Soil Moisture Mapping of Solano Nueva Vizcaya: A Comparison and Review

Soil moisture, defined as the presence or amount of water in the soil, is a critical element in hydrology, agriculture, and climate science, vital for understanding soil health, water cycle, and plant growth. Remote sensing, particularly using satellites like Sentinel-2 which is equipped with Multispectral instruments, offers a powerful tool for monitoring soil moisture across large areas. Therefore, this study was conducted to generate soil moisture maps of Solano Nueva Vizcaya through Geographic Information System by using Sentinel-2 Near InfraRed and Short Wavelength InfraRed bands for the years 2020 and 2025. It further aimed to determine the soil moisture level of the study area and compare soil moisture level differences between the years 2020 and 2025, and at the same time identify the minimum, maximum, and the mean soil moisture per barangay. The results revealed that the municipality of Solano has a minimum soil moisture level increased by 0.304678159, while the maximum decreased by 0.062563121. It was also revealed that most of the dry soil are located within the lowland areas and generally the results indicate a general increasing trend in soil moisture in Solano for the years 2020 and 2025.

Near InfraRed, Sentinel-2, Short Wavelength InfraRed, Soil Moisture

1. Abdulraheem, M.I,. Zhang, W., Li, S., Moshayedi, A.J., Farooque, A., & Hu, J. (2023). Advancement of Remote Sensing for Soil Measurements and Applications: A Comprehensive Review. Sustainability, 15(21), 15444. https://doi.or/10.3390/su152115444 [Google Scholar] [Crossref]

2. Allen, R. G., Pereira, L.S., Raes, D., & Smith, M. (1998). Crop Evapotranspiration: Guidelines for Computing Crop Water Requirements. FAO Irrigation and Drainage Paper. 56, 1-300. https//www.fao.org/3/X0490E/x0490e00.htm.Open access [Google Scholar] [Crossref]

3. Angelopoulou, T., Chabrillat, S., Pignatti, S., Milewski, R., Karyotis, K., Brell, M., Ruhtz, T., Bochtis D., & Zalidis, G. (2023). Evaluation of Airborne HySpex and Spaceborne PRISMA Hyperspectral Remote Sensing Data for Soil Organic Matter and Carbonates Estimation. Remote Sens. 2023, 15(4),1106. https://doi.org/10.3390/rs15041106 [Google Scholar] [Crossref]

4. Babaeian, E., Homaee, M., Montzka, C., Vereecken, H., Norouzi, A. A., & Van Genuchten, M. T. (2016). Soil Moisture Prediction of Bare Soil Profiles Using Diffuse Spectral Reflectance Information and vadose Zone Flow Modeling. Remote Sens. Environ. 187, 218-229. https://doi.org/10.1016/j.rse.2016.10.029 [Google Scholar] [Crossref]

5. Bindlish, N., & Cosh, M. H. (2013). Remote Sensing of Soil Moisture: Recent Advances and Challenges. Hydrology and Earth System Sciences. 17(6), 2217-2230. Open Access. [Google Scholar] [Crossref]

6. Burrough, P. A., & McDonnell, R. A. (1998). Principles of Geographical Information Systems. Oxford University Press. https://global.oup.com/academic/product/principles-of-information-systems-9780198742845. Open Access [Google Scholar] [Crossref]

7. Dorigo, W., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel M., & Gruber, A. (2017). ESCA CCI Soil Moisture for Improved Earth System Understanding: State-of-the-art and future directions. https://doi.org/10.1016/j.rse.2017.07.01 [Google Scholar] [Crossref]

8. Drusch, M., Del Bello, U., Carlier, S., Colin O., Fernandez, V., Gascon, F., & Plummer, S. (2012). Sentinel-2: ESA’s Optical High-Resolution Mission for GMES Operational Services. Proccedings of the Snetinel-2 Symposium, 1-12. Remote Sensing of Environment. https://ui.adsabs.harvard.edu/link_gateway/2012RSEnv.120...25D/doi:10.1016/j.rse.2011.11.026 Open Access [Google Scholar] [Crossref]

9. El Hajj, M., Baghdadi, N., Zribi, M., & Bazzi, H. (2017). Synergic Use of Sentinel-1 and Sentinel-2 Images for Operational Soil Moisture Mapping at High Spatial Resolution over Agricultural Areas. Remote Sensing, 9(12), 1292. https://doi.org/10.3390/rs9121292 [Google Scholar] [Crossref]

10. Entekhabi et.al. (2010). The Soil Moisture Active Passive (SMAP) Mission, Proceedings of the IEEE. https://www.researchgate.net/publication/224136559. Open Access [Google Scholar] [Crossref]

11. Esmaeili Sarteshnizi, R., Sahebi Vayghan, S., & Jazirian, I. (2023). Estimation of Soil Moisture Using Sentinel-1 and Sentinel-2 Images. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, X-4/W, 137144. https://doi.org/10.5194/isprs-annals-X-4WI-2022-137-2023 [Google Scholar] [Crossref]

12. European Space Agency (ESA). (2022). Sentinel-2: Multispectral Instrument, ESA. https://www.esa.int/Applications/Observing_the_Earth/Copernicus/Sentinel-2. Open Access [Google Scholar] [Crossref]

13. Gholizadeh, A., & Kopackova, V. (2019). Detecting Vegetation Stress as a Soil Contamination Proxy: A Review of Optical Proximal and Remote Sensing Techniques. Int. J. Environ. Sci. Technol. 2019, 16, 2511-2524. https:/link.springer.com/article/10.1007/s13762-019-02310-w. Open Access [Google Scholar] [Crossref]

