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At least 127 records · Page 7

Signals of Opportunity - Airborne Demonstrator (SoOP-AD): Instrument Overview, Performance During First Flights and Future Instrument Concept

The Signals of Opportunity Airborne Demonstrator (SoOp-AD) was developed as part of the NASA InstrumentIncubator Program (IIP) with the goal of maturing the use of SoOp from existing communication satellites in geostationaryorbit, operating within the heavily used P-band (under 500 MHz) spectrum for soil moisture observations. P-band offers the benefit of roughly five (5) times deeper soil penetration compared to conventional L-band methods. SoOp-AD operates in a bi-static radar configuration, and only requires reception of direct and scattered signals from the source satellite. In this paper we present an overview of the SoOp-AD instrument architecture, signal processing, internal calibration approach and preliminary results from flights over the Little Washita watershed in Oklahoma, USA. Finally, future steps towards a U-class ("cubesat") instrument concept based upon experience with the airborne demonstrator are presented.

Instrumentation and Photography↗

Evaluation of the Tau-Omega Model Over Bare and Wheat-Covered Flat and Periodic Soil Surfaces at P- and L-band

It has been over ten years since the successful launch of the first-ever dedicated satellite for global soil moisture monitoring; Soil Moisture and Ocean Salinity (SMOS). Looking towards the future, P-band (0.3-1 GHz) is a promising technique to replace or enhance the L-band (1.4 GHz) SMOS and SMAP (Soil Moisture Active Passive) missions because of an expected reduction in roughness and vegetation impact, leading to an improved soil moisture accuracy over rougher soil surfaces and more densely vegetated areas. Accordingly, this investigation evaluated the tau-omega model at P-band (0.75 GHz) using a tower-based experiment in Victoria, Australia, where brightness temperature observations were collected concurrently at P- and L-band over bare and wheat-covered flat and periodic soil surfaces. The potential to retrieve soil moisture without discriminating periodic and flat surfaces was investigated by applying the roughness and vegetation parameters calibrated for flat soil to retrieve the moisture of periodic soil. Results showed that P-band had a comparable RMSE across different roughness configurations (variations less than 0.016 m3/m3) for both bare and wheat-covered soil, while the L-band RMSE was only comparable for wheat-covered soil, indicating that periodic surfaces did not need to be discriminated in such scenarios. Conversely, a difference of 0.022 m3/m3 was observed for L-band with bare soil. A reduced vegetation impact was also demonstrated at P-band, with an RMSE of 0.029 m3/m3 achieved when completely ignoring the wheat existence with under 4-kg/m2 vegetation water content, whereas at L-band the RMSE increased to 0.063 m3/m3. This study therefore paves the way for a successful P-band radiometer mission for obtaining more accurate global soil moisture information.

P-band↗

On Soil Moisture Retrieval and Target Decomposition

None given. Paper covers earlier models for determining soil moisture and surface roughness from radar data. It covers problems with other models, how the data can be thrown off, and possible solutions to remedy those calculations.

soil↗

L Band Brightness Temperature Observations Over a Corn Canopy During the Entire Growth Cycle

During a field campaign covering the 2002 corn growing season, a dual polarized tower mounted L-band (1.4 GHz) radiometer (LRAD) provided brightness temperature (T(sub B)) measurements at preset intervals, incidence and azimuth angles. These radiometer measurements were supported by an extensive characterization of land surface variables including soil moisture, soil temperature, vegetation biomass, and surface roughness. During the period from May 22, 2002 to August 30, 2002 a range of vegetation water content (W) of 0.0 to 4.3 kg/square m, ten days of radiometer and ground measurements were available. Using this data set, the effects of corn vegetation on surface emissions are investigated by means of a semi-empirical radiative transfer model. Additionally, the impact of roughness on the surface emission is quantified using T(sub B) measurements over bare soil conditions. Subsequently, the estimated roughness parameters, ground measurements and horizontally (H)-polarized T(sub B) are employed to invert the H-polarized transmissivity (gamma-h) for the monitored corn growing season.

Joseph, Alicia T.↗

Soil Moisture Project Evaluation Workshop

Approaches planned or being developed for measuring and modeling soil moisture parameters are discussed. Topics cover analysis of spatial variability of soil moisture as a function of terrain; the value of soil moisture information in developing stream flow data; energy/scene interactions; applications of satellite data; verifying soil water budget models; soil water profile/soil temperature profile models; soil moisture sensitivity analysis; combinations of the thermal model and microwave; determing planetary roughness and field roughness; how crust or a soil layer effects microwave return; truck radar; and truck/aircraft radar comparison.

