Bit-Depth Enhancement (BDE) is designed to restore High-Bit-Depth (HBD) images from Low-Bit-Depth (LBD) input, but existing methods mostly fail to exploit their algorithmic advantages in extreme low-bit cases. In this paper, a new framework that combines noise shaping, latent space modeling, and adaptive weighting block (AWB) is proposed to solve the above problems. Firstly, based on the noise shaping method, Sigma-Delta (Σ-Δ) quantization is introduced to achieve low redundancy and high reversibility low-bit image construction. Second, based on the visual averaging property and Inverse Problem Transform (IPT), the conditional posterior distribution pz(HBD|LR) is introduced for the first time to model the relationship between low-resolution (LR) and HBD images in the latent space to achieve the recovery of high-frequency details. To realize effective interaction and adaptive fusion of spatial and bit information, the hierarchical feature discovery module (HFDM) is introduced in network construction to generate multiscale LBD-LR image pairs, while AWB dynamically fuses according to the amount of feature information. This framework reaches the leading level in both quantitative metrics (PSNR/SSIM) and visual quality, and provides a new idea for high-bit image generation.

Fine-grained visual referring and grounding are critical for enhancing scene understanding and enabling various real-world vision-language applications. Although recent studies have extended multimodal large language models (MLLMs) to these tasks, they still face significant challenges in fine-grained multi-target scenarios. To address this, we propose MTRAG, a pixel-level multi-target referring and grounding framework that leverages semantic-spatial collaboration. Specifically, we introduce a Channel Extension Mechanism (CEM) that enables a global image encoder to extract global semantics and multi-region representations while retaining background context, without extra region feature extractors. Moreover, we introduce a grounding branch for pixel-level grounding and design a Hybrid Adapter (HA) to fuse semantic features from the MLLM branch with spatial information from the grounding branch, thereby enhancing the semantic-spatial alignment. For training, we meticulously curate MTRAG-D, a dataset comprising single- and multi-target referring and grounding samples derived from existing datasets and newly synthesized free-form multi-target referring instruction-following data. We also present MTR-Bench, a benchmark for systematic evaluation of multi-target referring. Extensive experiments across five core tasks, including single- and multi-target referring and grounding as well as image-level captioning, show that MTRAG consistently outperforms strong baselines on both multi- and single-target tasks, while maintaining competitive image-level understanding. The code is available at https://github.com/deng-ai-lab/MTRAG.

Diffusion models, recognized for their effectiveness as generative priors, have become essential tools for addressing a wide range of visual challenges. Recently, there has been a surge of interest in leveraging Denoising processes for Regularization (DR) to solve inverse problems. However, existing methods often face issues such as mode collapse, which results in excessive smoothing and diminished diversity. In this study, we perform a comprehensive analysis to pinpoint the root causes of gradient inaccuracies inherent in DR. Drawing on insights from diffusion model distillation, we propose a novel approach called Consistency Regularization (CR), which provides stabilized gradients without the need for ODE simulations. Building on this, we introduce Hybrid Regularization (HR), a unified framework that combines the strengths of both DR and CR, harnessing their synergistic potential. Our approach proves to be effective across a broad spectrum of inverse problems, encompassing both linear and nonlinear scenarios, as well as various measurement noise statistics. Experimental evaluations on benchmark datasets, including FFHQ and ImageNet, demonstrate that our proposed framework not only achieves highly competitive results compared to state-of-the-art methods but also offers significant reductions in wall-clock time and memory consumption.

Generative models represented by diffusion models have recently shown great potential in image generation. They usually use a reverse iteration process to map noise into the data. However, for many real-world applications such as image restoration and translation, the model input comes from a distribution that is not random noise, making it difficult for these models to adapt directly to these tasks. In this paper, we introduce Image-to-Image Bayesian Flow Networks (I2I-BFNs), a novel framework for general-purpose image-to-image translation (I2I) that operates within the parameter space of distributions. This method upholds Gaussian distributions over pixel intensities, refining distribution parameters through closed-form Bayesian inference, steered by the network's predictions for the target image. An essential aspect of our approach is the utilization of the conditional image as a robust prior parameter, initializing the translation process from a deterministic, clean image to reduce variance and produce interpretable generation. Additionally, we introduce a skip sampling technique that enhances the efficiency of I2I-BFNs, facilitating rapid translation in diverse image restoration and general I2I tasks. Our experimental evaluations showcase the model's competitive edge in various settings, underscoring its efficacy and adaptability. This work contributes new insights and opportunities for the large-scale development of efficient conditional generation systems.

