Iranian Journal of Soil and Water Research

Iranian Journal of Soil and Water Research

Soil Moisture Modeling and Spatial Resolution Enhancement of SMAP Satellite Data at the Farm Scale Using Remote Sensing Data

Document Type : Research Paper

Authors
Mohadese sadat Fakhar 1
Abbas Kaviani 2
1 . Water Engineering Department, Faculty of Agricultural and Natural Resources, Imam Khomeini International University, Qazvin, Iran
2 Water Eng. and Science Dept., Imam Khomeini International University, Qazvin, Iran.
10.22059/ijswr.2026.405920.670047
Abstract
Nowadays, soil moisture estimation using SMAP satellite data is feasible at a temporal resolution of less than three days; however, the spatial resolution of these data is relatively coarse. In this study, a downscaling approach was applied to enhance the spatial resolution of SMAP soil moisture by integrating Sentinel-1 SAR and MODIS data. The study focused on estimating soil moisture in maize fields through field measurements collected from five farms at the Megsal Agro-Industrial Complex in the Qazvin Plain during the summer growing season of 2022. A Random Forest (RF) modeling approach was employed to estimate soil moisture, and six scenarios were defined, each representing a different combination of indices derived from Sentinel-1 and MODIS data as inputs to the RF model for SMAP soil moisture downscaling. The results indicate that among the evaluated scenarios, scenario RF6 in Farm 1 (M1) achieved the highest correlation with in-situ soil moisture (R = 0.97) and RMSE = 0.04 (cm³/cm³), followed by scenario RF5 with R = 0.96. The superior performance of RF5 and RF6 is attributed to the inclusion of the Leaf Area Index (LAI) and land cover maps. Moreover, the correlation between measured soil moisture and LAI was high (R = 0.88), highlighting the importance of vegetation indices in soil moisture estimation. Considering the limited availability of point-based soil moisture data and the need for continuous maps to monitor small farms, applying downscaling techniques can significantly improve the accuracy of SMAP data, making it a cost-effective and efficient tool for agricultural management and research.
Keywords
Downscaling
MODIS
Random Forest
SMAP
Soil Moisture
Subjects

Introduction

Soil moisture is one of the primary determinants governing energy exchange between the land surface and atmosphere, influencing plant growth, crop water use, and hydrological responses. In agricultural environments, particularly in semi-arid regions such as the Qazvin Plain of Iran, the precise monitoring of surface soil moisture is fundamental for optimizing irrigation practices and maintaining crop productivity under increasing water stress conditions. Recent advances in satellite remote sensing have enabled large-scale estimation of soil moisture; however, limitations remain regarding the spatial resolution of passive microwave sensors such as the Soil Moisture Active Passive (SMAP) mission. Although SMAP provides soil moisture observations with a revisit frequency of less than three days, its coarse spatial resolution (~9 km) restricts its applicability for field-scale agricultural monitoring where the spatial variability of soil moisture is significant.

To address this limitation, downscaling methods integrating Synthetic Aperture Radar (SAR) observations and optical vegetation indices have been developed to enhance SMAP spatial resolution without compromising temporal continuity. Sentinel-1 SAR provides backscatter information sensitive to surface roughness and soil moisture, while MODIS products offer vegetation and land surface parameters that characterize canopy cover, evapotranspiration, and water retention. In this study, a multi-sensor data fusion and machine-learning–based downscaling approach was applied to estimate soil moisture at field scale in maize farms within the Megsal agricultural complex. Specifically, the Random Forest (RF) algorithm was used due to its capacity to model nonlinear relationships and its robustness in handling high-dimensional predictor variables.

Materials and Methods

Study Area and Field Data

The study was conducted in five maize fields located in the Megsal agricultural complex in the Qazvin Plain during the 2022 growing season. The region is characterized by a semi-arid climate with warm summers, and irrigation is primarily supplied by groundwater. Soil texture across the fields is predominantly loam. Soil moisture measurements were collected from each field at a depth of 0–30 cm using the HH2 moisture probe. Sampling was repeated throughout the growing season to capture temporal fluctuations associated with irrigation events and plant development.

Remote Sensing Data

Three categories of remotely sensed data were employed:

Data Source

Parameter

Spatial/Temporal Resolution

SMAP L3

Soil moisture

9 km / 2–3 days

Sentinel-1 SAR

σ⁰VV backscatter

~20 m / 6–12 days

MODIS

NDVI, EVI, NDWI, LAI, LST, Albedo

250–1000 m / 8–16 days

Additionally, elevation and slope were derived from SRTM DEM to incorporate topographic variation influencing water redistribution.

