Assessment of Various Remote Sensing Technologies in Biomass and Nitrogen Content Estimation Using an Agricultural Test Field

Abstract

ABSTRACT:Multispectral and hyperspectral imaging is usually acquired by satellite and aircraft platforms. Recently, miniaturized hyperspectral 2D frame cameras have showed great potential to precise agriculture estimations and they are feasible to combine with lightweight platforms, such as drones. Drone platform is a flexible tool for remote sensing applications with environment and agriculture. The assessment and comparison of different platforms such as satellite, aircraft and drones with different sensors, such as hyperspectral and RGB cameras is an important task in order to understand the potential of the data provided by these equipment and to select the most appropriate according to the user applications and requirements. In this context, open and permanent test fields are very significant and helpful experimental environment, since they provide a comparative data for different platforms, sensors and users, allowing multi-temporal analyses as well. Objective of this work was to investigate the feasibility of an open permanent test field in context of precision agriculture. Satellite (Sentinel-2), aircraft and drones with hyperspectral and RGB cameras were assessed in this study to estimate biomass, using linear regression models and in-situ samples. Spectral data and 3D information were used and compared in different combinations to investigate the quality of the models. The biomass estimation accuracies using linear regression models were better than 90% for the drone based datasets. The results showed that the use of spectral and 3D features together improved the estimation model. However, estimation of nitrogen content was less accurate with the evaluated remote sensing sensors. The open and permanent test field showed to be suitable to provide an accurate and reliable reference data for the commercial users and farmers.

Estimating Biomass and Nitrogen Amount of Barley and Grass Using UAV and Aircraft Based Spectral and Photogrammetric 3D Features

“…We also evaluated the impact of the radiometric processing level on the results. This paper extends our previous work [55], which performed a preliminary study with the barley data using linear regression techniques. The major contributions of this study were the development and assessment of the integrated use of spectral and 3D features in the crop parameter estimation in different conditions, the comparison of RGB and hyperspectral imaging based remote sensing techniques and the consideration of impacts of various parameters, especially the flying height and the radiometric processing level on the results.…”

Remote estimation of crop canopy parameters by statistical regression algorithms for winter rapeseed using Sentinel-2 multispectral images

“…Dry Biomass (DBM) in g/m 2 : for DBM retrieval, the GPR model is selected with the most relevant bands being the blue, green, middle red-edge and near infrared (λ = 560; 740; 490; 842). In related studies [3,7,40,41] it was found that for crops or grass, DBM is best retrieved with visible and near-infrared reflectance which is in accordance with our results.…”

Optimizing Radiometric Processing and Feature Extraction of Drone Based Hyperspectral Frame Format Imagery for Estimation of Yield Quantity and Quality of a Grass Sward

“…The drone-based photogrammetric point clouds have also shown potential in agricultural crop biomass estimation (eg. Bendig et al, 2013;Li et al, 2016;Näsi et al, 2017).…”