Evaluating Soil Moisture–Precipitation Interactions Using Remote Sensing: A Sensitivity Analysis

The Essential Climate Variable (ECV) soil moisture (SM) datasets, originated from the European Space Agency, have revealed great potential for application in hydrology and agriculture. Hence, it is essential to continuously enhance the data quality and spatial completeness to satisfy the increasing scientific research requirements. In this study, we explore the potential possibility of Soil Moisture Active Passive (SMAP) datasets in filling the gaps of ECV SM. The comprehensive assessment results show that: (1) The data missing percent of gap-filled ECV decreases 20% on average, which can be one step closer to generate a seamlessly covered global land surface SM product with favorable quality. (2) Compared to the original ECV, the gap-filled ECV products express similar good response to the in-situ measurements, suggesting that the SMAP SM products could be taken to efficiently fill the gaps and consistently maintain favorable accuracy at the same time. (3) Compared to the in-situ measurements, the original ECV SM products demonstrate extremely high probability density peak percentages. Fortunately, this eminent high value could be effectively rectified through gap-filling progress using SMAP. Overall, this study conducts objective and detailed evaluation on the performance of applying SMAP to fill the gaps of ECV, and is expected to act as a valuable reference in ECV SM gap-filling method.

Abstract. An improved fine-scale soil moisture (SM) dataset at 1 km grid spacing, covering much of the eastern continental US, was generated by assimilating 9 km Soil Moisture Active Passive (SMAP) SM data into the v4.0.1 Noah-MP land surface model. With 12 ensemble members, the assimilation was carried out using the ensemble Kalman filter algorithm within NASA's Land Information System. The SM analysis for 2016 was fully validated against in situ observations from four different networks and compared with four other existing datasets. Results indicate that this SM analysis surpasses other datasets in top-layer SM distribution, including a machine-learning-based product, despite all SM estimates being less heterogeneous than observed. The analysis of anomalous errors suggests that large similarity in intrinsic errors is likely due to overlapping data sources among the selected SM datasets. More detailed evaluations were performed over two geographic areas. The observations collected by the Atmospheric Radiation Measurement facility in Oklahoma suggest that soil temperature and surface heat fluxes are concurrently simulated with good accuracy. Investigation into the 2016 southeastern US drought response further indicates drier conditions and higher evapotranspiration estimates compared to GLEAMv4.1. Notably, large errors are associated with grids having clay soil textures, underscoring the need for refined model treatments for specific soil types to further improve SM estimates. The dataset is publicly available on Zenodo at https://doi.org/10.5281/zenodo.14370563 (Tai et al., 2024).

Abstract. Soil moisture (SM) data records longer than 30 years are critical for climate change research and various applications. However, only a few such long-term global SM datasets exist, and they often suffer from large biases, low spatial resolution, or spatiotemporal incompleteness. Here, we generated a consistent and seamless global surface SM product (0–5 cm) spanning 1982–2021 using a deep learning (DL) model. The model was trained with the GLASS-MODIS SM product and was designed to integrate four decades of Advanced Very High Resolution Radiometer (AVHRR)-derived albedo and land surface temperature, the land component of the fifth generation of European ReAnalysis (ERA5-Land) SM, and terrain and soil texture datasets as input features. Considering the temporal autocorrelation of SM, we explored two types of DL models that are adept at processing sequential data, including three long short-term memory (LSTM)-based models, i.e., the basic LSTM, bidirectional LSTM (Bi-LSTM), and attention-based LSTM (AtLSTM), and a transformer model. We also compared the performance of the DL models with the tree-based eXtreme Gradient Boosting (XGBoost) model, known for its high efficiency and accuracy. Our results show that all four DL models outperformed the benchmark XGBoost model, with the AtLSTM model achieving the highest accuracy on the test set, particularly at high SM levels (>0.4m3m-3). These results suggest that under some challenging conditions, utilizing temporal information and adding an attention module can effectively enhance the estimation accuracy of SM. Subsequent analysis of attention weights revealed that the AtLSTM model could automatically learn the necessary temporal information from adjacent positions in the sequence, which is critical for accurate SM estimation. The best-performing AtLSTM model was then adopted to produce a four-decade seamless global SM dataset at 5 km spatial resolution, denoted as the GLASS-AVHRR SM product. Validation of the GLASS-AVHRR SM product using 45 independent International Soil Moisture Network (ISMN) stations prior to 2000 yielded a median correlation coefficient (R) of 0.73 and an unbiased root mean square error (ubRMSE) of 0.041 m3 m−3. When validated against SM datasets from three post-2000 field-scale COsmic-ray Soil Moisture Observing System (COSMOS) networks, the median R values ranged from 0.63 to 0.79, and the median ubRMSE values ranged from 0.044 to 0.065 m3 m−3. Further validation across 22 upscaled 9 km Soil Moisture Active Passive (SMAP) core validation sites indicated that it could well capture the temporal variations in measured SM and remained unaffected by the large wet biases present in the input ERA5-Land SM product. Moreover, characterized by complete spatial coverage and low biases, this four-decade, 5 km GLASS-AVHRR SM product exhibited high spatial and temporal consistency with the 1 km GLASS-MODIS SM product and contained much richer spatial details than both the long-term ERA5-Land SM product (0.1°) and European Space Agency Climate Change Initiative combined SM product (0.25°). The annual average GLASS-AVHRR SM dataset from 1982 to 2021 is available at https://doi.org/10.5281/zenodo.14198201 (Zhang et al., 2024b), and the complete product can be freely downloaded from https://glass.hku.hk/archive/SM/AVHRR/ (last access: 18 September 2025).