Gilbert, R. H.↗

A scheme for computing surface fluxes from mean flow observations

A computational method for estimating turbulent surface stress, sensible heat flux, and humidity flux from mean velocity, temperature, and humidity at a single height in the atmospheric surface layer is presented. The method is based on coupling a Monin-Obukhov similarity profile to a 'force-restore' formulation for the evolution of surface soil temperature to yield the local values of shear stress, heat flux, and surface temperature. In addition to reference-level mean flow properties, the parameters required to implement this method are the thermal heat capacity of the soil, the surface aerodynamic roughness, latitude, solar declination, surface albedo, surface emissivity, and atmospheric transmissivity. Sample calculations are presented for (1) constant atmospheric forcing at the reference level, and (2) variable atmospheric forcing corresponding to Kahle's (1977) measurements of windspeed, air temperature, and radiometer soil surface temperature under dry vegetatively sparse conditions in the Mohave Desert, Calif.

Hoffert, M. I.↗

Characterization of LANDSAT-4 TM and MSS Image Quality for Interpretation of Agricultural and Forest Resources

Systematic analysis of both image and numeric data shows that the overall spectral, spatial, and radiometric quality of the TM data are excellent. Spectral variations in fallow fields are due to the vaiability in soil moisture and surface roughness resulting from the various stages of field preparation for small grains production. Spectrally, the addition of the first TM short wave infrared band (Band 5) significantly enhanced ability to discriminate different crop types. Bands 1, 5, and 6 contain saturated pixels due to high albedo effects, low moisture conditions, and high radiant temperatures of granite and dry, bare soil on south facing slopes, respectively. Spatially, the two fold decrease in interpixel distance and four fold decrease in area per pixel between the TM and MSS allow for improved discrimination of small fields, boundary conditions, road and stream networks in rough terrain, and small forest clearings resulting from various forest management practices.

Degloria, S. D.↗

A semiempirical model for interpreting microwave emission from semiarid land surfaces as seen from space

A radiative-transfer model for simulating microwave brightness temperatures over land surfaces is described. The model takes into account sensor viewing conditions (spacecraft altitude, viewing angle, frequency, and polarization) and atmospheric parameters over a soil surface characterized by its moisture, roughness, and temperature and covered with a layer of vegetation characterized by its temperature, water content, single scattering albedo, structure, and percent coverage. In order to reduce the influence of atmospheric and surface temperature effects, the brightness temperatures are expressed as polarization ratios that depend primarily on the soil moisture and roughness, canopy water content, and percentage of cover. The sensitivity of the polarization ratio to these parameters is investigated. Simulation of the temporal evolution of the microwave signal over semiarid areas in the African Sahel is presented and compared to actual satellite data from the SMMR instrument on Nimbus-7.

Kerr, Yann H.↗

A Time Series Approach for Soil Moisture Estimation

Soil moisture is a key parameter in understanding the global water cycle and in predicting natural hazards. Polarimetric radar measurements have been used for estimating soil moisture of bare surfaces. In order to estimate soil moisture accurately, the surface roughness effect must be compensated properly. In addition, these algorithms will not produce accurate results for vegetated surfaces. It is difficult to retrieve soil moisture of a vegetated surface since the radar backscattering cross section is sensitive to the vegetation structure and environmental conditions such as the ground slope. Therefore, it is necessary to develop a method to estimate the effect of the surface roughness and vegetation reliably. One way to remove the roughness effect and the vegetation contamination is to take advantage of the temporal variation of soil moisture. In order to understand the global hydrologic cycle, it is desirable to measure soil moisture with one- to two-days revisit. Using these frequent measurements, a time series approach can be implemented to improve the soil moisture retrieval accuracy.

soil moisture↗

FLUXNET-CH4: a global, multi-ecosystem dataset and analysis of methane seasonality from freshwater wetlands

