The MOD10A1 daily snow cover product offers a fine temporal resolution, which is particularly important for time-critical applications. Although the reliability of this product was mostly verified by ground measurements before use, the inadequate representativeness of point-scale observations may lead to errors in the assessment of MOD10A1. In this study, based on the Google Earth Engine, MOD10A1 was thoroughly evaluated by a total of 20,131 binary snow maps generated from 30 m Landsat imagery for the 2000-2016 snow seasons in three typical snow regions across China. The three typical snow regions included Northern Xinjiang, Northeast China and the Tibetan Plateau. In general, the accuracy of MOD10A1 under clear sky conditions is 86.5% based on ground observations from meteorological stations and 90.3% based on Landsat imagery. Compared with ground measurements, snow cover derived from Landsat images is in better agreement with MOD10A1 due to a better spatial match. The error analyses also indicate that 1) compared with land cover, topography has larger effects on the accuracy of MOD10A1 over the three snow regions with statistically significant negative relationships, particularly in Northern Xinjiang (R = -0.604, P < 0.05); and 2) MOD10A1 shows a relatively lower accuracy of 81.8% from 2006 to 2008 due to the decrease in snow depth during this period (R = 0.58, P < 0.05) based on the Mann-Kendall test.

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.

The self-designed HaiYang-2B (HY-2B) satellite was launched on 24 October 2018 in China at 22:57 UT in a 99.34° inclination sun-synchronous orbit. The Scanning Microwave Radiometer (SMR) on the core observatory has the capability to provide near-real-time multi-channel brightness temperature (Tb) observations, which are designed mainly for improving the level of marine forecasting and monitoring, serving the development and utilization of marine resources. After internal calibration and ocean calibration, the first effort to retrieve land surface snow parameters was performed in this study, which obtained extremely low accuracy both in snow extent and snow mass. Accordingly, land inter-sensor calibration was carried out between SMR and the Advanced Microwave Scanning Radiometer 2 (AMSR2) in order to broaden the research and application of SMR data on the Earth’s land surface. Finally, we evaluated the consistency of the snow extent and snow mass derived from the initial and land-calibrated SMR data. The results indicated that a systematic SMR cold deviation whose magnitude depends on the channel is present for all the compared channels. After intercalibration, the conformity of the snow extent and snow mass were substantially improved compared to before; the relative bias of the snow extent and snow mass decreased from −49.97% to 2.97% and from −51.71% to 3.01%, respectively.

To improve the spatial density and quality of measurement points in multitemporal interferometric synthetic aperture radar, distributed scatterers (DSs) should be processed. An essential procedure in DS interferometry is phase estimation, which reconstructs a consistent phase series from all available interferograms. Influenced by the well-known suboptimality of coherence estimation, the performance of the state-of-the-art phase estimation algorithms is severely degraded. Previous research has addressed this problem by introducing the coherence bias correction technique. However, the precision of phase estimation is still insufficient because of the limited correction capabilities. In this paper, a modified phase estimation approach is proposed. Particularly, by incorporating the information on both interferometric coherence and the number of looks, a significant bias correction to each element of the coherence magnitude matrix is achieved. The bias-corrected coherence matrix is combined with advanced statistically homogeneous pixel selection and time series phase optimization algorithms to obtain the optimal phase series. Both the simulated and Sentinel-1 real data sets are used to demonstrate the superiority of this proposed approach over the traditional phase estimation algorithms. Specifically, the coherence bias can be corrected with considerable accuracy by the proposed scheme. The mean bias of coherence magnitude is reduced by more than 29%, and the standard deviation is reduced by more than 18% over the existing bias correction method. The proposed approach achieves higher accuracy than the current methods over the reconstructed phase series, including smoother interferometric phases and fewer outliers.

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.

Distributed scatterer (DS) decorrelation poses a challenge to multibaseline SAR interferometry. To overcome this challenge, the SqueeSAR retrieves an optimal phase time-series using a maximum likelihood estimation (MLE) method, which has been commonly used due to its remarkable effect. Unfortunately, however, the MLE’s performance is compromised for various reasons, such as inaccurate statistically homogeneous pixels (SHPs) and the bias in the estimator used. In this paper, we present an approach aiming to improve the MLE’s performance. The proposed approach includes the employment of the Kullback-Leibler divergence to realize more accurate SHP selection and the use of the second kind statistical estimator to mitigate the coherence bias. The performance of the conventional MLE is significantly improved by the proposed approach, making it close to its optimal performance. The experimental results on both simulated and real TerraSAR-X data demonstrate the improvements of the proposed approach with respect to the conventional MLE.