields along the east flight line were in clay; while along the west flight line, the soil was sandy loam which holds less moisture. Due to differences in the amount of green material, the pastures were wetter than the wheat fields. Most of the pastures average from 40-80% green material, while wheat averages from 90-100% green material. A large amount of green material transpired more water and depleted the soil water content faster than dead vegetation. Visicorder data found temperature differences between the rangeland and winter wheat fields. Pasture had a larger percentage of dead material with different thermal properties than live vegetation, and surface temperature was primarily dependent on insolation. Dead material transpired less, but warms up faster than wheat fields.
This dataset supports a broader study examining the effects of wetting and drying on soil layers across the eastern contiguous United States (CONUS). The dataset provides data generated from a laboratory moisture manipulation experiment. The contents include time series aerobic respiration and moisture; dissolved oxygen; sediment geochemistry data; and field metadata. Samples were collected as part of a collaboration between WHONDRS (Worldwide Hydrobiogeochemistry Observation Network for Dynamic River Systems; https://whondrs.pnnl.gov) and MONet (Molecular Observation Network; https://www.emsl.pnnl.gov/monet). The field samples (soil cores) were labeled as MEL_##_COR and subsequent subsamples begin with MEL_##. Additional subsamples were taken for the laboratory experiment and were labeled as EL_##. The labels from the MEL field samples and the EL subsamples can be mapped directly based on the digits following the prefix and underscore (i.e., EL_01 is a subsample from MEL_01). See the critical details section below for more details on sample naming and experimental design.For details on how to navigate this data package, see this infographic from the River Corridor SFA https://data.ess-dive.lbl.gov/portals/PNNLRiverCorridorSFA/About.In addition to this readme, this data package also includes a file-level metadata (FLMD) file that describes each file and a data dictionary (DD) that describes all column/row headers and variable definitions.This dataset is comprised of (1) a folder containing environmental context photos; (2) file-level metadata; (3) data dictionary; (4) field metadata; (5) readme; (6) international generic sample number (IGSN) mapping file; and (7) a subfolder with soil sample data from field samples and the incubation experiment. The sample data subfolder contains (1) effect size; (2) gravimetric moisture from field samples and incubation experiment; (3) respiration rates, raw dissolved oxygen values, and plots; (4) specific conductance, pH, and temperature from the incubation; (5) soil aggregates; (6) a summary containing median values of each data type for each treatment (wet and dry) in the incubation; (7) a summary containing averages for each data type of each soil layer; and (8) methods codes. All files are .csv, .pdf, .jpeg, or .jpg.
A major objective of soil moisture-related hydrological-research during NASA's SIR-C/X-SAR mission was to determine and compare soil moisture patterns within humid watersheds using SAR data, ground-based measurements, and hydrologic modeling. Currently available soil moisture-inversion methods using active microwave data are only accurate when applied to bare and slightly vegetated surfaces. Moreover, as the surface dries down, the number of pixels that can provide estimated soil moisture by these radar inversion methods decreases, leading to less accuracy and, confidence in the retrieved soil moisture fields at the watershed scale. The impact of these errors in microwave- derived soil moisture on hydrological modeling of vegetated watersheds has yet to be addressed. In this study a coupled water and energy balance model operating within a topographic framework is used to predict surface soil moisture for both bare and vegetated areas. In the first model run, the hydrological model is initialized using a standard baseflow approach, while in the second model run, soil moisture values derived from SIR-C radar data are used for initialization. The results, which compare favorably with ground measurements, demonstrate the utility of combining radar-derived surface soil moisture information with basin-scale hydrological modeling.
The goal of this project is to gain further understanding of soil moisture/temperature dynamics at different spatio-temporal scales and physical controls/parameters.We created a comprehensive GIS database, which has been accessed extensively by NASA Land Surface Hydrology investigators (and others), is located at the following URL: http://www.essc.psu.edu/nasalsh. For soil moisture field experiments such as SGP97, SGP99, SMEX02, and SMEX03, cartographic products were designed for multiple applications, both pre- and post-mission. Premission applications included flight line planning and field operations logistics, as well as general insight into the extent and distribution of soil, vegetation, and topographic properties for the study areas. The cartographic products were created from original spatial information resources that were imported into Adobe Illustrator, where the maps were created and PDF versions were made for distribution and download.
The soiling level of heliostat mirrors in Concentrated Solar Power (CSP) fields is one of the key factors that significantly influences optical efficiency. State-of-the-art methods of monitoring heliostats soiling levels still face various challenges, including slow speed, labor-intensive operations, resolution and accuracy constraints or interruptions to solar field operations. Here, we present a rapid, cost-effective, and non-intrusive method for mirror soiling detection based on polarimetric imaging, referred to as Polarimetric Imaging-based Mirror Soiling (PIMS). The compact PIMS device is designed for integration with unmanned aerial vehicles (UAVs), enabling rapid, large-area assessments of heliostat mirrors for efficient soiling detection. Our method utilizes the correlation between the Degree of Linear Polarization (DoLP) and surface soiling level based on Mie scattering theory and Monte Carlo simulations. Field deployment of the PIMS method requires minimal device installation, and its UAV-based operation allows for soiling detection without interrupting plant activities. The PIMS method holds the potential for mirror soiling detection across various concentrated solar power (CSP) plants and can be further adapted for other types of solar fields, such as parabolic trough systems.