Methane (CH4) emissions from natural landscapes constitute roughly half of global CH4 contributions to the atmosphere, yet large uncertainties remain in the absolute magnitude and the seasonality of emission quantities and drivers. Eddy covariance (EC) measurements of CH4 flux are ideal for constraining ecosystem-scale CH4 emissions due to quasi-continuous and high-temporal-resolution CH4 flux measurements, coincident carbon dioxide, water, and energy flux measurements, lack of ecosystem disturbance, and increased availability of datasets over the last decade. Here, we (1) describe the newly published dataset, FLUXNET-CH4 Version 1.0, the first open-source global dataset of CH4 EC measurements (available at https://fluxnet.org/data/fluxnet-ch4-community-product/, last access: 7 April 2021). FLUXNET-CH4 includes half-hourly and daily gap-filled and non-gap-filled aggregated CH4 fluxes and meteorological data from 79 sites globally: 42 freshwater wetlands, 6 brackish and saline wetlands, 7 formerly drained ecosystems, 7 rice paddy sites, 2 lakes, and 15 uplands. Then, we (2) evaluate FLUXNET-CH4 representativeness for freshwater wetland coverage globally because the majority of sites in FLUXNET-CH4 Version 1.0 are freshwater wetlands which are a substantial source of total atmospheric CH4 emissions; and (3) we provide the first global estimates of the seasonal variability and seasonality predictors of freshwater wetland CH4 fluxes. Our representativeness analysis suggests that the freshwater wetland sites in the dataset cover global wetland bioclimatic attributes (encompassing energy, moisture, and vegetation-related parameters) in arctic, boreal, and temperate regions but only sparsely cover humid tropical regions. Seasonality metrics of wetland CH4 emissions vary considerably across latitudinal bands. In freshwater wetlands (except those between 20°S to 20°N) the spring onset of elevated CH4 emissions starts 3 d earlier, and the CH4 emission season lasts 4 d longer, for each degree Celsius increase in mean annual air temperature. On average, the spring onset of increasing CH4 emissions lags behind soil warming by 1 month, with very few sites experiencing increased CH4 emissions prior to the onset of soil warming. In contrast, roughly half of these sites experience the spring onset of rising CH4 emissions prior to the spring increase in gross primary productivity (GPP). The timing of peak summer CH4 emissions does not correlate with the timing for either peak summer temperature or peak GPP. Our results provide seasonality parameters for CH4 modeling and highlight seasonality metrics that cannot be predicted by temperature or GPP (i.e., seasonality of CH4 peak). FLUXNET-CH4 is a powerful new resource for diagnosing and understanding the role of terrestrial ecosystems and climate drivers in the global CH4 cycle, and future additions of sites in tropical ecosystems and site years of data collection will provide added value to this database. All seasonality parameters are available at https://doi.org/10.5281/zenodo.4672601 (Delwiche et al., 2021). Additionally, raw FLUXNET-CH4 data used to extract seasonality parameters can be downloaded from https://fluxnet.org/data/fluxnet-ch4-community-product/ (last access: 7 April 2021), and a complete list of the 79 individual site data DOIs is provided in Table 2 of this paper.

FLUXNET-CH4↗

FLUXNET-CH 4 : a global, multi-ecosystem dataset and analysis of methane seasonality from freshwater wetlands

Methane (CH 4 ) emissions from natural landscapes constitute roughly half of global CH4 contributions to the atmosphere, yet large uncertainties remain in the absolute magnitude and the seasonality of emission quantities and drivers. Eddy covariance (EC) measurements of CH4 flux are ideal for constraining ecosystem-scale CH 4 emissions due to quasi-continuous and high-temporal-resolution CH 4 flux measurements, coincident carbon dioxide, water, and energy flux measurements, lack of ecosystem disturbance, and increased availability of datasets over the last decade. Here, we (1) describe the newly published dataset, FLUXNET-CH 4 Version 1.0, the first open-source global dataset of CH 4 EC measurements (available at https://fluxnet.org/data/fluxnet-ch4-community-product/, last access: 7 April 2021). FLUXNET-CH 4 includes half-hourly and daily gap-filled and non-gap-filled aggregated CH4 fluxes and meteorological data from 79 sites globally: 42 freshwater wetlands, 6 brackish and saline wetlands, 7 formerly drained ecosystems, 7 rice paddy sites, 2 lakes, and 15 uplands. Then, we (2) evaluate FLUXNET-CH 4 representativeness for freshwater wetland coverage globally because the majority of sites in FLUXNET-CH4 Version 1.0 are freshwater wetlands which are a substantial source of total atmospheric CH 4 emissions; and (3) we provide the first global estimates of the seasonal variability and seasonality predictors of freshwater wetland CH4 fluxes. Our representativeness analysis suggests that the freshwater wetland sites in the dataset cover global wetland bioclimatic attributes (encompassing energy, moisture, and vegetation-related parameters) in arctic, boreal, and temperate regions but only sparsely cover humid tropical regions. Seasonality metrics of wetland CH 4 emissions vary considerably across latitudinal bands. In freshwater wetlands (except those between 20°S to 20°N) the spring onset of elevated CH 4 emissions starts 3 d earlier, and the CH 4 emission season lasts 4 d longer, for each degree Celsius increase in mean annual air temperature. On average, the spring onset of increasing CH 4 emissions lags behind soil warming by 1 month, with very few sites experiencing increased CH 4 emissions prior to the onset of soil warming. In contrast, roughly half of these sites experience the spring onset of rising CH 4 emissions prior to the spring increase in gross primary productivity (GPP). The timing of peak summer CH 4 emissions does not correlate with the timing for either peak summer temperature or peak GPP. Our results provide seasonality parameters for CH 4 modeling and highlight seasonality metrics that cannot be predicted by temperature or GPP (i.e., seasonality of CH 4 peak). FLUXNET-CH 4 is a powerful new resource for diagnosing and understanding the role of terrestrial ecosystems and climate drivers in the global CH 4 cycle, and future additions of sites in tropical ecosystems and site years of data collection will provide added value to this database. All seasonality parameters are available at https://doi.org/10.5281/zenodo.4672601 (Delwiche et al., 2021). Additionally, raw FLUXNET-CH 4 data used to extract seasonality parameters can be downloaded from https://fluxnet.org/data/fluxnet-ch4-community-product/ (last access: 7 April 2021), and a complete list of the 79 individual site data DOIs is provided in Table 2 of this paper.

