AMSR-E Brightness Temperature Research Articles

Artificial Neural Network-Based Microwave Satellite Soil Moisture Reconstruction over the Qinghai–Tibet Plateau, China

Soil moisture is a key parameter for land-atmosphere interaction system; however, fewer existing spatial-temporally continuous and high-quality observation records impose great limitations on the application of soil moisture on long term climate change monitoring and predicting. Therefore, this study selected the Qinghai–Tibet Plateau (QTP) of China as research region, and explored the feasibility of using Artificial Neural Network (ANN) to reconstruct soil moisture product based on AMSR-2/AMSR-E brightness temperature and SMAP satellite data by introducing auxiliary variables, specifically considering the sensitivity of different combination of input variables, number of neurons in hidden layer, sample ratio, and precipitation threshold in model building. The results showed that the ANN model had the highest accuracy when all variables were used as inputs, it had a network containing 12 neurons in a hidden layer, it had a sample ratio 80%-10%-10% (training-validation-testing), and had a precipitation threshold of 8.75 mm, respectively. Furthermore, validation of the reconstructed soil moisture product (named ANN-SM) in other period were conducted by comparing with SMAP (April 2019 to July 2021) for all grid cells and in situ soil moisture sites (August 2010 to March 2015) of QTP, which achieved an ideal accuracy. In general, the proposed method is capable of rebuilding soil moisture products by adopting different satellite data and our soil moisture product is promising for serving the studies of long-term global and regional dynamics in water cycle and climate.

Spring thaw classification based on AMSR-E brightness temperature in the central Tibetan Plateau

ABSTRACT Changes in soil freeze/thaw dynamics in the Tibetan Plateau (TP) had significant influences on regional hydrology and ecosystem processes. We developed an algorithm to detect spring thaw onset in the central TP using microwave brightness temperature (Tb) data. We assumed that the Tb at lower frequencies is more sensitive to changes in soil freeze/thaw status, while the Tb at higher frequencies is subjected more to scattering effects of snow cover and vegetation. Therefore, the standard deviation of brightness temperature at 6-GHz horizontal polarization was used to detect soil F/T status, and a scattering index based on higher frequencies was used to constrain the scattering effects of snow cover. The algorithm was calibrated and validated with surface ground temperature and daily minimum air temperature. Results showed that our algorithm had a mean bias of 3.7 ~ 17.6 days and a RMSE of 7.5 ~ 19.7 days, and this method behaved better than the previous one based on all-frequency standard deviation of microwave Tb, which had a mean bias of 8.3 ~ 41.2 days and a RMSE of 13.4 ~ 27.6 days. Further validation is needed over more extensive area with diverse surface conditions.

SOIL MOISTURE RETRIEVAL USING CONVOLUTIONAL NEURAL NETWORKS: APPLICATION TO PASSIVE MICROWAVE REMOTE SENSING

Abstract. A empirical model is established to analyse the daily retrieval of soil moisture from passive microwave remote sensing using convolutional neural networks (CNN). Soil moisture plays an important role in the water cycle. However, with the rapidly increasing of the acquiring technology for remotely sensed data, it's a hard task for remote sensing practitioners to find a fast and convenient model to deal with the massive data. In this paper, the AMSR-E brightness temperatures are used to train CNN for the prediction of the European centre for medium-range weather forecasts (ECMWF) model. Compared with the classical inversion methods, the deep learning-based method is more suitable for global soil moisture retrieval. It is very well supported by graphics processing unit (GPU) acceleration, which can meet the demand of massive data inversion. Once the model trained, a global soil moisture map can be predicted in less than 10 seconds. What's more, the method of soil moisture retrieval based on deep learning can learn the complex texture features from the big remote sensing data. In this experiment, the results demonstrates that the CNN deployed to retrieve global soil moisture can achieve a better performance than the support vector regression (SVR) for soil moisture retrieval.

