Analysis of TerraSAR-X data and their sensitivity to soil surface parameters over bare agricultural fields

The sensitivity of TerraSAR-X radar signals to surface soil parameters has been examined over agricultural fields, using HH polarization and various incidence angles (26°, 28°, 50°, 52°). The results show that the radar signal is slightly more sensitive to surface roughness at high incidence (50°–52°) than at low incidence (26°–28°). The difference observed in the X-band, between radar signals reflected by the roughest and smoothest areas, reaches a maximum of the order of 5.5 dB at 50°–52°, and 4 dB at 26°–28°. This sensitivity increases in the L-band with PALSAR/ALOS data, for which the dynamics of the return radar signal as a function of soil roughness reach 8 dB at HH38°. In the C-band, ASAR/ENVISAT data (HH and VV polarizations at an incidence angle of 23°) are characterised by a difference of about 4 dB between the signals backscattered by smooth and rough areas.Our results also show that the sensitivity of TerraSAR-X signal to surface roughness decreases in very wet and frozen soil conditions. Moreover, the difference in backscattered signal between smooth and rough fields is greater at high incidence angles. The low-to-high incidence signal ratio (Δσ° = σ_26°–28°/σ_50°–52°) decreases with surface roughness, and has a dynamic range, as a function of surface roughness, smaller than that of the backscattering coefficients at low and high incidences alone. Under very wet soil conditions (for soil moistures between 32% and 41%), the radar signal decreases by about 4 dB. This decrease appears to be independent of incidence angle, and the ratio Δσ° is found to be independent of soil moisture.     

Effects of corn on C- and L-band radar backscatter: A correction method for soil moisture retrieval

This paper discusses the effects of vegetation on C- (4.75 GHz) and L- (1.6 GHz) band backscattering (σ^o) measured throughout a growth cycle at incidence angles of 15, 35 and 55°. The utilized σ^o data set was collected by a truck mounted scatterometer over a corn field and is supported by a comprehensive set of ground measurements, including soil moisture and vegetation biomass. Comparison of σ^o measurement against simulations by the Integral Equation Method (IEM) surface scattering model (Fung et al., 1992) shows that the σ^o measurements are dominated either by an attenuated soil return or by scattering from vegetation depending on the antenna configuration and growth stage. Further, the measured σ^o is found to be sensitive to soil moisture even at peak biomass and large incidence angles, which is attributed to scattering along the soil-vegetation pathway.For the simulation of C-band σ^o and the retrieval of soil moisture two methods have been applied, which are the semi-empirical water cloud model (Attema & Ulaby, 1978) and a novel method. This alternative method uses the empirical relationships between the vegetation water content (W) and the ratio of the bare soil and the measured σ^o to correct for vegetation. It is found that this alternative method is superior in reproducing the measured σ^o as well as retrieving soil moisture. The highest retrieval accuracies are obtained at a 35° incidence angle leading to RMSD's of 0.044 and 0.037 m³ m^− 3 for the HH and VV-polarization, respectively. In addition, the sensitivity of these soil moisture retrievals to W and surface roughness parameter uncertainties is investigated.     

Surface soil moisture estimation over dense crop using Envisat ASAR and Landsat TM imagery: an approach

This study focuses on developing a new method of surface soil moisture estimation over wheat fields using Environmental Satellite Advanced Synthetic Aperture Radar (Envisat ASAR) and Landsat Thematic Mapper (TM) data. The Michigan Microwave Canopy Scattering (MIMICS) model was used to simulate wheat canopy backscattering coefficients from experiment plots at incidence angles of 23° (IS2) and 43.9° (IS7). Based on simulated data, the scattering characteristics of a wheat canopy were first investigated in order to derive an optimal configuration of polarization (HH) and incidence angle (IS2) for soil moisture estimation. Then a parametric model was developed to describe wheat canopy backscattering at the optimal configuration. In addition, direct backscattering and two-way transmissivity of wheat crowns were derived from the TM normalized difference vegetation index (NDVI); direct ground backscattering was derived from surface soil moisture and TM NDVI; and backscattering from double scattering was derived from total backscattering. A semi-empirical model for soil moisture estimation was derived from the parametric model. Coefficients in the semi-empirical model were obtained using a calibration approach based on measured soil moisture, ASAR, and TM data. A validation of the model was performed over the experimental area. In this study, the root mean square error (RMSE) for the estimated soil moisture was 0.041 m³ m^?3, and the correlation coefficient between the measured and estimated soil moisture was 0.84. The experimental results indicate that the semi-empirical model could improve soil moisture estimation compared to an empirical model.     

