UAV based soil salinity assessment of cropland

Abstract

Increased soil salinity is a significant agricultural problem that decreases yields for common agricultural crops. Its dynamics require cost and labour effective measurement techniques and widely acknowledged methods are not present yet. We investigated the potential of Unmanned Aerial Vehicle (UAV) remote sensing to measure salt stress in quinoa plants. Three different UAV sensors were used: a WIRIS thermal camera, a Rikola hyperspectral camera and a Riegl VUX-SYS Light Detection and Ranging (LiDAR) scanner. Several vegetation indices, canopy temperature and LiDAR measured plant height were derived from the remote sensing data and their relation with ground measured parameters like salt treatment, stomatal conductance and actual plant height is analysed. The results show that widely used multispectral vegetation indices are not efficient in discriminating between salt affected and control quinoa plants. The hyperspectral Physiological Reflectance Index (PRI) performed best and showed a clear distinction between salt affected and treated plants. This distinction is also visible for LiDAR 2 measured plant height, where salt treated plants were on average 10 centimetres shorter than control plants. Canopy temperature was significantly affected, though detection of this required an additional step in analysis -Normalised difference Vegetation Index (NDVI) clustering. This step assured temperature comparison for equally vegetated pixels. Data combination of all three sensors in a multiple linear regression model increased the prediction power and for the whole dataset R 2 reached 0.46, with some subgroups reaching an R 2 of 0.64. We conclude that UAV borne remote sensing is useful for measuring salt stress in plants and a combination of multiple measurement techniques is advised to increase the accuracy.

Quantitative Estimation of Soil Salinity Using UAV-Borne Hyperspectral and Satellite Multispectral Images

“…Hyperspectral Imager 2.0 software (Rikola Ltd., Oulu, Finland) could help users of the UAV-borne Rikola hyperspectral sensor carry out sensor parameter settings, real-time imaging, image quality evaluation, and image preprocessing such as dark current correction. The dark current correction was carried out using a dark current measurement taken before the flight by covering the lens, and the raw images were converted to at-sensor-radiance images after the dark current correction [35]. The radiance images were then transformed into reflectance factor images through empirical line method using the measurement of the reference panel taken before each UAV flight [40].…”

“…Studies using UAV images for soil salinity detection and mapping were still rare. Ivushkin et al [35] have tried combining a WIRIS thermal camera, a Rikola hyperspectral camera and a Riegl VUX-SYS LiDAR scanner to measure salt stress in quinoa plants, and they found UAV-borne remote sensing to be a useful technique for salt stress measurements. Romero-Trigueros et al [36] concluded that the red and near-infrared bands were critical to assess the saline stress Citrus suffered from.…”

A Survey of Recent Extended Variants of the Traveling Salesman and Vehicle Routing Problems for Unmanned Aerial Vehicles

“…mounted on UAVs. Drones can also be useful for identifying diseased plants early and to establish an inventory of crops or to assess the soil salinity of arable land [7]. For industrial warehouses, UAVs are also useful for conveniently establishing inventories through RFID [8].…”

Biased Sampling Potentially Guided Intelligent Bidirectional RRT∗ Algorithm for UAV Path Planning in 3D Environment

“…Nowadays, Unmanned Aerial Vehicles (UAV) are being widely applied to military or civilian fields, e.g., mining surveillance [1], search and rescue operations [2], image recognition [3], cropland assessment [4], and so on. e increasing demands have brought in focus on reducing dependencies on human operators.…”