Unmanned aerial vehicles (UAVs) can offer higher spatial resolution images compared to satellites. In this experiment, an L-band microwave radiometer named PoLRa was equipped on an UAV to detect surface soil moisture and a new soil moisture retrieval algorithm was developed for it. Compared with the algorithm provided by suppliers of PoLRa, the new algorithm can significantly improve the accuracy of soil moisture and enhance spatial details. A comparison with ground-based soil moisture measurements showed that the UAV's spatial resolution reached 10 meters with an accuracy of 0.08 m3/m3. This demonstrates significant advantages for monitoring soil moisture at the farmland scale, making it applicable to precision agriculture, flash flood warnings, and drought monitoring in the future.

The U.S. Department of Energy Office of Legacy Management (DOE LM) is investigating options for future management of selected uranium mill tailings disposal cell covers as vegetated, evapotranspiration (ET) covers. ET limits drainage of water through the cell cover profile, while soil structure and drying by plants can increase radon diffusion; therefore, soil water content is a key performance parameter. This study used theoretical simulations to analyze the sensitivity of multi-frequency radar backscatter to soil moisture (SM) at different depths of an in-service DOE LM disposal cell. A machine-learning approach was then developed using Google Earth Engine to integrate multi-source observations and estimate SM across six soil layers from depths of 0-2 m. The model predictors included backscatter observations from satellite Synthetic Aperture Radar, vegetation and temperature products from optical-infrared sensors, and accumulated rainfall data from Daymet. The model was trained using in-situ SM measurements from 2019 and validated using data from 2014-2018 and 2020-2021. The approach produced accurate SM estimates for the six soil layers (R-values from 0.75 to 0.94; RMSE from 0.003 to 0.017 cm3/cm3; bias ~0.00 cm3/cm3). Additionally, the approach captured seasonal SM variability and spatial heterogeneity at 30-m resolution. The machine-learning based multi-source data fusion approach may characterize soil moisture dynamics at DOE LM disposal sites better than in situ measurements alone.

Rivers contribute to carrying biogeochemical constituents from terrestrial to marine biospheres. Land-margin ecosystems in rivers are critical ecosystems that provide an interaction between land and ocean. The ecosystems have experienced increasing climate and environmental changes in recent years. The variations of the surface water at the river mouth areas are closely linked with the changes in biogeochemical cycles, including greenhouse gas emission. Multi-source environmental remote sensing data records were utilized to analyze the relationship between surface open water fraction (Fw) and various environmental and biogeochemical parameters, such as precipitation, surface soil moisture, root-zone soil moisture, heterotrophic respiration, and CH4. The study focused on 253 river mouth locations, which were determined using the MERIT-Hydro map. The long-term (2002-2021) Fw data shows a strong, negative trend in the mean annual summer (Jun – Aug) Fw in northern low latitudes (45°N). The results show that 46% of the river mouths has a decreasing Fw trend, implying drier surface moisture conditions. The correlation results show the stronger correspondence between Fw and carbon variables at the river mouth.

Satellite microwave radiometers effectively monitor landscape freeze/thaw (FT) transitions but have difficulty distinguishing soil from other landscape properties, which can lower retrieval accuracy. Here, we applied a deep learning model for soil FT classification driven by daily brightness temperatures (TBs) from AMSR2 and SMAP, and trained on soil (~0-5cm depth) FT observations. The probability of frozen or thawed conditions was derived using a model cost function optimized using observational training data over the Northern Hemisphere (NH) and five year (2016-2020) study period. Results showed favorable accuracy against soil FT observations from ERA5 reanalysis (mean annual accuracy, MAE: 92.7%) and NH weather stations (MAE: 91.0%). Moreover, SMAP L-band (1.41 GHz) TBs provided enhanced soil FT performance over alternative retrievals derived using only AMSR2 inputs. FT accuracy was also consistent across different land covers and seasons. The results provide better soil FT precision to improve understanding of complex seasonal transitions and their influence on ecological processes and climate feedbacks.

Aspect-based sentiment classification aims to predict the sentiment polarity of an aspect term in a sentence. It has been verified that syntactic dependent trees, especially integrating with graph convolutional networks (GCN) can provide crucial syntactic features for sentiment classification. However, it can not consider the dependency label information between the aspect and context in the sentence, which contains rich semantic information. In this paper, we propose a bidirectional edge-enhanced graph convolutional networks (BE-GCN), which combines the syntactic structure and the dependency label information effectively. Specifically, we design an edge-enhanced module to dynamically update the dependency edge according to dependency relation and contextual information. Then the dependency edge can update the word representation reversely, thereby selectively outputting sentiment features according to the given aspect. Comparison experiments demonstrate the effectiveness of our model using dependency label information and syntactic structure.

