Abstract
In modern agriculture, the spatially differentiated assessment of the leaf area index (LAI) is of utmost importance to allow an adapted field management. Current hyperspectral satellite systems provide information with a high spectral but only a medium spatial resolution. Due to the limited ground sampling distance (GSD), hyperspectral satellite images are often insufficient for precision agricultural applications. In the presented study, simulated hyperspectral data of the upcoming Environmental Mapping and Analysis Program (EnMAP) mission (30 m GSD) covering an agricultural region were pan-sharpened with higher resolution panchromatic aisaEAGLE (airborne imaging spectrometer for applications EAGLE) (3 m GSD) and simulated Sentinel-2 images (10 m GSD) using the spectral preserving Ehlers Fusion. As fusion evaluation criteria, the spectral angle (αspec) and the correlation coefficient (R) were calculated to determine the spectral preservation capability of the fusion results. Additionally, partial least squares regression (PLSR) models were built based on the EnMAP images, the fused datasets and the original aisaEAGLE hyperspectral data to spatially predict the LAI of two wheat fields. The aisaEAGLE model provided the best results (R2cv = 0.87) followed by the models built with the fused datasets (EnMAP–aisaEAGLE and EnMAP–Sentinel-2 fusion each with a R2cv of 0.75) and the simulated EnMAP data (R2cv = 0.68). The results showed the suitability of pan-sharpened EnMAP data for a reliable spatial prediction of LAI and underlined the potential of pan-sharpening to enhance spatial resolution as required for precision agriculture applications.
Highlights
Ecological conditions and current management techniques have a strong influence on the spatial heterogeneity of agricultural fields
As a result of merging simulated Environmental Mapping and Analysis Program (EnMAP) data with an aisaEAGLE or Sentinel-2 panchromatic band, fused datasets were created, which have the spectral characteristics of EnMAP (82 spectral bands) and the spatial resolution of aisaEAGLE pan (Figure 4b) or Sentinel-2 pan (Figure 4c), respectively
Remote sensing can be regarded as one of the key tools in precision agriculture because it allows the spatial assessment of important crop parameters, and supports decision making for an adapted intra-field treatment
Summary
Ecological conditions and current management techniques have a strong influence on the spatial heterogeneity of agricultural fields. Multi- and hyperspectral remote sensing data can help to overcome these problems In this context, imaging airborne and satellite sensors provide spatial, spectral, and temporal information of agricultural fields that can be used to identify infield variability and can support decision making in precision agriculture [1,2,3,4,5]. Cost-effective and non-destructive assessment of relevant biochemical and structural vegetation properties is of utmost importance to characterize the crop status at leaf and canopy level In this regard, parameters like chlorophyll content, above ground biomass dry matter, nitrogen status, canopy water content and leaf area index (LAI) provide important information for describing current growth conditions, and can be converted into yield-driving state variables (e.g., dry mass increase), which were used for the re-parameterization of agricultural production models [6,7,8,9].
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