Understanding soil texture and organic matter content supports our understanding of hydrology and ecology of Arctic sites. Soil organic matter content and composition of sand, silt, and clay were measured from soils collected at the Teller 27 field site on the Seward Peninsula and at the Barrow Environmental Observatory (BEO) near Utqiaġvik, Alaska, on August 2nd and 6th 2024, respectively. Soil samples were collected from the active layer to varied depths. Precise location data were collected at each observation point using Avenza Maps on a mobile device. Sand, silt, clay, and organic matter percentages were measured at Desert Research Institute Soil Characterization and Quaternary Pedology Laboratory in Reno, NV. This dataset contains a *.csv file of soil properties, a *.kml file of measurement locations, a *.pdf user guide, a *.csv data dictionary, and a *.csv file level metadata.
Spatial correlation structures can describe the degree to which soil moisture at a specified location co-varies in time with that at other, remote locations. Using four years of warm season SMAP Level 2 near-surface soil moisture data, we compute these spatial correlation structures for points across North America. The character of these structures is seen to differ geographically; the structures found for the west-central US, for example, are significantly more spatially extensive. We then demonstrate how these structures can potentially be used to reconstruct soil moisture fields during the pre-SMAP era. In this exercise, we consider as "truth" the soil moistures produced in a long-term offline land surface model simulation (1980-2014) that utilizes precipitation forcing based on a high density of precipitation gauges. Then, for a given location within the continent, we construct an"estimated" soil moisture time series based solely on historical soil moisture information simulated at least 300 km distant from the location, using the SMAP-based spatial correlation structures to determine how to make best use of the remote information. The reconstructed soil moistures are found to have significant skill relative to the assumed truth, suggesting that the same approach, when applied in areas of low rain gauge density (i.e., in areas for which historically simulated soil moistures are necessarily inaccurate), could provide useful historical soil moisture estimates through the SMAP-guided extraction of relevant information from neighboring gauged regions.
The author has identified the following significant results. Ground truth of soil moisture content, and ambient air and soil temperatures were acquired concurrently with measurements of soil moisture in bare fields and fields covered with grass, corn, and soybeans obtained with 1.4 GHz and 5 GHz radiometers mounted on a truck. The biomass of the vegetation was sampled about once a week. The measured brightness temperatures over the bare fields were compared with those of radiative transfer model calculations using as inputs the acquired soil moisture and temperatures data with appropriate values of dielectric constants for soil-water mixtures. A good agreement was found between the calculated and measured results over 10 deg to 70 deg incident angles. The presence of vegetation reduced the sensitivity of soil moisture sensing. At 1.4 GHz the sensitivity reduction ranged from about 20% for 10 cm tall grassland cover to over 50 to 60% for the dense soybean field. At 5 GHz corresponding reduction in sensitivity ranged from approximately 70% to approximately 90%.
Understanding the spatial and temporal distribution of soil moisture near the soil surface is important to relate ground truth data to remotely sensed data using an electronically scanned thinned array radiometer (ESTAR). The research was conducted at the A-ARM EF site in the Little Washita Watershed in Chickasha Oklahoma. Soil moisture was measured on a 100 x 100-m grid on a quarter section (0.8 km by 0.8 km) size field where the DOE A-ARM SWATS is located. This site has several drainage channels and small ponds. The site is under four different land use practices, namely active pastureland, non-grazed pastureland covered with thick grass, forest area covered with trees, and a single residential area. Soil moisture was measured with a Time Domain Reflectometry (TDR) Delta-T 6-cm theta-probe and gravimetric soil moisture (GSM) technique for the top 6 cm of the soil depth. A fourth order polynomial equation was fitted to each probe calibration curve. The correlation between TDR and GSM measurement technique ranges from 0.81 to 0.91. Comparison of the spatial and temporal distribution of soil moisture measured by the TDR and GSM techniques showed very strong similarities. Such TDR probes can be used successfully to replace the GSM techniques to measure soil moisture content rapidly and accurately with site specific calibration.
In order to determine the significance of organic matter content on the microwave emissivity of soils when estimating soil moisture, field experiments were conducted in which 1.4 GHz microwave emissivity data were collected over test plots of sandy loam soil with different organic matter levels (1.8, 4.0, and 6.1 percent) for a range of soil moisture values. Analyses of the observed data show only minor variation in microwave emissivity due to a change in organic matter content at a given moisture level for soils with similar texture and structure. Predictions of microwave emissivity made using a dielectric model for aggregated soils exhibit the same trends and type of response as the measured data when appropriate values for the input parameters were utilized.