Soil moisture is a significant environmental factor that influences both the water and energy balance at the land-atmosphere interface. Therefore, proper assessment of the spatial and temporal distribution of soil moisture is crucial for many hydrological applications such as weather forecasting, agricultural water resource management and drought monitoring. This study involves the assimilation of Soil Moisture Active Passive (SMAP) soil moisture dataset within a land surface model and the evaluation of its performance in precise estimation of soil moisture by comparing the statistics with respect to standard European Space Agency’s Climate Change Initiative (ESA-CCI) soil moisture dataset. The Ensemble Kalman Filter technique has been used for assimilating SMAP soil moisture data using Noah-MP land surface model within NASA Land Information System (LIS) framework. The data assimilation (DA) framework includes Cumulative Distribution Function (CDF) matching for bias correction and twenty ensembles per tile. Meteorological forcings for the simulations have been taken from MERRA2 and IMD. Improvement or degradation due to DA has been analyzed in terms of the difference in anomaly correlation between open loop (OL) and DA soil moisture outputs with respect to the ESA-CCI soil moisture dataset over the entire Indian domain. The DA result shows improvement over larger areas in the case of MERRA2 forced simulations than IMD+MERRA2. The seasonal impact of DA in terms of the differences in DA and OL simulated soil moisture shows less variability in summer than winter. The results are validated with in-situ soil moisture datasets. Overall, the study shows that data assimilation is giving better results than open loop LSM simulation, which can be used for improved estimation of other water balance components.

Accurate soil moisture (SM) estimates with high spatial resolution are highly desirable for agricultural, hydrological, and environmental applications. This study developed a two-step reconstruction approach to obtain a high-quality and high-spatial-resolution (0.05°) SM dataset from microwave and model-based SM products, combining Bayesian three-cornered hat (BTCH) merging and machine/deep learning downscaling algorithms. Firstly, a three-cornered hat (TCH) method was used to analyze the uncertainty of seven SM products on four main land cover types in the Pearl River Basin (PRB). On this basis, the SM products with low uncertainty were merged using the BTCH method. Secondly, two machine/deep learning algorithms (random forest, RF, and long short-term memory, LSTM) were applied to downscale the merged SM data from 0.25° to 0.05° based on the relationship between SM and auxiliary variables. The overall performance of RF and LSTM downscaling models with/without antecedent precipitation were compared. The merged and downscaled SM results were validated against in situ observations and the China Meteorological Administration (CMA) Land Data Assimilation System (CLDAS) SM data. The results indicated the following: (1) The BTCH-based SM estimate outperformed the parent products and the AVE-based SM estimate (the arithmetic average), indicating that BTCH is a fusion approach that can effectively reduce data uncertainties and optimize weights. (2) The optimal time scale for the cumulative effect of precipitation on SM was 35 days during 2015–2020 in the PRB. SM estimations using RF and LSTM downscaling algorithms both had substantial improvement by considering the antecedent precipitation variable, both at the 0.25° and 0.05° spatial scales. Feature importance assessment also revealed the most important role of antecedent precipitation (30.01%). Moreover, the LSTM model with antecedent precipitation performed slightly better than the RF model with antecedent precipitation. (3) The downscaled SM results all mitigated the overestimation inherent in the original SM data, though they were inevitably limited by the performance of the original SM data and difficult to surpass. The developed two-step reconstruction approach was effective in generating an accurate SM dataset at a finer spatial scale for wide regional applications.

Ammonia-oxidizing archaea and bacteria (AOA, AOB) catalyze the first and rate-limiting step of nitrification. To examine their differential responses to the wetting of dry and salty arid soil, AOA and AOB amoA genes (encoding subunit A of the ammonia monooxygenase) and transcripts were enumerated in dry (summer) and wet (after the first rainfall) soil under the canopy of halophytic shrubs and between the shrubs. AOA and AOB were more abundant under shrub canopies than between shrubs in both the dry and wetted soil. Soil wetting caused a significant decrease in AOB abundance under the canopy and an increase of AOA between the shrubs. The abundance of the archaeal amoA gene transcript was similar for both the wet and dry soil, and the transcript-to-gene ratios were < 1 independent of niche or water content. In contrast, the bacterial amoA transcript-to-gene ratios were between 78 and 514. The lowest ratio was in dry soil under the canopy and the highest in the soil between the shrubs. The results suggest that the AOA are more resilient to stress conditions and maintain a basic activity in arid ecosystems, while the AOB are more responsive to changes in the biotic and abiotic conditions.

