Forests are the most extensive terrestrial ecosystems in the world. Their vertical structure information not only reflects the spatial structure characteristics of the forest but also the physiological and ecological processes. The existing studies on forest vertical structure through remote sensing often encounter challenges such as high costs, difficulties in acquiring effective data, or complexities in data processing. In this study, a new technology for detecting the vertical structure of vegetation is proposed and validated, which can obtain the vertical structure of vegetation by single flight. In order to adopt the new technology, a radar system was developed, incorporating a modular design scheme that emphasizes high integration and lightweight characteristics, and it was deployed on an unmanned aerial vehicle (UAV) flight platform. It consists of a main control unit, a signal processing unit, and a data recording unit, which weighs only 0.92 kg in total. Meanwhile, a novel algorithm is proposed to reconstruct the vertical structure of targets, effectively addressing critical challenges in UAV radar signal processing, such as strong system coupled signal, significant noise in radar signals, and high sidelobes in images. In order to validate the capability of the new technology, UAV flight experiments, as well as in situ observation, were carried out in typical vegetation areas. The proposed algorithm was used to process the acquired radar echoes, yielding a root-mean-square error (RMSE) of 1.33 m for the vegetation height compared to the ground synchronous measurement.

Quad-pol synthetic aperture radar (SAR) is one of the most effective approaches for dry and wet snow identification data, but its applicability is limited by the high cost of quad-pol SAR data. Dual-pol SAR such as Sentinel-1 has larger spatial coverage, longer time sequences, and freely accessible data, but there is still a highly uncertainty in dual-pol SAR to distinguish dry and wet snow due to limited polarimetric information. In this study, a pixel neighborhood-based snow identification algorithm was developed and verified using dual-pol C-band SAR data in Northern Xinjiang, China. A total of six decomposed parameters were obtained to characterize the polarimetric information of dual-pol SAR data by modifying the H-$\alpha $decomposition applicable to dual-pol SAR data. In the case of limited training samples, polarimetric features that were most sensitive to snow identification were selected as the optimal features for support vector machine (SVM), and the result derived from SVM was employed as the initial labels of Markov random field (MRF) model to separate dry and wet snow using iterative conditional mode (ICM). Then, the proposed algorithm, dual-pol SVM-MRF (DSVM-MRF), was validated and compared with previously published methods. The results show that the DSVM-MRF acquires the superior snow recognition with the overall accuracy (OA) and Kappa coefficient of 84.5% and 0.58%, respectively.

Distribution of snow and its melting is a critical factor affecting local weather, avalanche and flood forecasting, livelihood of people residing, and hydropower production. Most of the existing dry and wet snow identification methods were based on expensive quad-pol SAR with finite generalizability, while dual-pol SAR with larger coverage, longer time series and open availability has more advantages. In this study, an unsupervised algorithm for dry and wet snow discrimination, NSAE-WFCM, is proposed based on a variety of polarimetric features derived from H-α decomposition in dual-pol mode using C-band Sentinel-1 SAR data. NSAE-WFCM constructs a deep training network using the pixel neighborhood-based sparse autoencoder (NSAE) to optimize polarimetric parameters, and inputs reconstructed features with different weights into feature-weighted fuzzy C-means clustering (WFCM) to distinguish dry and wet snow for each underlying surface. Ground observation was carried out during the snow melting period of March 2021 in Altay, China, to validate dual-pol NSAE-WFCM method with an overall accuracy and kappa coefficient of 88.8% and 0.68, respectively. The results show that NSAE-WFCM’s accuracy is similar to that of the quad-pol SAR-based dry and wet snow result (90.0%), and significantly better than that of previously published approaches extended to dual-pol SAR, such as SVM (76.7%), H-α-Wishart (65.5%), SPAN-based threshold method (51.7%), and wet snow-based method (43.1%). Therefore, the NSAE-WFCM algorithm improves the ability to classify wet and dry snow based on dual-pol polarimetric features, overcomes the high dependence of existing methods on quad-pol SAR data, and reduces manual interpretation by using unsupervised clustering.

Snow density is an important variable in snowpack research. The comprehensive applicability evaluation of the snow density datasets is a prerequisite of these datasets for their applications in hydrology processes and climate change, as well as in snow equivalent water retrieval algorithms. In this letter, the applicability of three snow density datasets, including European ReAnalysis (ERA)-Interim, ERA5, and the newly released ERA5-Land datasets, was first assessed using two ground evaluation datasets with different land covers from seven snow survey courses and four densely sampled networks in China. The results show that the ERA-Interim dataset significantly overestimates snow density during the entire snow season, with an overall root mean square error (RMSE) larger than 112 kg/m3, and lacks temporal dynamics. The ERA5 and ERA5-Land datasets are generally in good agreement with the ground measurements in China. The averaged RMSEs of the ERA5 dataset are 56.2 kg/m3 against snow course sites and 28.3 kg/m3 versus the densely sampled measurements, and those of the ERA5-Land dataset are 56.6 and 28.4 kg/m3, respectively. However, the ERA5 and ERA5-Land datasets still underestimate snow density over time, especially for the middle and late snow seasons. These new findings are expected to provide valuable feedback to model developers to further enhance the accuracy of snow density datasets.