The capability of synergistic satellite flood monitoring and forecasts is crucial for improving disaster preparedness and mitigation. In this study, the Soil Moisture Active Passive (SMAP) fractional water (FW) data sets were used for flood mapping over southeast Africa during the Cyclone Idai event. We then developed a machine-learning approach with the support of Google Earth Engine (GEE) for 24-hour flood forecasting and 30-m inundation mapping using observations from SMAP and Landsat coupled with rainfall forecasts from Global Forecast System (GFS) 384-Hour Predicted Atmosphere Data. The forecast results for the Idai event captured the flood dynamics at 30-m resolution and showed inundation patterns consistent with independent satellite Synthetic Aperture Radar (SAR) observations. The approach provides new capacity for flood monitoring and forecasts from synergistic satellite observations and is particularly valuable for data sparse regions.

QP model with dual-channel algorithm could accurately represent the effect of surface roughness on emission at different polarization under big view angle. The purpose of this paper is to estimate long temporal series soil moisture product based on the QP algorithm, and compared it with JAXA, LPRM in Genhe basin. The results indicate that QP retrieval values are closest to the ground data but it have many missing values and high RMSE (around 0.15m3m-3); JAXA product has good correlation coefficients (around 0.4) but underestimates the ground data. LPRM product overestimates the ground data and it is found to be very noisy and unstable. Finally, In order to improve the model, this paper analysed the tendency of QP retrievals with satellite brightness temperature, and examined the influence of auxiliary data for retrievals.

FY-3B is the second satellite of FY3 (Feng Yun 3) series which was launched on November 5, 2010. One of the eleven instruments on board the FY-3B satellite is the Microwave Radiation Imager (MWRI) which is a high sensitive microwave radiometer [1]. It has 5 different frequencies from 10.65GHz to 89GHz with dual polarization. The MWRI instrument provides measurements of terrestrial, oceanic, and atmospheric parameters, including precipitation rate, sea ice concentration, snow water equivalent, soil moisture, atmospheric cloud water, and water vapor [2]. Soil moisture, as a key parameter in the drought monitoring, becomes especially concerned. The FY-3B/MWRI soil moisture product provides global observations of land surface soil moisture. In the summer of 2014, Henan Province which is located in the middle area of China suffered severe drought. The soil moisture of this area remained a very low level all along until significant precipitation finally came in last September. This paper will give an introduction of the application of FY-3B/MWRI soil moisture product during this drought event.

Accurate mapping of long-term global soil moisture is of great importance to earth science studies and a variety of applications. An approach for deriving volumetric soil moisture using satellite passive microwave radiometry from the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) was developed in this study. Unlike the major AMSR-E retrieval algorithms that assume fixed scattering albedo values over the globe, the proposed algorithm adopts a weighted averaging strategy for soil moisture estimation based on a dynamic selection of albedo values that are empirically determined. The resulting soil moisture retrievals demonstrate more realistic global patterns and seasonal dynamics relative to the baseline University of Montana soil moisture product. Quantitative analysis of the new approach against in situ soil moisture measurements over four study regions also indicates improvements over the baseline algorithm, with coefficients of determination (R2) between the retrievals and in situ measurements increasing by approximately 16.9% and 41.5% and bias-corrected root-mean-square errors decreasing by about 25.0% and 38.2% for ascending and descending orbital data records, respectively. The resulting algorithm is readily applied to similar microwave sensors, including the Advanced Microwave Scanning Radiometer 2, and its retrieval strategy is also applicable to other passive microwave sensors, including lower frequency (L-band) observations from the National Aeronautics and Space Administration Soil Moisture Active Passive mission.

Spatial and temporal variability in landscape freeze- thaw (FT) status at higher latitudes and elevations significantly impacts land surface water mobility and surface energy partitioning, with major consequences for regional climate, hydrological, ecological, and biogeochemical processes. With the development of new-generation spaceborne remote sensing instruments, future L-band missions, including the NASA Soil Moisture Active and Passive mission, will provide new operational retrievals of landscape FT state dynamics at moderate (~3 km) spatial resolution. We applied theoretical simulations of L-band radar backscatter using first-order radiative transfer models with two and three-layer modeling schemes to develop a modified seasonal threshold algorithm (STA) and FT classification study over Alaska using 100-m-resolution satellite Phased Array L-band Synthetic Aperture Radar (PALSAR) observations. The backscatter threshold distinguishes between frozen and nonfrozen states, and it is used to classify the predominant frozen or thawed status of a grid cell. An Alaska FT map for April 2007 was generated from PALSAR (ScanSAR) observations and showed a regionally consistent but finer FT spatial pattern than an alternative surface air temperature-based classification derived from global reanalysis data. Validation of the STA-based FT classification against regional soil climate stations indicated approximately 80% and 75% spatial classification accuracy values in relation to respective station air temperature and soil temperature measurement-based FT estimates. An investigation of relative spatial scale effects on FT classification accuracy indicates that the relationship between grid cell size and classified frozen or thawed area follows a general logarithmic function.