54 ENVIRONMENTAL SCIENCES↗

L-Band H Polarized Microwave Emission During the Corn Growth Cycle

Hourly L-band (1.4 GHz) horizontally (H) polarized brightness temperatures (T(sub B))'s measured during five episodes (more than two days of continuous measurements) of the 2002 corn growth cycle are analyzed. These T(sub B)'s measurements were acquired as a part of a combined active/passive microwave field campaign, and were obtained at five incidence and three azimuth angles relative to the row direction. In support of this microwave data collection, intensive ground sampling took place once a week. Moreover, the interpretation of the hourly T(sub B)'s could also rely on the data obtained using the various automated instruments installed in the same field. In this paper, the soil moisture and temperature measured at fixed time intervals have been employed as input for the tau-omega model to reproduce the hourly T(sub B). Through the calibration of the vegetation and surface roughness parameterizations, the impact of the vegetation morphological changes on the microwave emission and the dependence of the soil surface roughness parameter, h(sub r), on soil moisture are investigated. This analysis demonstrates that the b parameter, appearing in the representation of the canopy opacity, has an angular dependence that varies throughout the growing period and also that the parameter hr increases as the soil dries in a portion of the dry-down cycle. The angular dependence of the b parameter imposes the largest uncertainty on T(sub B) simulations near senescence as the response of b to the incidence is also affected by the crop row orientation. On the other hand, the incorporation of a soil moisture dependent h(sub r) parameterization was responsible for the largest error reduction of T(sub B) simulations in the early growth cycle.

Joseph, A. T.↗

Results of soil moisture flights during April 1974

The results presented here are derived from measurements made during the April 5 and 6, 1974 flights of the NASA P-3A aircraft over the Phoenix, Arizona agricultural test site. The purpose of the mission was to study the use of microwave techniques for the remote sensing of soil moisture. These results include infrared (10-to 12 micrometers) 2.8-cm and 21-cm brightness temperatures for approximately 90 bare fields. These brightness temperatures are compared with surface measurements of the soil moisture made at the time of the overflights. These data indicate that the combination of the sum and difference of the vertically and the horizontally polarized brightness temperatures yield information on both the soil moisture and surface roughness conditions.

Schmugge, T. J.↗

Remote sensing of snow

The snow parameters affecting sensor responses at different wavelengths are discussed. The effects of snow depth and background radiation on gamma ray sensors and of crystal size, contaminants, snow depth, liquid water, and surface roughness on visible and near-infrared sensors are considered. The influence of temperature, crystal size, and liquid water on thermal infrared sensors and of liquid water, crystal size, water equivalent depth, stratification, snow surface roughness, density, temperature, and soil condition on microwave sensors are addressed.