Sensitivity analysis (SA) of model parameters is of great importance for understanding, development, and application of models. However, the influence of snow microstructure variability on snow water equivalent retrieval from passive microwave measurements is still unclear. This article explores the parameter sensitivity of the microwave emission model of layered snowpacks (MEMLS) with improved born approximation (IBA) by using a quantitative global SA method, the extended Fourier amplitude sensitivity test (EFAST) algorithm. A deep analysis is conducted, including the sensitivity of passive microwave emission to snow parameters, the sensitivity variation analysis for different snow conditions, and the temporal properties of the parameter sensitivity. The results show the exponential correlation length, snow depth, and snow density are the three most sensitive parameters for snow without salt in the MEMLS model for the brightness temperature gradient at 18.7 and 36.5 GHz. For snow with a small salt content, the exponential correlation length, snow depth, snow temperature, and snow density are the four most sensitive parameters. Second, snow parameter variability highly affects the microwave radiation. The sensitivity values of microwave brightness temperature to snow depth gradually increase when the exponential correlation length is less than 0.25 mm and then slightly decreases with the increase of exponential correlation length and decreases along with the increase of snow density. Finally, our analysis highlights the importance to include the snow density, especially for deep snow depth, in the combination of sensitive factors in future multiparameter retrievals.

As an important element of the earth's surface, snow cover plays an important role in the global terrestrial ecosystem, climate change, water cycle and energy cycle. Snow depth (SD) provides information on the spatial distribution of snow cover and material energy information. It is also used to study the climatic effects of snow cover, water balance in the basin, snowmelt runoff simulation, and monitoring and evaluation of snow disasters. Snow depth data has become an indispensable basic supporting data in multi-disciplinary research. However, the current snow depth data is relatively poor in completeness and consistency and cannot meet the needs of related scientific research and industry applications, which will bring great confusion to users. This study attempts to use machine learning methods to effectively integrate snow depth data products from multiple sources to obtain a snow depth data set with high consistency in China. This paper chooses the passive microwave remote sensing data (WESTDC), ground-based data (Canadian Meteorological Centre, CMC) and Land surface models (Global Land Data Assimilation System, GLDAS; the NASA Modem-Era Retrospective Analysis for Research and Applications MERRA2; the European Centre for Medium-Range Weather Forecasts Interim Reanalysis, ERA-Interim) as the main SD data source of random forest model, and considering theinfluencing factors (e.g., land cover, snow class, forest cover fraction, surface roughness). The results show that the random forest fusion model method can effectively gather the advantages of each data source, improve the accuracy of snow depth estimation, and reduce the inconsistency between snow depth data from multiple sources. The correlation coefficient between the fused snow depth dataset and the observed snow depth can reach 0.87, and the root mean square error is 5.1 cm. Therefore, the multi-source snow depth fusion using random forest model can improve the accuracy of snow depth estimation.

This paper assessed the accuracy of four passive microwave (PM) sea ice concentration (SIC) products in polar regions by using twelve scenes MODIS images under clear-sky conditions. The SIC products include the DMSP SSMIS with Arctic Radiation and Turbulence Interaction Study Sea Ice (ASI) algorithm (SSMIS/ASI), the GCOM-W AMSR2 with NASA Bootstrap (BT) algorithm (AMSR2/BT), the Chinese Feng Yun-3B with enhanced NASA Team (NT2) algorithm (FY3B/NT2), and the Chinese Feng Yun-3C with NT2 (FY3C/NT2). An adapted optimal threshold method (i.e., the Otsu algorithm) was adopted to automatically classify the MODIS images into sea ice and water which were then aggregated to compare with the PM SIC. The results show that the averaged bias of PM SIC (PM SIC minus MODIS) is less than 6% in Arctic and ranges from -8% to 1 % in Antarctic, and the averaged root mean square error (RMSE) is less than 16% in the whole polar regions. Overall, the SSMIS/ASI product has better performance in Arctic, while the FY3/NT2 has lower bias and RMSE in Antarctic. Meanwhile, it is observed that the error metrics of PM SIC vary noticeably when compared to different MODIS images which may be caused by the diverse surface characteristics of different sea ice types.

