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The Qinghai–Tibetan Plateau (QTP), which has a unique and severe environment, suffers from the absence of rainfall gauges in western arid land. Using different precipitation products in this region would easily lead to contradictory results. To evaluate nine fine-resolution precipitation products in the QTP, we propose a "down to top" methodology, based on water balance and drought chain, by forecasting two accuracy assessment indices—multi-year precipitation bias and precipitation correlation. We assessed the biases of all products in the Jinsha–Yalong, Yellow, Heihe, Yangtze, Yarlung Zangbo catchments and interior drainage areas. And we assessed gauge-based correlation of precipitation products, based on the correlations between precipitation product-based effective drought index (EDI), Soil Moisture Active Passive (SMAP)-based soil moisture anomaly, and the moderate-resolution imaging spectroradiometer (MODIS)-based normalized difference vegetation index (NDVI) anomaly (R = 0.712, R = 0.36, and R = 0.785, respectively) for cross-sectional rainfall observations on the Tibetan Plateau in 2018. The results showed that ERA5-Land and IMERG merged precipitation dataset (EIMD) can efficiently close the water budget at the catchment scale. Moreover, the EIMD-based EDI exhibited the best performance in correlation with both the SMAP-based soil moisture anomaly and MODIS-based NDVI anomaly for the three main herbaceous species areas—Kobresia pygmaea meadow, Stipa purpurea steppe, and Carex moorcroftii steppe. Overall, we find that EIMD is the most accurate among the nine products. The annual average precipitation (2001–2018) was determined to be 568.16 mm in the QTP. Our assessment methodology has a remote sensing basis with low cost and can be used for other arid lands in the future. [ABSTRACT FROM AUTHOR]

The climate warming over the Third Pole is twice as large as that in other regions and glacier mass loss is considered to be more intensive in the region. However, due to the vast geographical differences, the characteristics of glacier mass loss might be very different between different parts of the Third Pole, such as between the southern and northern Third Pole. It is, therefore, very important to clarify the characteristics of glacier mass loss between different parts of the Third Pole, particularly between the southern and northern Third Pole. We selected the Yala Glacier in the Central Himalayas and the Qiyi Glacier in the Qilian Mountains to study the different characteristics of glacier mass loss between the southern and northern Third Pole using remote sensing data and in situ data. Based on the results, we found that the Yala Glacier has not only been in a status of mass loss but also in a status of intensive and accelerating mass loss. Our analysis showed that the average multi-year mass loss of the Yala Glacier is −736 mm w.e.a−1, with a maximum of −1815 mm w.e.a−1. At the same time, the Qiyi Glacier has experienced a mild glacier mass loss process compared with the Yala Glacier. The Qiyi Glacier's mass loss is −567 mm w.e.a−1 with a maximum of −1516 mm w.e.a−1. Our results indicate that the mass loss of the Yala Glacier is much stronger than that of the Qiyi Glacier. The major cause of the stronger mass loss of the Yala Glacier is from the decrease of glacier accumulation associated with precipitation decrease under the weakening Indian monsoon. Other factors have also contributed to the more intensive mass loss of the Yala Glacier. [ABSTRACT FROM AUTHOR]

This paper extends a new temperature and emissivity separation (TES) algorithm for retrieving land surface temperature and emissivity (LST and LSE) to the Advanced Geosynchronous Radiation Imager (AGRI) onboard Fengyun-4A, China's newest geostationary meteorological satellite. The extended TES algorithm was named the AGRI TES algorithm. The AGRI TES algorithm employs a modified water vapor scaling (WVS) method and a recalibrated empirical function over vegetated surfaces. In situ validation and cross-validation are utilized to investigate the accuracy of the retrieved LST and LSE. LST validation using the collected field measurements showed that the mean bias and RMSE of AGRI TES LST are 0.58 and 2.93 K in the daytime and −0.30 K and 2.18 K at nighttime, respectively; the AGRI official LST is systematically underestimated. Compared with the MODIS LST and LSE products (MYD21), the average bias and RMSE of AGRI TES LST are −0.26 K and 1.65 K, respectively. The AGRI TES LSE outperforms the AGRI official LSE in terms of accuracy and spatial integrity. This study demonstrates the good performance of the AGRI TES algorithm for the retrieval of high-quality LST and LSE, and the potential of the AGRI TES algorithm in producing operational LST and LSE products. [ABSTRACT FROM AUTHOR]

In this study, the net water flux (precipitation minus evaporation) over the Tibetan Plateau (TP) and its 12 drainage basins is estimated using ERA5. The terrestrial branch of the water cycle is investigated using the total water storage anomalies (TWSAs) derived from GRACE (Gravity Recovery and Climate Experiment) data and daily streamflow records collected in Zhimenda and Tangnaihai (two hydrological stations located in the upper Yangtze River Basin and upper Yellow River Basin). This work provides a preliminary assessment of discrepancies between model-derived and space-based observations in the atmospheric–terrestrial water cycle over the TP and its drainage basins. The results show that the net water fluxes occurring over the TP and the scale of its drainage basins are closely tied to local dynamics and physical processes and to large-scale circulation and atmospheric water vapor. ERA5 maintains the atmospheric water balance over the TP. ERA5-derived net water flux anomalies constitute a major component of the water cycle and correspond to GRACE-derived TWSAs. The water budget–based approach with the ERA5 and ITSG-Grace2018 datasets constrains the atmospheric–terrestrial water cycle over the TP and its drainage basins. Both the ERA5- and GRACE-derived estimates contain consistent long- and short-term variations over the TP. Discrepancies are evident at the drainage basin, while the ratio of signal to noise in both the ERA5 and GRACE datasets might cause discrepancies between estimates over relatively small or arid basins. Nevertheless, the observed good correspondence between ERA5- and GRACE-derived atmospheric–terrestrial water cycles over the TP highlights the potential value of the rational application of water resource information. [ABSTRACT FROM AUTHOR]

The estimation of downward surface shortwave radiation (DSSR) is important for the Earth's energy budget and climate change studies. This review was organised from the perspectives of satellite sensors, algorithms and future trends, retrospects and summaries of the satellite-based retrieval methods of DSSR that have been developed over the past 10 years. The shortwave radiation reaching the Earth's surface is affected by both atmospheric and land surface parameters. In recent years, studies have given detailed considerations to the factors which affect DSSR. It is important to improve the retrieval accuracy of cloud microphysical parameters and aerosols and to reduce the uncertainties caused by complex topographies and high-albedo surfaces (such as snow-covered areas) on DSSR estimation. This review classified DSSR retrieval methods into four categories: empirical, parameterisation, look-up table and machine-learning methods, and evaluated their advantages, disadvantages and accuracy. Further efforts are needed to improve the calculation accuracy of atmospheric parameters such as cloud, haze, water vapor and other land surface parameters such as albedo of complex terrain and bright surface, organically combine machine learning and other methods, use the new-generation geostationary satellite and polar orbit satellite data to produce high-resolution DSSR products, and promote the application of radiation products in hydrological and climate models. [ABSTRACT FROM AUTHOR]

Remotely sensed surface longwave downward radiation (LWDR) plays an essential role in studying the surface energy budget and greenhouse effect. Most existing satellite-based methods or products depend on variables that are not readily available from space such as, liquid water path, air temperature, vapor pressure and/or cloud-base temperature etc., which seriously restrict the wide applications of satellite data. In this paper, new nonlinear parameterizations and a machine learning-based model for deriving all-sky LWDR are proposed based only on land surface temperature (LST), column water vapor and cloud-top temperature (CTT), that are relatively readily available day and night for most satellite missions. It is the first time to incorporate the CTT in the parameterizations for estimating LWDR under the cloudy-sky conditions. The results reveal that the new models work well and can derive all-sky global LWDR with reasonable accuracies (RMSE <23 W/m2, bias <2.0 W/m2). The convenience of input data makes the new models easy to use, and thus will definitely expand the applicability of remotely sensed measurements in radiation budget fields and many land applications. [ABSTRACT FROM AUTHOR]