Potential of ASAR/ENVISAT for the characterization of soil surface parameters over bare agricultural fields

The objective of this investigation is to analyze the sensitivity of ASAR (Advanced Synthetic Aperture Radar) data to soil surface parameters (surface roughness and soil moisture) over bare fields, at various polarizations (HH, HV, and VV) and incidence angles (20°-43°). The relationships between backscattering coefficients and soil parameters were examined by means of 16 ASAR images and several field campaigns. We have found that HH and HV polarizations are more sensitive than VV polarization to surface roughness. The results also show that the radar signal is more sensitive to surface roughness at high incidence angle (43°). However, the dynamics of the radar signal as a function of soil roughness are weak for root mean square (rms) surface heights between 0.5 cm and 3.56 cm (only 3 dB for HH polarization and 43° incidence angle). The estimation of soil moisture is optimal at low and medium incidence angles (20°-37°). The backscattering coefficient is more sensitive to volumetric soil moisture in HH polarization than in HV polarization. In fact, the results show that the depolarization ratio σ_HH⁰/σ_HV⁰ is weakly dependent on the roughness condition, whatever the radar incidence. On the other hand, we observe a linear relationship between the ratio σ_HH⁰/σ_HV⁰ and the soil moisture. The backscattering coefficient ratio between a low and a high incidence angle decreases with the rms surface height, and minimizes the effect of the soil moisture.     

The use of a microwave backscatter model for retrieving soil moisture over bare soil

Abstract

Most attempts at predicting soil moisture from C-band microwave backscattering coefficients for bare soil are made by fitting experimental calibration relations obtained for limited ranges of incidence angle and soil surface roughness. In this paper, a more general approach is discussed using an inversion procedure to extend the use of a single experimental calibration relation to a wider range of incidence angle and surface roughness. A correcting function is proposed to normalize the backscattering coefficients to the conditions (incidence angle and surface roughness) of the calibration relation. This correcting function was derived from simulated data using the physical optics or KirchhofTs scatter model using the scalar approximation. Before discussing the inversion procedure, the backscattering coefficients calculated by the model have been compared with experimental data measured in the C-band, HH polarization and three incidence angles (Θ= 15°, 23°, 50°) under a wide range of surface soil moisture conditions (0.02H_v  0.35cm³ cm^-3) and for a single quite smooth soil surface roughness (0–011 _s  OOI4/n)m. The model was found to be experimentally validated from 15° to 23° of incidence and for surface soil moistures higher than 0-I0cm³cm^-3. For the inversion procedure, it is assumed to have a wider range of validity (15°  Θ 35° ) for ihc incidence angle. A sensitivity analysis of the model to errors on roughness parameter and incidence angle was performed in order to assess the feasability and suitability of the described inversion procedure.     

Active microwave measurement of soil water content

Measurements of radar backscatter from bare soil at 4.7, 5.9, and 7.1 GHz for incident angles of 0–70° have been analyzed to determine sensitivity to soil moisture. Because the effective depth of penetration of the radar signal is only about one skin depth, the observed signals were correlated with the moisture in a skin depth as characterized by the attenuation coefficient (reciprocal of skin depth). Since the attenuation coefficient is a monotonically increasing function of moisture density, it may also be used as a measure of moisture content over the distance involved, which varies with frequency and moisture content. The measurements show an approximately linear increase in scattering with attenuation coefficient of the soil at angles within 10° of vertical and all frequencies. At 4.7 GHz this increase continues relatively large out to 70° incidence, but by 7.1 GHz the sensitivity is much less even at 20° and practically gone at 50°.An inversion technique to determine how well the moisture content can be estimated from the scattered signal indicates good success for near-vertical angles and middle ranges of moisture density, with poorer success at smaller moisture densities and an anomaly in the data at the highest moisture density that must be resolved by further experimentation.     

