Soil Moisture Estimation Research Articles

Two-source energy balance schemes exploiting land surface temperature and soil moisture for continuous vineyard water use estimation

Two-source energy balance schemes exploiting land surface temperature and soil moisture for continuous vineyard water use estimation

Evaluation of Soil Moisture Retrievals from a Portable L-Band Microwave Radiometer

A novel Portable L-band radiometer (PoLRa), compatible with tower-, vehicle- and drone-based platforms, can provide gridded soil moisture estimations from a few meters to several hundred meters yet its retrieval accuracy has rarely been examined. This study aims to provide an initial assessment of the performance of PoLRa-derived soil moisture at a spatial resolution of approximately 0.7 m × 0.7 m at a set of sampling pixels in central Illinois, USA. This preliminary evaluation focuses on (1) the consistency of PoLRa-measured brightness temperatures from different viewing directions over the same area and (2) whether PoLRa-derived soil moisture retrievals are within an acceptable accuracy range. As PoLRa shares many aspects of the L-band radiometer onboard NASA’s Soil Moisture Active Passive (SMAP) mission, two SMAP operational algorithms and the conventional dual-channel algorithm (DCA) were applied to calculate volumetric soil moisture from the measured brightness temperatures. The vertically polarized brightness temperatures from the PoLRa are typically more stable than their horizontally polarized counterparts across all four directions. In each test period, the standard deviations of observed dual-polarization brightness temperatures are generally less than 5 K. By comparing PoLRa-based soil moisture retrievals against the simultaneous moisture values obtained by a handheld capacitance probe, the unbiased root mean square error (ubRMSE) and the Pearson correlation coefficient (R) are mostly below 0.05 m3/m3 and above 0.7 for various algorithms adopted here. While SMAP models and the DCA algorithm can derive soil moisture from PoLRa observations, no single algorithm consistently outperforms the others. These findings highlight the significant potential of ground- or drone-based PoLRa measurements as a standalone reference for the calibration and validation of spaceborne L-band synthetic aperture radars and radiometers. The accuracy of PoLRa-yielded high-resolution soil moisture can be further improved via standardized operational procedures and appropriate tau-omega parameters.

Integrating infiltration processes in hybrid downscaling methods to estimate sub-surface soil moisture

Soil moisture is a key variable in the water, energy, and carbon cycles. Mapping sub-surface soil moisture with fine spatial resolution requires integrating downscaling approaches and process-based models. However, the effectiveness of hybrid methods, such as regression kriging (RK), in enhancing soil moisture estimates through process-based parameter predictions remains inconclusive. This study aims to integrate infiltration processes into downscaling models to predict 1-km multi-layer soil moisture, while comparing performance of nonlinear and linear models, and evaluating RK improvements. Random forests (RF) and generalized linear model (GLM) were used to downscale surface soil moisture (0–5 cm) from 36-km Soil Moisture Active Passive satellite products to 1 km across the Qinghai-Tibet Plateau. Next, the soil moisture analytical relationship (SMAR) model was applied to simulate infiltration processes and obtain site-scale parameters. RK variants (RFRK and GLMRK) were applied to jointly predict the spatial distribution of multiple infiltration parameters, which were used in SMAR at 1-km grids to estimate sub-surface soil moisture (5–40 cm). The results showed that parameter calibration significantly enhanced sub-surface soil moisture simulation, reducing root mean square error (RMSE) by 61.2 % to 69.8 %, from 0.09 to 0.03. RF outperformed GLM across all depth intervals, providing higher prediction accuracy (average RMSE, RF: 0.07; GLM: 0.09). Moreover, RK enhanced the Nash-Sutcliffe efficiency coefficient (RFRK: 0.34; GLMRK: 0.28) and coefficient of determination (RFRK: 0.5; GLMRK: 0.38) by 7.7 %–13.3 % and 2.2 %–2.4 %. This study provides a reference for mapping multi-layer soil moisture through the integration of data-driven and knowledge-driven approaches in regional-scale study areas.

