Pang-Wei Liu

Sensitivity of GNSS-R Spaceborne Observations to Soil Moisture and Vegetation

Global navigation satellite systems-reflectometry (GNSS-R) is an emerging remote sensing technique that makes use of navigation signals as signals of opportunity in a multistatic radar configuration, with as many transmitters as navigation satellites are in view. GNSS-R sensitivity to soil moisture has already been proven from ground-based and airborne experiments, but studies using space-borne data are still preliminary due to the limited amount of data, collocation, footprint heterogeneity, etc. This study presents a sensitivity study of TechDemoSat-1 GNSS-R data to soil moisture over different types of surfaces (i.e., vegetation covers) and for a wide range of soil moisture and normalized difference vegetation index (NDVI) values. Despite the scattering in the data, which can be largely attributed to the delay-Doppler maps peak variance, the temporal and spatial (footprint size) collocation mismatch with the SMOS soil moisture, and MODIS NDVI vegetation data, and land use data, experimental results for low NDVI values show a large sensitivity to soil moisture and a relatively good Pearson correlation coefficient. As the vegetation cover increases (NDVI increases) the reflectivity, the sensitivity to soil moisture and the Pearson correlation coefficient decreases, but it is still significant.

Radar Remote Sensing of Agricultural Canopies: A Review

Observations from spaceborne radar contain considerable information about vegetation dynamics. The ability to extract this information could lead to improved soil moisture retrievals and the increased capacity to monitor vegetation phenology and water stress using radar data. The purpose of this review paper is to provide an overview of the current state of knowledge with respect to backscatter from vegetated (agricultural) landscapes and to identify opportunities and challenges in this domain. Much of our understanding of vegetation backscatter from agricultural canopies stems from SAR studies to perform field-scale classification and monitoring. Hence, SAR applications, theory, and applications are considered here too. An overview will be provided of the knowledge generated from ground-based and airborne experimental campaigns that contributed to the development of crop classification, crop monitoring, and soil moisture monitoring applications. A description of the current vegetation modeling approaches will be given. A review of current applications of spaceborne radar will be used to illustrate the current state of the art in terms of data utilization. Finally, emerging applications, opportunities and challenges will be identified and discussed. Improved representation of vegetation phenology and water dynamics will be identified as essential to improve soil moisture retrievals, crop monitoring, and for the development of emerging drought/water stress applications.

Impact of Moisture Distribution Within the Sensing Depth on L- and C-Band Emission in Sandy Soils

The performances of the soil moisture retrieval and assimilation algorithms using microwave observations rely on realistic estimates of brightness temperatures (TB) from microwave emission models. This study identifies circumstances when current models fail to reliably relate near-surface soil moisture to an observed TB at L-band; offers a plausible explanation of the physical cause of these failures; and recommends improvements needed so that L-band observations can provide reliable estimates of soil moisture, more universally. Physically consistent soil parameters and moisture at the surface were estimated by using dual-polarized C-band observations during an intensive field experiment, for an irrigation event and subsequent drydown. These derived parameters were used in conjunction with the in situ moisture in deeper layers and different moisture profiles within the moisture sensing depth to obtain estimates of H-pol TB at L-band, that provided best matches with the observed TB. The general assumptions of linear moisture distribution, with uniform or exponentially decaying weighting functions provided realistic TB during the later stages of the drydown. However, the RMSDs of the TBs were up to 10.37 K during the wet period. In addition, the use of one value of moisture representing the entire moisture sensing depth during this highly dynamic stage of the drydown provides unrealistic estimates of emissivity, and hence, TB at L-band. This study recommends use of a hydrological model to provide dynamic, realistic soil moisture profiles within the sensing depth and also an improved emissivity model that utilizes these detailed profiles for estimating TB.

Predicting L-band Microwave Attenuation through Forest Canopy using Directional Structuring Elements and Airborne Lidar

The L-band signals broadcast by GPS satellites are attenuated by vegetation, making it problematic, if not impossible, to predict the performance of the system in forested areas without some quantitative measure of the structure and density of the local forest canopy. Airborne laser swath mapping (ALSM) observations can be used to rapidly and remotely sample the structure and density of forested areas. We report here the results of a study performed to determine the attenuation of GPS signals in forests, by correlating changes in the signal-to-noise ratio (SNR) of the received GPS signals under different canopies, using three dimensional structure and density information about each canopy derived from ALSM observations. The results of this study verify that the loss of signal is strongly correlated with the local structure and density of the forest, and we demonstrate how the ALSM point cloud can be used to better predict the attenuation of the GPS signals. The results of this research also pertain to other modes of microwave transmission in forested areas, including satellite and cellular telephony, and the estimation of biomass from L-band radar.

