The Correlation between Proximal and Remote Sensing Methods for Monitoring Soil Water Content in Agricultural Applications
Abstract
:1. Introduction
2. Materials and Methods
2.1. Proximal Monitoring—Geoelectric Survey
2.2. Remote Monitoring—Satellite Reading
2.3. Monitoring of Meteorological Precipitation
2.4. Dataset and Statistical Analysis
- the measurements for each geographic soil coordinate of the electrical conductivity obtained from the ARP system;
- the difference in electrical conductivity detected by the surface sensors compared to those placed in the depth of the network of the transmitter nodes, with a frequency of 15 min;
- The spectral indices NDWI and MCARI2 are calculated by the Sentinel 2 source.
3. Results
3.1. Comparison between Sensor Grid Maps (R1) and Corresponding Satellite Maps (R2)
3.2. Comparison between Sensor Grid Maps (R1) and Corresponding Geoelectric Maps (R3)
3.3. Comparison between Geoelectric Maps (R3) and Corresponding Satellite Maps (R2)
4. Discussion
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Date | Condition | mm Rain (Last 5 Days Cumulative) |
---|---|---|
25 July 2020 | corn | 66.50 |
30 July 2020 | corn | 42.80 |
9 August 2020 | corn | 40.00 |
14 August 2020 | corn | 0.00 |
19 August 2020 | corn | 40.00 |
13 September 2020 | corn | 0.00 |
19 September 2020 | corn | 0.00 |
17 November 2020 | soil | 4.30 |
22 November 2020 | soil | 0.00 |
27 December 2020 | soil | 42.40 |
16 January 2021 | triticale | 0.00 |
15 February 2021 | triticale | 16.50 |
25 February 2021 | triticale | 0.00 |
2 March 2021 | triticale | 0.00 |
7 March 2021 | triticale | 0.00 |
17 March 2021 | triticale | 0.00 |
22 March 2021 | triticale | 0.00 |
Condition (Corn, Soil, Triticale) | Moisture (>40 mm) | Interaction Condition-Moiture | |
---|---|---|---|
R1-R2 (MCARI2) | *** (corn) | n.s. | *** |
R1-R2 (NDWI) | *** (corn) | *** | *** |
R1-R3 | *** (soil, triticale) | *** | *** |
R3-R2 (MCARI2) | *** (triticale) | *** | *** |
R3-R2 (NDWI) | *** (triticale) | *** | *** |
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Romano, E.; Bergonzoli, S.; Bisaglia, C.; Picchio, R.; Scarfone, A. The Correlation between Proximal and Remote Sensing Methods for Monitoring Soil Water Content in Agricultural Applications. Electronics 2023, 12, 127. https://doi.org/10.3390/electronics12010127
Romano E, Bergonzoli S, Bisaglia C, Picchio R, Scarfone A. The Correlation between Proximal and Remote Sensing Methods for Monitoring Soil Water Content in Agricultural Applications. Electronics. 2023; 12(1):127. https://doi.org/10.3390/electronics12010127
Chicago/Turabian StyleRomano, Elio, Simone Bergonzoli, Carlo Bisaglia, Rodolfo Picchio, and Antonio Scarfone. 2023. "The Correlation between Proximal and Remote Sensing Methods for Monitoring Soil Water Content in Agricultural Applications" Electronics 12, no. 1: 127. https://doi.org/10.3390/electronics12010127
APA StyleRomano, E., Bergonzoli, S., Bisaglia, C., Picchio, R., & Scarfone, A. (2023). The Correlation between Proximal and Remote Sensing Methods for Monitoring Soil Water Content in Agricultural Applications. Electronics, 12(1), 127. https://doi.org/10.3390/electronics12010127