Downscaling Modeling Scenarios

Six predictive model scenarios (RF1–RF6) were developed based on incremental combinations of remote sensing predictors, ranging from minimal configurations (LST and NDVI) to extended models incorporating vegetation indices, radar backscatter, land cover type, and terrain parameters. The Random Forest algorithm was implemented in the Google Earth Engine cloud environment. Model performance was assessed against ground measurements using:

·         Correlation coefficient (R)

·         Root Mean Square Error (RMSE)

·         Mean Absolute Error (MAE)

·         Normalized RMSE (NRMSE)

Results

Model performance varied substantially among the tested scenarios. Scenarios integrating vegetation structural parameters and land cover information (RF5 and RF6) consistently produced the highest accuracy across all fields. In Field M1, RF6 yielded the strongest performance (R = 0.97; RMSE = 0.04 cm³/cm³), indicating near‐exact correspondence to measured soil moisture. RF5 followed closely (R = 0.96), reflecting the influence of canopy structure on soil moisture retention.

The contribution of LAI was particularly significant, exhibiting a strong relationship with measured soil moisture (R = 0.88). This highlights the role of vegetation biomass in regulating canopy shading and transpiration dynamics, which in turn affects surface moisture availability. Scenarios lacking vegetation parameters (RF1–RF3) demonstrated lower accuracy (R = 0.51–0.75), confirming that soil temperature and backscatter alone are insufficient to represent field-scale moisture variability, especially during mid-season crop growth.

Temporal comparisons further revealed that downscaled soil moisture estimates effectively captured the progression of the maize growth cycle, including moisture increases following irrigation and declines during periods of high atmospheric demand. Models performed more consistently in fields with uniform irrigation pressure (linear systems) compared to strip irrigation fields where uneven water distribution increased spatial heterogeneity.

 

Conclusion

This research demonstrates that integrating Sentinel-1 SAR and MODIS-derived vegetation and surface parameters with SMAP soil moisture significantly improves spatial resolution and estimation accuracy at the field scale. The Random Forest–based downscaling approach produced high agreement with measured soil moisture, particularly when LAI and land cover information were included. The proposed approach offers a cost-effective and operationally feasible framework for precision irrigation planning, crop monitoring, and hydrological assessment in semi-arid agricultural systems. The results underscore the value of multi-sensor data fusion in bridging the gap between regional-scale satellite products and field-level management needs.

Funding

The study was funded by the Imam Khomeini International University, Qazvin, Iran.

Financial support for this research was provided by the Faculty of Agriculture and Natural Resources, Imam Khomeini International University, through the first author's thesis research grant as well as research grants awarded to the co-authors.

Authorship contribution

For research articles with several authors, a short paragraph specifying their individual contributions must be provided. The following statements should be used “Conceptualization, M.S. Fakhr and A. Kaviani; methodology, M.S. Fakhr and A. Kaviani; software, M. S. Fakhar; validation, M.S. Fakhr and A. Kaviani; formal analysis, M. S. Fakhar; investigation, M. S. Fakhar; resources, M. S. Fakhar; data curation, M.S. Fakhr and A. Kaviani; writing—original draft preparation, M. S. Fakhar; writing—review and editing, A. Kaviani; visualization, A. Kaviani; supervision, A. Kaviani; project administration, A. Kaviani; funding acquisition.

First author (graduate student): Sample preparation, conducting the experiments, data collection, performing calculations, statistical data analysis, interpretation of the results, and preparation of the original draft of the manuscript.

Second author (thesis supervisor): Study conceptualization and design, supervision of the research process, review and verification of the results, and revision, editing, and finalization of the manuscript.

Declaration of Generative AI and AI-assisted technologies in the writing process

No artificial intelligence tools were used in the article writing process.

Data availability statement

Data available on request from the authors.

Acknowledgements

The authors would like to express their sincere gratitude to the Vice-Chancellor for Research of Imam Khomeini International University (IKIU) for the financial support of this study.

Ethical considerations

The authors avoided data fabrication, falsification, and plagiarism, and any form of misconduct.

Conflict of interest

The authors declare no conflict of interest.

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Iranian Journal of Soil and Water Research
Volume 57, Issue 6
August 2026
Pages 1337-1358