Measuring soil moisture with imaging radars

An empirical algorithm for the retrieval of soil moisture content and surface Root Mean Square (RMS) height from remote.ly sensed radar data was developed using scatterorneter data. The algorithm is optimized for bare surfaces and requires two co-polarized channels at a frequency between 1.5 G1lz and 11 GHz. It gives best results for kh <2.5, p, < 35% and 0230° Omitting the usually weaker hv-polarized returns makes the algorithm less sensitive to system cross-talk and system noise, simplify the calibration process and adds robustness to the algorithm in the presence of vegetation. However, inversion results indicate that significant amounts of vegetation (NDVI > 0.4) cause the algorithm to underestimate soil moisture and overestimate RMS hc.ight. A simple criteria based on the @v/cJ~v ratio is developed to select the areas where the inversion is not impaired by the vegetation. The inversion accuracy is assessed on the original scatterometm data sets but also on several SAR data sets by comparing the derived soil moisture values with in-situ measurements collected over a variety of scenes between 1989 and 1994. Both spaceborne (SIR-C) and airborne (AIRSAR) data are used in the test. Over this large sample of conditions, the RMS error in the soil moisture estimate is found to be less than 3.5% soil moisture. (*): Jet Propulsion Laboratory, California Institute of Technology 4800 Oak Grove Drive, Pasadena CA 91009 (**) NASA Goddarcl Space IUight Center Code 974, Greenbelt,MlJ20771 E-mail: [email protected], nasa.gov Ph: (818) 3540497 Fx: (818) 3936943