Jiangyuan Zeng

Method for Soil Moisture and Surface Temperature Estimation in the Tibetan Plateau Using Spaceborne Radiometer Observations

A method for soil moisture and surface temperature estimation in the Tibetan Plateau (TP) using spaceborne radiometer observations was presented. Based on the physical basis that the 36.5-GHz (Ka-band) vertical brightness temperature is highly sensitive to the topsoil temperature, a new surface temperature model was developed using all ground measurements available from three networks named CAMP/Tibet, Maqu, and Naqu, established in the TP, which can significantly improve the accuracy of surface temperature derived from the land parameter retrieval model (LPRM). Then, the new surface temperature model, which was calibrated with in situ data, was integrated into the soil moisture retrieval algorithm proposed in this letter using Advanced Microwave Scanning Radiometer (AMSR-E) observations. The algorithm combines the vegetation optical depth and roughness into an integrated factor to avoid making unreliable assumptions and using auxiliary data to get these two parameters. Finally, the algorithm was validated by ground measurements from the dense Naqu network and was compared with NASA AMSR-E and Soil Moisture and Ocean Salinity (SMOS) official algorithms. The results show that the proposed algorithm can provide much more accurate soil moisture retrievals than the other two satellite algorithms in the Naqu network region. The algorithm can be applied to the areas with spare vegetation but may not be very suitable for densely vegetated surfaces.

A Preliminary Evaluation of the SMAP Radiometer Soil Moisture Product Over United States and Europe Using Ground-Based Measurements

The Soil Moisture Active Passive (SMAP) mission, which is the newest L-band satellite that is specifically designed for soil moisture monitoring, was launched on January 31, 2015. A beta quality version of the SMAP radiometer soil moisture product was recently released to the public. It is crucial to evaluate the reliability of this product before it can be routinely used in hydrometeorological studies at a global scale. In this paper, we carried out a preliminary evaluation of the SMAP radiometer soil moisture product against in situ measurements collected from three networks that cover different climatic and land surface conditions, including two dense networks established in the U.S. and Finland, and one sparse network set up in Romania. Results show that the SMAP soil moisture product is in good agreement with the in situ measurements, although it exhibits dry or wet bias at different network regions. It well reproduces the temporal evolution and anomalies of the observed soil moisture with a favorable correlation greater than 0.7. The overall ubRMSE (unbiased root mean square error) of SMAP product is 0.036 m3 · m-3, well within the mission requirement of 0.04 m3 · m-3. The error sources of SMAP soil moisture product may be associated with the parameterization of vegetation and surface roughness but still needs to be tested and confirmed in more extent. Considering that the algorithms are still under refinement, it can be reasonably expected that hydrometeorological applications will benefit from the SMAP radiometer soil moisture product.

A simplified physically-based algorithm for surface soil moisture retrieval using AMSR-E data

A simplified physically-based algorithm for surface soil moisture inversion from satellite microwave radiometer data is presented. The algorithm is based on a radiative transfer model, and the assumption that the optical depth of the vegetation is polarization independent. The algorithm combines the effects of vegetation and roughness into a single parameter. Then the microwave polarization difference index (MPDI) is used to eliminate the effects of surface temperature, and to obtain soil moisture, through a nonlinear iterative procedure. To verify the present algorithm, the 6.9 GHz dual-polarized brightness temperature data from the Advanced Microwave Scanning Radiometer (AMSR-E) were used. Then the soil moisture values retrieved by the present algorithm were validated by in-situ data from 20 sites in the Tibetan Plateau, and compared with both the NASA AMSR-E soil moisture products, and Soil Moisture and Ocean Salinity (SMOS) soil moisture products. The results show that the soil moisture retrieved by the present algorithm agrees better with ground measurements than the two satellite products. The advantage of the algorithm is that it doesn’t require field observations of soil moisture, surface roughness, or canopy biophysical data as calibration parameters, and needs only single-frequency brightness temperature observations during the whole retrieval process.

First Assessment of Sentinel-1A Data for Surface Soil Moisture Estimations Using a Coupled Water Cloud Model and Advanced Integral Equation Model over the Tibetan Plateau

