Sensitivity of a soil-moisture remote sensing algorithm at L band to variations in input parameters

This study investigated the uncertainties inherent in the estimation of soil moisture from variations inherent in the measurement of soil temperature, texture, bulk density, surface roughness, and vegetation water content. Algorithms to derive soil moisture from brightness temperature have been developed, tested and validated at point, field and spatial scales. These algorithms consist of empirical components and their sensitivities to variations in input parameters have not been adequately assessed. We investigated the sensitivity of a passive soil moisture retrieval algorithm that incorporates the Wang and Schmugge dielectric mixing model. Parameters were tested both individually and in combination. Sensitivity is quantified as the difference in observed volumetric soil moisture calculated with the mean parameters and plus or minus 3(sigma) of the individual parameter or their combinations. The algorithm is relatively insensitive to variations in surface temperature, soil temperature at 15 cm depth, as well as clay and sand contents. The algorithm is, however, sensitive to variations in bulk density (maximum volumetric moisture deviation of 11.4%) and surface roughness (9.0% deviation) and highly sensitive to vegetation water content (17.6% deviation). The algorithm is most sensitive to surface roughness and vegetation moisture content under wet conditions. The algorithm is insensitive to the simultaneous variations in surface temperature and clay content within the same textural class. Permutations of variations in bulk density, surface roughness and vegetation moisture content produce a significant compounding effect on sensitivity of the algorithm. Deviations in volumetric soil moisture resulting from the combination of variables often exceed the sum of the deviations due to the individual parameters tested alone. This study suggests that large variations in the determination of surface temperature will have negligible effect on the soil moisture derived using the algorithm. Slight deviations from the mean of the clay content will not significantly affect the precision of soil moisture calculated. On the other hand, precise values of bulk density, surface roughness, and vegetation moisture content are necessary in the algorithm for precise derivation of soil moisture from microwave remote sensing. Further studies on other mixing models at multi- frequencies are recommended.