Radar estimates of soil moisture over the Konza Prairie

Abstract

Radar backscatter measurements were made as a part of the First International satellite land surface climatology project Field Experiment (FIFE) to estimate soil moisture for use by other investigators. The helicopter-mounted radar was flown along selected transects that coincided with soil moisture measurements. The radar operated at microwave frequencies of 5-3 and 9 6 GHz and at selected incidence angles between 0° and 60°. Vertical polarization was used for two days in June of 1987 and horizontal polarization was used for three days in July and October of 1987.

The scattering coefficient data from different days were grouped by frequency and antenna angles and then related to soil moisture along the flight paths using linear regression. A measure of linearity for the regression, R², ranged between 0·9 and 0·5. The larger coefficients were for X -band measurements made at large antenna incidence angles, and the smaller coefficients were for C-band measurements made: at incidence angles near vertical.     

Measurement of radar backscatter from rough soil surfaces using linear and circular polarizations†

Radar backscatters from loam with a dry bulk density of 0·6g/cm³ have been measured at 9·9 GHz using both linear and circular polarizations. The sensitivity of radar return to soil moisture content has been obtained at five polarization combinations, HH, VV, HV, LR and LL (L and R denote the left-circular and the right-circular polarizations, respectively). Comparison of the moisture sensitivities shows that the sensitivity of HV is the highest among five polarizations and the sensitivity of LL is slightly higher than that of HH, VV and LR. Surface scatter theories are discussed in relation to the moisture sensitivities of five polarizations.     

Monitoring root-zone soil moisture through the assimilation of a thermal remote sensing-based soil moisture proxy into a water balance model

Two types of Soil Vegetation Atmosphere Transfer (SVAT) modeling approaches can be applied to monitor root-zone soil moisture in agricultural landscapes. Water and Energy Balance (WEB) SVAT modeling is based on forcing a prognostic root-zone water balance model with observed rainfall and predicted evapotranspiration. In contrast, thermal Remote Sensing (RS) observations of surface radiometric temperature (T_R) are integrated into purely diagnostic RS-SVAT models to predict the onset of vegetation water stress. While RS-SVAT models do not explicitly monitor soil moisture, they can be used in the calculation of thermal-based proxy variables for the availability of soil water in the root zone. Using four growing seasons (2001 to 2004) of profile soil moisture, micro-meteorology, and surface radiometric temperature measurements at the United States Department of Agriculture (USDA) Optimizing Production Inputs for Economic and Environmental Enhancements (OPE³) study site in Beltsville, MD, prospects for improving WEB-SVAT root-zone soil water predictions via the assimilation of diagnostic RS-SVAT soil moisture proxy information are examined. Results illustrate the potential advantages of such an assimilation approach relative to the competing approach of directly assimilating T_R measurements. Since T_R measurements used in the analysis are tower-based (and not obtained from a remote platform), a sensitivity analysis demonstrates the potential impact of remote sensing limitations on the value of the RS-SVAT proxy. Overall, results support a potential role for RS-SVAT modeling strategies in improving WEB-SVAT model characterization of root-zone soil moisture.     

