Multitemporal passive microwave mapping in MACHYDRO'90

MACHYDR0'90 was an experiment conducted in Pennsylvania in 1990 to study the synergistic use of remote sensors in multitemporal hydrologic studies. As part of this mission the pushbroom microwave radiometer was flown and used to produce brightness temperature maps. Verification studies and vegetation algorithms for mixed land cover areas are described     

Deriving land surface temperature from Landsat 5 and 7 during SMEX02/SMACEX

A sequence of five high-resolution satellite-based land surface temperature (T_s) images over a watershed area in Iowa were analyzed. As a part of the SMEX02 field experiment, these land surface temperature images were extracted from Landsat 5 Thematic Mapper (TM) and Landsat 7 Enhanced Thematic Mapper (ETM) thermal bands. The radiative transfer model MODTRAN 4.1 was used with atmospheric profile data to atmospherically correct the Landsat data. NDVI derived from Landsat visible and near-infrared bands was used to estimate fractional vegetation cover, which in turn was used to estimate emissivity for Landsat thermal bands. The estimated brightness temperature was compared with concurrent tower based measurements. The mean absolute difference (MAD) between the satellite-based brightness temperature estimates and the tower based brightness temperature was 0.98 °C for Landsat 7 and 1.47 °C for Landsat 5, respectively. Based on these images, the land surface temperature spatial variation and its change with scale are addressed. The scaling properties of the surface temperature are important as they have significant implications for changes in land surface flux estimation between higher-resolution Landsat and regional to global sensors such as MODIS.     

Remote sensing of land surface temperature: the directional viewingeffect

Land surface temperature and emissivity products are currently being derived from satellite and aircraft remote sensing data using a variety of techniques to correct for atmospheric effects. Implicit in the commonly employed approaches is the assumption of isotropy in directional thermal infrared exitance. The authors' theoretical analyses indicate angular variations in apparent infrared temperature will typically yield land surface temperature errors ranging from 1 to 4°C unless corrective measures are applied     

Correlating Rainfall with Remotely Sensed Microwave Radiation Using Physically Based Models

This simulation study evaluates the response of a 21-cm radiometer, measuring the radiation emitted by a bare soil, to varying accumulations of rain. We show that correlations between the decrease in emissivity after a rain storm and the total accumulation depend strongly on the physical characteristics of the soil which affect its capacity to hold water. These are primarily soil texture and pre-rain soil moisture. A method is also discussed which would use the numerical models with remotely sensed microwave brightness and surface physical temperatures, along with conventional weather data, to estimate the total accumulation.     

Estimation of surface roughness parameters from dual-frequencymeasurements of radar backscattering coefficients

A simple model was developed for estimating the surface roughness parameters of a bare soil field. The model uses a set of dual-frequency measurements of the field's radar backscattering coefficients, which can be matched to calculated results obtained with assumed values for the surface roughness parameters, as represented by the surface height standard deviation σ and its correlation lengths. Scatter plots of measured and calculated radar backscattering coefficients at the C -band (4.25-GHz) frequency versus those at L-band (1.5 GHz) show that it is feasible to estimate the surface roughness parameters using this technique. The estimated values for σ are in excellent agreement with those of measurements. However, there are discrepancies between the estimated and measured values for the correlation length L. For a very rough field, the geometrical optics model could be more appropriate for modeling the C-band data     

Liver microcirculation analysis by red blood cell motion modeling in intravital microscopy images

Intravital microscopy has been used to visualize the microcirculation by imaging fluorescent labeled red blood cells (RBCs). Traditionally, microcirculation has been modeled by computing the mean velocity of a few, randomly selected, manually tracked RBCs. However, this protocol is tedious, time consuming, and subjective with technician related bias. We present a new method for analyzing the microcirculation by modeling the RBC motion through automatic tracking. The tracking of RBCs is challenging as in each image, as many as 200 cells move through a complex network of vessels at a wide range of speeds while deforming in shape. To reliably detect RBCs traveling at a wide range of speeds, a window of temporal template matching is applied. Then, cells appearing in successive frames are corresponded based on the motion behavior constraints in terms of the direction, magnitude, and path. The performance evaluation against a ground truth indicates the detection accuracy up to 84% TP at 6% FP and a correspondence accuracy of 89%. We include an in-depth discussion on comparison of the microcirculation based on motion modeling from the proposed automated method against a mean velocity from manual analysis protocol in terms of precision, objectivity, and sensitivity.     

