Retrieving Soil Moisture from Spaceborne Passive Microwave Data
Soil moisture products will be generated from data acquired by the Advanced Microwave Scanning Radiometer (AMSR-E) on the Aqua satellite.
Spaceborne↗
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A method for estimating soil moisture availability
A method for estimating values of soil moisture based on measurements of infrared surface temperature is discussed. A central element in the method is a boundary layer model. Although it has been shown that soil moistures determined by this method using satellite measurements do correspond in a coarse fashion to the antecedent precipitation, the accuracy and exact physical interpretation (with respect to ground water amounts) are not well known. This area of ignorance, which currently impedes the practical application of the method to problems in hydrology, meteorology and agriculture, is largely due to the absence of corresponding surface measurements. Preliminary field measurements made over France have led to the development of a promising vegetation formulation (Taconet et al., 1985), which has been incorporated in the model. It is necessary, however, to test the vegetation component, and the entire method, over a wide variety of surface conditions and crop canopies.
Carlson, T. N.↗
The effects of vegetation cover on the radar and radiometric sensitivity to soil moisture
The measured effects of vegetation canopies on radar and radiometric sensitivity to soil moisture are compared to emission and scattering models. The models are found to predict accurately the measured emission and backscattering for various crop canopies at frequencies between 1.4 and 5.0 GHz, especially at theta equal to or less than 30 deg. Vegetation loss factors, L(theta), increase with frequency and are found to be dependent upon canopy type and water content. In addition, the radiometric power absorption coefficient of a mature corn canopy is 1.75 times that calculated for the radar. Comparison of an L-band radiometer with a C-band radar shows the two systems to be complementary in terms of accurate soil moisture sensing over the extreme range of naturally occurring soil moisture conditions.
Ulaby, F. T.↗
Resolution requirements for a soil moisture imaging radar
Image simulation techniques were employed to establish the relationship between spatial resolution and the accuracy of soil moisture estimated by an imaging radar. A spaceborne synthetic aperture radar at an orbital altitude of 600 km, operating at 4.5 GHz with HH polarization configuration and covering a ground swath width of 143 km corresponding to an angle of incidence range extending between 7-22 degrees relative to the vertical, provided the images simulated by a 17.7 km x 19.3 km test site. The role of resolution was evaluated by simulating various sets of images with resolutions ranging from 20 m x 20 m to 1 km x 1 km. Each simulated image was subjected to a soil moisture prediction algorithm whose input is limited to the image intensity level. Results show that the 1 km x 1 km resolution provides the best prediction accuracy for most soil moisture distributions except for drought-like conditions for which the 100 m x 100 m resolution is found to be preferred. In general it was found that the soil moisture of about 90% of the pixels is predicted with an error of + or - 20% of field capacity or less.
Dobson, C.↗
NASAs Soil Moisture Active Passive (SMAP) Mission and Opportunities For Applications Users
Water in the soil, both its amount (soil moisture) and its state (freeze/thaw), plays a key role in water and energy cycles, in weather and climate, and in the carbon cycle. Additionally, soil moisture touches upon human lives in a number of ways from the ravages of flooding to the needs for monitoring agricultural and hydrologic droughts. Because of their relevance to weather, climate, science, and society, accurate and timely measurements of soil moisture and freeze/thaw state with global coverage are critically important.
Mission↗
Active microwave measurements for estimating soil moisture in Oklahoma
Previous research has shown that active and passive microwave sensors can be used to estimate near surface soil moisture. An advantage of the active systems is that they have the capability of high spatial resolution from satellite platforms. Most of the previous research has been conducted on agricultural fields. This study examined the use of active microwave data for estimating the average surface soil moisture for several watersheds located near Chicasha, Oklahoma. Data were collected using P, L, and C band scatterometers, mounted on the National Aeronautics and Space Administration C130B aircraft flying at an altitude of 300 m. Soil moisture data were collected in conjunction with three flights during May 1978. Our results indicated that relationships between the scatterometer data and soil moisture do exist and agree with the results obtained at the University of Kansas.
Jackson, T. J.↗
The use of remotely sensed soil moisture data in large-scale models of the hydrological cycle
Manabe (1982) has reviewed numerical simulations of the atmosphere which provided a framework within which an examination of the dynamics of the hydrological cycle could be conducted. It was found that the climate is sensitive to soil moisture variability in space and time. The challenge arises now to improve the observations of soil moisture so as to provide up-dated boundary condition inputs to large scale models including the hydrological cycle. Attention is given to details regarding the significance of understanding soil moisture variations, soil moisture estimation using remote sensing, and energy and moisture balance modeling.