Longwave downward radiation (LWDR) is another major energy source received by the earth's surface apart from solar radiation. Its importance in regulating air temperature and balancing surface energy is enlarged especially under cloudy-sky conditions. Unfortunately, to date, a large number of efforts have been made to derive LWDR from space under only clear-sky conditions leading to difficulty in utilizing space-based LWDR in most models due to its spatio-temporal discontinuity. Currently, only a few studies are focused on LWDR estimation under cloudy skies, while their global application is still questionable. In this paper, an alternative strategy is proposed aiming to derive high-resolution (1 km) cloudy-sky LWDR by fusing collocated satellite multi-sensor measurements. The results show that the newly developed method works well and can derive LWDR at a better accuracy with RMSE < 27 W/m 2 and bias < 10 W/m 2 even under cloudy skies and at 1 km scales. By comparing the CCCM and SSF products of CERES, MERRA, ERA-interim and NCEP-CSFR over the Tibetan Plateau region, the new approach demonstrates its superiority in terms of accuracy, temporal variation and spatial distribution patterns of LWDR. Comprehensive comparison analysis also reveals that, except for the proposed product, the other four products (CERES, MERRA, ERA-interim and NCEP-CSFR) show a big difference from each other in the LWDR spatio-temporal distribution pattern and magnitude. The difference between these products can still be up to 60 W/m 2 even at the monthly scale, implying that large uncertainties exist in current LWDR estimations. More importantly, besides the higher accuracy of the proposed method, it provides unprecedented possibilities for jointly generating high-resolution global LWDR datasets by connecting the NASA's Earth Observing System-(EOS) mission (MODIS-AIRS/AMSU) and the Suomi National Polar-orbiting Partnership-(NPP) mission (VIIRS-CrIS/ATMS). Meanwhile, the scheme proposed in this study also gives some clues towards multiple data fusing in the remote sensing community. [ABSTRACT FROM AUTHOR]

The snow specific area (SSA) is a quantity to describe the size of snow grains, which controls the albedo of snow surface. The remote sensing retrieval of snow SSA is important for large scale energy and snow physical properties studies. The previous SSA retrieval algorithms are based on snow reflectance forward models with shape assumptions of snow grains, which may cause bias in the quantitative retrieval of SSA. In this paper, a new SSA retrieval algorithm based on a recently developed snow reflectance model is presented. In the reflectance model, the microstructure of snow medium is simulated by computer and the light scattering process is modeled by Monte Carlo ray tracing and radiative transfer theory. Validation shows the model can accurately simulate snow surface reflectance even at large incident or observation zenith angles. Three datasets were used to validate the SSA retrieval algorithm. The validation results show that the SSA can be accurately estimated using MODIS data. The difference of SSA by using reflectance of different wavelengths are studied by MODIS band 5 and band 6 data. The SSA retrieval algorithm is then applied to seasonal snow cover to study the temporal and spatial pattern of snow surface optical grain size. [ABSTRACT FROM AUTHOR]

Retrieval of integrated water vapor content (WVC) from remote sensing data is often ill-posed because of insufficient observational information. There are many factors that cause WVC changes, which yield instability in the accuracy of many traditional algorithms. To overcome this problem, we developed a novel fully-coupled paradigm for the robust retrieval of WVC from thermal infrared remote sensing data. Through the derivation of the physical radiative transfer equation, we determined two conditions that need to be satisfied for the deep learning retrieval paradigm of WVC. The first condition is that the input parameters and output parameters of the deep learning need to be able to build a complete set of solvable equations in theory. The second condition is that, if there is a strong relationship between input parameters and output parameters, it can be directly retrieved. If it is a weak relationship, we need to use prior knowledge to improve the portability and accuracy of the algorithm. The training and test data of deep learning is composed of representative solutions of physical methods and solutions of statistical methods. The representative solutions of the physical methods were obtained from the physical forward model, and the statistical solutions were obtained from multi-source data which can compensate for the defect that the physical model cannot simulate mixed pixels. MODIS L1B data was used for case analysis of paradigm retrieval, and the analysis indicated that four thermal infrared bands were usually needed as the input parameters of deep learning and the integrated water vapor content as the output parameter. When land surface temperature and emissivity were taken as prior knowledge, the root-mean-square error (RMSE) of the retrieved WVC was 0.07 g/cm2. The optimal accuracy RMSE was 0.27 g/cm2. When there was a strong correlation between input parameters and output parameters, i.e., if there were two bands that were very sensitive to WVC in the band combination, high-precision retrieval could also be achieved without prior knowledge. All the analyses show that the paradigm of deep learning coupling physics and statistics can accurately retrieve WVC, which is a significant improvement on the traditional method and solves the problem of lack of physical interpretation of deep learning. [ABSTRACT FROM AUTHOR]

Atmospheric water vapor plays an important role in hydrologic cycle and climate change of the Earth. A number of studies have focused on retrieval of the total precipitable water (TPW) using microwave or optical remote sensing. In this paper, the global quarter-degree gridded TPW over land was retrieved using water vapor sensitivity parameter ∆ Tb 18.7 /∆ Tb 23.8 based on the combination of AMSR-E and MODIS observations. There are two major improvements in the retrieval algorithm, including optimization of the estimation model of surface emissivity ∆ ε 18.7 /∆ ε 23.8 and correction of the terrain influence to the retrieval of TPW using DEM. To obtain a high resolution TPW, we also developed an algorithm to downscale the retrieved quarter-degree gridded TPW to a fine scale of 0.05° × 0.05° using DEM and NDVI. In addition, the downscaled TPW was further calibrated using high precision TPW from MODIS in the clear-sky condition to improve its accuracy. Finally, both quarter-degree and 0.05° × 0.05° gridded TPW were validated against SuomiNet GPS retrieved TPW on a global scale. The RMSE for the retrieved quarter-degree gridded global TPW is 3.45 mm, with a correlation coefficient of 0.95. In addition, the RMSE for the downscaled 0.05° × 0.05° gridded global TPW is 4.18 mm, with a correlation coefficient of 0.95. An obvious advantage of our algorithm compared with MODIS TPW product is that it can retrieve TPW under cloudy sky condition over land. The algorithm developed in this study can be easily transferred to AMSR2 on board GCOM-W1 and provides the long-term global daily TPW over land since the launch of Aqua to present day to support hydrologic cycle and climate change studies. [ABSTRACT FROM AUTHOR]

To provide hints for the design of novel acetylcholinesterase (AChE) inhibitors with higher potency and specificity, the binding modes of the (RS, S)-17b and (RS, R)-17b enantiomers on AChE were chosen to investigate by molecular docking and molecular dynamics simulation. The results show that the binding modes of (RS, S)-17b and (RS, R)-17b are clearly different from each other. In particular, the (RS, S)-17b and (RS, R)-17b enantiomers tend to be planar and bend conformations to interact with AChE, respectively. Furthermore, based on the binding mode on AChE and structure modification of (RS, S)-17b, two novel inhibitors (1 and 2) with higher inhibitory activity were designed. Our design strategy suggests that the number of N and O atoms should be increased, the 5, 6-dimethoxy should be transformed into ring and the indanone moiety should be ring-opening, which would result in generating potent and selective AChE inhibitors. [ABSTRACT FROM PUBLISHER]

