This research was conducted across five research farms located in Macon, Dallas, and Perry Counties within the Alabama Black Belt Region (ABBR) (Figure 1). The ABBR is a diverse region with rich agricultural land within the state of Alabama. The region forms part of the historical Black Belt Region of the southeastern United States [41][42].

The geographic region for ABBR encompasses 18 counties in central Alabama, including Sumter, Greene, Hale, Marengo, Choctaw, Perry, Dallas, Wilcox, Lowndes, Butler, Crenshaw, Montgomery, Pike, Bullock, Macon, Barbour, Pickens, and Russell. A significant geological feature of the ABBR is the Selma Chalk (Rotten limestone), a specific formation within the Selma Group [43][44]. The Selma Chalk, composed primarily of marl and chalk, was deposited during the Late Cretaceous period and is particularly prominent in the ABBR [45][46]. It serves as a foundation for the region’s calcareous, sediment-rich, fertile, dark soils, which support the cultivation of row and specialty crops such as cotton, wheat, corn, and vegetables, especially with irrigation, where feasible [42]. While most of the soils in the State of Alabama belong to the Ultisols soil order group, diverse soil textures are found in the research counties, including loams, sandy loams, silt loams, and sandy clay loams [46]. These counties have diverse soil characteristics and are of agricultural significance to the ABBR [34].

Figure 1. Study area showing ABBR counties and in-situ sensor data logger research farm locations.

In July of 2023, twenty TEROS 12 soil sensors and five ZL6 cellular data loggers, accessible via the ZENTRA cloud, were installed in 5 specialty crop farmers’ fields in Dallas, Perry, and Macon counties within the ABBR (Table 1). Each data logger was wired to four soil sensors, each measuring soil volumetric water content (VWC), temperature, and bulk electrical conductivity (EC) at varying depths of 15 cm, 20 cm, 25 cm, and 30 cm. Data loggers 1 and 2 were located in Macon County, data loggers 3 and 4 in Dallas County, and data logger 5 in Perry County. Since July 2023, these sensors have provided real-time measurements of soil conditions, providing over two years of records spanning multiple weather conditions, rainfall events, and crop growing seasons. The spatial distribution of sensors across three counties and five farms was designed to sample soil conditions for contrasting soil and agricultural management practices while still enabling cross-site comparison. Table 1 summarizes the data logger IDs, farm names, coordinates, elevation, county, and installation start times.

Table 1. Information on in-situ soil sensor data loggers.

Satellite imagery was acquired from August 2023 to October 2025, coinciding with the in-situ sensor monitoring period. This window covers two agricultural growing seasons for most crops in Alabama (April-October), allowing the analysis to focus on periods when vegetation is active [47][48]. Although harmonized multi-sensor products such as NASA’s Harmonized Landsat-Sentinel (HLS) dataset are available [49][50], this research intentionally used native Landsat 8/9 Operational Land Imager (OLI) and native Sentinel-2 Multispectral Instrument surface reflectance products as separate datasets to evaluate how each sensor individually captures soil moisture variability at the in-situ sites. Landsat 8/9 and Sentinel-2 imagery were obtained through Google Earth Engine (GEE), which provides cloud-based access to the official U.S. Geological Survey (USGS) and Copernicus/ESA surface reflectance archives [51]. The study used three collections: LANDSAT/LC08/C02/T1_L2, LANDSAT/LC09/C02/T1_L2, and COPERNICUS/S2_SR_HARMONIZED [52]. GEE distributes these datasets in their native formats, preserving the USGS Landsat Collection 2 Level-2 surface reflectance pre-processing and the ESA Level-2A atmospheric correction applied to Sentinel-2 imagery [53]-[54].