Salomonson, V. V.↗
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↗
Estimating Soil Moisture from Satellite Microwave Observations
Cooperative research in microwave remote sensing between the Hydrological Sciences Branch of the NASA Goddard Space Flight Center and the Earth Sciences Faculty of the Vrije Universiteit Amsterdam began with the Botswana Water and Energy Balance Experiment and has continued through a series of highly successful International Research Programs. The collaboration between these two research institutions has resulted in significant scientific achievements, most notably in the area of satellite-based microwave remote sensing of soil moisture. The Botswana Program was the first joint research initiative between these two institutions, and provided a unique data base which included historical data sets of Scanning Multifrequency Microwave Radiometer (SN4NM) data, climate information, and extensive soil moisture measurements over several large experimental sites in southeast Botswana. These data were the basis for the development of new approaches in physically-based inverse modelling of soil moisture from satellite microwave observations. Among the results from this study were quantitative estimates of vegetation transmission properties at microwave frequencies. A single polarization modelling approach which used horizontally polarized microwave observations combined with monthly composites of Normalized Difference Vegetation Index was developed, and yielded good results. After more precise field experimentation with a ground-based radiometer system, a dual-polarization approach was subsequently developed. This new approach realized significant improvements in soil moisture estimation by satellite. Results from the Botswana study were subsequently applied to a desertification monitoring study for the country of Spain within the framework of the European Community science research programs EFEDA and RESMEDES. A dual frequency approach with only microwave data was used for this application. The Microwave Polarization Difference Index (MPDI) was calculated from 37 GHz data and used to derive the one-way canopy transmissivity. Using a simple radiative transfer model, this information was combined with horizontally polarized 6.6 GHz SMMR observations to derive a 9-year time series of soil moisture for all of Spain at a one quarter degree spatial scale. Both day and night SMMR observations were used independently, in order to check the consistency of the results. A first order Fourier Transform was performed on the mean monthly soil moisture values to identify major characteristics of time series such as trend, amplitude, and phase shift.
Owe, M.↗
Benchmarking a Soil Moisture Data Assimilation System for Agricultural Drought Monitoring
Despite considerable interest in the application of land surface data assimilation systems (LDAS) for agricultural drought applications, relatively little is known about the large-scale performance of such systems and, thus, the optimal methodological approach for implementing them. To address this need, this paper evaluates an LDAS for agricultural drought monitoring by benchmarking individual components of the system (i.e., a satellite soil moisture retrieval algorithm, a soil water balance model and a sequential data assimilation filter) against a series of linear models which perform the same function (i.e., have the same basic inputoutput structure) as the full system component. Benchmarking is based on the calculation of the lagged rank cross-correlation between the normalized difference vegetation index (NDVI) and soil moisture estimates acquired for various components of the system. Lagged soil moistureNDVI correlations obtained using individual LDAS components versus their linear analogs reveal the degree to which non-linearities andor complexities contained within each component actually contribute to the performance of the LDAS system as a whole. Here, a particular system based on surface soil moisture retrievals from the Land Parameter Retrieval Model (LPRM), a two-layer Palmer soil water balance model and an Ensemble Kalman filter (EnKF) is benchmarked. Results suggest significant room for improvement in each component of the system.
soil moisture↗
Airborne time-series measurement of soil moisture using terrestrial gamma radiation
Terrestrial gamma radiation data and independent ground-based core soil moisture data are analyzed. They reveal the possibility of using natural terrestrial gamma radiation collected from a low-flying aircraft to make reliable real-time soil moisture measurements for the upper 20 cm of soil. The airborne data were compared to the crude ground-based soil moisture data set collected at the core sites.
Carroll, Thomas R.↗
Soil moisture measurements from airborne SAR
The preliminary results of algorithm development and testing for soil moisture retrieval at high incidence angles are reported. Based on first-order surface backscattering models, a physically based algorithm for retrieval of soil moisture was developed and evaluated using NASA/JPL aircraft synthetic aperture radar (SAR) data. It shows that the copolarization ratio is sensitive to soil moisture change but not to surface roughness at high incidence angles. This algorithm performed well at L-band and should be useful. This study suggests that incidence angles greater than 40 degrees are optimal for such monitoring.