An approach for deriving atmosphere total precipitable water vapor (PWV) and surface air temperature over land using satellite passive microwave radiometry from the Advanced Microwave Scanning Radiometer 2 (AMSR2) was developed in this study. The PWV algorithm is based on theoretical analysis and comparisons against similar retrievals from the Atmospheric Infrared Sounder (AIRS). The AMSR2 PWV retrievals compare favorably with AIRS operational PWV products ($R^{2}\geqslant 0.80$and rmse: 4.4–5.6 mm) and independent PWV observations from the SuomiNet North American Global Positioning System station network, with an overall mean rmse of 4.7 mm and more than 78% of absolute retrieval errors below 5 mm. The PWV retrievals were then applied within an AMSR2 multifrequency brightness temperature algorithm for deriving atmosphere-corrected surface air temperatures. The estimated temperatures agree favorably ($R^{2}>0.80$and$\hbox{rmse}<3.5\ \hbox{K}$) with independent weather station daily air temperature measurements spanning global climate and land cover variability. The resulting PWV estimates increase surface air temperature retrieval accuracy in our algorithm scheme. The AMSR2 algorithm is readily applied to similar microwave sensors including the AMSR for EOS and provides suitable performance and accuracy to support hydrologic, ecosystem, and climate change studies.

Earth observation satellites play a critical role in providing information for understanding the global water cycle, which dominates the Earth-climate system. However, limitations in observations will restrict our current ability to reduce the uncertainties in the information used to make decisions regarding to water use and management. Under the support of “Strategic Priority Research Program for Space Sciences” of the Chinese Academy of Sciences, a new satellite concept of global Water Cycle Observation Mission (WCOM) is proposed, aiming to provide higher accuracy and consistent measurements of key elements of water cycle from space, including soil moisture, ocean salinity, freeze-thaw, snow water equivalent and etc. The expected more consistent and accurate datasets would be used to refine existing long-time series of satellite measurements, to constrain hydrological model projections and to detect the trends necessary for global change studies.

In recent years, drought occurs frequently in China. Soil moisture, as a key parameter in the drought monitoring, becomes especially concerned. The FY-3B/MWRI soil moisture product provides global observations of land surface soil moisture. This paper gives a basic introduction of FY-3B/MWRI soil moisture product including the retrieval method used. Then an example in the application for drought monitoring will be demonstrated and finally we will evaluate the utility of MWRI soil moisture for drought monitoring.1

Endmember variability is one of reasons causing errors of model fits in spectral mixture analysis (SMA). The endmembers used in endmember matrix should be higher intra-class similarity and lower inter-class similarity, which depends on spectral wavelengths. The quantification of spectral similarity of intra- and inter-class helps to determine the appropriate endmember classes and optimize the endmember selection. In this study, five spectral matching algorithms were used to quantify the spectral similarity, and three spectral class-matching metrics based on the matching algorithms were used to analyze the spectral similarity of intra- and inter-class. The spectral class-matching metrics of a measured spectral library (ENVI spectral libraries) and MODIS image spectra were quantified and assessed to determine the appropriate the endmember classes for MODIS reflectance channels.

Knowledge of the vegetation phenological dynamics is crucial to the understanding of the Earth ecosystems and carbon cycles. A dual-frequency dual-polarization microwave vegetation index (MVI) has been developed recently for the Advanced Microwave Scanning Radiometer for the Earth Observing System; however, the noisy behavior of MVI limits its applications in the studies on terrestrial vegetation. In this letter, a method based on Fourier analysis is proposed to refine MVI. By excluding the high-frequency variations, the method helps to recover the trend of vegetation seasonal changes inherently contained in the raw MVI data series. Comparisons between the refined MVI and the normalized difference vegetation index (NDVI) data sets from years 2002 to 2005 show that the correlation between the refined MVI and NDVI is significantly increased for the three study sites. The refined MVI along with the optical vegetation indices provides complementary information on vegetation and can be served as a useful tool to monitor regional and global vegetation dynamics.

This study demonstrates a technique of estimating soil moisture using the L-band dual-polarization measurements and no soil roughness information. The development is based on the analysis of the simulated database using Advanced Integral Equation Model (AIEM) under SMAP sensor configurations. Through analyzing the current surface emission models the Hp model was selected as the basic model for development. The inversion technique is validated with both simulated data and ground experimental data. The inversion accuracy with RMSE (root-mean-square error) for simulated data and ground experimental data is 1.4% and 3.6%, respectively, which can meet the requirement of SMAP (soil moisture active and passive).

In this paper, a hybrid algorithm of retrieving soil moisture is developed. This method assembles the single channel algorithm (SCA) and the Qp-model based soil moisture inversion algorithm. The new algorithm does not require soil surface roughness parameter. The accuracy evaluation of algorithm shows that the new method can provide accurate soil moisture estimate in Tibetan Plateau (TP). Based on eight years (2003~2010) soil moisture data, multi-year changing trend of soil moisture of the TP is analyzed. The spatial pattern of soil moisture changing trend basically coincides with the trend of precipitation of the meteorological sites.

