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Abstract: Using the airborne Polarimetric L-band Imaging Synthetic aperture radar (PLIS) the impact of high revisit cycle and full polarimetric acquisitions on biomass retrieval was investigated by means of backscatter-based multi-temporal methods. Parametric and non-parametric models were used to relate reference biomass levels obtained from field plot measurements and high point density lidar data to backscatter intensities or polarimetric target decomposition components. Single-date retrieval using multiple independent variables provided lower estimation errors when compared to models using one independent variable with errors decreasing by 2% to 15%. The multi-temporal aggregation of daily biomass estimates did not improve the overall retrieval accuracy but provided more reliable estimates with respect to single-date methods. Backscatter intensities improved estimation accuracies up to 10% compared to polarimetric target decomposition components. Using all four polarizations increased the estimation accuracy marginally (2%) when compared to a dual-polarized system. The biomass estimation error was considerably reduced (up to 30%) only by decreasing the spatial resolution and was related to decreasing forest variability with increasing pixel size. These results indicate that, at least in semi-arid areas, future L-band missions would not significantly improve biomass estimation accuracy using backscatter-based modeling approaches despite their better spatial resolution, higher revisit cycles and the availability of fully polarimetric information. [Copyright &y& Elsevier]

Abstract: Soil Surface Roughness (SR) provides a representation of surface variability which can be an important factor in a range of modelling applications such as surface water flow and sediment/nutrient transport. Moreover, it is a crucial parameter for interpreting backscatter characteristics of Synthetic Aperture Radar (SAR) for agricultural application such as near-surface soil moisture retrieval. SR is typically estimated using manual profiles of height variation along short transects (1 to 3m). However, this approach can be very time consuming and often only a small number of transects can be measured in this way, which may not be adequate to characterize the spatial variability of SR across the landscape. This study investigated the feasibility of utilising airborne Light Detection and Ranging (LiDAR) observations as an alternative for mapping SR attributes across an agricultural environment in New South Wales, Australia. To that end, SR attributes were extracted from airborne LiDAR observations and compared with those extracted from an extensive ground survey of SR making use of manual pin-profilers. Results show that LiDAR-estimates of soil profile surface heights Root Mean Square (RMS) are both accurate (compared to manual profiles) and precise (repeatable stable estimates) for fields presenting bare or fallow conditions and either presenting no row structure or as long as the orientation of the LiDAR scan line is perpendicular to the row structure. In such cases results indicated a strong correlation between LiDAR-estimated and ground-measured RMS estimates (R2 >0.68, p<0.05), with an RMSE better than 0.81cm and bias smaller than 0.48cm from a 400m flight altitude. Moreover, estimates produced from repeat pass LiDAR datasets were consistent and highly correlated (R2 0.98) suggesting that the approach is precise and robust, provided that key tillage parameters (i.e. presence of vegetative material and row direction) can be pre-classified. LiDAR estimates of surface height RMS were shown to be accurate enough to allow the tracking of temporal changes in surface roughness due to farming activities. In contrast, LiDAR-derived surface Correlation Length (CL) estimates were not found to be a reliable proxy of the ground-measured CL. [Copyright &y& Elsevier]

