Evaluation of Calibration Method for Field Application of UAV‐Based Soil Water Content Prediction Equation

Abstract

The objective of this study is to monitor the water content of soil quickly and accurately using a UAV. Because UAVs have higher spatial and temporal resolution than satellites, they are currently becoming more useful in remote sensing areas. We developed a water content estimation equation using the color of the soil and suggested a calibration method for field application. Since the resolution of the images taken by the UAV is different according to the altitude, the water content estimation formula is developed by using the images taken at each altitude. In order to calibrate the color difference according to lighting conditions, the calibration method using field data were proposed. The results of the study showed an altitude-specific estimation equation using RGB values of the UAV image through linear regression. The appropriate number of field data needed for calibration for site application of the estimation equation was found between 4 and 10. On-site application results of the proposed calibration method showed RMSE accuracy of 1.8 to 2.9%. Thus, the water content estimation and calibration method proposed in this study can be used in effectively monitoring the water content of soil using UAVs.

A Literature Survey of Unmanned Aerial Vehicle Usage for Civil Applications

“…• Irrigation management: UAVs assist in obtaining critical irrigation data at any time and at a low cost (Kim et al 2019).…”

Quantifying Soil Particle Settlement Characteristics through Machine Vision Analysis Utilizing an RGB Camera

“…Digital image analysis is fast, inexpensive, and repeatable and has a wide range of applications [13][14][15][16][17]. As regards soils, digital image analysis is also being used to predict various soil properties such as water content [12,[18][19][20], density [12,20], soil cracks [21,22], permeability [23,24], settling velocity [25,26] and strength [27,28].…”

Characterization of the Relationship between the Loess Moisture and Image Grayscale Value

“…Drawing upon the principle of the remote sensing technique, the image-based methods have been proposed and increasingly applied to measure soil moisture content [ 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 ].The image-based technique commonly employs an ordinary digital camera to capture soil sample, and the moisture content is derived from soil color using the pre-established calibration curve. It is obvious that the calibration relationship between soil moisture content and its color is the key for employing the image-based technique.…”

“…Rahimi-Ajdadi et al [ 30 ] developed a machine vision model for predicting the soil moisture using image analysis; this model enables an estimate of soil moisture by only using three color features, including the mode of the color channel of blue and means of the color channels of normalized red and blue. Kim et al [ 31 ] developed a water content estimation equation using soil color and suggested a calibration method for field application of this equation to predict soil water content with unmanned aerial vehicle (UAV) images. Besides, Kirillova et al [ 32 ] investigated calibration methods for measuring the color of moist soils with digital cameras and showed that a decrease in soil moisture leads to a significant increase in lightness and yellowness.…”