|Bindlish, R - Science Systems, Inc.|
|Henlsey, S - Jet Propulsion Laboratory|
Submitted to: IEEE Transactions on Geoscience and Remote Sensing
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 5/9/2012
Publication Date: 3/1/2013
Publication URL: http://handle.nal.usda.gov/10113/56285
Citation: Mladenova, I., Jackson, T.J., Bindlish, R., Henlsey, S. 2013. Incidence angle normalization of radar backscatter data. IEEE Transactions on Geoscience and Remote Sensing. 51:1791-1804.
Interpretive Summary: One of the challenges with using the available operational satellite-based soil moisture data sets for regional agriculture and small-scale hydrologic applications is their coarse spatial resolution. Resolving this issue is one of the main objectives targeted by an upcoming soil moisture satellite called Soil Moisture Active Passive (SMAP), scheduled for launch in late 2014. Retrieval methods need to be developed and validated. Aircraft simulators can provide the necessary data but have limitations, specifically most observe the ground at multiple angles, as compared to the satellite, which views the Earth at a fixed angle. Here we developed an angular correction approach that allows a multi-angular aircraft data set to be modified to a fixed angle by employing statistical properties. The method was evaluated using aircraft observations. We found that the performance of the approach as well as the accuracy of the corrected data were independent of vegetation type, terrain properties and instruments characteristics. Furthermore, the approach does not rely on any prior instrument-specific knowledge, which makes it easily transferable. High resolution soil moisture products, such as the ones that are anticipated from SMAP will be of significant value for improved crop condition monitoring, water management and yield estimation.
Technical Abstract: NASA’s Soil Moisture Passive Active (SMAP) satellite (~2014) will include a radar system that will provide L-band multi-polarization backscatter at a constant incidence angle of 40º. During the pre-launch phase of the project there is a need for observations that will support the radar-based soil moisture algorithm development and validation. A valuable resource for providing these observations is the NASA Jet Propulsion Laboratory Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR). However, SMAP will observe at a constant incidence angle of 40 degrees and UAVSAR collects data over a wide range of incidence angles (25°-60°). In this investigation, a technique was developed and tested for normalizing UAVSAR data to a constant incidence angle. The approach is based on a histogram matching procedure. The data used to develop and demonstrate this approach were collected as part of the Canadian soil moisture experiment 2010 (CanEx-SM10). Land cover in the region included agriculture and forest. Evaluation was made possible by the acquisition of numerous overlapping UAVSAR flight lines that provided multiple incidence angle observations of the same locations. Actual observations at a 40º incidence angle were compared to the normalized data to assess performance of the normalization technique. An optimum technique should be able to reduce the systematic error (bias) to 0 dB and to lower the total root mean square error (RMSE) computed after correction to the level of the initial un-biased error (ubRMSE) present in the data set. The normalization approach developed here achieved both of these. Bias caused by the incidence angle variability was minimized to ~0 dB, whereas the un-biased error was reduced to approximately 3 dB for agricultural areas and 2.6 dB for forests; these values were consistent with the initial ubRMSE estimated using the un-corrected data. The un-biased error can be reduced further by aggregating the radar observations to a coarser grid spacing. The technique adequately adjusted the backscatter over the full swath width irrespective of the original incidence angle, polarization, and ground conditions (vegetation cover and soil moisture). In addition to providing a basis for fully exploiting UAVSAR (or similar aircraft systems) for SMAP algorithm development and validation, the technique could also be adapted to satellite radar systems. This normalization approach will also be beneficial in terms of reducing the number of flight lines required to cover a study area, which would eventually result in more cost-effective soil moisture field campaigns.