Knowledge of the spatial distribution of global soil moisture for a long period of time is crucial to the understanding of climate changes and land surface processes. The observations from spaceborne passive microwave sensors for more than 30 years are very valuable for generating historical soil moisture maps. Except those from the Soil Moisture and Ocean Salinity (SMOS) mission, which was launched in November 2009, most of the observations available are from sensors with frequencies much higher than L-band and are more sensitive to vegetation conditions. To rebuild long-term soil moisture data sets with an improved accuracy, a method to correct vegetation effects at X-band or higher frequencies using L-band soil moisture retrievals is explored in this study. Validations based on$b$-parameter values derived from both field-sampled soil moisture with noises added and the actual SMOS products indicate that the method can be applied to the observations from the Advanced Microwave Scanning Radiometer for the Earth Observing System or similar sensors and help to rebuild historical soil moisture data sets with a root-mean-square error less than 0.04$\hbox{m}^{3}/\hbox{m}^{3}$.

Image segmentation is a key procedure that partitions an image into homogeneous parcels in object-based image analysis (OBIA). Scale selection in image segmentation is always difficult for high-performance OBIA. This letter is aimed at scale selection before segmentation in OBIA and proposes a spatial statistics-based spatial bandwidth selection method based on mean-shift segmentation. This study uses Ikonos and Quickbird panchromatic images as the experimental data and then computes their semivariances to select the optimal spatial bandwidth for mean-shift segmentation. To validate this method and interpret the relationship between the semivariances and segmentation scale, this letter implements an image segmentation evaluation based on the homogeneity within and the heterogeneity between the segmentation parcels. The evaluation results basically support the proposed scale selection method based on the semivariogram. Consequently, the semivariogram-based spatial bandwidth selection method is practically meaningful for pre-estimating the appropriate scale and thus contributes to improving the performance and efficiency of OBIA.

In this letter, we investigated an inversion technique to estimate snow water equivalence (SWE) under Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) sensor configurations. Through our numerical simulations by the advanced integral equation model (AIEM), we found that the ground surface emission signals at 18.7 and 36.5 GHz were highly correlated regardless of the ground surface properties (dielectric and roughness properties) and can be well described by a linear function. It leads to a new development for describing the relationship between snow emission signals observed at 18.7 and 36.5 GHz as a linear function. The intercept (A) and slope (B) of this linear equation depend only on snow properties and can be estimated from the observations directly. This development provides a new technique that separates the snowpack and ground surface emission signals. With the parameterized snow emission model from a simulated database that was derived using a multiscattering microwave emission model (dense medium radiative transfer model-AIEM-matrix doubling) over dry snow covers, we developed an algorithm to estimate the SWE using the microwave radiometer measurements. Evaluations on this technique using both the model simulated data and the field experimental data with the airborne Polarimetric Scanning Radiometer data from National Aeronautics and Space Administration Cold Land Processes Experiment 2003 showed promising results, with root-mean-square errors of 32.8 and 31.85 mm, respectively. This newly developed inversion method has the advantages over the AMSR-E SWE baseline algorithm when applied to high-resolution airborne observations.

Active microwave sensors, especially high-frequency radar systems, are highly sensitive to snow pack parameters, including Snow Water Equivalent (SWE). With the availability of several X-band space-borne SAR systems, the study attempts to make use of multiple-angle SAR observations and develop relevant SWE inversion algorithms. Analysis was carried out based on parameterized scattering models for both soil surface and snowpack. It is found that the backscattering signals at two incident angles are well correlated for both soil surface and snowpack; and snow optical thickness can be well defined and estimated through snow volume scattering at two different angles. The snow and soil parameters can be estimated through two pairs of adjacent observations. The technique was tested using theoretical simulated database. Initial analysis shows that current technique needs to be further improved and a better estimation of single scattering albedo is needed.

In the article, the research of sub-pixel snow mapping was conducted using moderate resolution data of remote sensing for obtaining high-accuracy data of snow cover in Qinghai-Tibet Plateau. But the end-member database is very large in the research, so the amount of calculation is too great if all the end-members will be used in the unmixing of pixel. In light of the characteristics of end-member database, the method of combining use of typical and neighboring end-members was established for the pixel unmixing of MODIS data in Qinghai-Tibet Plateau. Through the method, the percentage data of snow cover have been obtained in the plateau. Based on ASTER data, the validation has been conducted for the unmixing result in the research, which is quite accurate and reliable.