Intercalibration of AMSR2 NASA Team 2 Algorithm Sea Ice Concentrations With AMSR-E Slow Rotation Data

Sea ice estimates from AMSR2 are intercalibrated with AMSR-E fields through a two-step process. First, slow rotation 2 r/min AMSR-E data is used to derive regression equations from colocated pairs of AMSR2 and AMSR-E brightness temperatures (Tbs). The regression equations are used to modify AMSR2 Tbs into AMSR-E equivalent Tbs that are then input into the NASA Team 2 (NT2) sea ice concentration algorithm used for the AMSR-E standard products. The regressed Tbs result in changes in sea ice concentration of a few percent compared to using the original un-regressed AMSR2 Tbs. Next, sea ice estimates from the F17 SSMIS sensor are used as a bridge to compare AMSR-E total sea ice extent estimates in 2010 with AMSR2 total sea ice extent estimates in 2013. Based on this comparison, a further adjustment is made to a weather filter threshold used in the NT2 algorithm to minimize the total extent bias between AMSR2 and AMSR-E using a double-differencing approach. The adjustments reduced apparent bias with AMSR-E from ∼200 000 km2 for the original unmodified AMSR2 Tbs to –700 and 4700 km2 for the Arctic and Antarctic, respectively. These differences are within the range of previous passive microwave sea ice intercalibrations. The adjusted AMSR2 sea ice fields provide a nearly 15-year time series of sea ice change; depending on the lifetime of AMSR2 and possible follow-on sensors, AMSR2 has the potential to be part of a multidecadal record of sea ice change.

A New Operational Snow Retrieval Algorithm Applied to Historical AMSR-E Brightness Temperatures

Snow is a key element of the water and energy cycles and the knowledge of spatio-temporal distribution of snow depth and snow water equivalent (SWE) is fundamental for hydrological and climatological applications. SWE and snow depth estimates can be obtained from spaceborne microwave brightness temperatures at global scale and high temporal resolution (daily). In this regard, the data recorded by the Advanced Microwave Scanning Radiometer—Earth Orbiting System (EOS) (AMSR-E) onboard the National Aeronautics and Space Administration’s (NASA) AQUA spacecraft have been used to generate operational estimates of SWE and snow depth, complementing estimates generated with other microwave sensors flying on other platforms. In this study, we report the results concerning the development and assessment of a new operational algorithm applied to historical AMSR-E data. The new algorithm here proposed makes use of climatological data, electromagnetic modeling and artificial neural networks for estimating snow depth as well as a spatio-temporal dynamic density scheme to convert snow depth to SWE. The outputs of the new algorithm are compared with those of the current AMSR-E operational algorithm as well as in-situ measurements and other operational snow products, specifically the Canadian Meteorological Center (CMC) and GlobSnow datasets. Our results show that the AMSR-E algorithm here proposed generally performs better than the operational one and addresses some major issues identified in the spatial distribution of snow depth fields associated with the evolution of effective grain size.

Inter-calibration of AMSR-E and AMSR2 Brightness Temperature

Inter-calibration of AMSR-E and AMSR2 Brightness Temperature

An Improvement of the Radiative Transfer Model Component of a Land Data Assimilation System and Its Validation on Different Land Characteristics

The paper reports the recent progress in the radiative transfer model (RTM) development, which serves as the observation operator of a Land Data Assimilation System (LDAS), and its validation at two Planetary Boundary Layer (PBL) stations with different weather and land cover conditions: Wenjiang station of humid and cropped field and Gaize station of arid and bare soil field. In situ observed micrometeorological data were used as the driven data of LDAS, in which AMSR-E brightness temperatures (TB) were assimilated into a land surface model (LSM). Near surface soil moisture content output from LDAS, together with the one simulated by a LSM with default parameters, were compared to the in-situ soil moisture observation. The comparison results successfully validated the capability of LDAS with new RTM to simulate near surface soil moisture at various environments, supporting that LDAS can generally simulate soil moisture with a reasonable accuracy for both humid vegetated fields and arid bare soil fields while the LSM overestimates near surface soil moisture for humid vegetated fields and underestimates soil moisture for arid bare soil fields.

