Understanding soil moisture dynamics at different depths is essential for irrigation management. This study evaluated the relationship between two satellite-derived moisture indices—the Normalized Difference Moisture Index (NDMI) and the Moisture Stress Index (MSI) and in-situ soil moisture measurements across the Alabama Black Belt region during the growing season. Soil moisture readings at depths of 15, 20, 25, and 30 cm were collected at five sensor locations between August 2023 and October 2025. Landsat 8/9 and Sentinel-2 imagery were processed to generate the moisture indices, which were then correlated with the sensor measurements. The results showed that, although both satellite data types had similar predictive performance for soil moisture at all depths, there were weak linear correlations at 15 - 20 cm that substantially increased at 25 - 30 cm. Landsat 8/9 soil moisture estimates were most accurate at 30 cm for both NDMI and MSI indices, whereas Sentinel-2 estimates were optimal at depths of between 25 and 30 cm. Both indices produced optimal correlations in the range of r ≈ 0.60 - 0.64. These results demonstrate that satellite-based moisture indices exhibit statistically significant relationships with subsurface soil moisture conditions, particularly at depths greater than 25 cm, necessary for agricultural water management.
References
[1]
Li, G., Long, H., Zhang, R., Drohan, P.J., Xu, A. and Niu, L. (2023) Stable Soil Moisture Alleviates Water Stress and Improves Morphogenesis of Tomato Seedlings. Horticulturae, 9, Article 391. https://doi.org/10.3390/horticulturae9030391
[2]
Plett, D.C., Ranathunge, K., Melino, V.J., Kuya, N., Uga, Y. and Kronzucker, H.J. (2020) The Intersection of Nitrogen Nutrition and Water Use in Plants: New Paths toward Improved Crop Productivity. Journal of Experimental Botany, 71, 4452-4468. https://doi.org/10.1093/jxb/eraa049
[3]
Wang, C., Fu, B., Zhang, L. and Xu, Z. (2019) Soil Moisture-Plant Interactions: An Ecohydrological Review. Journal of Soils and Sediments, 19, 1-9. https://doi.org/10.1007/s11368-018-2167-0
[4]
Bodner, G., Nakhforoosh, A. and Kaul, H. (2015) Management of Crop Water under Drought: A Review. Agronomy for Sustainable Development, 35, 401-442. https://doi.org/10.1007/s13593-015-0283-4
[5]
Camporese, M., Gumiere, S.J., Putti, M. and Botter, G. (2021) Efficient Irrigation of Maize through Soil Moisture Monitoring and Modeling. FrontiersinWater, 3, Article 627551. https://doi.org/10.3389/frwa.2021.627551
[6]
Ahmad, L., Shanono, N.J. and Nasidi, N.M. (2022) Toward Adoption of Drip Irrigation and Soil-Moisture Sensors by Small-Scale Farmers. FUDMA Journal of Sciences, 6, 259-270. https://doi.org/10.33003/fjs-2022-0603-961
[7]
Berthold, T.A., Ajaz, A., Olsovsky, T. and Kathuria, D. (2021) Identifying Barriers to Adoption of Irrigation Scheduling Tools in Rio Grande Basin. Smart Agricultural Technology, 1, Article 100016. https://doi.org/10.1016/j.atech.2021.100016
[8]
Ganjegunte, G. and Clark, J. (2017) Improved Irrigation Scheduling for Freshwater Conservation in the Desert Southwest U.S. Irrigation Science, 35, 315-326. https://doi.org/10.1007/s00271-017-0546-8
[9]
Quiring, S.M., Ford, T.W., Wang, J.K., Khong, A., Harris, E., Lindgren, T., et al. (2016) The North American Soil Moisture Database: Development and Applications. Bulletin of the American Meteorological Society, 97, 1441-1459. https://doi.org/10.1175/bams-d-13-00263.1
[10]
Kukal, M.S., Irmak, S. and Sharma, K. (2020) Development and Application of a Performance and Operational Feasibility Guide to Facilitate Adoption of Soil Moisture Sensors. Sustainability, 12, Article 321. https://doi.org/10.3390/su12010321
[11]
Liu, Y. and Yang, Y. (2022) Advances in the Quality of Global Soil Moisture Products: A Review. Remote Sensing, 14, Article 3741. https://doi.org/10.3390/rs14153741
[12]
Schwamback, D., Persson, M., Berndtsson, R., Bertotto, L.E., Kobayashi, A.N.A. and Wendland, E.C. (2023) Automated Low-Cost Soil Moisture Sensors: Trade-Off between Cost and Accuracy. Sensors, 23, Article 2451. https://doi.org/10.3390/s23052451
[13]
Sui, R. (2017) Irrigation Scheduling Using Soil Moisture Sensors. Journal of Agricultural Science, 10, Article 1. https://doi.org/10.5539/jas.v10n1p1
[14]
Sahaar, S.A. and Niemann, J.D. (2024) Estimating Rootzone Soil Moisture by Fusing Multiple Remote Sensing Products with Machine Learning. Remote Sensing, 16, Article 3699. https://doi.org/10.3390/rs16193699
