EGU2020-1826
https://doi.org/10.5194/egusphere-egu2020-1826
EGU General Assembly 2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Using Unmanned Aerial Vehicle to Obtain Digital Images and Estimating In-Situ Soil Water Content

Ching-Hsiung Wang1, Hong-Ru Lin2, Jyun-Lin Chen3, Shao-Yang Huang3, and Jet-Chau Wen3,4
Ching-Hsiung Wang et al.
  • 1National Yunlin university of science and technology, safety health and environmental engineering, engineering, Douliou, Taiwan (m10614043@yuntech.edu.tw)
  • 2Graduate School of Engineering Science and Technology, National Yunlin University of Science and Technology, 123, Section 3, University Road, Douliou, Yunlin 64002, Taiwan, (R.O.C.)
  • 3Research Center for Soil & Water Resources and Natural Disaster Prevention (SWAN), National Yunlin University of Science and Technology, 123, Section 3, University Road, Douliou, Yunlin 64002, Taiwan (R.O.C.)
  • 4Department and Graduate School of Safety Health and Environmental Engineering, National Yunlin University of Science and Technology, 123, Section 3, University Road, Douliou, Yunlin 64002, Taiwan (R.O.C.)

Soil water content (SWC) is a vital factor for soil sciences. Nowadays, there are many methods for estimating SWC, including the Time-domain reflectometry (TDR) and the Gravimetric method. Nevertheless, most of them may cause damages to soil structure and require a large workforce and resources. The optical method is a non-destructive and cost-efficient; therefore, recommended for SWC estimations.

This study analyses soil samples at the field site, as well as it uses aerial photo-shooting to obtain the digital image distribution of surface soil. Both soil samples and digital images were categorized into groups; 9 in total, depending on time parameters (one group equals one day). More specifically, the gravimetric method was selected for the SWC measurements in the laboratory, while the images were modified in such a way so to match the CIE 1931 XYZ color space resolution for further calculations. Then, comparing the CIE 1931 XYZ color space data with the Soil Water Content correlation of 9 groups by validation.

According to the findings, the sensitivity of CIE 1931 XYZ color space in SWC alternations is high. Additionally, it can be observed that the SWC result data of the model are similar to the SWC measurements; therefore, the CIE 1931 XYZ color space can be applied to agriculture and disaster prevention, and it is a cost-efficient method for SMC estimations, and it can provide several benefits.

How to cite: Wang, C.-H., Lin, H.-R., Chen, J.-L., Huang, S.-Y., and Wen, J.-C.: Using Unmanned Aerial Vehicle to Obtain Digital Images and Estimating In-Situ Soil Water Content, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-1826, https://doi.org/10.5194/egusphere-egu2020-1826, 2019

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