Global Terrestrial Water–Energy Coupling Across Scales

ABSTRACTTerrestrial water–energy coupling (WEC), in the form of a non‐linear relationship between Soil Moisture (SM) and evaporative fraction (EF, ratio of actual and potential evapotranspiration), controls critical ecohydrological processes. We investigate and parameterize the evolution of global SM–EF coupling from the field to remote‐sensing (RS)‐pixel. The field‐scale EF and SM were obtained from eddy covariance (EC) and SM sensors at the global FLUXNET and Texas Water Observatory sites. RS‐pixel‐scale EF and SM estimates were obtained from Moderate‐resolution Imaging Spectroradiometer (MODIS) and Soil Moisture Active Passive (SMAP) sensors, respectively. We estimate the effective thresholds of the WEC regimes from both EC and satellite datasets to highlight the influence of sub‐pixel‐scale heterogeneity, and, scaling and observational constraints on the evolution of WEC regimes from the field to RS‐pixel scale. We argue that the changes in land surface conditions add a temporal variability in the critical thresholds of terrestrial WEC at RS pixel scale. We compare the critical WEC thresholds of the water‐ and energy‐limited regimes with a SM drydown‐based approach and highlight the similarities between both methods in partitioning dominant WEC regimes. EF and SM are strongly coupled in dryland arid and semi‐arid regions compared to humid climates. WEC regimes and thresholds have strong interseason variability due to dynamic interactions between soil, vegetation and atmosphere at the RS‐pixel scale. In contrast, field‐scale SM‐EF coupling is influenced predominantly by soil conditions and land‐use/management practices. Hence, future development of Earth‐system/Land‐surface models must account for the inter‐scale differences in the coupling between terrestrial water and energy fluxes representative of the ‘effective’ processes at large spatial scales.

A novel validation of satellite soil moisture using SM2RAIN-derived rainfall estimates

Despite the importance of soil moisture (SM) in various applications and the need to validate satellite SM products, the current in situ SM network is still inadequate, even for developed country such as the United States. Recently, SM2RAIN (Soil Moisture to Rain) algorithm has prominently emerged as a bottom-up approach to derive rainfall data from SM. In this study, we evaluated whether SM2RAIN algorithm and rain gauges, which are more abundant and readily available than in situ SM, can be used to validate satellite-based SMAP SM estimates. Since errors in SMAP SM propagate to SMAP-derived rainfall, the skills of SM2RAIN might be able to provide insights on the accuracy of SMAP SM observations. While the correlation between SM2RAIN skills and SMAP SM skills was found to be statistically significant, the strength of the correlation varied among different climate zones and annual rainfall classes. Specifically, weaker correlations were observed in arid and lower rainfall regions (median R value of 0.12), while stronger correlations were found in temperate and higher rainfall regions (median R value of 0.54). In term of over/under-estimation tendencies, 56% of the stations had the same tendencies (SM2RAIN skills and satellite SM skills both have positive or negative PBIAS value).

Modeling European beech defoliation at a regional scale gradient in Germany from northern lowlands to central uplands using geo-ecological parameters, Sentinel-2 and National Forest Condition Survey data

Since 2018, severe droughts have affected a significant part of central Europe, causing premature leaf senescence in European beech (Fagus sylvatica L.). The correlation between the vitality of Fagus sylvatica L. and various geo-ecological and biological determinants (such as elevation, slope, aspect, tree age, and soil properties) concerning hydrological drought stress is still not well understood, especially when integrating multiple geographical datasets. In addition, the determination of crown condition by remote sensing and geo-ecological parameters is still under development; it would allow the assessment of an area-wide forest health status. Our analysis incorporated annual field data from the German National Forest Condition Survey (Waldzustandserhebung, WZE) as a response variable and employed geo-ecological parameters derived from a digital elevation model, soil properties and vegetation indices from a Sentinel-2 time series to explain and predict the crown defoliation of European beech throughout the drought-impacted period spanning 2016–2022 across the federal states Schleswig-Holstein, Lower Saxony, and Hesse of Germany. In a second step, the results of the modeling were used for mapping of crown defoliation in Hesse, Lower Saxony and Schleswig-Holstein. By employing Gradient Boosting Machines and Random Forest for regression analysis, the study uncovered the relationships between crown defoliation and the used predictors. Training was conducted on 80 % of the dataset, with the remaining 20 % serving as a test set for model validation. Regression findings based on static explanatory variable sets were improved by dynamic explanatory variables such as estimates of soil moisture, vegetation index metrics, and diameter at breast height. Furthermore, we identified key predictors for mapping crown defoliation of Fagus sylvatica L. and recommended using vegetation indices as additional predictors for future studies. The modeling results provided comparably accurate estimates compared to WZE estimates (R2 of 0.794 and RMSE of 7.646 %) during testing. Topographic and static soil predictors were significant, with soil moisture being a particularly influential variable for model optimization. Based on the predicted crown defoliation, beech trees with low to moderate crown defoliation predominated in beech distribution areas across the examined federal states, while a small number of beech trees with high defoliation were identified mostly in South Lower Saxony and Hesse. The annual variations in the proportions of beech trees showing increasing and decreasing crown defoliation indicate that the condition of the crown temporarily deteriorated when soil moisture decreased, but beech trees recovered after prolonged periods of drought. Additionally, beech trees in the study region exposed to declining soil moisture may suffer from medium-term declines in vitality. The predicted crown defoliation data can be utilized for future climate-adaptive management practices in European beech forests.

