Unmixing-Based Fusion of Hyperspatial and Hyperspectral Airborne Imagery for Early Detection of Vegetation Stress

Abstract

"© 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.” Upon publication, authors are asked to include either a link to the abstract of the published article in IEEE Xplore®, or the article’s Digital Object Identifier (DOI).Many applications require a timely acquisition of high spatial and spectral resolution remote sensing data. This is often not achievable since spaceborne remote sensing instruments face a tradeoff between spatial and spectral resolution, while airborne sensors mounted on a manned aircraft are too expensive to acquire a high temporal resolution. This gap between information needs and data availability inspires research on using Remotely Piloted Aircraft Systems (RPAS) to capture the desired high spectral and spatial information, furthermore providing temporal flexibility. Present hyperspectral imagers on board lightweight RPAS are still rare, due to the operational complexity, sensor weight, and instability. This paper looks into the use of a hyperspectral-hyperspatial fusion technique for an improved biophysical parameter retrieval and physiological assessment in agricultural crops. First, a biophysical parameter extraction study is performed on a simulated citrus orchard. Subsequently, the unmixing-based fusion is applied on a real test case in commercial citrus orchards with discontinuous canopies, in which a more efficient and accurate estimation of water stress is achieved by fusing thermal hyperspatial and hyperspectral (APEX) imagery. Narrowband reflectance indices that have proven their effectiveness as previsual indicators of water stress, such as the Photochemical Reflectance Index (PRI), show a significant increase in tree water-stress detection when applied on the fused dataset compared to the original hyperspectral APEX dataset (R-2 = 0.62, p 0.1). Maximal R-2 values of 0.93 and 0.86 are obtained by a linear relationship between the vegetation index and the resp., water and chlorophyll, parameter content maps.This work was supported in part by the Belgian Science Policy Office in the frame of the Stereo II program (Hypermix project-SR/00/141), in part by the project Chameleon of the Flemish Agency for Innovation by Science and Technology (IWT), and in part by the Spanish Ministry of Science and Education (MEC) for the projects AGL2012-40053-C03-01 and CONSOLIDER RIDECO (CSD2006-67). The European Facility for Airborne Research EUFAR (www.eufar.net) funded the flight campaign (Transnational Access Project 'Hyper-Stress'). The work of D. S. Intrigliolo was supported by the Spanish Ministry of Economy and Competitiveness program "Ramon y Cajal."Delalieux, S.; Zarco-Tejada, PJ.; Tits, L.; Jiménez Bello, MÁ.; Intrigliolo Molina, DS.; Somers, B. (2014). Unmixing-Based Fusion of Hyperspatial and Hyperspectral Airborne Imagery for Early Detection of Vegetation Stress. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 7(6):2571-2582. https://doi.org/10.1109/JSTARS.2014.2330352S257125827