Updated Screening Procedures for GPROF2010 over Land: Utilization for AMSR-E

AbstractThe Goddard profiling algorithm 2010 (GPROF2010) was revised for the Advanced Microwave Scanning Radiometer for Earth Observing System (EOS; AMSR-E) instrument. The GPROF2010 land algorithm was developed for the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI), which observes slightly different central frequencies than AMSR-E. A linear transfer function was developed to convert AMSR-E brightness temperatures to their corresponding TMI frequency for raining and nonraining instantaneous fields of view (IFOVs) using collocated brightness temperature and TRMM precipitation radar (PR) measurements. Previous versions of the algorithm separated rain from surface ice, snow, and desert using a series of empirical procedures. These occasionally failed to separate raining and nonraining scenes, leading to failed detection and false alarms of rain. The new GPROF2010, version 2 (GPROF2010V2), presented here, prefaced the heritage screening procedures by referencing annual desert and monthly snow climatologies to identify IFOVs where rain retrievals were unreliable. Over a decade of satellite- and ground-based observations from the Interactive Multisensor Snow and Ice Mapping System (IMS) and AMSR-E allowed for the creation of a medium-resolution (0.25° × 0.25°) climatology of monthly snow and ice cover. The scattering signature of rain over ice and snow is not well defined because of complex emissivity signals dependent on snow depth, age, and melting, such that using a static climatology was a more stable approach to defining surface types. GPROF2010V2 was subsequently used for the precipitation environmental data record (EDR) for the AMSR2 sensor aboard the Global Change Observation Mission–Water 1 (GCOM-W1).

Sub-pixel evaluation of sea ice roughness using AMSR-E data

The rapidly changing sea ice regime in the Arctic has necessitated an evaluation of sea ice roughness at smaller scales than those provided by satellites. In this article, we evaluate sub-pixel (<5.4 km) sea ice roughness using AMSR-E brightness temperature (Tb) 89 GHz data and in situ physical roughness data acquired using a helicopter-based laser system in the southern Beaufort Sea during April–June of 2008. The analysis shows a statistically significant correlation (r2 = 0.61, P-value < 0.05, regression line slope = –79.93) of Tb at horizontal polarization (H-pol) decreasing with increasing root mean square (RMS) heights. These results suggest that 89 H-pol is more sensitive (than vertical polarization (V-pol)) to the changes in physical roughness. The temporal evolution in AMSR-E Tb values at 89 H-pol and 89 V-pol shows a decrease from April to June. We conclude that solely the AMSR-E Tb at 5.4 km is insufficient to fully account for the changes occurring in the dielectric properties and surface roughness of sea ice at sub-pixel level of 1–4 km during April–June.

Monitoring of Alpine snow using satellite radiometers and artificial neural networks

Abstract The Alps represent an extremely complex environment in which snow properties suffer dramatic spatial variations that cannot easily be followed by space-borne microwave radiometers, due to their coarse spatial resolution: some studies demonstrated that the algorithms developed for global scale monitoring of the snow depth (SD) are unable to retrieve this parameter with a satisfactory accuracy on mountainous areas. An improved method for monitoring the Snow Depth (SD) on Alpine areas is presented here. Equivalent Brightness Temperature Tbeq at an enhanced spatial resolution, corrected for the effects of orography and forest coverage, were computed from the AMSR-E measurements by using ancillary information on land use, surface temperature, and a digital elevation model (DEM). These equivalent Tbeq values were used instead of the original AMSR-E measurements as inputs of an algorithm that estimates SD on a global scale basing on and Artificial Neural Network (ANN) techniques from AMSR-E brightness temperatures at X-, Ku- and Ka-bands, V-polarization. The improvement in the retrieval accuracy using these Tbeq equivalent values was evaluated using data collected during the winters between 2002 and 2011 on a test area located in the eastern part of the Italian Alps.