Evaluation of different orientation angle distributions within the X-Bragg scattering model for bare soil moisture estimation

In this paper, the applicability of three different orientation angle distributions of surface facets within the extended Bragg (X-Bragg) scattering model is investigated for estimation of soil moisture over bare surfaces using both Eigen-based and model-based polarimetric synthetic aperture radar (PolSAR) decomposition techniques. The three distributions considered for investigation in the X-Bragg model are uniform, half cosine, and the Lee distributions. In order to understand the sensitivity of the model using the three orientation angle distributions, key polarimetric parameters, such as scattering entropy (H), scattering anisotropy (A), scattering mechanism (α), cross-pol power (T₃₃), linear T₁₂ coherence (|γ_{(HH+VV)(HH–VV)}|), are simulated and analysed for various widths of distributions. The analysis of the simulated polarimetric parameters show that the Lee distribution has a reduced roughness validity range compared with the uniform and half cosine distributions. DLR E-SAR L-band data from the AgriSAR’2006 campaign over the Demmin test site in Northern Germany are inverted for soil moisture over bare surfaces. The inverted soil moisture from the physics-based X-Bragg model is compared with in situ measured TDR (time domain reflectometry) soil moisture values. The inversion results using the Eigen-based decomposition reveal similar root mean square error (RMSE = 14 vol.%) and inversion rates for three distributions. The model-based decomposition inversion results obtained at various fixed widths of distributions reveal that the Lee distribution shows less RMSE of 8 vol.% and high inversion rates for moderate surface roughness (ks = 0.5) as compared with half cosine and uniform distributions.     

Evaluation of the Oceansat‐1 Multi‐frequency Scanning Microwave Radiometer and its potential for soil moisture retrieval

The Multi‐frequency Scanning Microwave Radiometer (MSMR) aboard the Indian Space Research Organization—Oceansat‐1 platform measured land surface brightness temperature at a C‐band frequency and provided an opportunity for exploring large‐scale soil moisture retrieval during its two‐year period of operation. These data may provide a valuable extension to the Scanning Multichannel Microwave Radiometer (SMMR) and the Advanced Microwave Scanning Radiometer (AMSR) since they covered a portion of the time period between the two missions. This investigation was one of the first to utilize the MSMR data for a land application and, as a result, several data quality issues had to be addressed. These included geolocation accuracy, calibration (particularly over land), erroneous data, and the significance of anthropogenic radio‐frequency interference (RFI). Calibration of the low frequency channels was evaluated using inter‐comparisons between the Tropical Rainfall Measuring Mission/Microwave Imager (TRMM/TMI) and the MSMR brightness temperatures. Biases (TMI T _B>MSMR T _B) of 3.4 and 3.6 K were observed over land for the MSMR 10.65 GHz horizontal and vertical polarization channels, respectively. These results suggested that additional calibration of the MSMR data was required. Comparisons between the MSMR measured brightness temperature and ground measured volumetric soil moisture collected during the South Great Plain experiment (SGP99) indicated that the lower frequency and horizontal polarization observations had higher sensitivity to soil moisture. Using a previously developed soil emission model, multi‐temporal regional soil moisture distributions were retrieved for the continental United States. Comparisons between the MSMR based soil moisture and ground measured volumetric soil moisture indicated a standard error of estimate of 0.052 m³/m³.     

Validation of AMSR-E soil moisture using L-band airborne radiometer data from National Airborne Field Experiment 2006

AMSR-E has been extensively evaluated under a wide range of ground and climate conditions using in situ and aircraft data, where the latter were primarily used for assessing the T_B calibration accuracy. However, none of the previous work evaluates AMSR-E performance under the conditions of flood irrigation or other forms of standing water. Also, it should be mentioned that global soil moisture retrievals from AMSR-E typically utilize X-band data. Here, C-band based AMSR-E soil moisture estimates are evaluated using 1 km resolution retrievals derived from L-band aircraft data collected during the National Airborne Field Experiment (NAFE'06) field campaign in November 2006. NAFE'06 was conducted in the Murrumbidgee catchment area in southeastern Australia, which offers diverse ground conditions, including extensive areas with dryland, irrigation, and rice fields. The data allowed us to examine the impact of irrigation and standing water on the accuracy of satellite-derived soil moisture estimates from AMSR-E using passive microwave remote sensing. It was expected that in fields with standing water, the satellite estimates would have a lower accuracy as compared to soil moisture values over the rest of the domain. Results showed sensitivity of the AMSR-E to changes in soil moisture caused by both precipitation and irrigation, as well as good spatial (average R = 0.92 and RMSD = 0.049 m³/m³) and temporal (R = 0.94 and RMSD = 0.04 m³/m³) agreement between the satellite and aircraft soil moisture retrievals; however, under the NAFE'06 ground conditions, the satellite retrievals consistently overestimated the soil moisture conditions compared to the aircraft.     

