The newest soil moisture-dedicated satellite, the Soil Moisture Active Passive (SMAP) mission, provides global maps of soil moisture using concurrent L-band radar and radiometer acquisitions. To support the ongoing validation activities of SMAP soil moisture products, in this paper, we examined the retrieval accuracy of four SMAP soil moisture products by using well-calibrated and dense in situ measurements from the Little Washita Watershed network, one of the SMAP core validation sites with intensive ground sampling. The four SMAP products include the active (3 km), passive (36 km), active-passive (9 km), and the enhanced passive product which is a newly released soil moisture data set with a grid resolution of 9 km. Efforts on identifying the possible error sources of these products were also made for the purpose of improving the SMAP soil moisture algorithms. The results show that the passive and active-passive products can well capture the temporal dynamic of ground soil moisture with overall unbiased root-mean-square error (ubRMSE) values of 0.032 and 0.041 m³·m⁻³, respectively, which generally meet their mission requirement of 0.04 m³·m⁻³. In contrast, some irregular fluctuations exist in the active product, leading to an overall wet bias, which makes its accuracy a little poorer than its expected retrieval accuracy of 0.06 m³·m⁻³. The new enhanced passive product shows the lowest ubRMSE value of 0.026 m³·m⁻³ though it underestimates in situ measurements with a bias of 0.059 m³·m⁻³, revealing its great potential to substitute the active-passive product to provide global soil moisture measurements at a medium resolution of 9 km. The underestimation of SMAP surface temperature data may be one of the reasons that contribute to the dry bias of SMAP passive, active-passive, and enhanced passive products. The microwave polarization difference index and HV-polarized backscatter show good response to in situ soil moisture and may be considered in SMAP algorithms to further improve the accuracy of soil moisture retrievals. We expect that our findings can be fed back to improve the SMAP soil moisture algorithms and thus promote the application of SMAP soil moisture products in terrestrial water, energy, and carbon cycles.

The variation of polar sea ice is an indicator of polar environmental change, which plays an important role in the study of regional and global climate change. In this paper, the latest sea ice data sets from the European Space Agency Climate Change Initiative were firstly combined to comprehensively analyze the spatial and temporal variation of Arctic sea ice concentration (SIC) and sea ice thickness (SIT) from 2002 to 2017. The results show that during this period, the SIC of the Kara Sea and the Barents Sea decreases the most significantly, reaching −1.11%·year⁻¹. The Arctic annual average sea ice extent (SIE) and sea ice area (SIA) exhibit noticeable decreasing trends, reaching −0.0592 × 10⁶ km·year⁻¹ and −0.0628 × 10⁶ km·year⁻¹, respectively. Both of Arctic SIE and SIA vary seasonally, and they reach the minimum values of 4.97 × 10⁶ km² and 3.96 × 10⁶ km² in September and the maximum values of 14.58 × 10⁶ km² and 13.35 × 10⁶ km² in March, respectively. Moreover, the Arctic SIE and SIA anomaly series from 2002 to 2017 were analyzed based on the linear regression method. It is found the most noticeable decreasing trends of the Arctic SIE and SIA are from July to October. The maximum variation of the SIE is −1.06 × 10⁶ km²(10a)⁻¹, and the maximum variation of the SIA is −1.09 × 10⁶ km²(10a)⁻¹. The Arctic SIE and SIA present the smallest decreasing trends from April to May, which are −0.19 × 10⁶ km²(10a)⁻¹ and −0.2 × 10⁶ km²(10a)⁻¹, respectively. The Arctic SIT also displays a pronounced decreasing trend with −0.015 m·year⁻¹ in autumn and −0.018 m·year⁻¹ in winter. The annual average SIT reaches a minimum of 1.28 m in 2013 and a maximum of 1.66 m in 2005. Particularly, the SIT of the Hudson Bay decreases the most significantly, reaching −0.051 m·year⁻¹. Finally, the Arctic average SIT was tested by the Mann‐Kendall method. It is found that the SIT has a sudden change in 2007, from a slight increase in 2003–2007 to a significant decrease in 2007–2017. Overall, the decreasing trend of the Arctic sea ice (both SIC and SIT) is significant in the past decade. These findings can contribute to future research on polar regions and global climate change.

Persistent scatterer interferometric synthetic aperture radar (PSInSAR) technology provides a powerful tool for detecting ground surface displacements. However, one of its major limitations is an insufficient number of coherence points due to decorrelation. In this article, we propose an effective ground surface displacement monitoring approach based on the Stanford method for persistent scatterer (StaMPS). This approach dramatically increases the density of coherence points by jointly exploiting persistent scatterer (PS) and distributed scatterer (DS) points. In particular, we develop a new DS phase estimation method based on nonlinear optimization estimation (NLE) and exploit it in our approach. The NLE can obtain better DS phase noise reduction, can detect more coherence points, and requires slightly less computational time than conventional maximum-likelihood estimation (MLE). To demonstrate the effectiveness of our new approach, we apply it over 24 Envisat SAR images acquired over Tucson, Arizona in the USA. The coherence pixel density is increased substantially (> 6 times) compared with the conventional StaMPS-based PSInSAR approach. As for the improvement of our NLE over MLE, it can detect 11.7% more coherence pixels, can achieve better phase noise reduction, and needs approximately one third less computational time. These results validate the effectiveness of our new approach and suggest its great potential for land displacement measurements.

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.

Active and passive microwave signatures respond differently to the land surface and provide complementary information on the characteristics of the observed scenes. The objective of this paper is to explore the synergy of active radar and passive radiometer observations at the same spatial scale to constrain a discrete radiative transfer model, the Tor Vergata (TVG) model, to gain insights into the microwave scattering and emission mechanisms over grasslands. The TVG model can simultaneously simulate the backscattering coefficient and emissivity with a set of input parameters. To calibrate this model, in situ soil moisture and temperature data collected from the Maqu area in the northeastern region of the Tibetan Plateau, interpolated leaf area index (LAI) data from the Moderate Resolution Imaging Spectroradiometer LAI eight-day products, and concurrent and coincident Soil Moisture Active Passive (SMAP) radar and radiometer observations are used. Because this model needs numerous input parameters to be driven, the extended Fourier amplitude sensitivity test is first applied to conduct global sensitivity analysis (GSA) to select the sensitive and insensitive parameters. Only the most sensitive parameters are defined as free variables, to separately calibrate the active-only model (TVG-A), the passive-only model (TVG-P), and the active and passive combined model (TVG-AP). The accuracy of the calibrated models is evaluated by comparing the SMAP observations and the model simulations. The results show that TVG-AP can well reproduce the backscattering coefficient and brightness temperature, with correlation coefficients of 0.87, 0.89, 0.78, and 0.43 and root-mean-square errors of 0.49 dB, 0.52 dB, 7.20 K, and 10.47 K for σ HH⁰ , σ VV⁰ , TBH, and TBV, respectively. In contrast, TVG-A and TVG-P can only accurately model the backscattering coefficient and brightness temperature, respectively. Without any modifications of the calibrated parameters, the error metrics computed from the validation data are slightly worse than those of the calibration data. These results demonstrate the feasibility of the synergistic use of SMAP active radar and passive radiometer observations under the unified framework of a physical model. In addition, the results demonstrate the necessity and effectiveness of applying GSA in model optimization. It is expected that these findings can contribute to the development of model-based soil moisture retrieval methods using active and passive microwave remote sensing data.