[15]
Wang, Y., Zhao, J., Guo, Z., Yang, H. and Li, N. (2023) Soil Moisture Inversion Based on Data Augmentation Method Using Multi-Source Remote Sensing Data. Remote Sensing, 15, Article 1899. https://doi.org/10.3390/rs15071899
[16]
Xuan Wai, M.H., Huong, A. and Ngu, X. (2021) Soil Moisture Level Prediction Using Optical Technique and Artificial Neural Network. International Journal of Electrical and Computer Engineering, 11, Article 1752. https://doi.org/10.11591/ijece.v11i2.pp1752-1760
[17]
Zavala Díaz, N.A., Olivares-Rojas, J.C., Zavala Díaz, J., Reyes Archundia, E., Téllez Anguiano, A.D.C., Chávez Campos, G.M., et al. (2024) Study of Machine Learning Techniques for the Estimation of Soil Moisture in Agriculture. International Journal of Combinatorial Optimization Problems and Informatics, 15, 61-71. https://doi.org/10.61467/2007.1558.2024.v15i4.502
[18]
Taheri, M., Bigdeli, M., Imanian, H. and Mohammadian, A. (2025) An Overview of Machine-Learning Methods for Soil Moisture Estimation. Water, 17, Article 1638. https://doi.org/10.3390/w17111638
[19]
Bekele, D., Gela, A., Mengistu, D. and Derseh, A. (2023) Remote Sensing Based Soil Moisture Estimation for Agricultural Productivity: A Note from Lake Tana Sub Basin, NW Ethiopia. In: New Insights in Soil-Water Relationship, IntechOpen, 10. https://doi.org/10.5772/intechopen.109420
[20]
Chen, L., Hu, B., Sun, J., Xu, Y.J., Zhang, G., Ma, H., et al. (2025) Using Remote Sensing and Machine Learning to Generate 100-Cm Soil Moisture at 30-M Resolution for the Black Soil Region of China: Implication for Agricultural Water Management. Agricultural Water Management, 309, Article 109353. https://doi.org/10.1016/j.agwat.2025.109353
Suman, S., Srivastava, P.K., Petropoulos, G.P., Pandey, D.K. and O’Neill, P.E. (2020) Appraisal of SMAP Operational Soil Moisture Product from a Global Perspective. Remote Sensing, 12, Article 1977. https://doi.org/10.3390/rs12121977
[23]
Reichle, R.H., Liu, Q., Koster, R.D., Crow, W.T., De Lannoy, G.J.M., Kimball, J.S., etal. (2019) Version 4 of the SMAP Level-4 Soil Moisture Algorithm and Data Product. Journal of Advances in Modeling Earth Systems, 11, 3106-3130. https://doi.org/10.1029/2019ms001729
[24]
Mu, T., Liu, G., Yang, X. and Yu, Y. (2023) Soil-Moisture Estimation Based on Multiple-Source Remote-Sensing Images. Remote Sensing, 15, Article 139. https://doi.org/10.3390/rs15010139
[25]
Urban, M., Berger, C., Mudau, T.E., Heckel, K., Truckenbrodt, J., Onyango Odipo, V., et al. (2018) Surface Moisture and Vegetation Cover Analysis for Drought Monitoring in the Southern Kruger National Park Using Sentinel-1, Sentinel-2, and Landsat-8. Remote Sensing, 10, Article 1482. https://doi.org/10.3390/rs10091482
[26]
Wang, Q., Li, J., Jin, T., Chang, X., Zhu, Y., Li, Y., et al. (2020) Comparative Analysis of Landsat-8, Sentinel-2, and GF-1 Data for Retrieving Soil Moisture over Wheat Farmlands. Remote Sensing, 12, Article 2708. https://doi.org/10.3390/rs12172708
[27]
Entezari, M., Esmaeily, A. and Niazmardi, S. (2019) Estimation of Soil Moisture and Earth’S Surface Temperature Using Landsat-8 Satellite Data. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 4, 327-330. https://doi.org/10.5194/isprs-archives-xlii-4-w18-327-2019
[28]
Tajudin, N., Ya'acob, N., Mohd Ali, D. and Adnan, N.A. (2021) Soil Moisture Index Estimation from Landsat 8 Images for Prediction and Monitoring Landslide Occurrences in Ulu Kelang, Selangor, Malaysia. International Journal of Electrical and Computer Engineering, 11, Article 2101. https://doi.org/10.11591/ijece.v11i3.pp2101-2108
[29]
Tao, L., Ryu, D., Western, A. and Boyd, D. (2021) A New Drought Index for Soil Moisture Monitoring Based on MPDI-NDVI Trapezoid Space Using MODIS Data. Remote Sensing, 13, Article 122. https://doi.org/10.3390/rs13010122
[30]
Tao, L., Di, Y., Wang, Y. and Ryu, D. (2023) Normalized Temperature Drought Index (NTDI) for Soil Moisture Monitoring Using MODIS and Landsat-8 Data. Remote Sensing, 15, Article 2830. https://doi.org/10.3390/rs15112830
[31]
Tibkaew, A.P., Miyai, J., Buakhao, W. and Phonekeo, V. (2022) Soil Moisture Sensor Measurement and Vegetation-Soil-Water Related Indices—A Case Study in Mango Plantation, Nakhorn Ratchasima Province, Thailand. International Journal of Geoinformatics, 18, 71-90.