The spatiotemporal distribution of soil moisture over the Tibetan Plateau is important for understanding the regional water cycle and climate change. In this paper, the surface soil moisture in the northeastern Tibetan Plateau is estimated from time-series VV-polarized Sentinel-1A observations by coupling the water cloud model (WCM) and the advanced integral equation model (AIEM). The vegetation indicator in the WCM is represented by the leaf area index (LAI), which is smoothed and interpolated from Terra Moderate Resolution Imaging Spectroradiometer (MODIS) LAI eight-day products. The AIEM requires accurate roughness parameters, which are parameterized by the effective roughness parameters. The first halves of the Sentinel-1A observations from October 2014 to May 2016 are adopted for the model calibration. The calibration results show that the backscattering coefficient (σ°) simulated from the coupled model are consistent with those of the Sentinel-1A with integrated Pearson’s correlation coefficients R of 0.80 and 0.92 for the ascending and descending data, respectively. The variability of soil moisture is correctly modeled by the coupled model. Based on the calibrated model, the soil moisture is retrieved using a look-up table method. The results show that the trends of the in situ soil moisture are effectively captured by the retrieved soil moisture with an integrated R of 0.60 and 0.82 for the ascending and descending data, respectively. The integrated bias, mean absolute error, and root mean square error are 0.006, 0.048, and 0.073 m3/m3 for the ascending data, and are 0.012, 0.026, and 0.055 m3/m3 for the descending data, respectively. Discussions of the effective roughness parameters and uncertainties in the LAI demonstrate the importance of accurate parameterizations of the surface roughness parameters and vegetation for the soil moisture retrieval. These results demonstrate the capability and reliability of Sentinel-1A data for estimating the soil moisture over the Tibetan Plateau. It is expected that our results can contribute to developing operational methods for soil moisture retrieval using the Sentinel-1A and Sentinel-1B satellites.

Using an unmanned aerial vehicle for topography mapping of the fault zone based on structure from motion photogrammetry

ABSTRACT High-precision and high-resolution topography is the basis of the quantitative study of active faults. Light detection and ranging (lidar) is currently the most popular method for obtaining such data, but its relatively high cost greatly limits its use in many geoscience applications. Recently, with the rapid development of computer vision science and the growing application of small unmanned aerial vehicles (UAVs), Structure from Motion (SfM) photogrammetry shows great potential for providing topographic information of comparable resolution and precision to lidar surveys, but at significantly lower cost. In this study, we examined the applicability of SfM photogrammetry in modelling the topography of the fault zone using images acquired with a low-cost digital camera mounted on a UAV over the Haiyuan fault. The resolution and accuracy of the SfM-derived topography were evaluated in detail using existing airborne lidar data as a benchmark. The results show that the density of the point cloud generated by SfM photogrammetry is nearly 70 times higher than that from the airborne lidar. Furthermore, considering the errors in the lidar data itself, the precision of the SfM point cloud is comparable to that of the lidar point cloud. Overall, our results demonstrate that the UAV-based SfM photogrammetry method can provide an inexpensive, effective, and flexible alternative to airborne lidar for the topography mapping of the fault zone.

Soil Moisture Mapping from Satellites: An Intercomparison of SMAP, SMOS, FY3B, AMSR2, and ESA CCI over Two Dense Network Regions at Different Spatial Scales

A good knowledge of the quality of the satellite soil moisture products is of great importance for their application and improvement. This paper examines the performance of eight satellite-based soil moisture products, including the Soil Moisture Active Passive (SMAP) passive Level 3 (L3), the Soil Moisture and Ocean Salinity (SMOS) Centre Aval de Traitement des Donnees SMOS (CATDS) L3, the Japan Aerospace Exploration Agency (JAXA) Advanced Microwave Scanning Radiometer 2 (AMSR2) L3, the Land Parameter Retrieval Model (LPRM) AMSR2 L3, the European Space Agency (ESA) Climate Change Initiative (CCI) L3, the Chinese Fengyun-3B (FY3B) L2 soil moisture products at a coarse resolution of ~0.25°, and the newly released SMAP enhanced passive L3 and JAXA AMSR2 L3 soil moisture products at a medium resolution of ~0.1°. The ground soil moisture used for validation were collected from two well-calibrated and dense networks, including the Little Washita Watershed (LWW) network in the United States and the REMEDHUS network in Spain, each with different land cover. The results show that the SMAP passive soil moisture product outperformed the other products in the LWW network region, with an unbiased root mean square (ubRMSE) of 0.027 m3 m−3, whereas the FY3B soil moisture performed the best in the REMEDHUS network region, with an ubRMSE of 0.025 m3 m−3. The JAXA product performed much better at 0.25° than at 0.1°, but at both resolutions it underestimated soil moisture most of the time (bias < −0.05 m3 m−3). The SMAP-enhanced passive soil moisture product captured the temporal variation of ground measurements well, with a correlation coefficient larger than 0.8, and was generally superior to the JAXA product. The LPRM showed much larger amplitude and temporal variation than the ground soil moisture, with a wet bias larger than 0.09 m3 m−3. The underestimation of surface temperature may have contributed to the general dry bias found in the SMAP (−0.018 m3 m−3 for LWW and 0.016 m3 m−3 for REMEDHUS) and SMOS (−0.004 m3 m−3 for LWW and −0.012 m3 m−3 for REMEDHUS) soil moisture products. The ESA CCI product showed satisfactory performance with acceptable error metrics (ubRMSE < 0.045 m3 m−3), revealing the effectiveness of merging active and passive soil moisture products. The good performance of SMAP and FY3B demonstrates the potential in integrating them into the existing long-term ESA CCI product, in order to form a more reliable and useful product.

