Radiative Transfer Model Parameters Research Articles

L-Band Microwave Satellite Data and Model Simulations Over the Dry Chaco to Estimate Soil Moisture, Soil Temperature, Vegetation, and Soil Salinity

The Dry Chaco in South America is a semi-arid ecoregion prone to dryland salinization. In this region, we investigated coarse-scale surface soil moisture (<i>SM</i>), soil temperature, soil salinity and vegetation, using L-band microwave brightness temperature (<i>T</i>_B) observations and retrievals from the Soil Moisture Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP) satellite missions, Catchment Land Surface Model (CLSM) simulations, and in situ measurements within 26 sampled satellite pixels. Across these 26 sampled pixels, the satellite-based <i>SM</i> outperformed CLSM <i>SM</i> when evaluated against field data, and forward L-band <i>T</i>_B simulations derived from in situ <i>SM</i> and temperature performed better than those derived from CLSM estimates when evaluated against SMOS <i>T</i>_B observations. The surface salinity for the sampled pixels was on average only 4 mg&#x002F;g and only locally influenced the <i>T</i>_B simulations, when including salinity in the dielectric mixing model of the forward radiative transfer model (RTM) simulations. To explore the potential of retrieving salinity together with other RTM parameters to optimize <i>T</i>_B simulations over the entire Dry Chaco, the RTM was inverted using 10 years of multi-angular SMOS <i>T</i>_B data and constraints of CLSM <i>SM</i> and temperature. However, the latter modeled <i>SM</i> was not sufficiently accurate and factors such as open surface water were missing in the background constraints, so that the salinity retrievals effectively represented a bulk correction of the dielectric constant, rather than salinity per se. However, the retrieval of vegetation, scattering albedo and surface roughness resulted in realistic values.

The influence of canopy radiation parameter uncertainty on model projections of terrestrial carbon and energy cycling.

Reducing uncertainties in Earth System Model predictions requires carefully evaluating core model processes. Here we examined how canopy radiative transfer model (RTM) parameter uncertainties, in combination with canopy structure, affect terrestrial carbon and energy projections in a demographic land-surface model, the Ecosystem Demography model (ED2). Our analyses focused on temperate deciduous forests and tested canopies of varying structural complexity. The results showed a strong sensitivity of tree productivity, albedo, and energy balance projections to RTM parameters. Impacts of radiative parameter uncertainty on stand-level canopy net primary productivity ranged from ~2 to > 20% and was most sensitive to canopy clumping and leaf reflectance/transmittance in the visible spectrum (~400–750 nm). ED2 canopy albedo varied by ~1 to ~10% and was most sensitive to near-infrared reflectance (~800–1200 nm). Bowen ratio, in turn, was most sensitive to wood optical properties parameterization; this was much larger than expected based on literature, suggesting model instabilities. In vertically and spatially complex canopies the model response to RTM parameterization may show an apparent reduced sensitivity when compared to simpler canopies, masking much larger changes occurring within the canopy. Our findings highlight both the importance of constraining canopy RTM parameters in models and valuating how the canopy structure responds to those parameter values. Finally, we advocate for more model evaluation, similar to this study, to highlight possible issues with model behavior or process representations, particularly models with demographic representations, and identify potential ways to inform and constrain model predictions.

Uncertainty in soil moisture retrievals: An ensemble approach using SMOS L-band microwave data

The uncertainty of surface soil moisture (SM) retrievals from satellite brightness temperature (TB) observations depends primarily on the choice of radiative transfer model (RTM) parameters, prior SM information and TB inputs. This paper studies the sensitivity of several established and experimental SM retrieval products from the Soil Moisture Ocean Salinity (SMOS) mission to these choices at 11 reference sites, located in 7 watersheds across the United States (US). Different RTM parameter sets cause large biases between retrievals. Whereas typical RTM parameter sets are calibrated for SM retrievals, it is shown that a parameter set carefully optimized for TB forward modeling can also be used for retrieving SM. It is also shown that the inclusion of dynamic prior SM estimates in a Bayesian retrieval scheme can strongly improve SM retrievals, regardless of the choice of RTM parameters. The second part of this paper evaluates the ensemble uncertainty metrics for SM retrievals obtained by propagating a wide range of RTM parameters through the RTM, and the relationship with time series metrics obtained by comparing SM retrievals with in situ data. As expected for bounded variables, the total spread in the ensemble SM retrievals is smallest for wet and dry SM values and highest for intermediate SM values. After removal of the strong long-term SM bias associated with the RTM parameter values for individual ensemble members, the remaining anomaly ensemble SM spread shows higher values when SM deviates further from its long-term mean SM. This reveals higher-order biases (e.g. differences in variances) in the retrieval error, which should be considered when characterizing retrieval error. The time-average anomaly ensemble SM spread of 0.037 m3/m3 approximates the actual time series unbiased root-mean-square-difference of 0.042 m3/m3 between ensemble mean retrievals and in situ data across the reference sites.