The electromagnetic scattering by a dielectric cylinder of finite length is important in many applications, particularly for microwave remote sensing of vegetated terrain. Yet, not only a unified analytical solution is still elusive in providing the scattering cross sections but also other important aspects, such as the phase of the scattering amplitude function, energy conservation, and reciprocity relation, have been scantly touched in the literature. The treatment of a dielectric inhomogeneous cylinder of finite length brings forth new challenges. Taking on such challenges is the focus of this paper. The main plan of attack is to extend the virtual partition method, which is a T-matrix-based semi-analytical model, that we have previously proposed to treat scattering from a homogeneous dielectric cylinder of finite length, to the inhomogeneous cases. The effectiveness of the proposed method is validated numerically, including: 1) high-fidelity prediction of the copolarized and cross-polarized cross sections for arbitrary bistatic scattering configuration; 2) high-fidelity predictions of the phase of the scattering amplitude function; 3) verification of energy conservation; and 4) verification of the reciprocity theorem. [ABSTRACT FROM PUBLISHER]

This paper discusses the performance of preconditioned Krylov subspace methods at solving large-scale finite-element-formulated contact problems based on a finite element analysis program (FEAP) and portable, extensible toolkit for scientific computation (PETSc). Different combinations of the common preconditioners and Krylov subspace methods, namely conjugate gradient, generalized minimal residual, conjugate residual, biconjugate gradient, conjugate gradient square, biconjugate gradient stabilized, and transpose-free quasi-minimal residual method, are tested on the problems with rising size. Numerical experiments are carried out in respect of time consumption, parallelism, memory usage, and stability. The results show time consumption for most of combinations increases linearly with the growth of problem size, and block Jacobi and Jacobi-preconditioned conjugate gradient and conjugate residual methods have better potential for large-scale problems in the given and similar case. [ABSTRACT FROM AUTHOR]

Accurate quantitative global scale snow water equivalent information is crucial for meteorology, hydrology, water cycle and global change studies, and is of great importance for snow melt-runoff forecast, water resources management and flood control. With land surface process model and snow process model, the snow water equivalent can be simulated with certain accuracy, with the forcing data as input. However, the snow water equivalent simulated using the snow process models has large uncertainties spatially and temporally, and it may be far from the needs of practical applications. Thus, the large scale snow water equivalent information is mainly from remote sensing. Beginning with the launch of Nimbus-7 satellite, the research on microwave snow water equivalent remote sensing has developed for more than 30 years, researchers have made progress in many aspects, including the electromagnetic scattering and emission modeling, ground and airborne experiments, and inversion algorithms for future global high resolution snow water equivalent remote sensing program. In this paper, the research and progress in the aspects of electromagnetic scattering/emission modeling over snow covered terrain and snow water equivalent inversion algorithm will be summarized. [ABSTRACT FROM AUTHOR]

Water vapor plays a crucial role in atmospheric processes that act over a wide range of temporal and spatial scales, from global climate to micrometeorology. The determination of water vapor distribution in the atmosphere and its changing pattern is very important. Although atmospheric scientists have developed a variety of means to measure precipitable water vapor (PWV) using remote sensing data that have been widely used, there are some limitations in using one kind satellite measurements for PWV retrieval over land. In this paper, a new algorithm is proposed for retrieving PWV over land by combining different kinds of remote sensing data and it would work well under the cloud weather conditions. The PWV retrieval algorithm based on near infrared data is more suitable to clear sky conditions with high precision. The 23.5 GHz microwave remote sensing data is sensitive to water vapor and powerful in cloud-covered areas because of its longer wavelengths that permit viewing into and through the atmosphere. Therefore, the PWV retrieval results from near infrared data and the indices combined by microwave bands remote sensing data which are sensitive to water vapor will be regressed to generate the equation for PWV retrieval under cloud covered areas. The algorithm developed in this paper has the potential to detect PWV under all weather conditions and makes an excellent complement to PWV retrieved by near infrared data. Different types of surface exert different depolarization effects on surface emissions, which would increase the complexity of the algorithm. In this paper, MODIS surface classification data was used to consider this influence. Compared with the GPS results, the root mean square error of our algorithm is 8 mm for cloud covered area. Regional consistency was found between the results from MODIS and our algorithm. Our algorithm can yield reasonable results on the surfaces covered by cloud where MODIS cannot be used to retrieve PWV. [ABSTRACT FROM AUTHOR]

Light scattering models of snow are very important for the remote sensing of snow. Many previous models have used unrealistic assumptions about the snow particle shape and microstructure. In this paper, a new model is proposed, wherein a bicontinuous medium is used to simulate the snow microstructure, and geometric optics theory is used in combination with the Monte Carlo method to simulate the scattering properties of snow. Then, using the radiative transfer equation, the snow reflectance, including the polarized reflectance, can be simulated. Unlike other models that use Monte Carlo ray tracing, the new model is computationally efficient and can be used for massive simulations and practical applications. The simulation results of the new model are compared with the ground measurements and simulation results of a traditional model based on the Mie theory. Through validations and comparisons, the new model is shown to demonstrate a significantly improved capability in simulating the bidirectional reflectance of snow. The importance of the grain shape and microstructure modeling in the light scattering models of snow is confirmed by the comparison of the simulation results. [ABSTRACT FROM PUBLISHER]

Soil moisture (SM) and land surface temperature (LST) are entangled, and the retrieval of one of them requires a priori specification of the other one. Due to insufficient observational information, retrieval of LST and SM from passive microwave remote sensing data is often ill-posed, and the retrieval accuracy needs to be improved. In this study, a novel fully-coupled paradigm is developed to robustly retrieve SM and LST from passive microwave data, which integrates deep learning, physical methods, and statistical methods. The key condition of the general paradigm proposed by us is that the output parameters of deep learning can be uniquely determined by the input parameters theoretically through a certain mathematical equation. Firstly, the physical method is deduced based on the energy radiation balance equation. The nine unknowns require the brightness temperatures of nine channels to construct nine equations, and the solutions of the physical method equations are obtained by model simulation. Based on the derivation of the physical method, the solution of the statistical method is constructed using multi-source data. Secondly, the solutions of physical and statistical methods constitute the training and test data of deep learning, which is used to obtain the solution curve of physical and statistical methods. The retrieval accuracy of LST and SM is greatly improved by smartly utilizing the mutual prior knowledge of SM and LST and cross iterative optimization calculations. Finally, validation indicates that the mean absolute error of the retrieved SM and LST data are 0.027 m3/m3 and 1.38 K, respectively, at an incidence angle of 0–65°. A model-data-knowledge-driven and deep learning method can overcome the shortcomings of traditional methods and provide a paradigm for retrieval of other geophysical variables. The proposed paradigm not only has physical meaning, but also makes deep learning physically interpretable, which is a milestone in the retrieval of geophysical remote sensing parameters based on artificial intelligence technology. [ABSTRACT FROM AUTHOR]

Surface soil moisture is an important parameter in hydrology and climate investigations. Current and future satellite missions with L-band passive microwave radiometers can provide valuable information for monitoring the global soil moisture. A factor that can play a significant role in the modeling and inversion of microwave emission from land surfaces is the surface roughness. In this study, an L-band parametric emission model for exponentially correlated surfaces was developed and implemented in a soil moisture retrieval algorithm. The approach was based on the parameterization of an effective roughness parameter of H p in relation with the geometric roughness variables (root mean square height s and correlation length l ) and incidence angle. The parameterization was developed based on a large set of simulations using an analytical approach incorporated in the advanced integral equation model (AIEM) over a wide range of geophysical properties. It was found that the effective roughness parameter decreases as surface roughness increases, but increases as incidence angle increases. In contrast to previous research, H p was found to be expressed as a function of a defined slope parameter m = s 2 / l , and coefficients of the function could be well described by a quadratic equation. The parametric model was then tested with L-band satellite data in soil moisture retrieval algorithm over the Little Washita watershed, which resulted in an unbiased root mean square error of about 0.03 m 3 /m 3 and 0.04 m 3 /m 3 for ascending and descending orbits, respectively. [ABSTRACT FROM AUTHOR]