Retrieval of soil moisture in salinized farmland soil by multi-polarization SAR

ABSTRACT Synthetic Aperture Radar (SAR) is a potent instrument for estimating soil moisture across vast farmland areas. However, when soil salinity rises, existing SAR retrieval models for soil moisture become less accurate. Soluble salts in soil water alter the inherent correlation between the SAR backscattering coefficients and soil water, thus compromising the performance of retrieval models. The need to get surface roughness parameters from the field also limits the widespread application of the model. To address these issues, we developed a retrieval model for salty soil moisture assessment under small-scale roughness surfaces. First, we found a method to distinguish between slightly rough and rough surfaces using scattering entropy obtained from full-polarization decomposition. Then, we harnessed the co-polarization ratio to mitigate the effect of surface roughness in slightly rough conditions. Later, we developed a soil moisture retrieval model based on the co-polarization ratio, considering incidence angle, residual roughness, and soluble salt content. The validation of the model was verified using field surveying data, yielding an RMSE of 0.026 cm3 ·cm− 3 for the estimated soil moisture. This accuracy is comparable to or surpasses the level achieved by SAR for non-salinized soil moisture retrieval. Our findings showed that Scattering entropy is an effective parameter for distinguishing scattering mechanisms on farmland surfaces effectively. Specifically, when the scattering entropy of farmland is less than 0.5, the co-polarization ratio can mitigate some surface roughness effects. Additionally, when the soil-soluble salt content is incorporated, our model can accurately estimate the soil moisture of salty soil.

Rapid Estimation of Soil Moisture Using Spectroscopic Sensor

ABSTRACT Soil moisture plays a significant role in the global hydrological cycle in the field of agriculture, environment science, urban planning and construction, forestry and many more. Soil moisture can be determined using various approaches, however some of the limitations in these approaches are longer time duration, labor intensive, require installation and maintenance, expensive, lower resolution, etc. This research work proposes a soil moisture detection method which is portable, economic and time efficient. Spectroscopic sensor enables rapid soil moisture estimation using photodiodes of varying sensitivities and wavelengths across a broad spectrum of light, allowing precise identification and analysis of various samples based on their unique spectral fingerprints. In this study, a relation between sensor output data and soil moisture percentage value obtained from gravimetric method has been established. Using various regression methods, the highest value of correlation coefficient was found in case of polynomial function of order 3. Residual values of all wavelengths were then calculated by comparing the calculated with the expected moisture values obtained using the polynomial function. Result showed that 705 nanometers (nm) wavelength has the least residual value, thus signifying that the polynomial equation of order 3 at 705 nm wavelength perfectly demonstrates the relation between soil moisture and sensor reading. Furthermore, the results were verified using optimization method which showed that 705 nm wavelength has the highest closeness coefficient, thus verifying this model and observations. This model can be helpful in determining the soil moisture instantaneously instead of hours as seen in traditional gravimetric methods.

An improved optical trapezoid model to identify wetlands and irrigation water ponds in the arid irrigation district using Google Earth Engine