14. Jackson, T. J., & Hsu, A. Y. (2024). Soil Moisture Retrieval from Space: A Review. IEEE. Transaction on Geoscience and Remote Sensing, 42(8), 1740-1753. Open Access [Google Scholar] [Crossref]

15. Jafarbiglu, H., & Pourreza, A. A. (2022). A Comprehensive Review of Remote Sensing Platforms, Sensors, and Applications in Nut Crops. Comput.Election.Agric.2022. https://doi.org/10.1016/j.compag.2022.106844 [Google Scholar] [Crossref]

16. Jones, H.G., & Vaughan, R.A. (2011). Remote Sensing of Vegetation: Principles, Techniques, and Applications. Oxford University Press. https://doi.org/10.1111/j.1654-1103.2011.01319.x [Google Scholar] [Crossref]

17. Khanal, S., Fulton, J, & Shearer, S. (2017). An Overview of Current and Potential Applications of Thermal Remote Sensing in Precision Agriculture. Comput. Electron. Agric. 2017, 139, 22-32. https://doi.org/10.1016/j.compag.2017.05.001 [Google Scholar] [Crossref]

18. Manfreda, S., McCabe, M. F., Miller, P. E., Lucas, R., Pajuelo Madrigal, V., Mallinis, G., Ben Dor, E., Helman, D., Estes, L., & Ciraolo, G. (2018). On the Use of Unmanned Aerial Systems for Environmental Monitoring. Remote Sens. 2018, 10, 641. https://doi.org/10.3390/rs10040641 [Google Scholar] [Crossref]

19. Metcalfe, M., Cracknell, A. P., & Vaughan R. A. (1994). Remote Sensing of Soil Moisture Content: A Methodology for Estimating the Spatial Average in Large Areas. International Journal of Remote Sensing, 15(8), 1707-1717. https://www.mdpi.com/2072-4292/10/4/641 [Google Scholar] [Crossref]

20. NASA MODIS. (2023). MODIS Overview and Capabilities. “NASA Goddard Space Flight Center. https://modis.gsfc.nasa.gov/about/. Open Access [Google Scholar] [Crossref]

21. Rizzi, R. (2022). Active and Passive Microwave Remote Sensing for Soil Moisture Monitoring. Remote Sensing, 14(3), 578. Open Access [Google Scholar] [Crossref]

22. Sahbeni, G., Ngabire, M., Musyimi, P. K., & Szekely, B. (2023). Challenges and opportunities in Remote Sensing for Soil Salinization Mapping and Monitoring: A Review. https://doi.org/10.3390/rs15102540 [Google Scholar] [Crossref]

23. Schmugge, T. J. (1980). Remote Sensing of Soil Moisture: An Overview. Hydrological Sciences Journal, 25, 1-16. Open Access [Google Scholar] [Crossref]

24. Settu, P., & Ramaiah, M. (2024). Estimation of Sentinel-1 Derived Soil Moisture Using Modified Dubois Model. Environ Dev Sustain 26, 29677-29693. https://doi.org/10.1007/s10668-024-05460-1 [Google Scholar] [Crossref]

25. Veronese, F. et al. (2019). Estimating Soil Moisture from Sentinel-2 MSI Data Using Machine Learning Algorithms. Remote Sensing of Environment, 231, 111218. https://doi.org/10.1007/j.rse.2019.111218 [Google Scholar] [Crossref]

26. Yahia, O Guida, R., & Iervolino, P. (2021). Novel Weight-Based Approach for Soil Moisture Content Estimation Via Synthetic Aperture Radar, Multispectral and Thermal Infrared Data Fusion. https://doi.org/10.3390/s21103457 [Google Scholar] [Crossref]

27. Yao, H., Qin, R., & Chen, X. (2019). Unmanned Aerial Vehicle for Remote Sensing Applications: A Review. Remote Sens, 2019, 11, 1443. https://doi.org/10.3390/rs11121443 [Google Scholar] [Crossref]

28. Zhang, S., Roussel, N., Boniface, K., Ha, M. C., Frappart, F., Darrozes, J., Baup, F., & Calvet, J. C. (2017). Use of Reflected GNSS SNR Data to Retrieve Either Soil Moisture or Vegentation Height from a Wheat Crop. https://doi.org/10.5194/hess21-4767-2017 [Google Scholar] [Crossref]

29. Zribi, M., Kotti, F., Amri, R., Wagner, W., Shabou, M., Lili-Chabaane, Z, & Baghdadi, N, (2014). Soil Moisture Mapping in a Semiarid Region based on ASAR/Wide Swath Satellite Data. Water Resource Res. 2014, 50, 823-835. https://doi.org/10.1002/2012WR013405 [Google Scholar] [Crossref]

• Performance Analysis of Silicon PV Array Using Infrared Thermography and Detecting Temperature Non-Uniformities
• Dynamic Capabilities and Digital Transformation Performance: The Role of Organizational Agility in the Public Sector
• Comparative Effectiveness of Neural Mobilization Versus Positional Release Technique in Cervical Radiculopathy – An Experimental Study
• Silent Assistance for Emergencies (SAFE): A Mobile-Based Emergency Reporting Application for the Deaf and Mute Community
• Clean Energy Transition and Rural Health: Assessing the Impact of Solar Energy on Indoor Air Pollution and Well-Being in Khandwa District, M.P