Abstract: The Soil Moisture and Ocean Salinity (SMOS) mission, launched in November 2009, provides global maps of soil moisture and ocean salinity by measuring the L-band (1.4GHz) emission of the Earth''s surface with a spatial resolution of 40–50km. Uncertainty in the retrieval of soil moisture over large heterogeneous areas such as SMOS pixels is expected, due to the non-linearity of the relationship between soil moisture and the microwave emission. The current baseline soil moisture retrieval algorithm adopted by SMOS and implemented in the SMOS Level 2 (SMOS L2) processor partially accounts for the sub-pixel heterogeneity of the land surface, by modelling the individual contributions of different pixel fractions to the overall pixel emission. This retrieval approach is tested in this study using airborne L-band data over an area the size of a SMOS pixel characterised by a mix Eucalypt forest and moderate vegetation types (grassland and crops), with the objective of assessing its ability to correct for the soil moisture retrieval error induced by the land surface heterogeneity. A preliminary analysis using a traditional uniform pixel retrieval approach shows that the sub-pixel heterogeneity of land cover type causes significant errors in soil moisture retrieval (7.7%v/v RMSE, 2%v/v bias) in pixels characterised by a significant amount of forest (40–60%). Although the retrieval approach adopted by SMOS partially reduces this error, it is affected by errors beyond the SMOS target accuracy, presenting in particular a strong dry bias when a fraction of the pixel is occupied by forest (4.1%v/v RMSE, −3.1%v/v bias). An extension to the SMOS approach is proposed that accounts for the heterogeneity of vegetation optical depth within the SMOS pixel. The proposed approach is shown to significantly reduce the error in retrieved soil moisture (2.8%v/v RMSE, −0.3%v/v bias) in pixels characterised by a critical amount of forest (40–60%), at the limited cost of only a crude estimate of the optical depth of the forested area (better than 35% uncertainty). This study makes use of an unprecedented data set of airborne L-band observations and ground supporting data from the National Airborne Field Experiment 2005 (NAFE''05), which allowed accurate characterisation of the land surface heterogeneity over an area equivalent in size to a SMOS pixel. [Copyright &y& Elsevier]

Soil moisture will be mapped globally by the European Soil Moisture and Ocean Salinity (SMOS) mission to be launched in 2009. The expected soil moisture accuracy will be 4.0 %v/v. The core component of the SMOS soil moisture retrieval algorithm is the L-band Microwave Emission of the Biosphere (L-MEB) model which simulates the microwave emission at L-band from the soil–vegetation layer. The model parameters have been calibrated with data acquired by tower mounted radiometer studies in Europe and the United States, with a typical footprint size of approximately 10 m. In this study, aircraft L-band data acquired during the National Airborne Field Experiment (NAFE) intensive campaign held in South-eastern Australia in 2005 are used to perform the first evaluation of the L-MEB model and its proposed parameterization when applied to coarser footprints (62.5 m). The model could be evaluated across large areas including a wide range of land surface conditions, typical of the Australian environment. Soil moisture was retrieved from the aircraft brightness temperatures using L-MEB and ground measured ancillary data (soil temperature, soil texture, vegetation water content and surface roughness) and subsequently evaluated against ground measurements of soil moisture. The retrieval accuracy when using the L-MEB ‘default’ set of model parameters was found to be better than 4.0 %v/v only over grassland covered sites. Over crops the model was found to underestimate soil moisture by up to 32 %v/v. After site specific calibration of the vegetation and roughness parameters, the retrieval accuracy was found to be equal or better than 4.8 %v/v for crops and grasslands at 62.5-m resolution. It is suggested that the proposed value of roughness parameter H R for crops is too low, and that variability of H R with soil moisture must be taken into consideration to obtain accurate retrievals at these scales. The analysis presented here is a crucial... [Copyright &y& Elsevier]

The baseline radiometer brightness temperature ( Tb ) downscaling algorithm for NASA's Soil Moisture Active Passive (SMAP) mission, scheduled for launch in January 2015, is tested using an airborne simulation of the SMAP data stream. The algorithm synergistically uses 3 km Synthetic Aperture Radar (SAR) backscatter ( σ ) to downscale a 36 km radiometer Tb to 9 km. While the algorithm has already been tested using experimental datasets from field campaigns in the USA, it is imperative that it is tested for a comprehensive range of land surface conditions (i.e. in different hydro-climatic regions) before global application. Consequently, this study evaluates the algorithm using data collected from the Soil Moisture Active Passive Experiments (SMAPEx) in south-eastern Australia, that closely simulate the SMAP data stream for a single SMAP radiometer pixel over a 3-week interval, with repeat coverage every 2–3 days. The results suggest that the average root-mean-square error (RMSE) in downscaled Tb is 3.1 K and 2.6 K for h - and v -polarizations respectively, when downscaled to 9 km resolution. This increases to 8.2 K and 6.6 K when applied at 1 km resolution. Downscaling over the relatively homogeneous grassland areas resulted in 2 K lower RMSE than for the heterogeneous cropping area. Overall, the downscaling error was around 2.4 K when applied at 9 km resolution for five of the nine days, which meets the 2.4 K error target of the SMAP mission. [ABSTRACT FROM AUTHOR]