Hyperspectral Analysis for Protected Agriculture land cover mapping: A Remote Sensing Approach

Spaceborne and airborne remote sensing data serve as powerful tools for the analysis andmonitoring of both urban and agricultural territories, with diverse applications contingent upon spatialresolution. In recent years, remote sensing imagery has been utilized for the recognition of protectedagriculture landcovers, such as greenhouses and mulch. Various studies in the scientific literature havefocused on satellite sensors like Sentinel-2 and WorldView-3, mapping the presence of protectedagriculture surfaces and implementing specific indices for recognition.A recurrent limitation in these studies lies in the often insufficient spatial resolution of the sensors,particularly for identifying smaller-sized greenhouses. Additionally, spectral resolution is crucial. Whilesome laboratory studies analyse the spectral characteristics of plastic surfaces typical of protectedagriculture, they often neglect the issue of mixed pixels inherent in satellite or aerial detection.The aim of this study is to analyze images from the AVIRIS airborne sensor over the agricultural area ofSalerno in southern Italy. AVIRIS, a hyperspectral sensor with over 400 bands covering the visible (VIS) tothe shortwave infrared (SWIR) region, provided images with a spatial resolution of 1m and 3m. Wescrutinize these images to discern the spectral signatures of different types of greenhouses in the studyarea, subsequently comparing them with other land cover classes. For this, we employ supportive tools,including specific spectral indices and transformations such as Tasselled Cap and Principal ComponentsAnalysis (PCA). We implement the Region of Interest (ROI) separability technique to identify distinctivespectral features in the signatures of protected agriculture coverings that differentiate them from othersurfaces. Finally, the spectral signatures obtained from AVIRIS offer the opportunity to simulate spectralresponses of other satellite sensors with lower spatial and/or spectral resolutions, assessing the suitabilityof currently available data for recognizing this specific type of surface.