Spatial variability of SEBAL estimated root-zone soil moisture across scales

This study investigated the spatial scaling behaviour of root-zone soil moisture obtained from optical/thermal remote-sensing observations. The data for this study were obtained from Landsat and Moderate Resolution Imaging Spectroradiometer (MODIS) satellites on five different dates between early spring (April) and fall (September) in the years from 2000 to 2004 in the semi-arid middle Rio Grande Valley of New Mexico. Soil moisture data were obtained using the Surface Energy Balance Algorithm for Land (SEBAL) algorithm. The data were spatially aggregated and checked for power-law behaviour over a range of scales from 30 m to 15 km for Landsat and from 1 to 28 km for MODIS images. Results of this study demonstrate that power-law scaling of soil moisture in the middle Rio Grande area holds up to 1 km² pixel size, but is no longer valid beyond that scale. Whereas previous studies have studied soil moisture in the top 5 cm of the soil using radar and point measurements, our study uses SEBAL to estimate root-zone soil moisture. Our study is consistent with these previous studies in showing that variation in root-zone soil follows an empirical power law for pixel sizes of up to about 10⁶ m² and that there is an apparent break in the scaling at larger scales.     

Polarimetric SAR characterization of man-made structures in urban areas using normalized circular-pol correlation coefficients

Polarimetric Synthetic Aperture Radar (SAR) backscatter from man-made structures in urban areas is quite different than backscatter from predominantly natural areas. Backscatter from natural areas is often reflection symmetric; i.e., characterized by near zero values for covariance matrix off-diagonal terms of the form 〈S_HVS_HH^?〉, 〈S_HVS_VV^?〉 and their conjugates. A new approach is proposed to detect scattering from non-reflection symmetric structures using circular-pol, RR-LL, correlation coefficients, |ρ|. This method creates a normalization term, |ρ₀|, and then forms a ratio, |ρ|/|ρ₀|. The normalization term, |ρ₀|, contains the same diagonal terms of the covariance matrix. The 〈S_HVS_HH^?〉 and 〈S_HVS_VV^?〉 off-diagonal terms and their conjugates are purposely set to zero. The ratio, |ρ|/|ρ₀|, is rewritten as a product of separable helicity (τ) and orientation angle (θ) dependencies. The mathematical form of the τ dependence is a resonant singularity, or pole, term. This pole significantly enhances returns from man-made, high helicity, non-reflection symmetric structures. These structures have values of τ near the resonance value at τ = ± 1. Natural scatterers possess very strong RR / LL symmetry (τ ≈ 0) and the pole response for them is correspondingly weak. The dependence of |ρ|/|ρ₀| on the orientation angle (θ) is known from previous studies to be useful for measuring urban building alignments (relative to the azimuth direction) and measuring surface topography. The ratio |ρ|/|ρ₀| reduces much of the un-needed image detail of backscatter variations from natural areas of different surface roughness. This image simplification further facilitates detection of localized man-made targets.     

