Calibration of Satellite Imagery with Multispectral UAV Imagery
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RESEARCH ARTICLE
Calibration of Satellite Imagery with Multispectral UAV Imagery Kamal Jain1 • Akshay Pandey2 Received: 21 October 2020 / Accepted: 28 October 2020 Ó Indian Society of Remote Sensing 2020
Abstract Unmanned aerial vehicle (UAV)-based multispectral remote sensing has shown a tremendous potential normalized difference vegetation index (NDVI) for precision agriculture. In this study, data captured from a UAV equipped with a Multispectral Mica Sense Red Edge camera used as ground-truth information to calibrate Sentinel-2 imagery. UAV-based NDVI allowed crop estimation at 10-cm pixel resolution by discriminating no-green vegetation pixels. The reflectance value and NDVI of the crops at different stages were derived from both UAV and Sentinel-2 images. The UAV Multispectral mapping method used in this study provided advanced information about the physical conditions of the study area (Roorkee) and improved land feature delineation. The result shows that UAV data produced more accurate reflectance values than Sentinel-2 imagery. However, the accuracy of the vegetation index is not wholly dependent on the accuracy of the reflectance. The UAV-derived NDVI has relatively low sensitivity to the vegetation coverage and insignificantly affected by environmental factors compared to NDVI derived from Sentinel-2 image. Keywords Sentinel-2 UAV Multispectral Mica Sense Red Edge Vegetation indices
Introduction In remote sensing, medium-resolution imageries showed a high capacity to quantify the changes, development, and damages in croplands. The MODIS and Landsat images are used extensively for analysis. Sentinel-2 has finer-resolution images comparatively because of this; it constitutes a significant asset for this crop-based application (Inglada et al. 2015; Belgiu and Csillik 2018). However, these satellite resolutions are coarser to study the types of damages that affect the crops. In this sense, images provided by the high spatial resolution commercial satellites such as Deimos-2, GeoEye-2, QuickBird, WorldView-2 are
& Akshay Pandey [email protected] Kamal Jain [email protected] 1
Department of Civil Engineering, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India
2
Centre of Excellence in Disaster Management and Mitigation, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India
appropriate (Navrozidis et al. 2018; Johansen et al. 2018; C´ulibrk et al. 2011). Unmanned aerial vehicles-based remote sensing opens a broader window to acquire the images in a fast and easy way from the field for precision agriculture. Nowadays, unmanned aerial vehicles or systems (UAS) are gaining significant popularity, by its advantage of acquiring ultrahigh spatial resolution by flying at low altitude. UAV platforms (multirotors, swinglet, model helicopters, etc.) are coupled with imaging, ranging equipment’s. Positioning sensors can collect multispectral imagery at sub-cm resolution and offer great possibilities in the precision farming domain (IAG 2012; Primicerio et al. 2012; Bend
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