Compared with visible/infrared sensors, satellite data-based passive microwave radiometers could provide a more feasible method for retrieving precipitable water vapor (PWV). This paper presents a scheme that retrieves PWV over Beijing-Tianjin-Hebei region using satellite radiometer measurements from advanced microwave scanning radiometer (AMSR-E). For bare surfaces, the polarization difference ratio (PDR_WV) obtained from 23. 8 and 18. 7 GHz was found to be sensitive to PWV. For the surface covered by vegetation, surface emissivity was retrieved by AMSR-E with the help of the MODIS atmospheric profile product. Through analyzing the statistical relationship of emissivity polarization difference, an algorithm for retrieving PWV was built. Compared with the GPS results, the root mean square error of our algorithm is 7. 4 mm. Regional consistency was found between the results from MODIS and our algorithm. [ABSTRACT FROM AUTHOR]

Global warming induced by atmospheric CO2 has attracted increasing attention of researchers all over the world. Although space-based technology provides the ability to map atmospheric CO2 globally, the number of valid CO2 measurements is generally limited for certain instruments owing to the presence of clouds, which in turn constrain the studies of global CO2 sources and sinks. Thus, it is a potentially promising work to combine the currently available CO2 measurements. In this study, a strategy for fusing SCIAMACHY and GOSAT CO2 measurements is proposed by fully considering the CO2 global bias, averaging kernel, and spatiotemporal variations as well as the CO2 retrieval errors. Based on this method, a global CO2 map with certain UTC time can also be generated by employing the pattern of the CO2 daily cycle reflected by Carbon Tracker (CT) data. The results reveal that relative to GOSAT, the global spatial coverage of the combined CO2 map increased by 41.3% and 47.7% on a daily and monthly scale, respectively, and even higher when compared with that relative to SCIAMACHY. The findings in this paper prove the effectiveness of the combination method in supporting the generation of global full-coverage XCO2 maps with higher temporal and spatial sampling by jointly using these two space-based XCO2 datasets. [ABSTRACT FROM AUTHOR]

Abstract: To date, the light scattering models of snow consider very little about the real snow microstructures. The ideal spherical or other single shaped particle assumptions in previous snow light scattering models can cause error in light scattering modeling of snow and further cause errors in remote sensing inversion algorithms. This paper tries to build up a snow polarized reflectance model based on bicontinuous medium, with which the real snow microstructure is considered. The accurate specific surface area of bicontinuous medium can be analytically derived. The polarized Monte Carlo ray tracing technique is applied to the computer generated bicontinuous medium. With proper algorithms, the snow surface albedo, bidirectional reflectance distribution function (BRDF) and polarized BRDF can be simulated. The validation of model predicted spectral albedo and bidirectional reflectance factor (BRF) using experiment data shows good results. The relationship between snow surface albedo and snow specific surface area (SSA) were predicted, and this relationship can be used for future improvement of snow specific surface area (SSA) inversion algorithms. The model predicted polarized reflectance is validated and proved accurate, which can be further applied in polarized remote sensing. [Copyright &y& Elsevier]

In recent years, global warming caused by emission of CO 2 has attracted considerable attention from the public. Although the measurements from AIRS, GOSAT, SCIAMACHY and IASI have been frequently used to derive atmospheric CO 2 concentration, comprehensive quantification of the differences among these CO 2 products is still not fully investigated yet. In this paper, a series of strategies have been proposed to allow the CO 2 products from different instruments to be physically inter-comparable. Based on this, these CO 2 products are inter-compared in terms of magnitude and their spatiotemporal distributions. The results reveal that the correlations among these CO 2 products are relatively weak, and some discrepancies are detected in terms of the CO 2 spatiotemporal characteristics, demonstrating more efforts should be made in the future to improve the retrievals of CO 2. Their spatial coverage differences reflected in this study imply the great necessity to generate consistent products with improved spatial and temporal continuities by combining these CO 2 measurements. [ABSTRACT FROM AUTHOR]

Different electronic properties of B‒C‒N compounds come from different atomic arrangement. In this paper, three kinds of single-walled B4C4nanotubes were built and studied using density functional theory. One possible single-walled B4C4nanotube was calculated by CASTEP code. The structure and electronic properties were studied and discussed. It might be good conductor of electricity, based on the results of band structure, density of states and electron density. [ABSTRACT FROM AUTHOR]

The Qinghai-Tibet Plateau plays a very important role in studying severe weather in China and around the globe because of its unique characteristics. Moreover, the surface emissivities of the Qinghai-Tibet Plateau are also important for retrieving surface and atmospheric parameters. In the current study, a retrieval algorithm was developed to retrieve the surface emissivities of the Qinghai-Tibet Plateau. The developed algorithm was derived from the radiative transfer model and was first validated using simulated data from a one-dimensional microwave simulator. The simulated results show good precision. Then, the surface emissivities of the Qinghai-Tibet Plateau were retrieved using brightness temperatures from the advanced microwave-scanning radiometer and atmospheric profile data from the moderate resolution imaging spectroradiometer. Finally, the features of the time and space distribution of the retrieved results were analyzed. In terms of spatial characteristics, a spatial distribution consistency was found between the retrieved results and surface coverage types of the Qinghai-Tibet Plateau. In terms of time characteristics, the changes in emissivity, which were within 0.01 for every day, were not evident within a one-month time scale. In addition, surface emissivities are sensitive to rainfall. The reasonability of the retrieved results indicates that the algorithm is feasible. A time-series surface emissivity database on the Qinghai-Tibet Plateau can be built using the developed algorithm, and then other surface or atmospheric parameters would have high retrieval precision to support related geological research on the Qinghai-Tibet Plateau. [ABSTRACT FROM AUTHOR]

An ensemble method was used to combine three surface soil moisture products, retrieved from passive microwave remote sensing data, to reconstruct a monthly soil moisture data set for China between 2003 and 2010. Using the ensemble data set, the temporal and spatial variations of surface soil moisture were analyzed. The major findings were: (1) The ensemble data set was able to provide more realistic soil moisture information than individual remote sensing products; (2) during the study period, the soil moisture increased in semiarid regions and decreased in arid regions with anoverall drying trend for the whole country; (3) the soil moisture variation trends derived from the three retrieval products and the ensemble data differ from each other but all data sets show the dominant drying trend for the summer, and that most of the drying regions were in major agricultural areas; (4) compared with the precipitation trends derived from Global Precipitation Climatology Project data, it is speculated that climate change is a possible cause for the drying trend in semiarid regions and the wetting trend in arid regions; and (5) combining soil moisture trends with land surface temperature trends derived from Moderate Resolution Imaging Spectroradiomete, the study domain was divided into four categories. Regions with drying and warming trends cover 33.2%, the regions with drying and cooling trends cover 27.4%, the regions with wetting and warming trends cover 21.1% and the regions with wetting and cooling trends cover 18.1%. The first two categories primarily cover the major grain producing areas, while the third category primarily covers nonarable areas such as Northwest China and Tibet. This implies that the moisture and heat variation trends in China are unfavorable to sustainable development and ecology conservation. [ABSTRACT FROM AUTHOR]

Abstract: In this work, an algorithm for automatic subpixel snow mapping was developed for Moderate Resolution Imaging Spectroradiometer (MODIS) data of the Qinghai–Tibet Plateau. The algorithm consists of two parts: cloud removal and snow mapping. An approach to remove cloud was presented and it was demonstrated to remove thick and thin clouds well from MODIS images. Multiple-endmember spectral mixture analysis was used in the subpixel snow mapping of the Qinghai–Tibet Plateau, and multiple indices (including the normalized difference vegetation index and normalized difference snow index) were introduced to automatically select the endmembers. Additionally, the combined use of typical and neighboring endmembers was introduced into the unmixing of mixed pixels. Finally, highly accurate snow-cover data of the Qinghai–Tibet Plateau obtained with this algorithm were stored in a spatio-temporal database. The results of the subpixel snow mapping were validated with Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data recorded at the same time as corresponding MODIS data. According to the validation results, the correlation coefficients of the MODIS results versus the ASTER data exceed 0.9, and the root-mean-square errors are less than 0.2. [Copyright &y& Elsevier]