The application of compact polarimetric decomposition algorithms to L-band PolSAR data in agricultural areas

ABSTRACT

In this paper, the applicability of the recently developed compact polarimetric decomposition and inversion algorithm to estimate soil moisture under low agricultural vegetation cover is investigated using simulated L-band compact polarimetric synthetic aperture radar (PolSAR) data. The surface scattering component is separated from the volume component of the vegetation through a model-based compact polarimetric decomposition (m-α) under the assumption of randomly orientated vegetation volume and reflection symmetry. The extracted surface scattering component is compared with two physics-based, low frequency surface scattering models such as extended Bragg (X-Bragg) and polarimetric two scale model (PTSM) in order to invert soil moisture for corresponding model- and data-derived surface scattering mechanism parameter α_s. In addition to the parameter α_s from m-α decomposition, the applicability of other scattering mechanism parameters, such as δ (relative phase) and χ (degree of circularity) from m-δ and m-χ decompositions are also investigated for their suitability to invert soil moisture. The algorithm is applied on a time series of simulated L-band compact polarimetric E-SAR data from the AgriSAR’2006 campaign over the Görmin test site in Northern Germany. The compact PolSAR-derived soil moisture is validated against in situ time-domain reflectometry (TDR) measurements. Including various growth stages of three different crop types, the estimated soil moisture values indicate an overall root mean square error (RMSE) of 9–12 and 9–15 vol.% using the X-Bragg model and the PTSM, respectively. The inversion rate for vegetation covered soils ranges from 5% to 40% including all phenological stages of the crops and different soil moisture conditions (range from 4 to 34 vol.%). The time series of soil moisture inversion results using compact polarimetry reveal that the developed algorithm is less sensitive to wet soils under growing agriculture crops due to less sensitivity of scattering mechanism parameters α_s and χ for ε_s > 20. Thus, further developments and investigations are needed to invert soil moisture for compact PolSAR data with high inversion rates and consistently less RMSE (<5 vol.%) over the various crop growing season.     

Comparison of ERS-2 SAR and Landsat TM imagery for monitoring agricultural crop and soil conditions

Studies over the past 25 years have shown that measurements of surface reflectance and temperature (termed optical remote sensing) are useful for monitoring crop and soil conditions. Far less attention has been given to the use of radar imagery, even though synthetic aperture radar (SAR) systems have the advantages of cloud penetration, all-weather coverage, high spatial resolution, day/night acquisitions, and signal independence of the solar illumination angle. In this study, we obtained coincident optical and SAR images of an agricultural area to investigate the use of SAR imagery for farm management. The optical and SAR data were normalized to indices ranging from 0 to 1 based on the meteorological conditions and sun/sensor geometry for each date to allow temporal analysis. Using optical images to interpret the response of SAR backscatter (σ^o) to soil and plant conditions, we found that SAR σ^o was sensitive to variations in field tillage, surface soil moisture, vegetation density, and plant litter. In an investigation of the relation between SAR σ^o and soil surface roughness, the optical data were used for two purposes: (1) to filter the SAR images to eliminate fields with substantial vegetation cover and/or high surface soil moisture conditions, and (2) to evaluate the results of the investigation. For dry, bare soil fields, there was a significant correlation (r²=.67) between normalized SAR σ^o and near-infrared (NIR) reflectance, due to the sensitivity of both measurements to surface roughness. Recognizing the limitations of optical remote sensing data due to cloud interference and atmospheric attenuation, the findings of this study encourage further studies of SAR imagery for crop and soil assessment.     

Low power highly sensitive platform for gas sensing application

This paper reports a low power miniaturized MEMS based integrated gas sensor with 36.84 % sensitivity (ΔR/R₀) for as low as 4 ppm (NH₃) gas concentration. Micro-heater based gas sensor device presented here consumes very low power (360 °C at 98 mW/mm²) with platinum (Pt) micro-heater. Low powered micro-heater is an essential component of the metal oxide based gas sensors which are portable and battery operated. These micro-heaters usually cover less than 5 % of the gas sensor chip area but they need to be thermally isolated from substrate, to reduce thermal losses. This paper elaborates on design aspects of micro fabricated low power gas sensor which includes ‘membrane design’ below the microheater; the ‘cavity-to-active area ratio’; effect of silicon thickness below the silicon dioxide membrane; etc. using FEM simulations and experimentation. The key issues pertaining to process modules like fragile wafer handling after bulk micro-machining; lift-off of platinum and sensing films for the realization of heater, inter-digitated-electrodes (IDE) and sensing film are dealt with in detail. Low power platinum microheater achieving 700 °C at 267 mW/mm² are fabricated. Temperature calculations are based on experimentally calculated thermal coefficient of resistance (TCR) and IR imaging. Temperature uniformity and localized heating is verified with infrared imaging. Reliability tests of the heater device show their ruggedness and repeatability. Stable heater temperature with standard deviation (σ) of 0.015 obtained during continuous powering for an hour. Cyclic ON–OFF test on the device indicate the ruggedness of the micro-heater. High sensitivity of the device for was observed for ammonia (NH₃), resulting in 40 % response for ~4 ppm gas concentration at 230 °C operating temperature.     