ABSTRACT In arid regions, wetlands support ecosystem with high biodiversity and productivity. Meanwhile, irrigation during the fallow period often results in the temporary formation of water ponds in the cropland, which plays a critical role in agricultural productivity. The prompt identification of wetlands and irrigation water ponds in irrigation districts is important for the protection of local wetland ecosystem and the effective management of irrigation water. Utilizing the OPtical TRApezoid Model (OPTRAM) for soil moisture estimation, we have proposed an improved version of OPTRAM aimed to identify wetlands and irrigation water ponds in irrigation district using Sentinel-2 data through Google Earth Engine platform. Different criteria were used for the identification of wetland and irrigation water pond, given the greater diversity associated with wetlands. Parameters for the wet edge determined from OPTRAM were used to identify wetlands, while an additional threshold was added to the model and calibrated in accordance with the irrigated area to identify irrigation water ponds. The improved OPTRAM was applied in Hetao Irrigation District (HID) of Northwest China from 2016 to 2023, where autumn irrigation applied in late autumn after crop harvesting was considered. The identified distributions of wetland and autumn irrigation were validated with observations from field survey and statistical data, and were also compared with other remote sensing products. Results show that the proposed model is effective in identifying both wetlands and irrigation ponds. Considering the wetlands, the distances from identified boundaries to the actual boundaries obtained from field surveys were calculated, revealing the mean absolute error of 6.99 m, alongside a validation accuracy ratio of 0.94 for points located within wetlands. For autumn irrigation extent, the overall accuracy is 0.90 based on the observations from field survey, with mean absolute relative errors for irrigated areas across four sub-irrigation districts recorded as 20.55%, 8.10%, 12.83%, and 11.38%, respectively, when compared with statistical data. The majority of wetlands within HID are small in size and scattered, with different trends observed in the wetland areas of sub-irrigation districts and Wuliangsuhai Lake. Autumn irrigated croplands are mainly concentrated in Jiefangzha sub-irrigation district while being scattered across other sub-irrigation districts, depicting an overall decreasing trend in the autumn irrigated area. In summary, the proposed model performed well in identifying both wetlands and irrigation ponds, and can offer valuable support for the conservation of wetland ecosystem and irrigation management.

Study of Machine Learning Techniques for the Estimation of Soil Moisture in Agriculture

Soil moisture is crucial in various fields and monitoring it to guide irrigation is challenging. Machine learning has emerged as a promising tool to predict soil moisture levels accurately. This study evaluates machine learning techniques for this task, training models with meteorological variables and direct soil moisture measurements. Four machine learning algorithms were implemented, highlighting the Gradient Boosting Regressor as the most effective. In addition, a processed data set that combines meteorological and soil moisture measurements is presented, hoping it will be helpful for future research. This approach seeks to improve the compression and predictability of soil moisture, which is crucial for agricultural planning and water management in agriculture

Intermittent Drip Irrigation Soil Wet Front Prediction Model and Effective Water Storage Analysis

The depth and width of drip infiltration play a critical role in designing effective irrigation strategies. However, existing models primarily focus on continuous irrigation and fail to predict wetting patterns under intermittent drip irrigation. This study developed an infiltration model to estimate soil moisture depth and width under intermittent drip irrigation and identified strategies that enhance effective water storage. Indoor soil box simulations were conducted, with continuous drip irrigation as the control. Results showed that intermittent irrigation increased infiltration width and reduced depth, maximizing water storage efficiency. We recommend adopting an intermittent irrigation system with 1.5 h of irrigation followed by a 0.5 h interval, repeated four times. This system increased effective water storage by up to 16.23% compared to continuous irrigation. The proposed method is suitable for sandy loam farmland in southern Xinjiang and can significantly improve water use efficiency in arid regions.

Assessing the influence of model inputs on performance of the EMT + VS soil moisture downscaling model for a large foothills region in Northern Colorado

Soil moisture is an important variable in the hydrologic cycle and a key consideration for off-road vehicle mobility and many other applications. Soil moisture can be estimated at coarse resolutions (∼10 km grid cells) using microwave satellites or land surface models, but these estimates must be downscaled to be useful for such applications. The Equilibrium Moisture from Topography Plus Vegetation and Soil (EMT + VS) model has been proposed as a tool to downscale soil moisture to 10–40 m resolutions. However, it was developed by considering small catchments and determining the model inputs from field data. Although it produced accurate results, it has not been tested using the spatial extents and data from microwave satellites or land surface models. The objective of this study is to test the EMT + VS soil moisture downscaling method for such conditions, examining how the choice of model inputs affects the accuracy of the resulting soil moisture estimates. The model is applied to a 4,000-ha ranch in Northern Colorado that includes diverse topography, vegetation, and soils. The results are compared to in situ soil moisture observations collected at 86 locations on 10 dates that span a wide range of conditions. The study finds that the EMT + VS model can reproduce much of the space–time variation in the observations, and the model outperforms its coarse resolution input and two other soil moisture estimation methods. The model can produce similar results using a variety of coarse resolution soil moisture inputs and fine resolution (30 m, 10 m, or 3 m) topographic inputs, but the choice of satellite, date, and index used to characterize vegetation impacts the accuracy of the soil moisture results.