Landsat 8 and 9 provide data with 30m spatial resolution and 16-day temporal resolution, with a combined temporal resolution of approximately 8 days between the two. Sentinel-2 Level-2A imagery offers 10 - 20 m spatial resolution with a roughly 5-day revisit time over the study area [53]-[54]. These datasets provide the spectral configuration needed to compute moisture indices and align satellite overpasses with study counties and field sensors. County boundaries for Macon, Dallas, and Perry were used as spatial filters within GEE to ensure that only scenes intersecting the study area were queried. This filtering minimized unnecessary data downloads and facilitated temporal alignment with the in-situ sensor record. For each retained scene, key metadata, which includes acquisition date, cloud statistics, scene identifiers, and Landsat WRS Path/Row, were exported for subsequent cloud-threshold analysis and scene selection. For Sentinel-2, the S2_SR_ HARMONIZED collection in GEE ensures that mixed-resolution bands are resampled onto a common grid before export. This internal harmonization prevents spatial misalignment between Band 8 (10 m) and Band 11 (20 m) and ensures consistent pixel-level extraction of pixel values at sensor locations.

All downloaded Landsat and Sentinel scenes were clipped to the exact county boundaries of Macon, Dallas, and Perry using official county shapefiles and projected into a common coordinate system (UTM Zone 16N - EPSG: 32616). The original radio-metric properties of each data set were preserved to avoid smoothing or blending effects that could alter the moisture index-soil moisture relationship. For Landsat 8/9, surface reflectance scaling (Equation (1)) was applied according to the standard USGS Collection 2 transform [53]:

Reflectance = 0.0000275 × DN - 0.2(1)

This correction was applied before the index computation to convert digital numbers (DN) to the soil moisture biophysical index product. Sentinel-2 Level-2A (S2_SR_HARMONIZED) is provided directly in a corrected surface reflectance, so no additional scaling of DN values was necessary. Cloud screening was performed using the scene-level cloud metrics provided in the satellite metadata. To assess the influence of cloud contamination on MI-SM relationships, three thresholds (≤15%, ≤10%, and ≤5%) were evaluated. The ≤5% subset was selected for the primary analysis in this study to ensure the highest data quality, while higher thresholds were used during preliminary assessment to evaluate sensitivity to cloud filtering. The overall methodological workflow integrating satellite data processing, in-situ soil moisture aggregation, dataset harmonization, and statistical analysis is summarized in Figure 2.

Records of volumetric water content (VWC) at 15-minute intervals for four study depths (15, 20, 25, and 30 cm) at all five research farms were obtained from field soil sensor data loggers via the Zentra cloud platform. Time-series processing and

Figure 2. Overview of the methodological workflow integrating in-situ soil moisture measurements and satellite-derived moisture indices for statistical analysis.

quality control were conducted before integrating measured soil data with satellite-derived indices. First, the 15-minute readings were aggregated to hourly averages to reduce high-frequency noise. To synchronize with satellite overpass times, daily soil moisture values were aggregated over the 16:00-17:00 UTC window. Satellite acquisition times extracted from scene-level metadata for this study ranged between approximately 16:00 and 16:45 UTC across Landsat 8/9 and Sentinel-2 observations. The selected averaging window was therefore chosen to minimize temporal mismatch between satellite and in-situ soil sensor measurements. These aggregated values were used as the in-situ VWC and matched to each satellite’s acquisition date. In all cases, soil moisture values were matched to the same calendar day as the satellite acquisition, with no temporal shifting applied.

A quality control assessment was conducted using the whole record from August 2023 to October 2025. For each depth, pairwise Pearson correlations were computed across all five sites to evaluate temporal analysis and identify potential sensor anomalies. Cross-site correlations ranged from approximately 0.65 to 0.94, with correlation strength generally increasing with depth. This pattern is consistent with stronger hydraulic buffering and reduced short-term variability in deeper soil layers. No sensor exhibited physically inconsistent behavior over the two years, indicating that the in-situ network provided a reliable ground-truth reference for evaluating satellite-derived moisture indices.

Descriptive statistics were also computed from daily VWC measurements from soil sensors, spanning the entire observation period (August 2023 - October 2025). For each site and depth, mean, standard deviation (SD), minimum, maximum, and coefficient of variation (CV) were calculated (Table 2). This revealed variability across the five sites. The statistics characterized site-level soil moisture conditions across seasons and provided contextual information for subsequent statistical analysis. The descriptive statistics were, however, computed independently of satellite acquisition dates to avoid sampling bias.