Shi, Jiancheng↗
An evaluation of the spatial resolution of soil moisture information
Rainfall-amount patterns in the central regions of the U.S. were assessed. The spatial scales of surface features and their corresponding microwave responses in the mid western U.S. were investigated. The usefulness for U.S. government agencies of soil moisture information at scales of 10 km and 1 km. was ascertained. From an investigation of 494 storms, it was found that the rainfall resulting from the passage of most types of storms produces patterns which can be resolved on a 10 km scale. The land features causing the greatest problem in the sensing of soil moisture over large agricultural areas with a radiometer are bodies of water. Over the mid-western portions of the U.S., water occupies less than 2% of the total area, the consequently, the water bodies will not have a significant impact on the mapping of soil moisture. Over most of the areas, measurements at a 10-km resolution would adequately define the distribution of soil moisture. Crop yield models and hydrological models would give improved results if soil moisture information at scales of 10 km was available.
Hardy, K. R.↗
Soil Moisture Active Passive Mission Mass Properties Optimization and Analysis
The Soil Moisture Active Passive (SMAP) mission creates global soil moisture measurements every 2-3 days by spinning a lightweight, ~6 meter diameter deployable mesh reflector. This represents the largest spinning antenna ever to be used on a NASA satellite. The paper discusses the mass properties challenges that were overcome in implementing the mission, including the definition of a combined imbalance term called the Effective Product of Inertia, the selection of an optimum Spun Instrument focal length and edge offset that fostered a reduced mass properties sensitivity, the inclusion of late game Spun Instrument configuration flexibility, the use of balance mass locations that leveraged the large moment arm provided by the reflector, and a subassembly measurement campaign that reduced mass properties uncertainties on testable components.
Hendry, Morgan L., IV↗
Measurement of soil moisture trends with airborne scatterometers
The author had identified the following significant results. Repeated looks at surfaces that maintain constant roughness can provide an estimate of soil moisture in the surface, when appropriate radar look angles are used. Significant influence due to differences in soil moisture can be detected in the 13.3 GHz and 1.6 GHz scatterometer returns. Effects of normal crop densities have little influence on the surface soil moisture estimate, when appropriate look angles are used. It appears that different look angles are optimum for different frequencies to avoid effects from vegetation. Considering the frequency and look angles used on the Seasat-A imaging radar, differences in soil moisture should produce as much as 9 db difference in return on that system.
Blanchard, B. J.↗
Central California Disasters: Incorporating Satellite-Derived Precipitation and Soil Moisture Products into Flood Preparedness and Emergency Management in California
Atmospheric rivers are a major contributor to extreme precipitation events and flooding in California. This study assessed the feasibility of using Earth observation data to monitor precipitation, soil moisture, and flooding from atmospheric rivers in the Salinas River Watershed in central California during the winter of 2022-2023. The team analyzed satellite derived precipitation estimates from the Global Precipitation Measurement mission, soil moisture data from the Soil Moisture Active Passive radiometer, and synthetic aperture radar imagery from Sentinel-1 to detect flood inundation. Global Precipitation Measurement precipitation estimates were found to underestimate rainfall by 33% to 52% compared to rain gauge data. The team found the Soil Moisture Active Passive dataset to be a valuable flooding indicator that they could visualize through various graphical representations, although its accuracy still needs verification with in situ flood maps. Sentinel-1 synthetic aperture radar imagery provided mapping of flood inundation extent along the Salinas River and surrounding areas and the Blue Spot model appears to be a good predictor of pluvial flood zones. The findings suggest Earth observations can enhance precipitation and flooding monitoring by the California Department of Water Resources, though further verification is needed. Integration of these datasets with vulnerability indices highlighted communities at highest risk during the 2022-2023 flooding events.
Abhinav Banthiya↗
Microwave radiometer experiment of soil moisture sensing at BARC test site during summer 1981
Soil moisture was measured by truck mounted microwave radiometers at the frequencies of 1.4 GHz, 5 GHz, and 10.7 GHz. The soil textures in the two test sites were different so that the soil type effect of microwave radiometric response could be studied. Several fields in each test site were prepared with different surface roughnesses and vegetation covers. Ground truth on the soil moisture, temperature, and the biomass of the vegetation was acquired in support of the microwave radiometric measurements. Soil bulk density for each of the fields in both test sites was sampled. The soils in both sites were measured mechanically and chemically. A tabulation of the measured data is presented and the sensors and operational problems associated with the measurements are discussed.
Wang, J.↗
Orbiting multi-beam microwave radiometer for soil moisture remote sensing
The effects of soil moisture and other factors on soil surface emissivity are reviewed and design concepts for a multibeam microwave radiometer with a 15 m antenna are described. Characteristic antenna gain and radiation patterns are shown and losses due to reflector roughness are estimated.
Shiue, J. C.↗