Foster, J. L.↗

Conceptual aspects of a statistical-dynamical approach to represent landscape subgrid-scale heterogeneities in atmospheric models

A parameterization of land surfaces based on a statistical-dynamical approach is presented. With this approach, the most important characteristics of the soil-plant-atmosphere system that affect the partition of energy (e.g., plant stomatal conductance, soil humidity, and surface roughness) are represented by a pdf rather than by a single 'representative' value. A primary simplified version of this parameterization is used to estimate the land-surface energy fluxes that are produced at the grid scale by various distributions of stomatal conductance under a broad range of environmental conditions. To demonstrate the approach's potential, results are compared with the same fluxes calculated with a big leaf model using the mean stomatal conductance that corresponds to the distributions. Large absolute and relative differences are obtained between the two schemes for many combinations of stomatal conductance pdfs and environmental conditions.

Avissar, Roni↗

Microwave remote sensing and its application to soil moisture detection

The author has identified the following significant results. Experimental measurements were utilized to demonstrate a procedure for estimating soil moisture, using a passive microwave sensor. The investigation showed that 1.4 GHz and 10.6 GHz can be used to estimate the average soil moisture within two depths; however, it appeared that a frequency less than 10.6 GHz would be preferable for the surface measurement. Average soil moisture within two depths would provide information on the slope of the soil moisture gradient near the surface. Measurements showed that a uniform surface roughness similar to flat tilled fields reduced the sensitivity of the microwave emission to soil moisture changes. Assuming that the surface roughness was known, the approximate soil moisture estimation accuracy at 1.4 GHz calculated for a 25% average soil moisture and an 80% degree of confidence, was +3% and -6% for a smooth bare surface, +4% and -5% for a medium rough surface, and +5.5% and -6% for a rough surface.

Newton, R. W.↗

Multifrequency microwave radiometer measurements of soil moisture

Ground-based microwave radiometer experiments are carried out to investigate the effects of moisture, temperature, and roughness on microwave emission from bare soils. The measurements are made at frequencies of 0.6-0.9, 1.4, and 10.7 GHz using van-mounted radiometers to observe prepared soil sites in Kern County, CA. Brightness temperature variations of approximately 15 K at 1.4 GHz and 25 K at 10.7 GHz are observed as a result of diurnal changes in the soil temperature. Increasing the soil moisture content from 2% to 15% by volume is found to result in brightness temperature decreases of approximately 70 K at 0.775 and 1.4 GHz and 40 K at 10.7 GHz, depending, to a lesser extent, on polarization and viewing angle. The results attest the significance of soil temperature in deriving soil moisture from microwave radiometer measurements. Comparisons of the microwave measurements with theoretical predictions using smooth surface models give reasonable agreement and support previous results of this nature obtained with other soil types.

Njoku, E. G.↗

Synergism of active and passive microwave data for estimating bare surface soil moisture

Active and passive microwave sensors were applied effectively to the problem of estimating the surface soil moisture in a variety of environmental conditions. Research to date has shown that both types of sensors are also sensitive to the surface roughness and the vegetation cover. In estimating the soil moisture, the effect of the vegetation and roughness are often corrected either by acquiring multi-configuration (frequency and polarization) data or by adjusting the surface parameters in order to match the model predictions to the measured data. Due to the limitations on multi-configuration spaceborne data and the lack of a priori knowledge of the surface characteristics for parameter adjustments, it was suggested that the synergistic use of the sensors may improve the estimation of the soil moisture over the extreme range of naturally occurring soil and vegetation conditions. To investigate this problem, the backscattering and emission from a bare soil surface using the classical rough surface scattering theory were modeled. The model combines the small perturbation and the Kirchhoff approximations in conjunction with the Peak formulation to cover a wide range of surface roughness parameters with respect to frequency for both active and passive measurements. In this approach, the same analytical method was used to calculate the backscattering and emissivity. Therefore, the active and passive simulations can be combined at various polarizations and frequencies in order to estimate the soil moisture more actively. As a result, it is shown that (1) the emissivity is less dependent on the surface correlation length, (2) the ratio of the backscattering coefficient (HH) over the surface reflectivity (H) is almost independent of the soil moisture for a wide range of surface roughness, and (3) this ratio can be approximated as a linear function of the surface rms height. The results were compared with the data obtained by a multi-frequency radiometer-scatterometer system working at frequencies between 3.0 GHz to 11.0 GHz. The data were acquired over bare soil surfaces with moisture variations due to freezing and thawing and roughness changes due to rain and erosion. The model predictions are shown to be in reasonable agreement with the data. In addition, it was shown that the same ratio when calculated from the data shows almost no dependence on the soil moisture. Finally, a simple technique which combines the backscattering coefficient at HH polarization (active sensing) with the emissivity at H polarization (passive sensing) is suggested for retrieving the soil moisture from bare soil surfaces.

Saatchi, Sasan S.↗