Search

Most polar-orbit-satellite based passive microwave radiometers, e.g. AMSR-2 and the FY-3B Microwave Radiation Imager (FY3B-MWRI), have the revisit cycles of ~2 days in low latitudes. This indicates that requirement of complete global coverage on the daily basis has not been met. On the other hand, using synergistic observations from different radiometers seems promising to enhance the temporal resolution of observations in such areas, whereas cross-radiometer inter-calibration is essential prior to their synergism. For this purpose, conventional inter-calibration models are usually built based on linear relations of brightness temperature (TB) between different radiometers. However, the linear model has relatively poor performance in low latitudes primarily due to wet surface conditions. In this study, we propose an improved non-linear model for calibrating MWRI TB against AMSR-2 TB at 18.7 GHz and 23.8 GHz channels. Global land surface temperature (LST) is estimated using the post-calibration MWRI TB data and then validated against AMSR-2 based LST, MODIS based LST, and in situ near-surface air temperature datasets in low latitudes respectively. The validation results show that LST outcome based on the improved non-linear inter-calibration method has smaller global Root Mean Square Errors (RMSEs) below 3.5 K, compared with the RMSEs of ~6 K obtained from the conventional linear inter-calibration method and from the results without inter-calibration. Further analysis also shows that synergistic observations between AMSR-2 and MWRI are capable of shortening the maximum global revisit cycle of AMSR-2 LST from ~2 days to less than 1.2 days. These results indicate that the improved inter-calibration method is effective for obtaining daily LST datasets with quasi-complete global coverage and reliable accuracy.

International audience; L-band radiometer measurements collected over the Dome C area from 2010 to 2015 indicated that the brightness temperature (T B) was relatively stable at vertical (V) polarization (standard deviation lower than 1 K at annual scale), while it was slightly more variable at horizontal (H) polarization. During the 2014-2015 austral summer, an exceptional situation was recorded by both the DOMEX ground radiometer and the European Space Agency (ESA)'s Soil Moisture and Ocean Salinity (SMOS) satellite. From November 2014 to March 2015, T B H showed a progressive and significant increase until 20 March 2015 when it sharply decreased by about 5 K (at 52.5 o incidence angle) within a few days. In parallel to the increase in T B H, glaciological and meteorological in situ measurements showed a wind speed that was lower than usual and a low-density snow layer being progressively set up on the surface. This was consistent with the exceptional hoar event observed, as well as with snow accumulation on the surface. On the other hand, the decrease in T B H was related to the passing over Dome C of a storm that removed or compacted the layer of light snow on the surface. The WALOMIS (Wave Approach for LOw-frequency MIcrowave emission in Snow) snow-emission model was used with in situ measurements of the snowpack as inputs for evaluating the effect of changes observed on the snow surface in T B H. The simulations indicated that the surface snow density variations were sufficient for predicting the increasing and decreasing trends of the T B H. However, the thickness variations of the superficial layer were essential so as to obtain a better agreement with the SMOS observations. This result confirmed that the L-band T B H was affected by the snow properties of the top centimeters of the snowpack, in spite of the large penetration depth (hundreds of meters). Both the surface snow density and the thickness of the superficial layer were relevant, due to coherent interference effects.

Recent studies showed that machine learning (ML) algorithms (e.g., artificial neural network (ANN) and support vector machine (SVM)) reasonably reproduce passive microwave brightness temperature observations over snow-covered land as measured by the Advanced Microwave Scanning Radiometer (AMSR-E). However, these studies did not explore the sensitivities of the ML algorithms relative to ML inputs in order to determine the behavior and performance of each algorithm. In this current study, normalized sensitivity coefficients are computed to diagnose ML performance as a function of time and space. The results showed that when using the ANN, approximately 20% of locations across North America are relatively sensitive to snow water equivalent (SWE). However, more than 65% of locations in the SVM-based brightness temperature (Tb) estimates are sensitive relative to perturbations in SWE at all frequency and polarization combinations explored in this study. Further, the SVM-based results suggest the algorithm is sensitive in both shallow and deep SWE, SWE with and without overlying forest canopy, and during both the snow accumulation and snow ablation seasons. Therefore, these findings suggest that compared with the ANN, the SVM could potentially serve as a more efficient and effective measurement model operator within a Tb data assimilation framework for the purpose of improving SWE estimates across regional- and continental-scales.