Table 2. Descriptive statistics of daily volumetric water content (m³/m³) at four depths (15-30 cm) across the five study sites for the observation period (August 2023 - October 2025).

Moisture indices were computed from the Short-Wave Infrared Band 1 (SWIR1) and Near-Infrared (NIR) bands of Landsat 8/9 and Sentinel-2. For Landsat, Band 5 (NIR) and Band 6 (SWIR1) were used. For Sentinel-2, Band 8 (NIR) and Band 11 (SWIR1) were used, consistent with established practice for NDMI and MSI computation. Sentinel-2 provides NIR (Band 8) at 10 m resolution and SWIR1 (Band 11) at 20 m. When accessed through GEE, Band 8 was resampled to 20 m to ensure pixel alignment with Band 11 during export. This harmonization enables accurate NDMI and MSI calculation from co-registered pixels and introduces only minor smoothing relative to native 10-m NIR data. Because all analyses relied on point-level pixel extraction at sensor coordinates, the impact of this resampling on index values was negligible. Two moisture-sensitive spectral indices were then calculated for each scene:

Moisture Stress Index (MSI): The MSI is a spectral index used to quantify water content or stress in vegetation based on spectral reflectance ratios of the moisture-sensitive shortwave infrared (SWIR) and the less-sensitive near-infrared (NIR) bands (Equation (2)). Higher values signify greater moisture stress or less moisture content in vegetation and vice versa for lower values of MSI [34];

Normalized Difference Moisture Index (NDMI): The NDMI is a biophysical spectral reflectance index used to assess moisture content in vegetation and the broader environment (Equation (3)). High NDMI values indicate high vegetation water content, whereas low values indicate lower water content [55].

Index computation was performed in ERDAS IMAGINE, using batch workflows with Python scripts to generate MSI and NDMI for each Landsat and Sentinel scene. After index products were created, pixel values for NDMI and MSI were extracted at the exact coordinates of each data logger location. For each date where satellite imagery and soil moisture readings overlapped, a record was stored containing site ID, county, date, NDMI, MSI, and soil moisture (VWC) at each depth (15, 20, 25, 30 cm). This produced a harmonized dataset for analysis. Following all preprocessing, only dates with valid, non-missing soil moisture data and corresponding NDMI and MSI values were retained. These harmonized records formed the analysis-ready dataset for subsequent correlation and regression.

Statistical analysis was performed to quantify the relationship between satellite-derived moisture indices (NDMI and MSI) and in situ soil moisture at each location and depth. All statistical computations were performed in Python (v3.12) [56], with additional verification in Microsoft Excel to ensure consistency of regression outputs and summary statistics. To characterize these relationships, both Pearson (r) and Spearman correlation (ρ) coefficients were computed between NDMI, MSI, and soil moisture readings at 15, 20, 25, and 30 cm. Pearson correlations captured linear trends (Equation (4)). In contrast, Spearman correlations identified monotonic trends that might persist even when linearity was weaker. Spearman’s correlation coefficient evaluates the strength of association based on the rank ordering of paired moisture index and soil moisture (MI-SM) values (Equation (5)), which can be informative when the MI-SM relationship is affected by local heterogeneity or non-linear trends [57][58]. Initial correlations were computed using all twenty in-situ soil sensors (IDs 1-5) to provide a regional perspective on depth-specific behavior. Pearson and Spearman coefficients were calculated using the following standard formulas in Equations (4) and (5) [56][58]:

where n is the sample size, x is the independent variable, and y is the dependent variable, and d_i is the difference between the ranks of paired observations.

For a refined regression modeling, one representative site was selected from each county to maintain balanced geographic coverage across Macon, Dallas, and Perry Counties (Data Logger IDs 1, 4, 5). Selection criteria included 1) sufficient paired observations after harmonization and ≤5% cloud filtering across both Landsat 8/9 and Sentinel-2 to support stable statistical analysis, and 2) consistent and lower site-level variability in soil moisture behavior across depths (Table 2), during the initial screening analysis. This approach preserved regional representation while focusing on sites exhibiting stable vertical moisture structure. Site-level differences in soil and crop conditions were acknowledged as potential influences on MI-SM relationships. To evaluate predictive accuracy, simple linear regression models (Equation (6)) were fitted separately for each satellite, index, and depth combination:

Soil Moisture = (a × Index) + b.(6)

where a - is the regression slope representing the rate of change in soil moisture per unit change in the satellite moisture index, and b is the intercept representing the predicted soil moisture value when the index is zero.