Validation is an important and particularly challenging task for remote sensing of soil moisture. A key issue in the validation of soil moisture products is the disparity in spatial scales between satellite and in situ observations. Conventional measurements of soil moisture are made at a point, whereas satellite sensors provide an integrated area/volume value for a much larger spatial extent. In this paper, four soil moisture networks were developed and used as part of the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) validation program. Each network is located in a different climatic region of the U.S., and provides estimates of the average soil moisture over highly instrumented experimental watersheds and surrounding areas that approximate the size of the AMSR-E footprint. Soil moisture measurements have been made at these validation sites on a continuous basis since 2002, which provided a seven-year period of record for this analysis. The National Aeronautics and Space Administration (NASA) and Japan Aerospace Exploration Agency (JAXA) standard soil moisture products were compared to the network observations, along with two alternative soil moisture products developed using the single-channel algorithm (SCA) and the land parameter retrieval model (LPRM). The metric used for validation is the root-mean-square error (rmse) of the soil moisture estimate as compared to the in situ data. The mission requirement for accuracy defined by the space agencies is 0.06 m3/m3. The statistical results indicate that each algorithm performs differently at each site. Neither the NASA nor the JAXA standard products provide reliable estimates for all the conditions represented by the four watershed sites. The JAXA algorithm performs better than the NASA algorithm under light-vegetation conditions, but the NASA algorithm is more reliable for moderate vegetation. However, both algorithms have a moderate to large bias in all cases. The SCA had the lowest overall rmse with a small bias. The LPRM had a very large overestimation bias and retrieval errors. When site-specific corrections were applied, all algorithms had approximately the same error level and correlation. These results clearly show that there is much room for improvement in the algorithms currently in use by JAXA and NASA. They also illustrate the potential pitfalls in using the products without a careful evaluation.

Vegetation biomass is an important parameter in the carbon cycle study. In this paper, a new technique to estimate aboveground vegetation wet biomass based on the Microwave Vegetation Indices (MVIs), which are computed through the observed brightness temperature of AMSR-E/Aqua under two adjacent frequencies, has been developed. The MVIs can provide significant new information compared with the conventional optical vegetation indices 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 where the microwave sensor can ¿see¿ through. We know that the absorption effect of vegetation canopy is mostly controlled by the total wet biomass. In this technique, we first retrieve the single scattering albedo and the optical thickness based on model simulations under AMSR-E configuration. Then, the estimated above two properties are used to derive the absorption fraction of vegetation. Finally, it can be related to the aboveground vegetation wet biomass.

Snow Water Equivalent (SWE) is a crucial parameter in the studies of hydrology and climatology. Estimating SWE by using high-resolution radar systems, especially those capable of providing high-frequency observations, is an important task in the microwave studies. In this paper, a parameterized snow scattering model was first developed to provide the model basis for the snow inversion problems. A scheme based on the parameterized model, analysis on the depolarization factor as well as the scattering and extinction relationships between X and Ku-band snow backscattering signals was then developed for SWE inversion. Initial evaluation on the technique was made through theoretically simulated database. The estimated SWE is found to be well correlated with simulated SWE with an acceptable accuracy.

This paper investigated the emission behavior at 18.7 GHz, 36.5 GHz and 89 GHz over the snow-cover surface and after snow completely removed surface at the Local Scale Observation Site (LSOS) in Fraser, Colorado, USA with 55° incidence angle) using one-layer and two-layer emission model, which is based on the radiative transfer by Matrix Doubling approach with the dense media theory and the surface scattering Model. From the comparisons with the GBMR-7 observation on Feb. 21, both the two-layer emission model and one-layer emission model could predict the observed brightness temperature over snow-covered surface well, but the polarization difference predicted by two-layer emission model was relatively smaller than one-layer model did. In addition, we attempted to interpret the emission magnitude and polarization separation of snow-removed surface by incorporating a transition layer below the soil medium. We also demonstrated the effect of snow fraction on the brightness temperature difference at 18.7 GHz and 36.5 GHz over snow-cover surface with the field observation and model simulation.

A simple linear regression algorithm to retrieve LST was presented in this paper. AvgSurfT from GLDAS was used to fill up the data gaps of MODIS LST due to cloudiness in the Tibet Plateau. The objective of our algorithm is to establish multi-liner regress equations between brightness temperature of AMSR-E and the combined temperature from MODIS and GLDAS under different land cover types, and then we retrieved LST using AMSR-E between brightness according to the coefficients obtained from regress equation. The retrieved LST and MODIS LST were examined to evaluate the algorithm. It was found that the retrieved results were good under the surface type categories of shrub, crop and grass due to the minimal variety of the land surface emissivity.

In order to evaluate the accuracy of snow water equivalent (SWE) inversion algorithm for passive microwave sensor Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) in Western China, we compared SWE got from AMSR-E daily SWE product with the ground measurements from 15 meteorological stations in Tibetan plateau. The results show AMSR-E overestimate SWE in this regions and the RMSE is 21mm Tibetan plateau. Through incorporating snow fraction factor, a new empirical algorithm estimate snow depth and SWE have been developed in Tibet. This new algorithm appeared higher accuracy than AMSR-E. Due to complex topography, shallow patchy snow and frozen grounds covered at the Tibetan Plateau, this technique didn't show good results. In future we will focus on how to evaluate and eliminate the effects of these factors quantitatively on SWE retrieval.