Highly accurate observations at various scales on the land surface are urgently needed for the studies of many areas, such as hydrology, meteorology, and agriculture. With the rapid development of remote sensing techniques, remote sensing has had the capacity of monitoring many factors of the Earth's land surface. Especially, the space-borne microwave remote sensing systems have been widely used in the quantitative monitoring of global snow, soil moisture, and vegetation parameters with their all-weather, all-time observation capabilities and their sensitivities to the characteristics of land surface factors. Based on the electromagnetic theories and microwave radiative transfer equations, researchers have achieved great successes in the microwave remote sensing studies for different sensors in recent years. This article has systematically reviewed the progresses on five research areas including microwave theoretical modeling, microwave inversion on soil moisture, snow, vegetation and land surface temperatures. Through the further enrichment of remote sensing datasets and the development of remote sensing theories and inversion techniques, remote sensing including microwave remote sensing will play a more important role in the studies and applications of the Earth systems. [ABSTRACT FROM AUTHOR]

High temporal resolution and spatially complete (seamless) land surface temperature (LST) play a crucial role in numerous geoscientific aspects. This paper proposes a data fusion method for producing hourly seamless LST from Himawari-8 Advanced Himawari Imager (AHI) data. First, the high-quality hourly clear-sky LST was retrieved from AHI data by an improved temperature and emissivity separation algorithm; then, the hourly spatially complete China Land Data Assimilation System (CLDAS) LST was calibrated by a bias correction method. Finally, the strengths of the retrieved AHI LST and bias-corrected CLDAS LST were combined by the multiresolution Kalman filter (MKF) algorithm to generate hourly seamless LST at different spatial scales. Validation results showed the bias and root mean square error (RMSE) of the fused LST at a finer scale (0.02°) were −0.65 K and 3.38 K under cloudy sky conditions, the values were −0.55 K and 3.03 K for all sky conditions, respectively. The bias and RMSE of the fused LST at the coarse scale (0.06°) are −0.46 K and 3.11 K, respectively. This accuracy is comparable to the accuracy of all-weather LST derived by various methods reported in the published literature. In addition, we obtained the consistent LST images across different scales. The seamless finer LST data over East Asia can not only reflect the spatial distribution characteristics of LST during different seasons, but also exactly present the diurnal variation of the LST. With the proposed method, we have produced a 0.02° seamless LST dataset from 2016 through 2021 that is freely available at the National Tibetan Plateau Data Center. It is the first time that we can obtain the hourly seamless LST data from AHI. [ABSTRACT FROM AUTHOR]

Applications of microwave remote-sensing data in land data assimilation are a topic of current interest and importance due to their high temporal and spatial resolution and availability. However, there have been few studies on land surface sub-grid scale heterogeneity and calculating microwave wetland surface emissivity when directly assimilating gridded Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) satellite brightness temperature (BT) data to estimate soil moisture. How to assimilate gridded AMSR-E BT data for land surface model (LSM) grid cells including various land cover types, especially wetland, is worthy of careful study. The ensemble Kalman filter (EnKF) method is able to resolve the non-linearity and discontinuity in forecast and observation operators, and is widely used in land data assimilation. In this study, considering the influences of land surface sub-grid scale heterogeneity, a satellite data simulation scheme based on the National Center for Atmosphere Research (NCAR) Community Land Model version 2.0 (CLM2.0), microwave Land Emissivity Model (LandEM), Shuffled Complex Evolution (SCE-UA) algorithm and AMSR-E BT observation data is presented to simulate AMSR-E BT data and calibrate microwave wetland surface emissivity; then, a soil moisture data assimilation scheme is developed to directly assimilate the gridded AMSR-E BT data, which consists of the CLM2.0, LandEM and EnKF. The experimental results indicate that the calibrated microwave wetland surface emissivities possess excellent transportability, and that the assimilation scheme is practical and can significantly improve soil moisture estimation accuracy. This study provides a promising solution to improve soil moisture estimation accuracy through directly assimilating gridded AMSR-E BT data for various land cover types such as bare soil, vegetation, snow, lake and wetland. [ABSTRACT FROM AUTHOR]

We develop a new technique for terrain correction of coarse resolution satellite microwave radiometer measurements using the high-resolution digital elevation model (DEM). With the high-resolution DEM and a facet model, we first simulated the terrain influences that resulted from both effects of local incidence and polarization rotation (PR) on the microwave radiometer measurements at the different scales of the radiometer footprints. The emissivity signals of bare surfaces are more sensitive on incidence and PR than vegetated surfaces. So the emissivities at different incidence angles of the bare surface generated by the advanced integral equation model (AIEM) were used to simulate the terrain effects on the satellite measurements. It was found that (1) the PR effect at the satellite footprint can be corrected using the absolute mean PR angle from all facets within the footprint, and (2) the satellite measurements can be described as from a mean incidence angle. Both angles can be obtained from all facets within the footprint with the high-resolution DEM and the satellite viewing geometry. Therefore, a corresponding terrain-correction technique was developed for satellite brightness temperature measurements. We also demonstrate the terrain effects on the satellite measurements from the advanced microwave scanning radiometer–earth observing system (EOS) (AMSR-E). [ABSTRACT FROM AUTHOR]

Microwave radiometers have many applications because of their penetration ability. However, two major problems remain that obstruct the development of microwave research. One factor that limits their commercial application is the relatively low resolution of microwave radiometers. The other is the non-uniform spatial resolution for each frequency of the radiometer. The resolution mismatch becomes a critical consideration when observations at two or more frequencies must be combined. In this paper, we have used the Backus-Gilbert method to solve these two problems, while AMSR-E is chosen as the research object. First, we derived the Backus-Gilbert method in detail. The simulated data were then used to decide the optimum parameters in the Backus-Gilbert method. To enhance the resolution, the Backus-Gilbert method has been applied to the AMSR-E data, which covered the Mexico Gulf and the Amazon River. After resolution was enhanced, detailed information was obtained and compared with visible high resolution data. To match the resolution, the AMSR-E data from the Oklahoma Little Washed were used to compute the Microwave Vegetation Index (MVI), which was developed by J. C. Shi. Compared to the original MVIs, the information contained in the MVIs that were processed by the Backus-Gilbert method is more reliable. [ABSTRACT FROM AUTHOR]

Four satellites with S-band synthetic aperture radar (SAR) onboard, along with four optical satellites, form the Huan Jing (HJ) constellation, which consists of the Chinese Environment and Disaster Monitoring Satellites that are designed for fast monitoring of the dynamics of soil moisture and other environmental variables. A 24-hour revisit time of the future four HJ SAR satellites enables an estimation of soil moisture change by using multi-temporal observations. The algorithm for estimating soil moisture change has been developed for HJ SAR in four steps: (a) theoretical simulation of radar backscattering from vegetated and bare soil with first-order scattering model and the Advanced Integral Equation Method (AIEM); (b) vegetation correction based on a simplified scattering model and ancillary optical observations of the HJ constellation; (c) simplification of a radar backscattering model for bare soil to separate the effects of surface roughness and soil moisture; and (d) estimation of soil moisture change from multi-temporal SAR observations. The comparison between field measurements and soil moisture retrieved by the proposed algorithm demonstrates good accuracy. [ABSTRACT FROM AUTHOR]