Soil Moisture Active Passive (SMAP) mission of NASA was launched in January 2015. Currently, SMAP has an L-band radiometer and a defunct L-band radar with a rotating 6-m mesh reflector antenna. On July 7th, 2015, the SMAP radar malfunctioned and became inoperable. Consequently, the production of high-resolution active-passive soil moisture product got hampered, and only ~2.5 months (April 15th, 2015 to July 7th, 2015) of data remain available. Therefore, during the SMAP post-radar phase, many ways were examined to restart the high-resolution soil moisture product generation of the SMAP mission. One of the feasible approaches was to substitute the SMAP radar with other available SAR data. Sentinel-1A/Sentinel-1B SAR data was found most suitable for combining with the SMAP radiometer data because of its nearly similar orbit configuration that allows overlapping of their swaths with a minimal time difference, a key feature/requirement for the SMAP active-passive algorithm. The Sentinel interferometric wide swath (IW) mode acquisition also provides the co-polarized and cross-polarized observations required for the SMAP active-passive algorithm. However, some differences do exist between the SMAP and Sentinel SAR data. They are mainly: 1) Sentinel has a C-band SAR whereas SMAP operates at L-band; 2) Sentinel has multiple incidence angles within its swath, and SMAP has one single incidence angle; and 3) Sentinel 1A/B Interferometric Wide (IW) swath width is ~250 km as compared to SMAP with 1000 km swath width. On any given day, the narrow swath width of the Sentinel observations significantly reduces the overlap spatial coverage between SMAP and Sentinel as compared to the original SMAP radar and radiometer swath coverage. Hence, the temporal resolution (revisit interval) suffers due to narrow overlapped swath width and degrades from 3 days to 12 days. One advantage of using very high-resolution resolution Sentinel-1A/Sentinel-1B data in the SMAP active-passive algorithm is the potential of obtaining the disaggregated brightness temperature and thus soil moisture at a much finer spatial resolution of 3 km and 1 km at global extent. The assessment of high-resolution product at 3 km and 1 km using the soil moisture calibration and validations sites shows reasonable accuracy of ~0.05 m3/m3. The SMAP-Sentinel1 active-passive high-resolution product is now available to the public (new version released in October 2018) through NSIDC (NASA DAAC). The duration of this product is from April 2015 to current date.

The Helsinki University of Technology (HUT) snow emission model forms the basis of the European Space Agency's GlobSnow snow water equivalent (SWE) product (Takala et al., 2011). The model applies a semi-empirical radiative transfer calculation to account for the interaction of the snow medium with microwaves; separate components are applied to account for vegetation, the atmosphere, and emission from the ground surface. For the retrieval of SWE, an innovative method is used to account for spatial and temporal variability in snow conditions by retrieving an effective parameter describing the scattering behavior of microwaves in the snow (a proxy indicator of the microwave effective snow grain size). In this study, the influence of differing snow conditions, as well as varying land cover, on the retrieved microwave effective snow grain size was analyzed. Passive microwave measurements were acquired using tower-based, mobile sled-based and airborne radiometers in a mixed forest environment near Sodankyla, Finland. Forward simulations at the tower site over an entire winter period showed that the use of an empirical relation to modify the classical in situ measured grain size produced HUT model bias errors less than 6 K on average at 19 and 37 GHz from a ~ 20–80 cm deep boreal snowpack. Model simulations for airborne and sled-based observations showed that using a simplified 2-layer representation of the snowpack improves simulation biases and RMSE, although modification of the measured grain size was again necessary to achieve these results, regardless of the layering configuration. The microwave effective grain size retrieved from HUT predictions was closely related to a simple average grain size measured in situ, both in terms of magnitude and temporal trend. This is an important finding as the retrieval scheme of Takala et al. (2011) relies on the microwave effective grain size to retain a degree of physical basis for values to be generally applicable over larger areas, which is challenging because this free parameter also accounts for errors unrelated to the effective snow grain size (i.e. vegetation, soil conditions, scene heterogeneity, etc.).

