J.A.J. Berni

Thermal and Narrowband Multispectral Remote Sensing for Vegetation Monitoring From an Unmanned Aerial Vehicle

Two critical limitations for using current satellite sensors in real-time crop management are the lack of imagery with optimum spatial and spectral resolutions and an unfavorable revisit time for most crop stress-detection applications. Alternatives based on manned airborne platforms are lacking due to their high operational costs. A fundamental requirement for providing useful remote sensing products in agriculture is the capacity to combine high spatial resolution and quick turnaround times. Remote sensing sensors placed on unmanned aerial vehicles (UAVs) could fill this gap, providing low-cost approaches to meet the critical requirements of spatial, spectral, and temporal resolutions. This paper demonstrates the ability to generate quantitative remote sensing products by means of a helicopter-based UAV equipped with inexpensive thermal and narrowband multispectral imaging sensors. During summer of 2007, the platform was flown over agricultural fields, obtaining thermal imagery in the 7.5-13-mum region (40-cm resolution) and narrowband multispectral imagery in the 400-800-nm spectral region (20-cm resolution). Surface reflectance and temperature imagery were obtained, after atmospheric corrections with MODTRAN. Biophysical parameters were estimated using vegetation indices, namely, normalized difference vegetation index, transformed chlorophyll absorption in reflectance index/optimized soil-adjusted vegetation index, and photochemical reflectance index (PRI), coupled with SAILH and FLIGHT models. As a result, the image products of leaf area index, chlorophyll content (C ab), and water stress detection from PRI index and canopy temperature were produced and successfully validated. This paper demonstrates that results obtained with a low-cost UAV system for agricultural applications yielded comparable estimations, if not better, than those obtained by traditional manned airborne sensors.

A new era in remote sensing of crops with unmanned robots

In the 1980s, remote-sensing technology and methods were proposed as a solution for environmental problems because they could continuously monitor the earth’s surface. A number of satellites were launched operating in active (i.e., providing their own illumination) and passive (i.e., recording the natural radiation) modes with capabilities ranging from monitoring large spatial swaths at high temporal resolution to high-spatialresolution imaging at low repeat cycles. The emerging technology and its potential outcomes were oversold, however: current applications in precision agriculture are limited to high-spatialresolution satellite sensors providing coarse spectral resolution and sparsely sampled revisit times. Key constraints for successful application of remote sensing in precision agriculture include, among others, very high spatial resolution (pixel sizes of <1m), access to visible, near-infrared, and thermal spectral bands, and use of bandwidths allowing estimation of key crop biophysical parameters such as the concentration of chlorophyll a and b, xanthophylls, carotenoids, anthocyanins, water, and dry matter, as well as leaf-area index and crop temperature. Availability of imaging at critical cropphenological stages combined with fast turnaround times is an additional key factor. Since the combination of all of these factors cannot be met with current satellite sensors, applications of remote sensing in agriculture are limited to ‘demonstration’ studies in dedicated experimental fields using high-resolution airborne sensors, crop classification for inventory purposes, and planning studies. Nevertheless, although airborne remote sensing has proved its potential, limitations for actual implementation are driven by the cost of imaging campaigns with full-size airplanes, and the financial and technical difficulties associated with frequent image acquisition. Current methods for remote detection of plant-physiology status rely therefore almost enFigure 1. High-resolution multispectral imaging of an orchard acquired with a fixed-wing unmanned aerial vehicle (UAV).