ALBERT

Description: The first Chinese operational Ku-band scatterometer on board Haiyang-2A (HY-2A), launched in August 2011, is designed for monitoring the global ocean surface wind. This study estimates the quality of the near-real-time (NRT) retrieval wind speed and wind direction from the HY-2A scatterometer for 36 months from 2012 to 2014. We employed three types of sea-surface wind data from oceanic moored buoys operated by the National Data Buoy Center (NDBC) and the Tropical Atmospheric Ocean project (TAO), the European Centre for Medium Range Weather Forecasting (ECMWF) reanalysis data (ERA-Interim), and the advanced scatterometer (ASCAT) to calculate the error statistics including mean bias, root mean square error (RMSE), and standard deviation. In addition, the rain effects on the retrieval winds were investigated using collocated Climate Prediction Center morphing method (CMORPH) precipitation data. All data were collocated with the HY-2A scatterometer wind data for comparison. The quality performances of the HY-2A NRT wind vectors data (especially the wind speeds) were satisfactory throughout the service period. The RMSEs of the HY-2A wind speeds relative to the NDBC, TAO, ERA-Interim, and ASCAT data were 1.94, 1.73, 2.25, and 1.62 m·s−1, respectively. The corresponding RMSEs of the wind direction were 46.63°, 43.11°, 39.93°, and 47.47°, respectively. The HY-2A scatterometer overestimated low wind speeds, especially under rainy conditions. Rain exerted a diminishing effect on the wind speed retrievals with increasing wind speed, but its effect on wind direction was robust at low and moderate wind speeds. Relative to the TAO buoy data, the RMSEs without rain effect were reduced to 1.2 m·s−1 and 39.68° for the wind speed direction, respectively, regardless of wind speed. By investigating the objective laws between rain and the retrieval winds from HY-2A, we could improve the quality of wind retrievals through future studies.

Description: Satellite remote sensing has advanced the understanding of the climate system in the short period of observations. This study highlights remote sensing discoveries that were not detected by climate models or conventional observations, and suggests future challenges for the robust determination of climate trends. Nature Climate Change 3 875 doi: 10.1038/nclimate1908

Description: A convolutional neural network (CNN) algorithm was developed to retrieve the land surface temperature (LST) from Advanced Microwave Scanning Radiometer 2 (AMSR2) data in China. Reference data were selected using the Moderate Resolution Imaging Spectroradiometer (MODIS) LST product to overcome the problem related to the need for synchronous ground observation data. The AMSR2 brightness temperature (TB) data and MODIS surface temperature data were randomly divided into training and test datasets, and a CNN was constructed to simulate passive microwave radiation transmission to invert the surface temperature. The twelve V/H channel combinations (7.3, 10.65, 18.7, 23.8, 36.5, 89 GHz) resulted in the most stable and accurate CNN retrieval model. Vertical polarizations performed better than horizontal polarizations; however, because CNNs rely heavily on large amounts of data, the combination of vertical and horizontal polarizations performed better than a single polarization. The retrievals in different regions indicated that the CNN accuracy was highest over large bare land areas. A comparison of the retrieval results with ground measurement data from meteorological stations yielded R2 = 0.987, RMSE = 2.69 K, and an average relative error of 2.57 K, which indicated that the accuracy of the CNN LST retrieval algorithm was high and the retrieval results can be applied to long-term LST sequence analysis in China.