Plot-scale brightness temperature (T B ) measurements at 6.9, 19, 37, and 89 GHz were acquired in forest, open, and lake environments near Churchill, Manitoba, Canada with mobile sled-based microwave radiometers during the 2009–2010 winter season. Detailed physical snow measurements within the radiometer footprints were made to relate the microwave signatures to the seasonal evolution of the snowpack, and provide inputs for model simulations with the Helsinki University of Technology (HUT) snow emission model. Large differences in depth, density, and grain size were observed between the three land cover types. Plot-scale simulations with the HUT model showed a wide range in simulation accuracy between sites and frequencies. In general, model performance degraded when the effective grain size exceeded 2 mm and/or there was an ice lens present in the pack. HUT model performance improved when simulations were run regionally at the satellite scale (using three proportional land cover tiles: open, forest, and lake) and compared to Advanced Microwave Scanning Radiometer (AMSR-E) measurements. Root mean square error (RMSE) values ranged from approximately 4 to 16 K depending on the frequency, polarization, and land cover composition of the grid cell. Snow water equivalent (SWE) retrievals produced using forward T B simulations with the HUT model in combination with AMSR-E measurements produced RMSE values below 25 mm for the intensive study area. Retrieval errors exceeded 50 mm when the scheme was applied regionally.

Vegetation phenology characterizes seasonal life-cycle events that influence the carbon cycle and land–atmosphere water and energy exchange. We analyzed global phenology cycles over a six year record (2003–2008) using satellite passive microwave remote sensing based Vegetation Optical Depth (VOD) retrievals derived from daily time series brightness temperature (Tb) measurements from the Advanced Microwave Scanning Radiometer on EOS (AMSR-E) and other ancillary data inputs. The VOD parameter derives vegetation canopy attenuation at a given microwave frequency (18.7 GHz) and varies with canopy height, density, structure and water content. An error sensitivity analysis indicates that the retrieval algorithm can resolve the VOD seasonal cycle over a majority of global vegetated land areas. The VOD results corresponded favorably (p

The algorithms designed to estimate snow water equivalent (SWE) using passive microwave measurements falter in lake-rich high-latitude environments due to the emission properties of ice covered lakes on low frequency measurements. Microwave emission models have been used to simulate brightness temperatures (Tbs) for snowpack characteristics in terrestrial environments but cannot be applied to snow on lakes because of the differing subsurface emissivities and scattering matrices present in ice. This paper examines the performance of a modified version of the Helsinki University of Technology (HUT) snow emission model that incorporates microwave emission from lake ice and sub-ice water. Inputs to the HUT model include measurements collected over brackish and freshwater lakes north of Inuvik, Northwest Territories, Canada in April 2008, consisting of snowpack (depth, density, and snow water equivalent) and lake ice (thickness and ice type). Coincident airborne radiometer measurements at a resolution of 80 × 100 m were used as ground-truth to evaluate the simulations. The results indicate that subsurface media are simulated best when utilizing a modeled effective grain size and a 1 mm RMS surface roughness at the ice/water interface compared to using measured grain size and a flat Fresnel reflective surface as input. Simulations at 37 GHz (vertical polarization) produce the best results compared to airborne Tbs, with a Root Mean Square Error (RMSE) of 6.2 K and 7.9 K, as well as Mean Bias Errors (MBEs) of −8.4 K and −8.8 K for brackish and freshwater sites respectively. Freshwater simulations at 6.9 and 19 GHz H exhibited low RMSE (10.53 and 6.15 K respectively) and MBE (−5.37 and 8.36 K respectively) but did not accurately simulate Tb variability (R = −0.15 and 0.01 respectively). Over brackish water, 6.9 GHz simulations had poor agreement with airborne Tbs, while 19 GHz V exhibited a low RMSE (6.15 K), MBE (−4.52 K) and improved relative agreement to airborne measurements (R = 0.47). Salinity considerations reduced 6.9 GHz errors substantially, with a drop in RMSE from 51.48 K and 57.18 K for H and V polarizations respectively, to 26.2 K and 31.6 K, although Tb variability was not well simulated. With best results at 37 GHz, HUT simulations exhibit the potential to track Tb evolution, and therefore SWE through the winter season.