[32]
Lamichhane, M., Mehan, S. and Mankin, K.R. (2025) Soil Moisture Prediction Using Remote Sensing and Machine Learning Algorithms: A Review on Progress, Challenges, and Opportunities. Remote Sensing, 17, Article 2397. https://doi.org/10.3390/rs17142397
[33]
Malakhov, D. and Tsychuyeva, N. (2020) Calculation of the Biophysical Parameters of Vegetation in an Arid Area of South-Eastern Kazakhstan Using the Normalized Difference Moisture Index (NDMI). Central Asian Journal of Environmental Science and Technology Innovation, 4, 189-198.
[34]
Welikhe, P., Quansah, J.E., Fall, S. and McElhenney, W. (2017) Estimation of Soil Moisture Percentage Using Landsat-Based Moisture Stress Index. Journal of Remote Sensing & GIS, 6, Article 1000200.
[35]
Li, M., Sun, H. and Zhao, R. (2023) A Review of Root Zone Soil Moisture Estimation Methods Based on Remote Sensing. Remote Sensing, 15, Article 5361. https://doi.org/10.3390/rs15225361
[36]
Peng, J. and Loew, A. (2017) Recent Advances in Soil Moisture Estimation from Remote Sensing. Water, 9, Article 530. https://doi.org/10.3390/w9070530
[37]
Babaeian, E. and Tuller, M. (2023) The Feasibility of Remotely Sensed Near-Infrared Reflectance for Soil Moisture Estimation for Agricultural Water Management. Remote Sensing, 15, Article 2736. https://doi.org/10.3390/rs15112736
[38]
Celik, M.F., Isik, M.S., Yuzugullu, O., Fajraoui, N. and Erten, E. (2022) Soil Moisture Prediction from Remote Sensing Images Coupled with Climate, Soil Texture and Topography via Deep Learning. Remote Sensing, 14, Article 5584. https://doi.org/10.3390/rs14215584
[39]
Mohanty, B.P., Cosh, M.H., Lakshmi, V. and Montzka, C. (2017) Soil Moisture Remote Sensing: State-of-the-Science. Vadose Zone Journal, 16, 1-9. https://doi.org/10.2136/vzj2016.10.0105
[40]
Chen, Y., Palta, J.A., Wu, P. and Siddique, K.H.M. (2019) Crop Root Systems and Rhizosphere Interactions. Plant and Soil, 439, 1-5. https://doi.org/10.1007/s11104-019-04154-2
[41]
S.1643-117th Congress (2021–2022): Alabama Black Belt National Heritage Area Act (2021-05-13). https://www.congress.gov/bill/117th-congress/senate-bill/1643
[42]
Quansah, J., Doria, R. and Fall, S. (2025) Evaluating the Performance of the National Water Model: A Spatiotemporal Analysis of Streamflow Forecasting. Water, 17, Article 2950. https://doi.org/10.3390/w17202950
[43]
Winemiller, T. (2009) Black Belt Region in Alabama. https://encyclopediaofalabama.org/article/black-belt-region-in-alabama/
[44]
Smith, E.A., Johnson, L.C., Langdon Jr, D.W., Aldrich, T.H. and Cunningham, K.M. (1894) Report on the Geology of the Coastal Plain of Alabama. https://books.google.com/books?id=BzsxAQAAMAAJ&printsec=frontcover&source=gbs_ge_summary_r&cad=0#v=onepage&q&f=false
[45]
Stephenson, L.W. and Monroe, W.H. (1938) Stratigraphy of Upper Cretaceous Series in Mississippi and Alabama. AAPGBulletin, 22, 1639-1657. https://doi.org/10.1306/3d933022-16b1-11d7-8645000102c1865d
[46]
Soil Survey Staff, Natural Resources Conservation Service, United States Department of Agriculture (2025) Web Soil Survey. http://websoilsurvey.sc.egov.usda.gov/
[47]