Construction of Drought Indices Based on Passive Microwave Remote Sensing AMSR-E Data over Huaihe River Basin

On the basis of previous work, this paper aims to build several proper drought indices based on the basic computation for the band information of passive microwave remote sensing AMSR-E data in Huaihe river basin. Compared with measured soil moisture, optimal drought indices have been selected to explore the spatio-temporal variation laws of drought conditions and its impact factors. The results indicate that there are satisfactory negative correlations between MPDIs (Microwave Polarization Difference Index) and observed soil moisture on the whole, which means the more negative the index is, the more serious the drought will be. Besides, MPDIs at frequency 69GHz and 187GHz calculated by AMSR-E brightness temperature data are much closer to the variation trend of soil moisture than those obtained from other bands.

Algorithm Development of Monitoring Daily Near Surface Freeze/Thaw Cycles Using AMSR-E Brightness Temperatures

Algorithm Development of Monitoring Daily Near Surface Freeze/Thaw Cycles Using AMSR-E Brightness Temperatures

Dual-polarized ratio algorithm for retrieving Arctic sea ice concentration from passive microwave brightness temperature

We present a new algorithm for retrieving sea ice concentration from the AMSR-E data, the dual-polarized ratio (DPR) algorithm. The DPR algorithm is developed using vertically and horizontally polarized brightness temperatures at the same channel of 36.5 GHz. It depends on the ratio of dual-polarized emissivity, α, which is determined empirically at about 0.92 by remotely sensed brightness temperature in winter and used for the other seasons as well. The ice concentration retrieved by the DPR is compared with those by the NT2 and ABA algorithms. Since the main difference among these algorithms takes place in marginal ice zones, 17 marginal ice zones are chosen. The retrieved ice concentrations in these zones are examined by the ice concentration obtained by the MODIS data. The mean error, root-mean-square error and mean absolute error of the DPR algorithm are relatively better than those from the other two algorithms. The results of this study illustrate that the DPR algorithm is a more accurate algorithm for retrieving sea ice concentration from the AMSR-E brightness temperature, and can be used for operational purposes.

Quantifying the relationships between lake fraction, snow water equivalent and snow depth, and microwave brightness temperatures in an arctic tundra landscape

This study investigates the effects of sub-grid lake variability on brightness temperature measurements and snow mass retrieval from passive microwave satellite data in the North Slope of Alaska. In situ snow measurements of water equivalent, depth and density collected from field surveys during 1996–2004 snow seasons were collocated with gridded SSM/I brightness temperatures. Sub-grid lake fraction was computed from a high-resolution land cover map derived from LANDSAT imagery. Another dataset was created consisting of multi-year time series of SSM/I and AMSR-E brightness temperatures and computed lake fraction. Consistent with other studies, it was found that lake fraction was negatively correlated with in situ snow depth and snow water equivalent and positively correlated with snow density, which indicates that lakes have a direct effect on snow cover distribution: They accumulate less but denser snow than surrounding land areas. Additionally, lake fraction was positively correlated with the brightness temperature measurements at 18GHz and above, but negatively correlated with the AMSR-E brightness temperatures at low frequency channels (6 and 10GHz), with the highest correlation values for the SSM/I (r=0.57) and AMSR-E (r=0.65) at 37GHz dominating the response. Brightness temperature-based lake fraction algorithms were derived using stepwise regression. Performance assessment showed that the AMSR-E algorithm was superior to the SSM/I algorithm due to the use of the low frequency measurements not available from the SSM/I instrument. Combined lake fraction- and SSM/I brightness temperature-based snow depth and snow water equivalent algorithms were also derived using stepwise regression, with improved performance compared to conventional brightness temperature-based algorithms.

Global Land Surface Temperature Retrieval with AMSR-E Brightness Temperature and MODIS Land Cover Type Products

AMSR-E brightness temperature data and MODIS land cover type data are combined to retrieval land surface temperature(LST).Using the MODIS data,we classified the global land surface into 16 types,and make assumption that in each land surface type,the emissivity of each channel is constant separately.Based on this assumption and the land surface types,we build land surface retrieval algorithms for each land surface types.Before the algorithms were built,we chose the pixels that with above 90% single land type area and pixels that the MODIS land surface temperature exceed 273 K.This is because the emissivity is dubious in snow cover,ice cover,and mixed-pixels.Coequally,considering the precipitation effect,we winkled the rainfall pixel from our dataset.Finally,we employed the algorithms to retrieval global land surface temperature of Jan to Oct,2004.Compared with the MODIS land surface temperature product,the result is fine,with RMSE of 2~4 K.