A Comprehensive Analysis of Rough Soil Surface Scattering and Emission Predicted by AIEM With Comparison to Numerical Simulations and Experimental Measurements

Theoretical modeling plays a significant role as forward and inverse problem in active and passive microwave remote sensing. Understanding the validity and limitations of the models is essential for model refinements and, perhaps more importantly, model applications. Motivated by these, this paper presents a comprehensive analysis of the scattering, both backscattering and bistatic scattering, and emission of rough soil surface predicted by the advanced integral equation model (AIEM), a well-established theoretical model. Numerically simulated data, covering a wide range of surface parameters, and in situ measurement data set of well-characterized bare soil surfaces were used to evaluate the performance of AIEM in predicting the scattering coefficient and microwave emissivity over a wide range of geometric parameters and ground surface conditions. The results show that the AIEM predictions are generally in good consistency with both numerical simulations and experiment measurements in terms of angular, frequency, and polarization dependences, except for some deviations in a few cases (e.g., at large incident angles and dry soil conditions). Extensive comparison confirms the effectiveness and practicability of AIEM for both scattering and emission of rough soil surface. Possible explanations for the discrepancy between the model prediction and data are given, together with suggestions for model usage and refinements.

Radar Response of Off-Specular Bistatic Scattering to Soil Moisture and Surface Roughness at L-Band

This letter investigates the bistatic radar response of soil moisture and surface roughness of bare soil surfaces at L-band using the advanced integral equation model (AIEM). It focuses on the use of bistatic geometries away from the specular region. To better explore the potential of bistatic scattering for soil moisture sensing, both polarized and angular scattering coefficients, and their combinations, are evaluated using a defined sensitivity index. The results show that sensitivity is enhanced in a bistatic mode compared with the monostatic case. Using a combination of dual polarized and angular data suppresses an undesired impact of the surface correlation function. Moreover, the forward region is preferred to soil moisture sensing regardless of the surface correlation function. Among the combinations investigated, the dual angular observation reduces the influence of surface roughness, preserves a good sensitivity to soil moisture response, and thus seems to be a good candidate for soil moisture sensing in bistatic configuration.

Full-Wave Simulation and Analysis of Bistatic Scattering and Polarimetric Emissions From Double-Layered Sastrugi Surfaces

In this paper, a physically based numerical electromagnetic approach, by solving Maxwells equations, is developed to investigate the scattering and the emission from double-layered media, where both the top and bottom interfaces are random sastrugi surfaces with a random horizontal shift between the two corresponding negative-slope facets of both interfaces. Numerical simulations are illustrated for bistatic scattering and four Stokes parameters at L-, C-, X-, and K-bands. Results indicate that the bistatic scattering and Stokes parameters are asymmetric for the double-sastrugi media, whereas for the other two structures, sastrugi surface alone and sastrugi surface with a planar bottom boundary, their scattering and the first two Stokes parameters are even symmetric relative to the azimuthal angle of 90°, and the third and fourth Stokes parameters are odd symmetric. In particular, the Stokes results no longer have the strong coherent fluctuations in angular variations shown in periodic double-layered structures because the random double-layered structure eliminates the coherent interference. It is interesting to observe that the internal total reflection may cancel if the coupled interactions between the two sastrugi interfaces are very strong, which results in decreasing scattering at the L-band, and its Stokes parameters are similar to those of the sastrugi alone. Numerical results also reveal that the maxima of cross-polarized specular scattering from double-sastrugi structures can be observed at cross incidence; however, they are at a deep dip for the latter two statistical symmetric structures. The sastrugi-sastrugi structure, being geometrically anisotropic, is capable of generating strong cross-polarized scattering and, subsequently, significant amounts of the third and fourth Stokes. The azimuthal patterns, at a viewing angle of 55°, of four Stokes parameters, although more complex, are feature rich where two striking extrema of third and fourth Stokes are presented. Compared with the L-band, the C-band presents strong azimuthal dependence of four Stokes. The simulation results offer deeper insights into the scattering and emission process in sastrugi surface and may lead to better retrieval of surface parameters from radar or radiometric measurements.

A method for monitoring hydrological conditions beneath herbaceous wetlands using multi-temporal ALOS PALSAR coherence data

Reed marshes, the world’s most widespread type of wetland vegetation, are undergoing major changes as a result of climate changes and human activities. The presence or absence of water in reed marshes has a significant impact on the whole ecosystem and remains a key indicator to identify the effective area of a wetland and help estimate the degree of degeneration. Past studies have demonstrated the use of interferometric synthetic aperture radar (InSAR) to map water-level changes for flooded reeds. However, the identification of the different hydrological states of reed marshes is often poorly understood. The analysis given in this paper shows that L-band interferometric coherence is very sensitive to the water surface conditions beneath reed marshes and so it can be used as classifier. A method based on a statistical analysis of the coherence distributions for wet and dry reeds using InSAR pairs was, therefore, investigated in this study. The experimental results were validated by in-situ data and showed very good agreement. This is the first time that information about the water cover under herbaceous wetlands has been derived using interferometric coherence values. This method can also effectively and easily be applied to monitor the hydrological conditions beneath other herbaceous wetlands.