Articles | Volume 25, issue 5
https://doi.org/10.5194/hess-25-2739-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-25-2739-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
25 May 2021
Research article |
| 25 May 2021
| 25 May 2021
Advances in soil moisture retrieval from multispectral remote sensing using unoccupied aircraft systems and machine learning techniques
Earth System Science, Stanford University, Stanford, CA, USA
Anna Fryjoff-Hung
Center for Information Technology in the Interest of Society and the Banatao Institute, University of California, Merced, Merced, CA, USA
Andreas Anderson
Center for Information Technology in the Interest of Society and the Banatao Institute, University of California, Merced, Merced, CA, USA
Joshua H. Viers
Center for Information Technology in the Interest of Society and the Banatao Institute, University of California, Merced, Merced, CA, USA
Department of Civil and Environmental Engineering, University of California, Merced, Merced, CA, USA
Teamrat A. Ghezzehei
Center for Information Technology in the Interest of Society and the Banatao Institute, University of California, Merced, Merced, CA, USA
Life and Environmental Science, University of California, Merced, Merced, CA, USA
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- How to combine socioeconomic assessment and remote sensing methods to recover and group farm plots at risk of abandonment C. Calafat-Marzal et al. 10.1080/1747423X.2023.2234921
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- Quantification of soil water content by machine learning using enhanced high-resolution ERT F. Meng et al. 10.1016/j.jhydrol.2024.131994
- The Potential of Optical UAS Data for Predicting Surface Soil Moisture in a Peatland across Time and Sites R. de Lima et al. 10.3390/rs14102334
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- Enhancing soil moisture retrieval in semi-arid regions using machine learning algorithms and remote sensing data X. Duan et al. 10.1016/j.apsoil.2024.105687
- Precision Irrigation Soil Moisture Mapper: A Thermal Inertia Approach to Estimating Volumetric Soil Water Content Using Unmanned Aerial Vehicles and Multispectral Imagery K. Wienhold et al. 10.3390/rs16101660
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- Satellite-Based Estimation of Soil Moisture Content in Croplands: A Case Study in Golestan Province, North of Iran S. Bandak et al. 10.3390/rs15082155
- Soil Moisture Mapping with Moisture-Related Indices, OPTRAM, and an Integrated Random Forest-OPTRAM Algorithm from Landsat 8 Images U. Acharya et al. 10.3390/rs14153801
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- Enhanced root zone soil moisture monitoring using multitemporal remote sensing data and machine learning techniques A. Nouraki et al. 10.1016/j.rsase.2024.101354
- Recent Advances in Dielectric Properties-Based Soil Water Content Measurements M. Abdulraheem et al. 10.3390/rs16081328
- Spatial mapping of soil moisture content using very-high resolution UAV-based multispectral image analytics S. Khose & D. Mailapalli 10.1016/j.atech.2024.100467
- Use of Unmanned Aerial Vehicles for Monitoring Pastures and Forages in Agricultural Sciences: A Systematic Review W. Santos et al. 10.3390/drones8100585
- Applications of Machine Learning and Remote Sensing in Soil and Water Conservation Y. Kim et al. 10.3390/hydrology11110183
- Analysis of Unmanned Aerial System (UAS) Sensor Data for Natural Resource Applications: A Review B. Fraser et al. 10.3390/geographies2020021
- Spatial-temporal constraints for surface soil moisture mapping using Sentinel-1 and Sentinel-2 data over agricultural regions Z. Ya'nan et al. 10.1016/j.compag.2024.108835
- A Remote Sensing Driven Soil Moisture Estimator: Uncertain Downscaling With Geostatistically Based Use of Ancillary Data M. Karamouz et al. 10.1029/2022WR031946
- UAV-enabled approaches for irrigation scheduling and water body characterization M. Yadav et al. 10.1016/j.agwat.2024.109091
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Latest update: 22 Nov 2024
Short summary
We took aerial photos of a grassland area using an unoccupied aerial vehicle and used the images to estimate soil moisture via machine learning. We were able to estimate soil moisture with high accuracy. Furthermore, by analyzing the machine learning models we developed, we learned how different factors drive the distribution of moisture across the landscape. Among the factors, rainfall, evapotranspiration, and topography were most important in controlling surface soil moisture distribution.
We took aerial photos of a grassland area using an unoccupied aerial vehicle and used the images...