Snow monitoring at regional and continental scale is an important task in Cryosphere studies. Space-borne active microwave sensors are capable of delivering long-term, all-weather observations of snow at large scale and providing information on key snow parameters, such as snow water equivalent (SWE). However, the complexity of snowpack makes it difficult to model the microwave scattering and retrieve snow parameters. A computationally efficient snow scattering model with simple terms is desirable for studying microwave interactions with snow and developing inversion techniques. In this study, a parameterized dry snow scattering model for analyzing X-band and Ku-band snow measurements is developed. The parameterized model is built from the microwave signal database simulated from a wide range of snowpack conditions by a theoretical model, which accounts for multiple-scattering effects and has been well-validated. The parameterized model is simple and reliable, with RMSEs 0.20 dB, 0.24 dB and 0.43 dB for VV, HH and VH polarizations, respectively.

ERS Wind Scatterometer provides capability of the multiple angles by their three different look antennas, In this study, we evaluate whether the multi-incidence angle observations can help on improving surface soil moisture estimations. With the theoretical surface backscattering model - the Advanced Integral Equation Model (AIEM), we first simulated a surface backscattering database with a wide range of surface roughness and soil moisture properties at different incident angles. Then, a parameterized surface backscattering model is developed using the simulated database. The newly developed simple model has the roughness function that can be described by a single combined roughness parameter from the commonly used surface roughness descriptors (RMS height and correlation length). This makes it possible to be used as an inversion model. We will demonstrate this simple model development, its accuracy, and inversion test by using the ground measurements from the Intensive Observation Period (IOP'98) field campaign in 1998 of the Global Energy and Water Experiment (GEWEX) Asian Monsoon Experiment Tibet (GAME/Tibet).

Effectively monitoring vegetation water is essential to improve our understanding of agriculture and hydrology. Vegetation water content is often estimated using vegetation indices derived from optical satellite sensors. In this study, we introduced the new microwave vegetation indices (MVIs) and derived the new MVIs using observation from the Advanced Microwave Scanning Radiometer (AMSR-E). To demonstrate the potential of the proposed MVIs, we compared them with vegetation water content which were obtained using ground based observations of vegetation water content and reflectance from a MultiSpectral Radiometer (MSR) during the National Airborne Field Experiment 2006 (NAFE'06), as well as the coincident Landsat 5 TM data. The estimated vegetation water content were averaged and compared with daily MVIs derived from AMSR-E on EASE-GRID pixels. The results of comparison between the MVIs and crop water content on EASE-GRID pixels demonstrated that the MVIs could be used to monitor the vegetation water content, which suggests additional all weather information and a potential linkage of the two data sources. In combination with vegetation indices derived from conventional optical sensor, MVIs provide a possible complementary dataset for monitoring global vegetation from space.

The parameterized emission model of dry snow developed by Jiang et al. could be used to simulate the microwave emission signal for one layer snow. On the basis of sensitivity analysis, this simple snow parameterized model is firstly compared with the HUT model using the PSR observation with the corresponding snow pits data over North Park area in Feb., 2003. At lower frequencies, both of the two models underestimated the measurements, while the parameterized model was closer to the PSR at 36.5 GHz, since the parameterized model considered the multiscattering in the snow layer. Finally, with this parameterized model, we performed the brightness temperature simulation of the Polarimetric Scanning Radiometer data similar as AMSR-E, with the outputs from the Snow Model. The difference between the simulated TBs and the measurements of PSR was as large as 20K, even more at l0.7 GHz. Through analysis, the discrimination was possibly either linked with the emission model or due to snow surface simulations. This comparison case made us to better understand how accurate the simulations could be in reality.

Microwave scattering from snow is difficult to model due to the complexity and heterogeneity of natural snow. In this paper, we developed a multi-layer, multi-scattering model 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 truck-mounted L- and Ku-band scatterometers (frequencies are 1.25 GHz and 15.5 GHz) at Local-Scale Observation Site (LSOS) of NASA Cold- land Processes Field Experiment (CLPX) during Third Intensive Observation Period (IOP3). It was found that model predictions were in good agreement with field observations with proper particle size selected. Analysis on scatterer shape, multiple scattering and snow stratification effects were also made based on model simulations.

