Search

Unmanned airborne systems (UAS), particularly the class of UAS referred to as small-unmanned airborne systems (S-UAS), have the potential to revolutionize the science, practice, and role of remote sensing. S-UAS-collected remote sensing data differ from that acquired from larger airborne and space-borne platforms in myriad ways. To provide an indication of the novel remote sensing capabilities that S-UAS are poised to enable and identify research priorities for realizing the full potential of remote sensing from these novel platforms, characteristics of S-UAS platforms and their impact on data and information products are analysed in the context of remote sensing model and the remote sensing communication model. Results indicate that S-UAS will not only enable a range of novel remote sensing capabilities but also present clear challenges to the remote sensing community. These challenges, including increased data volume, a paucity of appropriate analysis approaches, and restrictions on autonomous operation (both regulatory and technological), point towards several near-term research priorities. [ABSTRACT FROM AUTHOR]

There are two main parameters describing the amount of water in vegetation: the gravimetric water content (GWC) and the equivalent water thickness (EWT). In this study, we investigated the applicability of hyperspectral water-sensitive indices from canopy spectra for estimating canopy EWT (CEWT) and GWC. First, the spectral reflectance’s response to different levels of canopy water content was analysed and a noticeable increase in the slope of the near-infrared (NIR) shoulder of the canopy spectrum was observed. Next, the correlation between the CEWT and various hyperspectral water-sensitive indices was investigated. It was found that all of the indices could retrieve the CEWT of winter wheat well, with the coefficients of determination (R2) all being higher than 0.80. Finally, the retrieval performance of these indices for canopy GWC was evaluated and no significant correlation was observed between canopy GWC and the water-sensitive indices except for the spectral ratio index in the NIR shoulder region (NSRI). These results showed that the traditional water-sensitive vegetation indices are more sensitive to CEWT than to GWC, especially when the LAI is not highly correlated with the GWC, and that the NSRI is a potential vegetation index for use in the retrieval of GWC. [ABSTRACT FROM PUBLISHER]