Abstract: Snow Water Equivalent (SWE) is a crucial parameter in the study of climatology and hydrology. Active microwave remote sensing is one of the most promising techniques for estimating the distribution of SWE at high spatial resolutions in large areas. Development of reliable and accurate inversion techniques to recover SWE is one of the most important tasks in current microwave researches. However, a number of snow pack properties, including snow density, particle size, crystal shape, stratification, ground surface roughness and soil moisture, affect the microwave scattering signals and need to be properly modeled and exploited. In this paper, we developed a multi-layer, multi-scattering model for dry snow based on recent theoretical advances in snow and surface modeling. In the proposed multi-layer model, Matrix Doubling method is used to account for scattering from each snow layer; and Advanced Integral Equation Model (AIEM) is incorporated into the model to describe surface scattering. Comparisons were made between the model predictions and field observations from NASA Cold Land Processes Field Experiment (CLPX) during Third Intensive Observation Period (IOP3) and SARALPS-2007 field experiment supported by ESA. The results indicated that model predictions were in good agreement with field observations. With the confirmed confidence, the analyses on multiple scattering, scatterer shape, and snow stratification effects were further made based on the model simulations. Furthermore, a parameterized snow backscattering model with a simple form and high computational efficiency was developed using a database generated by the multiple-scattering model. For a wide range of snow and soil properties, this parameterized model agrees well with the multiple-scattering model, with the root mean square error 0.20dB, 0.24dB and 0.43dB for VV, HH and VH polarizations, respectively. This simplified model can be useful for the development of SWE retrieval algorithm and for fast simulations of radar signals over snow cover in land data assimilation systems. [Copyright &y& Elsevier]

Abstract: The selectivity of noise and coupling for coherence biresonance (CBR) and array-enhanced coherence biresonance (AECBR) in coupled neural systems has been investigated. It is shown that, depending on the coupling strength and noise intensity, various coherence behaviors and phenomena are exhibited, including CBR, coherence resonance without tuning, AECBR and undamped signal transmission. There exist optimal coupling and noise regions for the occurrence of CBR and AECBR in the transmission of noise-induced oscillations (NIOs). [Copyright &y& Elsevier]

Abstract: Vegetation indices are valuable in many fields of geosciences. Conventional, visible-near infrared, indices are often limited by the effects of atmosphere, background soil conditions, and saturation at high levels of vegetation. In this study, we will establish the theoretical basis for our new passive microwave vegetation indices (MVIs) based on data from the Advanced Microwave Scanning Radiometer (AMSR-E) on the Aqua satellite. Through the analysis of numerical simulations by surface emission model, the Advanced Integral Equation Model (AIEM), we found that bare soil surface emissivities at different frequencies can be characterized by a linear function with parameters that are dependent on the pair of frequencies used. This makes it possible to minimize the surface emission signal and maximize the vegetation signal when using multi-frequency radiometer measurements. Using a radiative transfer model (ω–τ model), a linear relationship between the brightness temperatures observed at two adjacent radiometer frequencies can be derived. The intercept and slope of this linear function depend only on vegetation properties and can be used as vegetation indices. These can be derived from the dual-frequency and dual-polarization satellite measurements under assumption that there is no significant impact of the polarization dependence on the vegetation signals. To demonstrate the potential of the new microwave vegetation indices, we compared them with the Normalized Difference of Vegetation Index (NDVI) derived using MODIS the optical sensor at continental and global scales. The major purpose of this paper is to describe the concept and techniques involved in these newly developed MVIs and explore the general relationships between these MVIs and NDVI. In this first investigation, the information content of NDVI and MVIs, both are qualitative indices, was compared by examining its response in global pattern and to seasonal vegetation phenology. The results indicate that the MVIs can provide significant new information since the microwave measurements are sensitive not only to the leafy part of vegetation properties but also to the properties of the overall vegetation canopy when the microwave sensor can “see” through it. In combination with conventional optical sensor derived vegetation indices, they provide a possible complementary dataset for monitoring global short vegetation and seasonal phenology from space. [Copyright &y& Elsevier]

Snow water equivalent (SWE) is one of the key parameters for many applications in climatology, hydrology, and water resource planning and management. Satellite-based passive microwave sensors have provided global, long-term observations that are sensitive to SWE. However, the complexity of the snowpack makes modeling the microwave emission and inversion of a model to retrieve SWE difficult, with the consequence that retrievals are sometimes incorrect. Here we develop a parameterized dry snow emission model for analyzing passive microwave data, including those from the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) at 10.65 GHz, 18.7 GHz, and 36.5 GHz for SWE estimation. We first evaluate a multiple-scattering microwave emission model that consists of a single snow layer over a rough surface by comparing model calculations with data from two field measurements, from the Cold Land Process Experiment (CLPX) in 2003 and from Switzerland in 1995. This model uses the matrix doubling approach to include incoherent multiple-scattering in the snow, and the model combines the Dense Media Radiative Transfer Model (DMRT) for snow volume scattering and emission with the Advanced Integral Equation Model (AIEM) for the randomly rough snow/ground interface to calculate dry snow emission signals. The combined model agrees well with experimental measurements. With this confirmation, we develop a parameterized emission model, much faster computationally, using a database that the more physical multiple-scattering model generates. For a wide range of snow and soil properties, this parameterized model''s results are within 0.013 of those from the multiple-scattering model. This simplified model can be applied to the simulation of the microwave emission signal and to developing algorithms for SWE retrieval. [Copyright &y& Elsevier]

Due to the success of the SMOS (Soil Moisture and Ocean Salinity) and SMAP (Soil Moisture Active Passive) missions, new satellite missions are on the horizon. The current and future missions can benefit from investigations that seek to improve retrieval algorithms that quantitatively map global soil moisture and vegetation optical depth (tau) from Earth's microwave emissions. In this study, we explore multi-angular and multi-frequency approaches for the retrieval of soil moisture and vegetation tau, considering the payload configurations of current and future satellite missions (such as the Copernicus Imaging Microwave Radiometer, the Water Cycle Observation Mission, and the Terrestrial Water Resources Satellite) using a new set of ground observations. Two ground-based microwave radiometry datasets collected in Inner Mongolia during the Soil Moisture Experiment in the Luan River from July to August 2017 (cropland) and August to September 2018 (grassland) are used for this study. The corn field, which covers an entire growth period, indicated that the degree of information increases linearly as the number of channels (in terms of the incidence angle and frequency) increases, and that the multi-frequency observations contain slightly more independent information than do the multi-angular observations under the same number of channels. The polarization difference in brightness temperature is sensitive to both soil moisture and vegetation water content, especially at L-band due to its penetrating ability. Soil moisture explains most of the variance in frequency differences of brightness temperature at adjacent frequencies (L- & C-bands, C- & X-bands), while the variance in incidence-angle differences of brightness temperature is mostly associated with the vegetation water content. A multi-channel collaborative algorithm (MCCA) is developed based on the two-component version of the omega-tau model, which utilizes information from collaborative channels expressed as an analytical form of brightness temperature at the core channel to rule out the parameters to be retrieved. Results of soil moisture retrieval show that the multi-angular approach used by the MCCA generally has a better performance, unbiased root mean square difference (ubRMSD) varying from 0.028 cm3/cm3 to 0.037 cm3/cm3, than the multi-frequency approach (ubRMSD from 0.028 cm3/cm3 to 0.089 cm3/cm3) for the corn field. This is attributed to the dependence of vegetation tau on the frequency being more significant than that on the incidence angle. Except for in the C- & X-band combination, the multi-frequency approach used by the MCCA performs better (ubRMSD from 0.018 cm3/cm3 to 0.023 cm3/cm3) than the multi-angular version (ubRMSD from 0.026 cm3/cm3 to 0.034 cm3/cm3) for the grass field due to reduced vegetation effects for this type of cover. It is affirmed that increasing the number of observation channels could make the soil moisture retrieval more robust, but might also limit the retrieval performance, as the probability that the model estimations will not match the observations is increased. This study provides new insights into the design of potential satellite missions to improve soil moisture retrieval. A satellite with simultaneous multi-angular and multi-frequency observation capabilities is highly recommended. • Independent information contained in multi-frequency and multi-angular observations • Variance decomposition of brightness temperature into soil and vegetation property • A multi-channel collaborative algorithm is developed for soil moisture retrieval. • Multi-angular approach is optimal for retrievals when vegetation is significant. • Soil moisture retrievals get more robust when increasing observation channels. [ABSTRACT FROM AUTHOR]

