Crop Separability from Individual and Combined Airborne Imaging Spectroscopy and UAV Multispectral Data

Jonas E Böhler,Mathias Kneubühler,Michael E Schaepman

doi:10.3390/rs12081256

Abstract

Crop species separation is essential for a wide range of agricultural applications—in particular, when seasonal information is needed. In general, remote sensing can provide such information with high accuracy, but in small structured agricultural areas, very high spatial resolution data (VHR) are required. We present a study involving spectral and textural features derived from near-infrared (NIR) Red Green Blue (NIR-RGB) band datasets, acquired using an unmanned aerial vehicle (UAV), and an imaging spectroscopy (IS) dataset acquired by the Airborne Prism EXperiment (APEX). Both the single usage and combination of these datasets were analyzed using a random forest-based method for crop separability. In addition, different band reduction methods based on feature factor loading were analyzed. The most accurate crop separation results were achieved using both the IS dataset and the two combined datasets with an average accuracy (AA) of >92%. In addition, we conclude that, in the case of a reduced number of IS features (i.e., wavelengths), the accuracy can be compensated by using additional NIR-RGB texture features (AA > 90%).

Highlights

The accurate quantification of crop species in agricultural areas is crucial for various tasks such as decision-making and monitoring [1,2,3]
We present a study involving spectral and textural features derived from near-infrared (NIR) Red Green Blue (NIR-RGB) band datasets, acquired using an unmanned aerial vehicle (UAV), and an imaging spectroscopy (IS) dataset acquired by the Airborne Prism EXperiment (APEX)
This study presents a methodology for separating crops in a highly fragmented landscape with small structured plots, as in the Swiss Plateau, based on two different datasets

Summary

Introduction

The accurate quantification of crop species in agricultural areas is crucial for various tasks such as decision-making and monitoring [1,2,3]. Remote sensing has proved to be a viable alternative to human observations in the field [6,7] Many studies of this kind are, conducted with satellite data [8,9], but the spatial resolution of the majority of these data is too low to provide accurate results at the field level in highly fragmented agricultural areas with small field plots [6,10,11]. Lower flying platforms are an option capable of collecting data at very high spatial resolutions Using remote sensing, such studies for crop separation have been conducted with unmanned aerial vehicles (UAVs) [12,13,14] or airborne imaging spectrometers (IS) [15,16]

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