Data-driven spatio-temporal estimation of soil moisture and temperature based on Lipschitz interpolation

This study estimates agricultural soil variables using a non-parametric machine learning technique based on Lipschitz interpolation. This method is adapted for the first time to learn spatio-temporal dynamics, accounting for two-dimensional spatial and one temporal coordinate inputs separately. The estimator is validated on real agricultural data, addressing challenges like measurement noise and quantization. The experimental setup, including an edge layer with measurement devices and a cloud layer for data storage and processing, is detailed. Despite its simplicity, the method presents a compelling alternative to Gaussian processes and neural networks.

Generation of root zone soil moisture from the integration of an all-weather satellite surface soil moisture estimates and an analytical model: A preliminary result in China

In addition to surface soil moisture (SSM) data, root zone soil moisture (RZSM) is particularly significant for irrigation decision-making and agricultural drought warnings since water uptake generally occurs through the root systems of plants. In this study, a daily/0.05° RZSM dataset covering the major land areas of China was generated from the synergistic use of satellite-derived SSM estimates and a RZSM-SSM analytical model. First, based on the SSM and RZSM datasets generated from the fifth generation of atmospheric reanalysis (ERA5-Land), the Markov Chain Monte Carlo algorithm was applied to optimize the soil moisture analytical relationship (SMAR) model to determine four essential parameters pixel-to-pixel over the study area. In addition, the optimized SMAR parameters were integrated with satellite-derived daily/0.05° SSM to predict the daily/0.05° RZSM in China. Finally, due to the lack of in situ RZSM measurements ground truth at a spatial resolution of 0.05°, two RZSM products, namely the Soil Moisture Active Passive (SMAP)-based RZSM and the China Meteorological Administration Land Data Assimilation System (CLDAS)-based RZSM, were used as references to preliminarily assess the estimated data. The results showed overall fair correlations between SMAP_RZSM and estimated RZSM (correlation coefficient R varying from 0.557 to 0.655), and between CLDAS_RZSM and estimated RZSM (correlation coefficient R varying from 0.496 to 0.596) over four typical days representing spring, summer, autumn, and winter in 2019. Although the proposed approach may overestimate or underestimate RZSM compared to the two referenced products in different seasons, an overall acceptable accuracy with unbiased root mean square error of ∼ 0.070–0.090 cm3/cm3 was obtained.

Machine learning‐driven microwave imaging for soil moisture estimation near leaky pipe

AbstractCharacterizing soil moisture around drip irrigation pipes is crucial for precise and optimized farming. Machine learning (ML) approaches are particularly suitable for this task as they can reduce uncertainties caused by soil conditions and the drip pipe positions, using features extracted from relevant datasets. This letter addresses local moisture detection in the vicinity of dripping pipes using a portable microwave imaging system. The employed ML approach is fed with two dimensional images generated using back projection as a radar‐based algorithm and the Born approximation as an inverse scattering method, based on spatio‐temporal (collected data at various positions over the soil surface and at different time points.) measurements at various frequencies. The study investigates the performance of K‐nearest neighbour (KNN) and convolutional neural networks (CNN) algorithms for moisture classification based on these imaging techniques. We also explore the potential of KNN and CNN for moisture estimation around the plant roots and in the presence of pebbles. In general, CNN outperforms KNN in moisture content detection from microwave data, especially after applying imaging algorithms. A combination of CNN as the ML approach and the back projection algorithm to provide training data, yielded accuracy more than other models for moisture content estimation. Also, the practical results demonstrate that our method can detect soil moisture with an estimation error of less than 10%.

Enhancing spatial resolution of satellite soil moisture data through stacking ensemble learning techniques

Soil moisture (SM) is a critical variable influencing various environmental processes, but traditional microwave sensors often lack the spatial resolution needed for local-scale studies. This study develops a novel stacking ensemble learning framework to enhance the spatial resolution of satellite-derived SM data to 1 km in the Urmia basin, a region facing significant water scarcity. We integrated in-situ SM measurements (obtained using time-domain reflectometry [TDR]), Soil Moisture Active Passive (SMAP) and Advanced Microwave Scanning Radiometer 2 (AMSR2) SM products, Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature and vegetation indices, precipitation records, and topography data. Ten base machine-learning models were evaluated using the Complex Proportional Assessment (COPRAS) method, and the top-performing models were selected as base learners for the stacking ensemble. The ensemble model, incorporating Random Forest, Gradient Boosting, and XGBoost, significantly improved SM estimation accuracy and resolution compared to individual models. The XGBoost and Gradient Boosting meta-models achieved the highest accuracy, with an unbiased root mean square error (ubRMSE) of 1.23% m3/m3 and a coefficient of determination (R2) of 0.97 during testing, demonstrating the exceptional predictive capabilities of our approach. SHapley Additive exPlanations (SHAP) analysis revealed the influence of each base model on the ensemble’s predictions, highlighting the synergistic benefits of combining diverse models. This study establishes new benchmarks for soil moisture monitoring by showcasing the potential of ensemble learning to improve the spatial resolution and accuracy of satellite-derived SM data, providing crucial insights for environmental science and agricultural planning, particularly in water-stressed regions.