Accurate long-term daily cloud-gap-filled fractional snow cover products are essential for climate change and snow hydrological studies in the Asian Water Tower (AWT) region, but existing Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover products are not sufficient. In this study, the multiple-endmember spectral mixture analysis algorithm based on automatic endmember extraction (MESMA-AGE) and the multistep spatiotemporal interpolation algorithm (MSTI) are used to produce the MODIS daily cloud-gap-filled fractional snow cover product over the AWT region (AWT MODIS FSC). The AWT MODIS FSC products have a spatial resolution of 0.005° and span from 2000 to 2022. The 2745 scenes of Landsat-8 images are used for the areal-scale accuracy assessment. The fractional snow cover accuracy metrics, including the coefficient of determination (R2), root mean squared error (RMSE) and mean absolute error (MAE), are 0.80, 0.16 and 0.10, respectively. The binarized identification accuracy metrics, including overall accuracy (OA), producer's accuracy (PA) and user's accuracy (UA), are 95.17 %, 97.34 % and 97.59 %, respectively. Snow depth data observed at 175 meteorological stations are used to evaluate accuracy at the point scale, yielding the following accuracy metrics: an OA of 93.26 %, a PA of 84.41 %, a UA of 82.14 % and a Cohen kappa (CK) value of 0.79. Snow depth observations from meteorological stations are also used to assess the fractional snow cover resulting from different weather conditions, with an OA of 95.36 % (88.96 %), a PA of 87.75 % (82.26 %), a UA of 86.86 % (78.86 %) and a CK of 0.84 (0.72) under the MODIS clear-sky observations (spatiotemporal reconstruction based on the MSTI algorithm). The AWT MODIS FSC product can provide quantitative spatial distribution information on snowpacks for mountain hydrological models, land surface models and numerical weather prediction in the Asian Water Tower region. This dataset is freely available from the National Tibetan Plateau Data Center at 10.11888/Cryos.tpdc.272503 (Jiang et al., 2022) or from the Zenodo platform at 10.5281/zenodo.10005826 (Jiang et al., 2023a). [ABSTRACT FROM AUTHOR]

Soil moisture (SM) is an important parameter in water cycle research. Rapid and accurate monitoring of SM is critical for hydrological and agricultural applications, such as flood detection and drought characterization. The Global Navigation Satellite System (GNSS) uses L-band microwave signals as carriers, which are particularly sensitive to SM and suitable for monitoring it. In recent years, with the development of Global Navigation Satellite System–Reflectometry (GNSS-R) technology and data analysis methods, many studies have been conducted on GNSS-R SM monitoring, which has further enriched the research content. However, current GNSS-R SM inversion methods mainly rely on auxiliary data to reduce the impact of non-target parameters on the accuracy of inversion results, which limits the practical application and widespread promotion of GNSS-R SM monitoring. In order to promote further development in GNSS-R SM inversion research, this paper aims to comprehensively review the current status and principles of GNSS-R SM inversion methods. It also aims to identify the problems and future research directions of existing research, providing a reference for researchers. Firstly, it introduces the characteristics, usage scenarios, and research status of different GNSS-R SM observation platforms. Then, it explains the mechanisms and modeling methods of various GNSS-R SM inversion research methods. Finally, it highlights the shortcomings of existing research and proposes future research directions, including the introduction of transfer learning (TL), construction of small models based on spatiotemporal analysis and spatial feature fusion, and further promoting downscaling research. [ABSTRACT FROM AUTHOR]

Radar at high frequency is a promising technique for fine-resolution snow water equivalent (SWE) mapping. In this paper, we extend the Bayesian-based Algorithm for SWE Estimation (BASE) from passive to active microwave (AM) application and test it using ground-based backscattering measurements at three frequencies (X and dual Ku bands; 10.2, 13.3, and 16.7 GHz), with VV polarization obtained at a 50° incidence angle from the Nordic Snow Radar Experiment (NoSREx) in Sodankylä, Finland. We assumed only an uninformative prior for snow microstructure, in contrast with an accurate prior required in previous studies. Starting from a biased monthly SWE prior from land surface model simulation, two-layer snow state variables and single-layer soil variables were iterated until their posterior distribution could stably reproduce the observed microwave signals. The observation model is the Microwave Emission Model of Layered Snowpacks 3 and Active (MEMLS3&a) based on the improved Born approximation. Results show that BASE-AM achieved an RMSE of ∼ 10 cm for snow depth and less than 30 mm for SWE, compared with the RMSE of ∼ 20 cm snow depth and ∼ 50 mm SWE from priors. Retrieval errors are significantly larger when BASE-AM is run using a single snow layer. The results support the potential of X- and Ku-band radar for SWE retrieval and show that the role of a precise snow microstructure prior in SWE retrieval may be substituted by an SWE prior from exterior sources. [ABSTRACT FROM AUTHOR]

Presents an algorithm which was developed to provide estimation of soil moisture and surface roughness parameter, from quad-polarized synthetic aperture radar measurements. What the algorithm is based on; Application of the algorithm for soil moisture inversion; Examination of the sensitivity of soil moisture variation to the co-polarized signals; Results obtained.

Corrigendum to "Precipitation differences cause contrast patterns of glacier-melt water supplied discharge of two glacier basins between northern and southern Third Pole" [Sci. The authors would like to apologize that, due to their indiligence, there is an error detected, that is the affiliations of the authors of this paper, Ruzhen Yao and Jiancheng Shi have to be corrected. The corrected author list and affiliations are shown above. [Extracted from the article]

Surface longwave net radiation (SLNR) is the key component of the surface radiation budget. SLNR is estimated by using the conventional method, in which surface longwave downward radiation (SLDR) and surface longwave upward radiation (SLUR) are estimated separately, and then SLNR is calculated by subtracting SLUR from SLDR. However, uncertainties in SLDR and SLUR may accumulate and propagate into the calculated SLNR, affecting the SLNR estimate accuracy. This paper proposes a direct algorithm that links clear-sky SLNR with the top-of-atmosphere (TOA) radiances in MODIS channels 29, 31 and 32 and column water vapour (CWV). The multivariate regression (MVR) and extreme gradient boosting (×GBoost) were employed to establish the relationships sequentially with collocated ground measurements and satellite data for high-latitude, mid-latitude, and low-latitude areas. According to the training and validation results, the GBoost model performs slightly better than the MVR model, which indicates that weak nonlinearity exists between clear-sky SLNR and its predictors. The absolute biases of the XGBoost models are approximately 1.0 Wm−2, and the RMSEs of the XGBoost model are 17.77, 27.33, and 19.60 Wm−2 in high-latitude, mid-latitude, and low-latitude areas. These results indicate that nonlinear relationships exist between clear-sky SLNR and predictors, but this nonlinearity is not strong. The superiority of the direct algorithm over the conventional method was also demonstrated. In the future, we will collect more data to improve the performance of the proposed direct algorithm especially in high-latitude and mid-latitude areas. [ABSTRACT FROM AUTHOR]

This article presents a comprehensive subpixel water mapping algorithm to automatically produce routinely open water fraction maps in the Tibetan Plateau (TP) with the Moderate Resolution Imaging Spectroradiometer (MODIS). A multi-index threshold endmember extraction method was applied to select the endmembers from MODIS images. To incorporate endmember variability, an endmember selection strategy, called the combined use of typical and neighboring endmembers, was adopted in multiple endmember spectral mixture analysis (MESMA), which can assure a robust subpixel water fractions estimation. The accuracy of the algorithm was assessed at both the local scale and regional scale. At the local scale, a comparison using the eight pairs of MODIS/Landsat 8 Operational Land Imager (OLI) water maps demonstrated that subpixels water fractions were well retrieved with a root mean square error (RMSE) of 7.86% and determination coefficient (R2) of 0.98. At the regional scale, the MODIS water fraction map in October 2014 matches well with the TP lake data set and the Global Lake and Wetland Database (GLWD) in both latitudinal and longitudinal distribution. The lake area estimation is more consistent with the reference TP lake data set (difference of −3.15%) than the MODIS Land Water Mask (MOD44W) (difference of −6.39%). [ABSTRACT FROM AUTHOR]