Landslide susceptibility mapping using downscaled AMSR-E soil moisture: A case study from Cleveland Corral, California, US

As soil moisture increases, slope stability decreases. Remotely sensed soil moisture data can provide routine updates of slope conditions necessary for landslide predictions. For regional scale landslide investigations, only remote-sensing methods have the spatial and temporal resolution required to map hazard increases. Here, a dynamic physically-based slope stability model that requires soil moisture is applied using remote-sensing products from multiple Earth observing platforms. The resulting landslide susceptibility maps using the advanced microwave scanning radiometer (AMSR-E) surface soil moisture are compared to those created using variable infiltration capacity (VIC-3L) modeled soil moisture at Cleveland Corral landslide area in California, US. Despite snow cover influences on AMSR-E surface soil moisture estimates, a good relationship between the downscaled AMSR-E's surface soil moisture and the VIC-3L modeled soil moisture is evident. The AMSR-E soil moisture mean (0.17 cm³/cm³) and standard deviation (0.02 cm³/cm³) are very close to the mean (0.21 cm³/cm³) and standard deviation (0.09 cm³/cm³) estimated by VIC-3L model. Qualitative results show that the location and extent of landslide prone regions are quite similar. Under the maximum saturation scenario, 0.42% and 0.49% of the study area were highly susceptible using AMSR-E and VIC-3L model soil moisture, respectively.     

Comparing the utility of microwave and thermal remote-sensing constraints in two-source energy balance modeling over an agricultural landscape

A two-source (soil + vegetation) energy balance model using microwave-derived near-surface soil moisture as a key boundary condition (TSM_SM) and another scheme using thermal-infrared (radiometric) surface temperature (TSM_TH) were applied to remote sensing data collected over a corn and soybean production region in central Iowa during the Soil Moisture Atmosphere Coupling Experiment (SMACEX)/Soil Moisture Experiment of 2002 (SMEX02). The TSM_SM was run using fields of near-surface soil moisture from microwave imagery collected by aircraft on six days during the experiment, yielding a root mean square difference (RMSD) between model estimates and tower measurements of net radiation (Rn) and soil heat flux (G) of approximately 20 W m^− 2, and 45 W m^− 2 for sensible (H) and latent heating (LE). Similar results for H and LE were obtained at landscape/regional scales when comparing model output with transect-average aircraft flux measurements. Flux predictions from the TSM_SM and TSM_TH models were compared for two days when both airborne microwave-derived soil moisture and radiometric surface temperature (T_R) data from Landsat were available. These two days represented contrasting conditions of moderate crop cover/dry soil surface and dense crop cover/moist soil surface. Surface temperature diagnosed by the TSM_SM was also compared directly to the remotely sensed T_R fields as an additional means of model validation. The TSM_SM performed well under moderate crop cover/dry soil surface conditions, but yielded larger discrepancies with observed heat fluxes and T_R under the high crop cover/moist soil surface conditions. Flux predictions from the thermal-based two-source model typically showed biases of opposite sign, suggesting that an average of the flux output from both modeling schemes may improve overall accuracy in flux predictions, in effect incorporating multiple remote-sensing constraints on canopy and soil fluxes.