Description: Monitoring global vegetation dynamics is of great importance for many environmental applications. The vegetation optical depth (VOD), derived from passive microwave observation, is sensitive to the water content in all aboveground vegetation and could serve as complementary information to optical observations for global vegetation monitoring. The microwave vegetation index (MVI), which is originally derived from the zero-order model, is a potential approach to derive VOD and vegetation water content (VWC), however, it has limited application at dense vegetation in the global scale. In this study, we preferred to use a more complex vegetation model, the Tor Vergata model, which takes into account multi-scattering effects inside the vegetation and between the vegetation and soil layer. Validation with ground-based measurements proved this model is an efficient tool to describe the microwave emissions of corn and wheat. The MVI has been derived through two methods: (i) polarization independent ( MVI B P ) and (ii) time invariant ( MVI B T ), based on model simulations at the L band. Results show that the MVI B T has a stronger sensitivity to vegetation properties compared with MVI B P . MVI B T is used to retrieve VOD and VWC, and the results were compared to physical VOD and measured VWC. Comparisons indicated that MVI B T has a great potential to retrieve VOD and VWC. By using L band time-series information, the performance of MVIs could be enhanced and its application in a global scale could be improved while paying attention to vegetation structure and saturation effects.

Description: Due to the availability of observations and the effectiveness of bias correction, it is still a challenge to assimilate data from the polar orbit satellites into a limited-area and frequently updated model. This study assessed the initial application of satellite radiance data from multiple platforms in the Rapid-refresh Multi-scale Analysis and Prediction System (RMAPS). Satellite radiance data from the advanced microwave sounding unit-A (AMSU-A) and microwave humidity sounding (MHS) were used. Two 12-day retrospective runs were conducted to evaluate the impact of assimilating satellite radiance data on 0–24 h forecasts using RMAPS. The forecasts, initialized from analyses with and without satellite radiance data, were verified against observations. The results showed that satellite radiance data from AMSU-A and MHS had a positive impact on the initial conditions and the forecasts of RMAPS, even over the relatively data-rich area of North China. Compared to the control run that only assimilated conventional observations, an improvement of about 36.8% can be obtained for the temperature bias between 300 hPa and 850 hPa and 0.65% for the average RMSE. Satellite radiance observations from 1200 UTC contribute relatively significantly (77.8%) to the bias improvement of the initial temperature field. For the wind at 10 m, the bias and root-mean-square error (RMSE) both had a reduction for the 0–12 h forecast range. An improvement can be also found for the skill score of the 3-h accumulated rainfall below 10.0 mm in the first 12 h of the forecast range. There was a slight improvement in the skill score of the 6-h accumulated rainfall above 50 mm over North China, with a 20.7% improvement for the first 12 h of the forecast. The inclusion of satellite radiance observations was found to be beneficial for the initial temperature, which consequently improved the forecast skill of the 0–12 h range in the RMAPS.

Description: Abstract Clouds play a significant role in the derivation of land surface temperature (LST) from optical remote sensing. The estimation of LST under cloudy‐sky conditions has been a great challenge for the community for a long time. In this study, a scheme for recovering the LST under cloudy skies is proposed by accounting for the solar‐cloud‐satellite geometry (SCSG) effect, through which the LSTs of shadowed and illuminated pixels covered by clouds in the image are estimated. The validation shows that the new scheme can work well and has reasonable LST accuracy with a root mean square error (RMSE) 〈 4.9K and bias 〈 3.5K. The application of the new method to the MODIS and Landsat‐8 data reveals that the LSTs under cloud layers can be reasonably recovered, and that the fraction of valid LSTs in an image can be correspondingly improved. The method is not data specific, instead, it can be used in any optical remote sensing images as long as the proper input variables are provided. As an alternative approach to derive cloudy‐sky LSTs based only on optical remote sensing data, it gives some new ideas to the remote sensing community, especially in the fields of surface energy balance.

Description: Owing to the temporal and spatial variability of the emissivity spectra, problems remain in the interpretation and application of satellite passive microwave data over vegetation-covered surfaces. The commonly used microwave land emissivity model, developed by Weng et al. (2001) and implemented into the community radiative transfer model (CRTM), treats vegetation-covered surfaces as a three-layer medium. This simplification comes at the cost of accuracy. In this study, to reduce bias in the modeling of microwave emissions from short vegetation-covered surfaces, two modifications are made. First, vegetation was considered as a multilayered medium including leaves and stems to simulate volumetric absorption and scattering. The results suggest that the calculated brightness temperatures well agree with field experiments under different incidence angles for low soil moisture and sparse crop cover. On the other hand, large errors from the measurements are found for high soil moisture content and dense crop cover. Second, the advanced integral equation model (AIEM) was also used to improve the simulation of reflectivity from rough soil surfaces. Comparisons with field experimental data show that the determination coefficient between the calculated and measured brightness temperatures significantly increased and the root-mean-square errors remarkably decreased. The average improvement using the proposed approach is about 80% and 59% in accuracy for the vertical and horizontal polarization, respectively.