The Soil Moisture Experiment in the Luan River (SMELR) was conducted from 2017 to 2018 in the semi-arid Luan River watershed located in the North of China. One of the objectives of SMELR is to serve as an assessment tool and demonstration for a new Terrestrial Water Resources Satellite (TWRS) concept with one-dimensional synthetic aperture microwave techniques, for which soil moisture retrieval under variable satellite observing configurations (mainly in terms of incidence angels) is the greatest challenge. This proposed mission is targeted to provide continuity for the current satellite L-band microwave observations, and further improve the accuracy and spatial resolution of soil moisture mapping through the synergistic use of active, passive and optical remote sensing data. Multi-resolution, multi-angle and multi-spectral airborne data were obtained four times over a 70 km by 12 km area in the Shandian River basin, and one time over a 165 km by 5 km area that includes the Xiaoluan River basin. The near surface soil moisture (0 cm–5 cm) was measured extensively on the ground in fifty 1 km by 2 km quadrats (targeted to compare with the airborne radiometer), and two hundred and fifty 200 m by 200 m quadrats corresponding to radar observations. Two networks were established for continuous measurement of the soil moisture and temperature profile (3 cm, 5 cm, 10 cm, 20 cm, 50 cm) and precipitation in the Shandian and Xiaoluan River basin, respectively. Supporting ground measurements also included ground temperature, vegetation water content, surface roughness, continuous measurement of microwave emission and backscatter at a pasture site, reflectance of various land cover types, evapotranspiration and aerosol observations. Preliminary results within the experimental area indicate that (1) the near surface soil moisture spatial variability at a 200 m scale was up to ~0.1 cm3/cm3 at an intermediate value of ~0.35 cm3/cm3. (2) The difference of soil and vegetation temperature in grass and croplands reach its maximum of 11 K around solar noon time, and the soil temperature gradient is largest at around 15 P.M. (3) Both the airborne and ground measurements cover a wide range of conditions. The L-band active and passive observations exhibit a large variation of ~30 dB and ~80 K, respectively, corresponding to soil moisture range from 0.1 cm3/cm3 to 0.5 cm3/cm3. The sensitivity of both active and passive data to soil moisture is compared at corresponding spatial resolutions and show high information complementarity for better accuracy and resolution soil moisture retrieval. • First airborne optical and active-passive microwave experiment conducted in China • Demonstration of the active-passive microwave synergy at varying incidence angles • Addressing prelaunch science questions of the terrestrial water resources mission [ABSTRACT FROM AUTHOR]

Soil moisture (SM) and vegetation optical depth (VOD) estimates using passive microwave remote sensing at L-band (1.4 GHz) are essential for attaining a better understanding of water exchanges at the land-atmosphere interface. However, current retrieval algorithms often ignore the polarization dependence of vegetation effects. This study proposed a parameter self-calibrating framework for the multi-channel collaborative algorithm (MCCA) and presented a new SM and the first polarization-dependent VOD product based on the dual-polarized L-band observations at a fixed angle (40°) from the NASA Soil Moisture Active Passive (SMAP) mission. The parameter self-calibrating framework utilizes an information theory-based approach to obtain surface roughness and effective scattering albedo globally. Furthermore, the MCCA does not require auxiliary data for vegetation or soil moisture to constrain the retrieval process. Comparison with other SM and VOD products, such as MT-DCA version 5, DCA, SCA-H, SCA-V from SMAP Level-3 products version 8, and SMAP-IB, demonstrate analogous spatial patterns. The MCCA-derived SM exhibits the lowest unbiased root mean square deviation (ubRMSD, about 0.055 m3/m3), followed by SMAP-IB and DCA (0.061 m3/m3), with an overall Pearson's correlation coefficient of 0.744 (SMAP-IB performed best with R = 0.764) when evaluated against in-situ observations from 18 dense soil moisture networks. The MCCA generates VOD values for both vertical and horizontal polarization, demonstrating a slight polarization difference of vegetation effect at the satellite scale. Both VODs exhibit a strong linear relationship with above-ground biomass and canopy height. The polarization difference of VODs is primarily observed in densely vegetated and arid areas. • No ancillary data are used to calibrate parameters or constrain the cost function. • MCCA SM shows lower uncertainty and similar correlation compared to SMAP products. • The first SMAP VOD product that takes account of its polarization dependence. • VOD at H-polarization is greater than VOD at V-polarization over dense vegetation. [ABSTRACT FROM AUTHOR]

The Terrestrial Water Resources Satellite (TWRS) campaign is a planned Chinese candidate satellite mission, and a one-dimensional synthetic aperture technology will be used, resulting in variant incidence angles for collecting synchronous active-passive observations at L-band, which would make brightness temperature (Tb) downscaling especially challenging when aiming to improve the spatial resolution of soil moisture measurements. In this study, two active-passive Tb downscaling algorithms, the time-series regression (TSR) and spectral analysis (SA) algorithms, are assessed comprehensively based on airborne experimental datasets. The results with data collected during the Soil Moisture Experiment 2002 (SMEX02) showed that both approaches could provide a reliable downscaled Tb at the same incidence angle. Based on the ground and airborne active-passive observations under variant incidence angles from the Soil Moisture Experiment in the Luan River (SMELR) it can be shown that the linear relationship between Tb and σ is still robust under the case of variant incidence angles, and Tb (both h- and v-pol) is better correlated to σvv for most cases than σhh. Both downscaling approaches can be applied to active-passive observations under varying incidence angles. Moreover, SA method performed better than the TSR method according to the lower RMSE values and higher correlation. [ABSTRACT FROM AUTHOR]

The effect of topography on shortwave downward radiation (SWDR) is interest in the geoscience. However, such effects are rarely quantiatively and systematically evalulated, especially over the Tibetan Plateau region. With the geostationaly satellite measurements and topographic radiation model, this study reveals a heightened significance of topography on SWDR with increasing slope. Particularly in abrupt terrain (slopes >15°) the impact becomes pronounced, wherein the topographic radiative forcing (TRF) contributes 9.5% of the annual‐average SWDR. And the ratio of TRF to SWDR reaches a peak during winter, exceeding 150%. In annual‐average scales, the SWDR is 169 ± 38.4 W/m2 and the corresponding TRF is 16.2 ± 22.6 W/m2. Seasonal variations manifest on northern and southern slopes, with the sourthern slopes significant in summer, while the northern ones significant in winter. Notably, topographic effects persist across spatial scales and remain evident at 5 km resolution, emphasizing the necessity of considering topography in SWDR product utilization. Plain Language Summary: Shortwave downward radiation is the main source of surface energy. In mountainous areas, the terrain significantly alters the amount of received SWDR. This study comrephensively examines the topographic influence on SWDR across the Tibetan Plateau for the first time. We investigate the influence of diverse topographic factors on the distribution of surface shortwave radiation in mountainous terrains. With the slope increasing, the impact of topography on SWDR becomes more and more significant. The topographic impact of northern and sourthern slopes behaves obvious seasonal variations, with the sourthern slopes significant in summer, while the northern ones significant in winter. With the increasing of spatial scale, the topographic effect gradually decreases and tends to be stable, but it can never disappear. As remote sensing data resolution coarsens, topographic radiative forcing diminishes, but even at 5 km resolution, terrain significantly affects SWDR distribution. Key Points: In abrupt slopes, the annual‐average proportion of topographic radiative forcing to shortwave downward radiation (SWDR) can reach up to 9.5%The proportion of topographic radiative forcing to SWDR exceeding 150% in winter on the northern slopesDespite decreasing influence with coarser spatial resolutions, topographic effects persist even at 5 km [ABSTRACT FROM AUTHOR]