Impact of Bias Correction Methods on Estimation of Soil Moisture When Assimilating Active and Passive Microwave Observations

In this paper, bias correction approaches are investigated to understand their impact on assimilating active and/or passive microwave observations on near-surface soil moisture (SM) estimates. Synthetic and field observations were assimilated in a soil-vegetation-atmosphere transfer model linked with an integrated active-passive model at L-band for bare soil. The two bias correction methods included in this study are the online bias correction with feedback (BCWF) with extended implementation with nonlinear observation operators and the simultaneous state parameter (SSP) update. New equations for BCWF were derived for the case of nonlinear observation operators because current versions of this approach were not applicable for improving SM by assimilating microwave observations. In SSP, the bias is compensated by tunning the values of the parameters. The two approaches resulted in similar accuracy for improving SM estimates compared with the uncorrected estimates. SSP showed the highest certainty for both synthetic and field observations. Using the bias correction methods, the mean estimates of SM improved by up to 88%, 87%, and 94%, when passive, active, and active-passive synthetic observations were assimilated, respectively, compared with the open-loop estimates. In contrast, when assimilating field observations from the Eleventh Microwave Water Energy Balance Experiment, the mean estimates of SM improved by up to 44%, 18%, and 48%, when passive, active, and active-passive observations were assimilated, respectively, compared with open-loop estimates. The decrement in improving the SM estimates suggests sources of uncertainty other than those from model parameters and forcings.

Utilizing complementarity of active/passive microwave observations at L-band for soil moisture studies in sandy soils

In this study, sensitivity of active and passive (AP) observations at L-band to near-surface SM was analyzed for bare sandy soils. The complementarity of AP microwave observations was used to obtain realistic SM profile that matched well with both AP observations during dynamic moisture conditions. Active observations exhibit less sensitivity to SM changes and higher sensitivity to surface roughness than passive observations. Based upon these findings, the observed brightness temperatures (TBs) were used to estimate a SM profile using an emission model. The backscatter (σ°) observations were used to estimate root mean square height (s) and correlation length (cl) using a backscatter model. The estimated SM profile, s, and cl resulted in RMSDs of 4.55K and 0.81dB between the observed and modeled TB and σ° values, respectively, for the rough surface. This study demonstrates the integrated use of AP to improve SM estimates.

Assimilation of Active and Passive Microwave Observations for Improved Estimates of Soil Moisture and Crop Growth

An ensemble Kalman Filter-based data assimilation framework that links a crop growth model with active and passive (AP) microwave models was developed to improve estimates of soil moisture (SM) and vegetation biomass over a growing season of soybean. Complementarities in AP observations were incorporated to the framework, where the active observations were used to optimize surface roughness and update vegetation biomass, while passive observations were used to update SM. The framework was implemented in a rain-fed agricultural region of the southern La-Plata Basin during the 2011-2012 growing season, through a synthetic experiment and AP observations from the Aquarius mission. The synthetic experiment was conducted at a temporal resolution of 3 and 7 days to match the current AP missions. The assimilated estimates of SM in the root zone and dry biomass were improved compared to those from the cases without assimilation, during both 3- and 7-day assimilation scenarios. Particularly, the 3-day assimilation provided the best estimates of SM in the near surface and dry biomass with reductions in RMSEs of 41% and 42%, respectively. The absolute differences of assimilated LAI from Aquarius were

Automatic forest canopy removal algorithm for underneath obscure target detection by airborne lidar point cloud data

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Soil moisture and vegetation impact in GNSS-R TechDemosat-1 observations

compared to the MODIS LAI indicating that the performance of assimilation was similar to the MODIS product at a regional scale. This study demonstrates the potential of assimilation using AP observations at high temporal resolution such as those from soil moisture active passive (SMAP) for improved estimates of SM and vegetation parameters.

Scattering modeling of dynamic soybean during SMAPVEX16-MicroWEX

The thermal imaging cameras can see the heat signature of people, boats, and vehicles in total darkness as well as through smoke, haze, and light fog, but not through the forest canopy. This study develops a novel algorithm to help detecting obscure targets underneath forest canopy and mitigate the vegetation problem for those bare ground point extraction filters as well. By examining our automatically processed results with actual LiDAR data, the forest canopy is successfully removed where all obscure vehicles or buildings underneath canopy can now be easily seen. Besides, the occluded rate of forest canopy and the detailed underneath x-y point distribution can be easily obtained accordingly. This will be very useful for predicting the performance of occluded target detection with respect to various object locations.