Observations from visible, infrared and microwave satellite instruments are integrated to detect dust storm over northwestern China. Microwave measurements are used to detect the dust storm underneath ice clouds, while visible and infrared measurements are utilized for delineating the cloud-free dust systems. Detection is based on microwave polarized brightness temperature differences (Δ T b = T bv − T bh ) among two channels of 89 GHz and 23.8 GHz and infrared brightness temperature difference (BTD) between channels at 11 and 12 μm. It is shown that the integrated approach is better than the method solely based on infrared BTD in storm detection, especially for those dust systems covered by ice clouds. This approach is applied for the Asia dust storms cases using the data from the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Advanced Microwave Scanning Radiometer (AMSR-E) onboard Aqua satellite.

Optimal deconvolution (ODC) utilizes the footprint overlap in microwave observations to estimate the earth's brightness temperatures (TB). This paper examines the accuracy of ODC-estimated TB compared with a standard averaging technique. Because brightness temperatures cannot be independently verified, we constructed synthetic True TB for accuracy assessment. We assigned TB at a high spatial resolution (1 km) grid and computed the True TB by spatial averaging of the assigned TB to a lower resolution earth grid (25 km), selected to match the resolution of products generated from the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E). We used the sensor antenna response function along with the 1-km assigned TB to generate synthetic observations at AMSR-E footprint locations. These synthetic observations were subsequently deconvolved in the ODC technique to estimate TB at the lower resolution earth grid. The ODC-estimated TB and the simple grid cell averages of the synthetic observations were compared with the True TB allowing us to quantify the efficacy of each technique. In areas of high TB contrast (such as boundaries of water bodies), ODC performed significantly better than averaging. In other areas, ODC and averaging techniques produced similar results. A technique similar to ODC can be effective in delineating water bodies with significant clarity. That will allow microwave observations to be utilized near the shorelines, a trouble spot for the currently used averaging techniques.

The microwave brightness temperatures over a frequency range from 1 GHz to 100 GHz were monitored below a large beech tree (Fagus silvatica L.) from August 1987 to August 1988, and several observations were added in the following 2 years until the beech was felled. The data were used to compute microwave transmissivities and opacities of the vegetation canopy. The results show that the transmissivities are influenced by wind, temperature, and liquid water, leaves play a dominant role, especially at frequencies above 5 GHz. With increasing frequency, also, the inhomogeneity of branches and leaves becomes more and more pronounced. An interesting result is the anticorrelation found between the microwave opacity of the test beech and a regional forest-damage parameter. These results are based on the full 4-year observation period. Potential applications of microwave radiometric tree observations are the study of the waterstatus in vegetation, the estimation of forest-canopy parameters, and the development and the validation of microwave propagation models.

Experimental and theoretical investigations have shown that microwave emission from canopies depends on crop type and physical conditions. However, since the sensitivity of the brightness temperature to vegetation parameters is rather low, accurate observations and suitable models are necessary to extract geophysical parameters from radiometer data. The most significant results, obtained over the past years on the basis of measurements carried out with passive microwave sensors at 10 GHz and 36 GHz, are summarized in this paper. The relations between the microwave vegetation indexes and plant biophysical parameters are discussed, using a simple slab model that considers vegetation as a uniform absorbing and scattering medium upon the soil surface. It is shown that simultaneous observations at 10 GHz and 36 GHz, in V and H polarizations, especially if combined with thermal infrared measurements, are useful in separating different crops and in identifying moisture conditions of the surfaces. The difference (δTN), between the normalized temperatures at 36 GHz and 10 GHz, and the polarization index (PI) at 10 GHz are functions of biomass and can be related to the leaf area index of some crops.