This paper evaluated the consistency of four microwave remotely sensed sea ice concentration (SIC) products with respect to a reanalysis SIC dataset in polar regions during the period of 2015-2017. The remotely sensed SIC products include the Chinese Feng Yun-3B with enhanced NASA Team (NT2) sea ice algorithm (FY3B/NT2), the Chinese Feng Yun-3C with NT2 (FY3C/NT2), the DMSP SSMIS with Arctic Radiation and Turbulence Interaction Study Sea Ice (ASI) algorithm (SSMIS/ASI), and the GCOM-W AMSR2 with NASA Bootstrap (BT) algorithm (AMSR2/BT). The OISSTV2 (NOAA Optimum Interpolation 1/4 Degree Daily Temperature Analysis Version 2) dataset was adopted as a reference to compare and evaluate the performance of four satellite-based SIC products. The results show that remotely sensed SIC values are generally in good consistency with OISSTV2. Meanwhile, it is observed that different products have different bias in polar regions. Overall, the SSMIS/ASI product has better performance during the whole period, demonstrating that ASI algorithm may be more potential in SIC estimation. Our results also illustrate the spatial and temporal distribution characteristic of discrepancy between microwave remotely sensed SIC products and reanalysis dataset for the whole Arctic and Antarctic regions. The large difference for all the four SIC products mostly occurs in summer and marginal ice zone, indicating a great deal of uncertainty of satellite SIC products in this period and areas. The results will be useful to find possible errors in the satellite SIC products for further algorithm improvement.

Accurate monitoring of the global snow cover is important in understanding the impact of the climate on snow cover. Google Earth Engine (GEE) is a cloud computing platform dedicated to satellite imagery and other Earth observation data. Taking the Landsat TM data as true data, this paper evaluated and analyzed MODIS snow cover products by GEE in snow seasons. Using GEE JavaScript program, we obtain that error rate of MODIS snow cover product, the missing rate during the snow season with finally 88.94% of overall average accuracy in the test sites. The results showed that the GEE platform can be used to assess accurately the snow products with high efficiency.

The first evaluation of GaoFen-3 SAR radiation characteristics and its potential for quantitative parameter estimation in land surface is presented in this letter. Based on two triangle corner reflectors, five impulse response property parameters are calculated to assess image quality of GaoFen-3 SAR, and results show that the peak sidelobe ratio is superior to system specification and the integrated sidelobe ratio cannot be assessed by misconduct in the field campaign mainly for strong background scattering (not low enough). Five distributed target parameters are extracted from three categories and compared with Radarsat-2 and Sentinel-1A quasi-synchronous images in two days. The results indicate good radiometric resolution (RR) of 3 dB and good equivalent number of looks closed to 1 for GaoFen-3 single-look complex product, which proves GaoFen-3 SAR image is at the same quality level with the other two C-band SAR images and its RR meets system design of 3.5 dB. Then, the backscatter coefficient of these three SAR images is compared at bare soil area after GaoFen-3's incidence angle being normalized to 43° by Oh2004 empirical model, and the results reveal the problem of several decibels lower for GaoFen-3 in absolute radiometric calibration. Finally, the capability of surface parameter estimation is justified by statistical relation of GaoFen-3 co-polarization backscatter coefficient and bare soil moisture content, indicating its good potential for quantitative applications in land after solving the problem in calibration.

For supporting the applications of GF-3 and GEOSAR satellite projects, which have been approved by Chinese government, a multi surface parameter inversion algorithm using alternative Radarsat-2 polarimetric images is tested and improved in this study. Result shows that, though the algorithm can retrieve vegetation height and water content with relatively good accuracies, soil moisture and surface roughness inversed results are poor when the above two vegetation parameters retrieved values as inputs to inverse two soil parameters. Analyzing the reason, scattering model could amplify the errors caused by the two retrieved vegetation parameters, which are inputted as “true value” in the model. The conclusion of this study is that, surface parameters can not be inversed step by step as the idea of engineering, scientists should focus on certain parameter(s), and take other parameters as disturbance factors to eliminate or suppress.

SAR knowledge base system is a comprehensive application platform for typical objects interpretation, which includes the database of microwave scattering models, backscattering measurement data and SAR image. The paper introduces the design of the whole system and gives a detail description for each database in the system. The comprehensive knowledge rules for SAR application is developed based on the system, and a case of SAR classification is shown using the expert rules. The result of the classification illustrates the advantages of SAR knowledge base system.

A new soil moisture retrieval algorithm developed in this paper, using C- and K-band microwave radiometer channels of Ocean Salinity Satellite (OSS). In this new algorithm, K-band(23.8GHz) brightness temperature (BT) is used to estimate land surface temperature, and C-band BT in H polarization used to retrieve soil moisture by τ - ω model, in which soil roughness (h) and vegetation parameters (τ) are combined in a single factor for their similar change trends with BT. The validation is done using AMSR-E data of the same channels with Naqu soil moisture monitoring network data in the central Tibetan Plateau, result shows the new algorithm has very good accuracy, at correlation coefficient, bias and RMSE.