USDA NASS (2010) Field Crops Usual Planting and Harvesting Dates (Agricultural Handbook 628). United States Department of Agriculture. https://esmis.nal.usda.gov/sites/default/release-files/vm40xr56k/dv13zw65p/w9505297d/planting-10-29-2010.pdf
[48]
Alabama Cooperative Extension System (2018) Planting Guide for Home Gardening in Alabama (ANR-0063). https://www.aces.edu/blog/topics/lawn-garden/planting-guide-for-home-gardening-in-alabama/
[49]
NASA (2023) Harmonized Landsat and Sentinel-2 (HLS). https://hls.gsfc.nasa.gov/
[50]
Ju, J., Zhou, Q., Freitag, B., Roy, D.P., Zhang, H.K., Sridhar, M., et al. (2025) The Harmonized Landsat and Sentinel-2 Version 2.0 Surface Reflectance Dataset. Remote Sensing of Environment, 324, Article 114723. https://doi.org/10.1016/j.rse.2025.114723
[51]
Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D. and Moore, R. (2017) Google Earth Engine: Planetary-Scale Geospatial Analysis for Everyone. Remote Sensing of Environment, 202, 18-27. https://doi.org/10.1016/j.rse.2017.06.031
[52]
Google Earth Engine (n.d.) Earth Engine Data Catalog. https://developers.google.com/earth-engine/datasets
[53]
Earth Resources Observation and Science (EROS) Center (2020) Landsat 8-9 Operational Land Imager/Thermal Infrared Sensor Level-2, Collection 2 [Dataset]. U.S. Geological Survey. https://doi.org/10.5066/P9OGBGM6
[54]
European Space Agency (n.d.) Sentinel-2. https://www.esa.int/Applications/Observing_the_Earth/Copernicus/Sentinel-2
[55]
European Space Agency (2026) Sentinel-2 Level 2A Surface Reflectance. Copernicus Data Space Ecosystem Documentation. https://documentation.dataspace.copernicus.eu/Data/SentinelMissions/Sentinel2.html#sentinel-2-level-2a-surface-reflectance
[56]
U.S. Geological Survey (n.d.) Normalized Difference Moisture Index (NDMI). https://www.usgs.gov/landsat-missions/normalized-difference-moisture-index
Shaqiri, M., Iljazi, T., Kamberi, L. and Ramani-Halili, R. (2023) Differences between the Correlation Coefficients Pearson, Kendall and Spearman. Journal of Natural Sciences and Mathematics of UT, 8, 392-397
[59]
El-Hashash, E.F. and Shiekh, R.H.A. (2022) A Comparison of the Pearson, Spearman Rank and Kendall Tau Correlation Coefficients Using Quantitative Variables. Asian Journal of Probability and Statistics, 20, 36-48. https://doi.org/10.9734/ajpas/2022/v20i3425
[60]
Wang, H., Muller, J.D., Tatarinov, F., Yakir, D. and Rotenberg, E. (2022) Disentangling Soil, Shade, and Tree Canopy Contributions to Mixed Satellite Vegetation Indices in a Sparse Dry Forest. Remote Sensing, 14, 3681. https://doi.org/10.3390/rs14153681
[61]
METER Group (n.d.) TEROS 12—Advanced Soil Moisture Sensor+Temperature and EC. https://metergroup.com/products/teros-12/
[62]
Markham, B., Barsi, J., Kvaran, G., Ong, L., Kaita, E., Biggar, S., et al. (2014) Landsat-8 Operational Land Imager Radiometric Calibration and Stability. RemoteSensing, 6, 12275-12308. https://doi.org/10.3390/rs61212275
[63]
Optical Mission Performance Cluster Service (2025) Data Quality Report—Sentinel-2 MSI L2A (Issue 87.0, OMPC.CS.DQR.002.06-2025). Copernicus Space Component. https://sentiwiki.copernicus.eu/__attachments/1673423/OMPC.CS.DQR.002.06-2025-Sentinel-2-MSI-L2A-DQR-July-2025-87.pdf?inst-v=d0190039-b174-4e67-b630-27bc085e66cf