Validation of satellite-based soil moisture algorithms and products is particularly challenging due to the disparity of scales of the two observation methods. Validation programs for the Advanced Microwave Scanning Radiometer-E (AMSR-E) instrument on the Aqua satellite is currently ongoing. As part of the AMSR-E validation activities several networks of ground based in-situ soil moisture sensors were established in research watersheds. These networks provide estimates of the average soil moisture over the watersheds and surrounding areas that approximate the size of the AMSR-E passive microwave footprint. Four watersheds in different vegetation/climate regions of the U.S. were selected. All instrumentation was installed prior to the launch of AMSR-E in 2002. There are now over five years of observations available. Quality control of the data has included short term field experiments at some of the watersheds to verify calibration and scaling. The National Aeronautics and Space Administration (NASA) and Japanese Aerospace Exploration Agency (JAXA) soil moisture products were compared to the network observations, along with an alternative algorithm. The results indicate that each algorithm has different performance statistics that depend upon the site. A positive outcome of the analysis is that it appears that the algorithms have the potential to perform within acceptable error bounds. Preliminary results indicate the single channel algorithm performs very well in all four watersheds. These results are not final because the products of both agencies are undergoing revisions. The issues addressed here are common to both current and future satellite missions.

Major uncertainties in deriving vegetation indices from satellite measurements are the effects of atmosphere and background soil conditions. Through numerical simulations by surface emission model - Advanced Integral Equation Model (AIEM), we found that bare surface emissivities at different frequencies can be well characterized by a linear function with parameters that are dependent on the pair of frequencies to be used. This makes it possible to minimize the surface emission signal and maximize the vegetation signal when using multifrequency radiometer measurements. Using the radiative transfer model (ω-τ model), a linear relationship between the brightness temperatures observed at two adjacent radiometer frequencies can be derived. It can be shown that the microwave vegetation index derived by the intercept and slope of this linear function depends only on vegetation properties and can be derived from the dual-frequency and dual-polarization measurements. We will demonstrate the theoretical basis of this new microwave vegetation index and show comparisons of the microwave derived vegetation index with the optical sensor derived NDVI measurements.

A novel method, which employs both a matrix doubling algorithm and the first-order solution of a radiative transfer (RT) equation for modeling microwave backscattering from forest, is presented in the paper. The method is based on the assumption that a forest canopy can be divided into a number of distinct horizontal vegetation layers over a dielectric half-space rough surface. The scattering phase matrix of each layer is calculated by either matrix doubling to account for the multiple-scattering effect or first-order solution of an RT equation, depending on the scattering characteristics of the layer. The first-order solution of the RT equation is used for the trunk layer while the matrix doubling technique is applied to both the crown layer and understory. The advanced integral equation model and reflectivity matrix are used to calculate the noncoherent and coherent surface boundary conditions. Comparisons between model predictions and field measurements on radar backscattering coefficients for a walnut orchard showed a good agreement at both L-band and X-band and for all three polarizations. Comparative analyses of model predictions for backscattering from a forest medium calculated using the combined model, first-order RT model, and the standard matrix doubling model were also presented. Understory effects, that can significantly change the weight of each scattering mechanism, were also evaluated by using the combined method.

Abstract—A microwave scattering model based on second-order solution of radiative transfer equation has been developed for dry snow. Advanced integral equation model (AIEM) and a semi-empirical model were included in the model to account for ground contribution. Also, ellipsoid grain shape was adopted to describe ice particle. This model was compared with the ground-based scatterometer data (frequencies are 1.25GHz and 15.5 GHz) from NASA Cold-land Processes Field Experiment (CLPX). Inputs to the model were from Local-Scale Observation Site (LSOS) snow pit measurements, except that particle size and shape were computed as free parameters. The comparison shows that the model agrees well with the field data. Also from the comparison, it could be seen that particle shape had a significant effect on the cross-polarization signals. Keywords- snow; second-order model; CLPX I.

This paper presents a study to demonstrate the potential of a spaceborne K/sub u/-band APR (active phase-array radar) - Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) to monitor snow cover in Tibetan Plateau. A method based on the difference of backscattering from snow covered ground with zenithal/non-zenithal incidence angles was carried out and validated by a second-order RT model. In the end, we use the TRMM/PR data from 2003-01 to 2004-04 to do the snow monitor of several areas in Tibetan Plateau.

The automatic extraction of contour lines from scanned topographic map is one of the challenging subjects in GIS. Most of the methods available rely heavily on image processing on pixel scale and pay little attention to spatial relationships, which are inherent characteristics of contour lines. This work brings forward a new scheme aiming at the effective realization of automatic extraction of contour lines. Based on mathematical morphology, this scheme tries to acquire spatial relationships of contour lines and make use of them for matching and connecting broken contour lines. The whole process of automatic extraction of contour lines includes the following aspects: acquisition of contour line image by color separation, erasion of image noise, image skeletonizing process, line tracking process, acquisition of spatial relationships by dilation of contour lines, matching and connecting broken contour lines based on spatial relationships, compression and output of vector data. Software was designed according to the scheme and experiments on scanned maps further proved the effectiveness of the method.