Surface soil moisture is an important parameter in draught monitoring and crop yield estimation, it is important to obtain spatial-temporal soil moisture information in large range. Microwave signal is much related to dielectric constant of object observed, and soil dielectric constant is determined by soil moisture, which was the basis of the use of microwave remote sensing technology for soil moisture monitoring. To solve the transformation of soil moisture and soil dielectric constant, the Dobson semi-empirical model was used to build a simulated database, and then the Hallikainen formula calibrated by the least square regression method at 1.26/1.4/3.2/5.3/6.9 and 9.6GHz frequency-points were performed to set up the simplified models to transform the real part of the dielectric constant to the soil volumetric moisture content. The validations were performed shows that the simplified models have good accuracy and practicability.

A physically-based algorithm for surface soil moisture retrieval in the Tibetan Plateau using passive microwave remote sensing was presented. The algorithm is based on a radiative transfer model and the assumption that the vegetation optical depth is polarization independent. It combines the effects of vegetation and roughness as a single parameter and uses the microwave polarization difference index (MPDI) to eliminate the effects of surface temperature and obtain soil moisture through a nonlinear iterative procedure. The advantage of this algorithm is that it needs only one frequency brightness temperature observations, and requires no field observations of roughness, soil moisture or vegetation data sets during the whole retrieval process. Finally, the algorithm was tested with the 6.9 GHz dual-polarized brightness temperature data from the Advanced Microwave Scanning Radiometer (AMSR-E) and compared with NASA official algorithm using in situ soil moisture from 20 stations in the Tibetan Plateau. The results show that the soil moisture retrieved by the algorithm is more consistent with ground measurements than the NASA soil moisture products.

A technique combined CFAR and EF detection method is presented to improve the power transmission tower detection in synthetic aperture radar (SAR) image. CFAR can detect power transmission tower as the point like targets used the intensity information. EF features is sensitive to target geometric feature, which can consider the shape of power transmission tower. The paper takes advantage of intensity information and geometric feature of targets to detect the power transmission tower, which has the regular shape. The test results using real SAR images show good performance in multitarget situation and heterogeneous environment.

The paper develops a new technique enhancing synthetic aperture radar (SAR) resolution as well as suppressing sidelobes based on adaptive weighting technique. Spatially Variant Apodization (SVA) is a nonlinear sidelobe reduction method without lose the resolution of mainlobe. The paper's method applies 2D SVA on the SAR image. And then it is detailed analyzed about the effect of extrapolated signal bandwidth after the processing. An inverse weight function is used to equalize the spectrum to obtain the extrapolated signal bandwidth. A modified noninteger Nyquist SVA formulation is used to suppress sidelobes after extrapolation. Examples of 1D case and 2D case demonstrate enhanced image resolution with sidelobe reduction.

As we know, SRTM (Shuttle Radar Topography Mission) DEM has been widely used to evaluate the multi-decadal elevation changes of mountain glaciers. Recently, a new global elevation dataset known as GDEM, based on the ASTER satellite imagery has also been released. In this paper various kinds of data on SDG(Small Dongkemadi Glacier) were collected to evaluate the potential of GDEM used in this field, including the GDEM, SRTM DEM, topographic map for 1969 and Landsat ETM+ image for 2000. The elevation change from GPS survey data (2007) and DEM(1969) on SDG is used to validate the results here. The comparison analysis shows the thickness change detected by SRTM DEM is consistent with the GPS measurements, however, the result from ASTER GDEM is far away from the field work on SDG, although the overall distribution is right. The experiment results will cause our serious attentions to the usage of GDEM in glacial field.

The paper provides an efficient extrapolation algorithm to enhance resolution as well as reduce sidelobes, which is based on ASR. The processing of algorithm is simple to operate. Simulation experiments show the validity of the algorithm. Comparing to the Fourier method, the proposed algorithm obtains better results.

One of the driving forces of the development of SAR image formation has been to obtain better and better image resolution. Conventional radar imaging methods based on Fourier transform provide good resolution as long as the backscattered data is available over a large bandwidth and a sufficient aspect region. The paper proposes a 2D Unitary ESPRIT superresolution SAR imaging method exploiting that the SAR image in phase history domain is a band-pass function with a main frequency support domain. Thus, the problem of superresolution SAR imaging is transformed to solve sinusoid harmonic estimation, which can be solved by 2D Unitary ESPRIT. From the experiments using simulation and measured data, we can see better resolution obtained by the method of the paper than the FFT method.