A simple retrieval method of land surface temperature from AMSR-E passive microwave data—A case study over Southern China during the strong snow disaster of 2008

The analysis of the passive microwave radiance transfer equation certifies that there is a linear relationship between satellite-generated brightness temperatures (BT) and in situ observation temperature and that land surface temperature (LST) is largely influenced by vegetation cover conditions. Microwave polarization difference index (MPDI) is an effective indicator for characterizing the land surface vegetation cover density. Based on the analysis of LST models from AMSR-E BT with 6.9GHz MPDI intervals at 0.04, 0.02 and 0.01, respectively, this paper developed a simplified LST regression model with MPDI-based five land cover types, combining observation temperatures from 86 meteorological observation stations. The study shows that smaller MPDI intervals can obtain higher accuracy of AMSR-E LST simulation, and that the combination of HDF Explorer and ArcGIS software was useful for automatically processing the pixel latitude, longitude and BT information from the AMSR-E HDF imagery files. The RMSE of the five LST simulation algorithms is between 1.47 and 1.92°C, with an average LST retrieval error of 0.91–1.30°C. Besides, only 7 polarization bands and 5 land surface types are required by the proposed simplified model. The new LST simulation models appears to be more effective for producing LST compared to past most studies, of which the accuracy used to be more than 2°C. This study is one of the rare applications that combine the meteorological observation temperature with MPDI to produce the LST regression analysis algorithms with less RMSE from AMSR-E data. The results can be referred to similar areas of the world for LST retrieval or land surface process research, in particular under extreme bad weather conditions.

Development of a tundra-specific snow water equivalent retrieval algorithm for satellite passive microwave data

Airborne and satellite brightness temperature ( T B) measurements were combined with intensive field observations of sub-Arctic tundra snow cover to develop the framework for a new tundra-specific passive microwave snow water equivalent (SWE) retrieval algorithm. The dense snowpack and high sub-grid lake fraction across the tundra mean that conventional brightness temperature difference approaches (such as the commonly used 37 GHz–19 GHz) are not appropriate across the sub-Arctic. Airborne radiometer measurements (with footprint dimensions of approximately 70 × 120 m) acquired across sub-Arctic Canada during three field campaigns during the 2008 winter season were utilized to illustrate a slope reversal in the 37 GHz T B versus SWE relationship. Scattering by the tundra snowpack drives a negative relationship until a threshold SWE value is reached near 130 mm at which point emission from the snowpack creates a positive but noisier relationship between 37 GHz T B and SWE. The change from snowpack scattering to emission was also evident in the temporal evolution of 37 GHz T B observed from satellite measurements. AMSR-E brightness temperatures (2002/03–2006/07) consistently exhibited decreases through the winter before reaching a minimum in February or March, followed by an increase for weeks or months before melt. The cumulative absolute change (Σ|Δ37 V|) in vertically polarized 37 GHz T B was computed at both monthly and pentad intervals from a January 1 start date and compared to ground measured SWE from intensive and regional snow survey campaigns, and climate station observations. A greater (lower) cumulative change in |Δ37 V| was significantly related to greater (lower) ground measured SWE ( r 2 = 0.77 with monthly averages; r 2 = 0.67 with pentad averages). Σ|Δ37 V| was only weakly correlated with lake fraction: monthly r 2 values calculated for January through April 2003–2007 were largely less than 0.2. These results indicate that this is a computationally straightforward and viable algorithmic framework for producing tundra-specific SWE datasets from the complete satellite passive microwave record (1979 to present).