Description: Snow water equivalent (SWE) is a key parameter in the Earth’s energy budget and water cycle. It has been demonstrated that SWE can be retrieved using active microwave remote sensing from space. This necessitates the development of forward models that are capable of simulating the interactions of microwaves and the snow medium. Several proposed models have described snow as a collection of sphere- or ellipsoid-shaped ice particles embedded in air, while the microstructure of snow is, in reality, more complex. Natural snow usually forms a sintered structure following mechanical and thermal metamorphism processes. In this research, the bi-continuous vector radiative transfer (bi-continuous-VRT) model, which firstly constructs snow microstructure more similar to real snow and then simulates the snow backscattering signal, is used as the forward model for SWE estimation. Based on this forward model, a parameterization scheme of snow volume backscattering is proposed. A relationship between snow optical thickness and single scattering albedo at X and Ku bands is established by analyzing the database generated from the bi-continuous-VRT model. A cost function with constraints is used to solve effective albedo and optical thickness, while the absorption part of optical thickness is obtained from these two parameters. SWE is estimated after a correction for physical temperature. The estimated SWE is correlated with the measured SWE with an acceptable accuracy. Validation against two-year measurements, using the SnowScat instrument from the Nordic Snow Radar Experiment (NoSREx), shows that the estimated SWE using the presented algorithm has a root mean square error (RMSE) of 16.59 mm for the winter of 2009–2010 and 19.70 mm for the winter of 2010–2011.

Description: Endmember selection is the basis for sub-pixel land cover classifications using multiple endmember spectral mixture analysis (MESMA) that adopts variant endmember matrices for each pixel to mitigate errors caused by endmember variability in SMA. A spectral library covering a large number of endmembers can account for endmember variability, but it also lowers the computational efficiency. Therefore, an efficient endmember selection scheme to optimize the library is crucial to implement MESMA. In this study, we present an endmember selection method based on vector length. The spectra of a land cover class were divided into subsets using vector length intervals of the spectra, and the representative endmembers were derived from these subsets. Compared with the available endmember average RMSE (EAR) method, our approach improved the computational efficiency in endmember selection. The method accuracy was further evaluated using spectral libraries derived from the ground reference polygon and Moderate Resolution Imaging Spectroradiometer (MODIS) imagery respectively. Results using the different spectral libraries indicated that MESMA combined with the new approach performed slightly better than EAR method, with Kappa coefficient improved from 0.75 to 0.78. A MODIS image was used to test the mapping fraction, and the representative spectra based on vector length successfully modeled more than 90% spectra of the MODIS pixels by 2-endmember models.

Description: We develop an accurate and fast forward model for calculating the compact closed-form high-order perturbative solutions of the problem of three-dimensional (3D) radiation and propagation electromagnetic fields in a layered structure with multilayer rough interfaces. The proposed method for the fast forward model is firstly demonstrated by strictly theoretical formulas derivation in the framework of classical small perturbation method (SPM) without other else approximation and equivalent process. The kernel functions of high-order are proposed for calculating the radar cross sections with more efficiency and clear physical meanings for better use in practice. What is more, we give the clear physical interpretation of the first-order fully polarimetric electromagnetic wave scattering mechanism in the layered structure with multilayer rough interfaces. The proposed forward model is necessary to insure a successful inversion process. Furthermore, the high-order SPM solutions derived by employing the proposed method are validated with existing methods and numerical results. Finally, we study the performance of the high-order fully polarimetric electromagnetic wave scattering according to the numerical results and analyze the scattering enhancement phenomena.