For each model, slope, intercept, Pearson r, coefficient of determination (r²), p-value, standard error, and root-mean-square error (RMSE) were extracted to assess statistical significance and model performance. After applying the preprocessing and harmonization steps, paired satellite-sensor datasets for Landsat 8/9 and Sentinel-2 spanning August 2023 to October 2025 during the growing season were obtained. Paired observation counts after harmonization and filtering were summarized by county and satellite (Table 3). Counts represent aggregated observations across data loggers within each county. Because each satellite observation was matched with soil moisture measurements at all four depths, counts were

Table 3. Summary of paired observation counts (n) by county and satellite following harmonization and filtering.

consistent across depths within each county. Across all datasets, MSI values ranged from 0.5 to 1.5, while NDMI values typically spanned −0.2 to 0.25, reflecting expected stress-to-moisture gradients within the canopy.

Initial correlation across all twenty in-situ sensors within the research counties established a baseline before a refined analysis. Across all sensors, a consistent depth-dependent structure emerged. For Landsat 8/9, correlations were weak at shallow layers (15 - 20 cm), with NDMI producing r ≈ 0.13 at 15 cm and r ≈ 0.18 at 20 cm. At 25 cm, correlations strengthened (NDMI r ≈ 0.34), while the strongest relationships occurred at 30 cm (NDMI r ≈ 0.47; MSI r ≈ −0.45). Sentinel-2 exhibited a similar depth-dependent structure but with stronger overall associations. At 15 cm, NDMI produced r ≈ 0.34, increasing to r ≈ 0.44 at 20 cm and reaching r ≈ 0.50 - 0.53 at 25 - 30 cm depths. MSI exhibited negative correlations with NDMI, consistent with its stress-based formulation, in which higher MSI values indicate greater moisture stress and, therefore, lower soil moisture. Across both sensors, MI-SM coupling strengthened with increasing depth, with deeper layers exhibiting the most stable relationships. To further examine site-level structure while maintaining balanced geographic coverage, a representative subset of one site per county was analyzed in greater detail. This refinement preserved the depth-dependent pattern observed across the whole dataset and enabled a more precise evaluation of MI-SM behavior.

3.1.1. Landsat 8/9

Landsat 8/9 exhibited a clear depth-dependent pattern in MI-SM coupling, within the refined subset (Table 4). The performance improves from shallow layers (15 - 20 cm) to 30 cm depth (Table 4). Under the strictest cloud condition threshold (≤5%, n = 80), NDMI at 15 cm produced r ≈ 0.11, while MSI yielded r ≈ −0.12.

Table 4. Pearson and Spearman correlations between Landsat 8/9 indices and in-situ soil moisture.

Correlations increased only slightly at 20 cm (NDMI r ≈ 0.16; MSI r ≈ −0.17), while NDMI at 25 cm achieved r ≈ 0.43, with MSI showing r ≈ −0.43. At 30 cm, Landsat reached its highest correlations, with NDMI r ≈ 0.62 and MSI r ≈ −0.64. Results indicate poor correlations between NDMI and MSI and soil moisture at shallow soil layers/depths, whereas the strongest correlations occur at 30 cm depth. However, the Pearson and Spearman coefficients were almost identical at each depth, reinforcing that Landsat’s MI-SM relationships were predominantly linear.