To study the effect of a frozen soil layer on the microwave signatures of bare soil, we carried out measurements on frozen and unfrozen fields in winter. Our ground-based radiometer — scatterometer system, RASAM, works at frequencies between 3 GHz and 11 GHz. The brightness temperature was measured at horizontal and vertical polarization, the backscattering coefficient at HH, VV, HV, and VH polarization, all at incidence angles from 0° to 70°. In addition, we measured soil parameters such as moisture, roughness, and texture. The results are subdivided into three sections. First the microwave parameters measured during two diurnal freeze — thaw cycles of the soil are shown. For thawing the soil has three layers wet/frozen/wet and therefore different microwave properties than for freezing. Second frozen soil (thickness of the frozen layer is larger than the penetration depth of the radiation) is compared with unfrozen soil. For the horizontal polarization we separate the reflectivity into the Fresnel reflectivity and a roughness factor. The active data are compared with the geometrical-optics model at the higher frequencies and with the small-perturbation model at the lower frequencies. In addition the data are used to determine the sensitivity of the microwave parameters to soil liquid water content. Third the growth of a frozen soil layer is monitored. These data are used to test layered media models. Due to the scale height of surface roughness, which is of the order of the penetration depth of the radiation, the models do not agree well with the measurements. We find much better fits with a semiempirical formula.

The sensitivity of microwave (MW) emission to physical conditions of vegetation has been assessed by means of ground-based microwave and infrared radiometers. Measurements on corn and wheat have shown an inverse correlation between the normalized brightness temperature (TN) from the Ka band (36 GHz) and the atmospheric water vapor pressure (VP) at the top of vegetation. From this observation, we show that a crop water stress index can be calculated by means of down-looking MW sensors, provided air temperature is known. A polarization index (PI) dependent only on microwave measurements was shown to be related to crop water stress.

Normalized 18-GHz microwave brightness temperatures, T(B), and a vegetation index determined from satellite radiometer data are combined with climatically modeled surface moisture estimates to constrain a simple physically based soil moisture model. It is found that the normalized T(B) values correlated well with soil moisture when the data were segregated by vegetation index range, but less so when all the data were combined. By using the vegetation index parameter, the model is shown to account for about 70 percent of the variability in modeled surface soil moisture.

The microwave radiometric measurements made by the Skylab 1.4 GHz radiometer and by the 6.6 GHz and 10.7 GHz channels of the Nimbus-7 Scanning Multichannel Microwave Radiometer were analyzed to study the large-area soil moisture variations of land surfaces. Two regions in Texas, one with sparse and the other with dense vegetation covers, were selected for the study. The results gave a confirmation of the vegetation effect observed by ground-level microwave radiometers. Based on the statistics of the satellite data, it was possible to estimate surface soil moisture in about five different levels from dry to wet conditions with a 1.4 GHz radiometer, provided that the biomass of the vegetation cover could be independently measured. At frequencies greater than about 6.6 GHz, the radiometric measurements showed little sensitivity to moisture variation for vegetation-covered soils. The effects of polarization in microwave emission were studied also.

Over most of the microwave spectrum, raindrops both absorb and scatter radiation producing large changes in brightness temperatures relative to clear or cloudy conditions. Since the structure of rain varies substantially for different rain rates and climatological backgrounds, the raindrop size distribution, the rain-layer thickness and the ice clouds above the rain layer are all important inputs to the model computations. The subsequent modeling involves applying the Mie theory to derive the absorption and scattering effects and the radiative transfer computations to derive the brightness temperature. The radiative transfer calculation is based upon a variational iterative approach which takes account of the multiple scattering effect of the rain layer. Results over both ocean and land backgrounds are demonstrated. It is shown that over ocean background, which is “cold” in the microwave region, lower frequencies are sufficiently sensitive to rain conditions. Over land background, higher frequencies have to be utilized in order to obtain the sensitivity to rain rates. It is also demonstrated that by using discrimination tests of the radiometric data, the rain/no rain decision can be made and the rainfall rate can be retrieved from a statistical inversion technique. The shortcomings of this technique are: (1) the assumption of a homogeneous field of view at different wavelengths can be erroneous, (2) the actual physical structure of the rain layer may be significantly different from the one used in the climatological approach, and (3) the additional uncertainty which may be introduced because of the limitations of the radiative transfer algorithm.