Triple Collocation-Based Model Error Estimation of VIC-Simulated Soil Moisture at Spatial and Temporal Scales in the Continental United States in 2010–2020

Triple Collocation-Based Model Error Estimation of VIC-Simulated Soil Moisture at Spatial and Temporal Scales in the Continental United States in 2010–2020

Geographic analysis of soil moisture recorded and estimated in Erbil governorate

Calculating of soil moisture and its monitoring is one of the important processes of water balance and sustainable agriculture. The intended goal of this study is to determine soil moisture values in Erbil Governorate and reveal the correlation coefficient and the degree of influence between its values with the elevation and total annual rainfall. The importance of the study lies In its discussion of the accuracy of extracting soil moisture values using satellite images, by comparing its results with the soil moisture values recorded in the governorate’s climate stations, the study reached a set of conclusions, the most important of which is: The results of using satellite images to estimate soil moisture are inaccurate. It is closer than guessing, and the results of applying the linear regression process indicated that there was a significant statistical relationship between the soil moisture values recorded with the two factors of elevation and the total annual rainfall, where the probability values were less than 0.01 and the values of the degree of influence between them reached 0.583 and 0.491 for each of them, respectively.‏

Soil moisture estimation in the unsaturated zone using surface wave measurements and hybrid modeling framework

Abstract Soil moisture plays a critical role in influencing various facets of ecosystem dynamics. The preference for measuring soil moisture without physical intrusion has been desirable for precise assessments while minimizing disruptions to soil structural, hydraulic, and biological characteristics. In this study, we explored the potential of surface elastic waves as a proxy to estimate soil moisture profiles to a depth of 1.05 m at intervals of 0.1 m. We conducted a multichannel analysis of surface waves (MASW) survey and measured soil moisture at depths of 0.15 m and 0.35 m. To address the limited availability of soil moisture measurements, we developed a mechanistic soil moisture model as a substitute for measured soil moisture profiles. Our results showed that as soil moisture increased, the propagation of surface waves became more pronounced due to reduced frictional resistance. However, it was not straightforward to link measured surface wave responses and subsurface soil moisture profile. To address these challenges, we developed a convolutional neural network (CNN) with the inputs of the frequency-velocity and frequency-wavenumber images obtained from the measured surface waves. We found that the integration of MASW and CNN proved effective in estimating soil moisture profiles to a depth of 1.05 m at intervals of 0.1 m without causing disturbances to the soil (MAE = 0.0035 m3 m−3). This study suggested that the combined use of surface waves and CNN hold promise in measuring soil moisture profiles without physical disruptions. As such, the proposed approach could serve as a viable alternative to noninvasive soil moisture sensors.

An Integrated Approach to the Regional Estimation of Soil Moisture

Automatic or smart irrigation systems benefit irrigation water management. However, measurement sensor networks in automatic irrigation systems are complex, and maintenance is essential. Regional soil moisture estimation avoids the multiple measurements necessary when deploying an irrigation system. In this sense, a fuzzy estimation approach based on decision-making (FEADM) has been used to obtain soil moisture point estimates. However, FEADM requires intelligent weather adjustment based on spatial features (IWeCASF) to perform regional soil moisture estimation. The IWeCASF-FEADM integrated approach for regional soil moisture estimation is developed in this work. IWeCASF provides the inputs for FEADM. FEADM is performed R times; R is the number of checkpoints at which a point estimate is obtained. In this way, regional estimation is achieved when the set of R soil moisture point estimates is completed. Additionally, IWeCASF-FEADM considers the irrigation water records, which are not included in either method individually. This method can detect when the soil moisture is deficient in a region, allowing actions to prevent water stress. This regional estimation reduces an irrigation system’s operational and maintenance complexity. This integrated approach has been tested over several years by comparing the results of regional soil moisture estimation with measurements obtained at many points in the study region.