A soil moisture assimilation scheme based on the ensemble Kalman filter using microwave brightness temperature

This study presents a soil moisture assimilation scheme, which could assimilate microwave brightness temperature directly, based on the ensemble Kalman filter and the shuffled complex evolution method (SCE-UA). It uses the soil water model of the land surface model CLM3.0 as the forecast operator, and a radiative transfer model (RTM) as the observation operator in the assimilation system. The assimilation scheme is implemented in two phases: the parameter calibration phase and the pure soil moisture assimilation phase. The vegetation optical thickness and surface roughness parameters in the RTM are calibrated by SCE-UA method and the optimal parameters are used as the final model parameters of the observation operator in the assimilation phase. The ideal experiments with synthetic data indicate that this scheme could significantly improve the simulation of soil moisture at the surface layer. Furthermore, the estimation of soil moisture in the deeper layers could also be improved to a certain extent. The real assimilation experiments with AMSR-E brightness temperature at 10.65 GHz (vertical polarization) show that the root mean square error (RMSE) of soil moisture in the top layer (0–10 cm) by assimilation is 0.03355 m3 · m−3, which is reduced by 33.6% compared with that by simulation (0.05052 m3 · m−3). The mean RMSE by assimilation for the deeper layers (10–50 cm) is also reduced by 20.9%. All these experiments demonstrate the reasonability of the assimilation scheme developed in this study.

Geostatistical Characterization of Snow-Depth Structures on Sea Ice Near Point Barrow, Alaska—A Contribution to the AMSR-Ice03 Field Validation Campaign

The objective of this paper is to characterize spatial properties of snow-depth structures and their role as indicators of sea-ice properties and sea-ice-morphogenetic processes, and to provide quantitative measures of sea-ice properties that may be utilized in analyses of passive-microwave data. Snow-depth data collected near Point Barrow, Alaska, as part of the AMSRIce03 Field Validation Campaign for Advanced Microwave Scanning Radiometer (AMSR)-E-Sea-Ice Products from NASA earth-observing-systems satellite AQUA, are analyzed and compared to P-3 polarimetric scanning radiometer (PSR) data, a proxy for AMSR-E brightness temperatures. The approach taken in the analysis is geostatistical characterization. Various functions of first and second order are calculated for the snow-depth profiles, then geostatistical classification parameters are extracted and combined into feature vectors, on which the characterization is based. The complexity of sea ice requires a generalization of the method by introduction of the hyperparameter concept. Results include a quantitative characterization of sea-ice provinces from field transects in the Beaufort Sea, Chukchi Sea, and Elson Lagoon, which represent a good subset of Arctic sea-ice types, an internal segmentation of the longer profiles, and a derivation of surface-roughness length and of sea-ice-type complexity. PSR data reflect complexity of spatial snow-depth structures as captured in multidimensional feature vectors and, less directly, snow-depth and surface-roughness length. These results indicate that passive-microwave data in general may be affected by spatial snow depth and surface roughness, with a dependence on scale and quantified by geostatistical classification

VALIDATION OF AMSR-E ICE CONCENTRATION & THICKNESS IN THE OKHOTSK SEA

The data from aircraft and ship campaigns in February 2003 in conjunction with high resolution satellite images for validating sea ice concentrations and thickness derived from AMSR-E brightness temperatures are presented. Patrol Vessel SOYA conducted time series observations of sea ice parameters such as ice type, floe size, snow thickness, density, grain size on sea ice and ice surface temperature, and salinity. Surface data by ship were used to interpret aircraft microwave (PSR) and visible channel data obtained by the high resolution Landsat-7 and MODIS images. The latter data were utilized to interpret the ice concentration derived from AMSR-E. The co-registered images of aircraft PSR, Landsat-7 and MODIS data exhibit good coherence in signatures. In highly consolidated ice cover, the ice concentrations were in good agreement within 5 to 10% in ice concentration. However, in highly divergent areas, the derived ice concentration has a negative bias due to the dominant presence of new ice. The new ice has relatively lower emmissivity than first year ice which is snow cover and is affected with waves and wetness by flooding. The relationship between the thickness and brightness temperature of sea ice was obtained in detail