Detecting land cover change at the Jornada Experimental Range, New Mexico with ASTER emissivities

Multispectral thermal infrared remote sensing of surface emissivities can detect and monitor long term land vegetation cover changes over arid regions. The technique is based on the link between spectral emissivities within the 8.5-9.5 μm interval and density of sparsely covered terrains. The link exists regardless of plant color, which means that it is often possible to distinguish bare soils from senescent and non-green vegetation. This capability is typically not feasible with vegetation indices. The method is demonstrated and verified using ASTER remote sensing observations between 2001 and 2003 over the Jornada Experimental Range, a semi-arid site in southern New Mexico, USA. A compilation of 27 nearly cloud-free, multispectral thermal infrared scenes revealed spatially coherent patterns of spectral emissivities decreasing at rates on the order of 3% per year with R² values of ∼ 0.82. These patterns are interpreted as regions of decreased vegetation densities, a view supported by ground-based leaf area index transect data. The multi-year trend revealed by ASTER's 90-m resolution data are independently confirmed by 1-km data from Terra MODIS. Comparable NDVI images do not detect the long-term spatially coherent changes in vegetation. These results show that multispectral thermal infrared data, used in conjunction with visible and near infrared data, could be particularly valuable for monitoring land cover changes.     

Monte Carlo Simulation of the Effect of Soil Moisture Variation on the Microwave Emission from Soils

In this paper, results of a Monte Carlo simulation of the effect of noise on the relationship between the microwave emissivity of soil and its moisture content are presented. It is found that whenever the magnitude of the noise for the independent variable, in this case the soil moisture, is increased, both the slope of the regression and the correlation coefficient decrease. In párticular, when the noise has a magnitude equivalent to a coefficient of variation of 0.25, the slope and correlation coefficient are in good agreement with those obtained from the data of a 21-cm airborne microwave radiometer which was flown over a test site in Hand County, South Dakota. The comparison was made using a linear relationship to determine the estimated emissivity from the ground measurements of soil moisture. The linear relationship was derived from a radiative transfer model calculation of the microwave emissivities using realistic soil-moisture profiles. The effect of surface roughness was included in the relationship, and the variability of the surface roughness was also simulated by a Monte Carlo technique.     

Passive Microwave Soil Moisture Research

During the four years of the AgRISTARS Program, significant progress was made in quantifying the capabilities of microwave sensors for the remote sensing of soil moisture. In this paper we discuss the results of numerous field and aircraft experiments, analysis of spacecraft data, and modeling activities which examined the various noise factors such as roughness and vegetation that affect the interpretability of microwave emission measurements. While determining that a 21-cm wavelength radiometer was the best single sensor for soil moisture research, these studies demonstrated that a multisensor approach will provide more accurate soil moisture information for a wider range of naturally occurrring conditions.     

A dielectric model of the vegetation effects on the microwaveemission from soils

A layer of vegetation over the soil surface absorbs some of the radiation emitted from the soil and emits at its own temperature. This results in a reduction of the information in the microwave radiation about the soil surface. To study this problem further the authors use the model of F.T. Ulaby and M.A. El-Rayes (1987) for the dielectric constant of vegetation to estimate the absorption loss and optical depth, τ, of plant canopies for frequencies between 1 and 40 GHz. The authors treated τ as the product of a vegetation parameter b and vegetation water content, VW. They compared both the linear and square root (refractive) mixing models with the observed data in terms of the b parameter. These data were obtained from published reports on the values of τ and VW for crops ranging from prairie grass to corn and soybeans. The data fit the curve for the refractive model quite well. For the refractive model the value of b was independent of VW, while for the linear model there was some dependence on VW. For both models b is roughly proportional to the frequency