3.1.2. Sentinel-2

Sentinel-2 also exhibited a depth-dependent correlation with soil moisture, however, with stronger overall MI-SM relationships than Landsat, especially at shallow and mid-depth levels (Table 5). Under the strictest cloud condition threshold (≤5%, n = 73), Sentinel correlation had NDMI r ≈ 0.49 and MSI r ≈ −0.48 at 15 cm depth, while that for 20 cm was NDMI r ≈ 0.51 and MSI r ≈ −0.50. These values are higher compared to Landsat, and indicate moderate MI-SM relationships, with Spearman coefficients (ρ ≈ 0.54 - 0.55) consistently exceeding Pearson r. At depths of 25 - 30 cm, the strongest Sentinel-2 MI-SM relationships occurred, where NDMI correlations ranged from r ≈ 0.60 to 0.61, with MSI exhibiting similar values in the opposite direction. Overall, Sentinel-2 maintained robust MI-SM relationships for mid- and deep-layer (25 - 30) soil moisture.

A notable pattern, however, emerged: Spearman correlations for Sentinel-2 were consistently slightly higher than the corresponding Pearson values for all depth levels. This suggests a strong monotonic MI-SM relationship even when linearity was less pronounced.

Table 5. Pearson and Spearman correlations between Sentinel 2 indices and in-situ soil moisture.

NDMI and MSI provided biophysical indications of vegetation and soil moisture status. NDMI increases as vegetation and soil moisture levels increase. On the other hand, MSI increases with increasing moisture stress and decreasing soil moisture levels. Across all satellite data types and depths, the two indices performed similarly, with only minor differences in correlation strength. For Landsat 8/9, MSI consistently produced slightly stronger absolute correlations than NDMI at all depths, although the advantage was slight (typically 0.005 - 0.01 in |r|). At 30 cm, where Landsat performed best, both indices reached nearly identical strengths (|r| ≈ 0.60 - 0.64). Despite these differences, both indices followed the same depth pattern: weak relationships at 15 - 20 cm, stronger sensitivity at 25 cm, and the most stable indicator at 30 cm. This consistency indicates that NDMI and MSI are essentially interchangeable for soil-moisture estimation in this landscape, with neither index showing a clear systematic advantage. To illustrate their spatial behavior, Figures 3-6 present MSI and NDMI maps for September 2025 conditions across Macon, Dallas, and Perry counties, highlighting county-level variations in

Figure 3. MSI-based Moisture maps for Dallas (a), Perry (b), and Macon (c) counties for September 2025, derived from Landsat 9.

Figure 4. NDMI-based moisture maps for Dallas (a), Perry (b), and Macon (c) counties for September 2025, derived from Landsat 9.

Figure 5. MSI-based moisture maps for Dallas (a), Perry (b), and Macon (c) counties for September 2025, derived from Sentinel 2.

Figure 6. NDMI-based Moisture maps for Dallas (a), Perry (b), and Macon (c) counties for September 2025, derived from Sentinel 2.

vegetation moisture conditions during the growing season. The maps show that moisture conditions are not uniform across counties. Some areas appear drier (with higher MSI and lower NDMI values), while others seem relatively wetter. NDMI and MSI also display inverse spatial patterns, consistent with their physical formulation. The Sentinel-2 maps (Figure 5 and Figure 6) also show slightly finer spatial detail, consistent with its higher spatial resolution. Any preference for one index over the other should therefore be guided more by conceptual framing (moisture or stress emphasis) and modeling choices.

Regression modeling was further used to assess the relative strength of the MI-SM relationships across Landsat 8/9 and Sentinel-2, at different soil depths. Separate linear models were fitted for each index (Table 6), and both indices produced statistically significant regressions (p < 0.01). For Landsat 8/9, NDMI and MSI both produced strong and statistically significant regressions at 30 cm depth, with the lowest RMSE values among all regressions, indicating that Landsat can provide highly accurate linear predictions of soil moisture at this depth. For Sentinel, the slightly lower RMSE value at 30 cm compared to 25 cm suggests tighter coupling between Sentinel indices and Soil moisture at deeper depths. Scatterplots with fitted lines (Figures 7(a)-(d)) reinforce these results, showing

Table 6. Regression performance for MI-SM relationships across Landsat 8/9 and Sentinel-2.

Figure 7. MI-SM scatterplots with fitted linear regression lines for 30 cm soil depth for Landsat 8/9 (a) MSI; (b) NDMI and Sentinel-2 (c) MSI; (d) NDMI.

clear MI-SM linear relationships at 30 cm depth.