Optimization of a Radiative Transfer Forward Operator for Simulating SMOS Brightness Temperatures over the Upper Mississippi Basin

AbstractThe Soil Moisture Ocean Salinity (SMOS) satellite mission routinely provides global multiangular observations of brightness temperature TB at both horizontal and vertical polarization with a 3-day repeat period. The assimilation of such data into a land surface model (LSM) may improve the skill of operational flood forecasts through an improved estimation of soil moisture SM. To accommodate for the direct assimilation of the SMOS TB data, the LSM needs to be coupled with a radiative transfer model (RTM), serving as a forward operator for the simulation of multiangular and multipolarization top of the atmosphere TBs. This study investigates the use of the Variable Infiltration Capacity model coupled with the Community Microwave Emission Modelling Platform for simulating SMOS TB observations over the upper Mississippi basin, United States. For a period of 2 years (2010–11), a comparison between SMOS TBs and simulations with literature-based RTM parameters reveals a basin-averaged bias of 30 K. Therefore, time series of SMOS TB observations are used to investigate ways for mitigating these large biases. Specifically, the study demonstrates the impact of the LSM soil moisture climatology in the magnitude of TB biases. After cumulative distribution function matching the SM climatology of the LSM to SMOS retrievals, the average bias decreases from 30 K to less than 5 K. Further improvements can be made through calibration of RTM parameters related to the modeling of surface roughness and vegetation. Consequently, it can be concluded that SM rescaling and RTM optimization are efficient means for mitigating biases and form a necessary preparatory step for data assimilation.

Regional Leaf Area Index Retrieval Based on Remote Sensing: The Role of Radiative Transfer Model Selection

Physically-based approaches for estimating Leaf Area Index (LAI) using remote sensing data rely on radiative transfer (RT) models. Currently, many RT models are freely available, but determining the appropriate RT model for LAI retrieval is still problematic. This study aims to evaluate the necessity of RT model selection for LAI retrieval and to propose a retrieval methodology using different RT models for different vegetation types. Both actual experimental observations and RT model simulations were used to conduct the evaluation. Each of them includes needleleaf forests and croplands, which have contrasting structural attributes. The scattering from arbitrarily inclined leaves (SAIL) model and the four-scale model, which are 1D and 3D RT models, respectively, were used to simulate the synthetic test datasets. The experimental test dataset was established through two field campaigns conducted in the Heihe River Basin. The results show that the realistic representation of canopy structure in RT models is very important for LAI retrieval. If an unsuitable RT model is used, then the root mean squared error (RMSE) will increase from 0.43 to 0.60 in croplands and from 0.52 to 0.63 in forests. In addition, an RT model’s potential to retrieve LAI is limited by the availability of a priori information on RT model parameters. 3D RT models require more a priori information, which makes them have poorer generalization capability than 1D models. Therefore, physically-based retrieval algorithms should embed more than one RT model to account for the availability of a priori information and variations in structural attributes among different vegetation types.

Global Calibration of the GEOS-5 L-Band Microwave Radiative Transfer Model over Nonfrozen Land Using SMOS Observations

Abstract A zero-order (tau-omega) microwave radiative transfer model (RTM) is coupled to the Goddard Earth Observing System, version 5 (GEOS-5) catchment land surface model in preparation for the future assimilation of global brightness temperatures (Tb) from the L-band (1.4 GHz) Soil Moisture Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP) missions. Simulations using literature values for the RTM parameters result in Tb biases of 10–50 K against SMOS observations. Multiangular SMOS observations during nonfrozen conditions from 1 July 2011 to 1 July 2012 are used to calibrate parameters related to the microwave roughness h, vegetation opacity τ and/or scattering albedo ω separately for each observed 36-km land grid cell. A particle swarm optimization is used to minimize differences in the long-term (climatological) mean values and standard deviations between SMOS observations and simulations, without attempting to reduce the shorter-term (seasonal to daily) errors. After calibration, global Tb simulations for the validation year (1 July 2010 to 1 July 2011) are largely unbiased for multiple incidence angles and both H and V polarization [e.g., the global average absolute difference is 2.7 K for TbH(42.5°), i.e., at 42.5° incidence angle]. The calibrated parameter values depend to some extent on the specific land surface conditions simulated by the GEOS-5 system and on the scale of the SMOS observations, but they also show realistic spatial distributions. Aggregating the calibrated parameter values by vegetation class prior to using them in the RTM maintains low global biases but increases local biases [e.g., the global average absolute difference is 7.1 K for TbH(42.5°)].