A field experiment was conducted utilizing artificial arrangements of plant components during the summer of 1982 to examine the effects of corn canopy structure and plant water content on microwave emission. Truck-mounted microwave radiometers at C (5 GHz) and L (1.4 GHz) band sensed vertically and horizontally polarized radiation concurrent with ground observations of soil moisture and vegetation parameters. Results indicate that the orientation of cut stalks and the distribution of their dielectric properties through the canopy layer can influence the microwave emission measured from a vegetation/soil scene. The magnitude of this effect varies with polarization and frequency and with the amount of water in the plant, disappearing at low levels of vegetation water content. Although many of the canopy structures and orientations studied in this experiment are somewhat artificial, they serve to improve understanding of microwave energy interactions within a vegetation canopy and to aid in the development of appropriate physically based vegetation models.

The use of satellite microwave data to determine snowpack properties is investigated through calculation of theoretical microwave brightness temperatures and comparison of the computed brightness temperatures with actual satellite microwave measurements. Archived data from the Nimbus-5 and Nimbus-6 Electrically Scanning Microwave Radiometers (ESMR), as well as data from the Nimbus-7 Scanning Multifrequency Microwave Radiometer (SMMR), are analyzed for a study area in the north-central United States. The results of the investigation indicate that snow boundaries can usually be defined by the 37-GHz or 18 (19) GHz data because of the sharp decrease in brightness temperature when going from land to a snow surface. For dry snow conditions, the 37 GHz data display a decrease in brightness temperature with snow depth due to the stronger volume scattering effect of the deeper snow; at 18–19 GHz, the sensitivity to snow depth is not as significant. The onset of snowmelt can be determined at both microwave frequencies investigated (18–19 GHz and 37 GHz) because of the significant increase in the brightness temperature with melting due to the decrease in volume scattering that occurs in the presence of free water.

Areas of the Canadian high plains, the Montana and North Dakota high plains, and the steppes of central Russia have been studied in an effort to determine the utility of spaceborne microwave radiometers for monitoring snow depths in different geographic areas. Significant regression relationships between snow depth and microwave brightness temperatures were developed for each of these homogeneous areas. In each of the study areas investigated in this paper, Nimbus-6 (0.81 cm) ESMR data produced higher correlations than Nimbus-5 (1.55 cm) ESMR data in relating microwave brightness temperature to snow depth. It is difficult to extrapolate relationships between microwave brightness temperature and snow depth from one area to another because different geographic areas are likely to have different snowpack conditions.

The Satellite Microwave Radiometer (SAMIR-II) on board the second Indian remote sensing satellite, Bhaskara-II, measured microwave radiation at 19.35, 22.235, and 31.4 GHz. These measurements are primarily influenced by water vapor, cloud liquid water, and wind speed. The effect of collinearity among SAMIR-II channels has been examined to determine the effectiveness of a two- or three-channel subset for retrieving precipitable water (PW). The analysis indicated a decreasing correlation coefficient value from 0.87 to 0.65 with increasing frequency separation due to dynamical interdependence of meteorological variables on brightness temperature measurements. A correlation coefficient of 0.65 between two window channels, 19.35 and 31.4 GHz, is suggestive of using either of the two channels for water vapor retrieval. Performance of SAMIR-II channels has therefore been evaluated by retrieving (PW) using regression technique with all the three channels of SAMIR-II and its subset of the two channels, 19.35/22.235 and 22.235/31.4 GHz and comparing the results from NOAA-7 supplied PW and limited radiosonde derived PW. Comparison of PW derived from the regression models and NOAA-7 gave an rms difference of 0.7 to 1.0 g/cm 2 . Retrievals of PW using 19.35 and 22.235 GHz channels and its comparison with near concurrent coastal radiosonde observations gave an rms difference of 0.32 g/cm 2 , comparable to the accuracy (0.2–0.4 g/cm 2 ) obtained from previous satellite-borne microwave radiometers.