Evaluation of soil moisture extremes for agricultural productivity in the Canadian prairies

Ground‐based, air‐borne, and space‐borne remote sensing techniques have evolved over the past several decades and provided many new techniques for estimating various land surface attributes at multiple scales related to mass and energy dynamics. The vadose zone, encompassing land surface, root zone, and the deeper soil profile down to the groundwater table, is a complex domain, particularly related to various hydrologic and biological processes across different scales. In this zone, spatial distributions and temporal dynamics of soil moisture and evapotranspiration (ET), including their interdependence, are critical to climate feedback, hydrology, and plant canopy health. Their temporal and spatial variability across catchments affect surface and subsurface runoff, modulates evaporation and transpiration, determines the extent of groundwater recharge and contaminant transport, and initiates or sustains feedback between the land surface and the atmosphere. With the recent development and deployment of various remote sensing platforms working with different techniques such as optical, microwave, gravitational, infrared, and other sensors, improved temporal and spatial measurement or estimates of soil moisture, evapotranspiration, soil hydraulic parameters, soil salinity, and vegetation attributes are possible. In this special section “Remote Sensing for Vadose Zone Hydrology,” 14 contributions on fundamental and applied studies using different remote sensing platforms, including satellite retrieval algorithm development, data assimilation techniques, scaling issues, ground validation, and field applications in the context of vadose zone hydrology are presented. The foci of these papers range across root zone soil moisture retrieval and variability, evapotranspiration dynamics and distribution, agricultural water management, soil hydraulic and mechanical property estimation, ecosystems assessment, land–atmosphere interaction, and land surface hazard assessment. Here we organized the summary of these papers in four overlapping sections including (i) estimation, variability, scaling, and data assimilation of soil moisture by microwave remote sensing; (ii) estimating evapotranspiration by remote sensing and water management applications; (iii) estimating vadose zone properties by remote sensing; and (iv) ground‐based soil moisture for calibration and validation of microwave remote sensing.

The disappearance of EPTC (ethyl N,N-di-n-propylthiolcarbamate) from soils was studied by steam distilling the remaining EPTC from the soil samples and analyzing the distillates. The most important factor affecting the loss of EPTC from soil was the amount of moisture in the soil. During the first 15 minutes after spraying on the soil surface, 20% of the applied EPTC disappeared from dry soil, 27% from moist soil and 44% from wet soil. The loss was 23, 49, and 69% after one day and 44, 68, and 90% after six days on dry, moist, and wet soils, respectively. No significant loss of EPTC occurred the first few hours after applying granular EPTC to dry soil or soil containing 4 to 6% moisture, but on soil at 15% moisture the loss from granules was 60% in two hours. There appeared to be a critical moisture level for each soil, above which large losses of EPTC occurred after application to the surface or after shallow incorporation. Depth of incorporation was the second most important factor in EPTC loss from soils. Incorporation to at least 2 to 3 inches was necessary to prevent large losses of EPTC when light rain or sprinkler irrigation followed treatment. EPTC was lost at a faster rate on sunny days than cloudy days. Increasing the temperature from 32 to 60 F, increased the rate of EPTC vaporization from moist soil but had no effect on the loss from dry soil.

Abstract. Soil moisture (SM) datasets are critical to understanding the global water, energy, and biogeochemical cycles and benefit extensive societal applications. However, individual sources of SM data (e.g., in situ and satellite observations, reanalysis, offline land surface model simulations, Earth system model – ESM – simulations) have source-specific limitations and biases related to the spatiotemporal continuity, resolutions, and modeling and retrieval assumptions. Here, we developed seven global, gap-free, long-term (1970–2016), multilayer (0–10, 10–30, 30–50, and 50–100 cm) SM products at monthly 0.5∘ resolution (available at https://doi.org/10.6084/m9.figshare.13661312.v1; Wang and Mao, 2021) by synthesizing a wide range of SM datasets using three statistical methods (unweighted averaging, optimal linear combination, and emergent constraint). The merged products outperformed their source datasets when evaluated with in situ observations (mean bias from −0.044 to 0.033 m3 m−3, root mean square errors from 0.076 to 0.104 m3 m−3, Pearson correlations from 0.35 to 0.67) and multiple gridded datasets that did not enter merging because of insufficient spatial, temporal, or soil layer coverage. Three of the new SM products, which were produced by applying any of the three merging methods to the source datasets excluding the ESMs, had lower bias and root mean square errors and higher correlations than the ESM-dependent merged products. The ESM-independent products also showed a better ability to capture historical large-scale drought events than the ESM-dependent products. The merged products generally showed reasonable temporal homogeneity and physically plausible global sensitivities to observed meteorological factors, except that the ESM-dependent products underestimated the low-frequency temporal variability in SM and overestimated the high-frequency variability for the 50–100 cm depth. Based on these evaluation results, the three ESM-independent products were finally recommended for future applications because of their better performances than the ESM-dependent ones. Despite uncertainties in the raw SM datasets and fusion methods, these hybrid products create added value over existing SM datasets because of the performance improvement and harmonized spatial, temporal, and vertical coverages, and they provide a new foundation for scientific investigation and resource management.

Remote detection of bare soil moisture using a surface-temperature-based soil evaporation transfer coefficient

The difficulty of accurate and large-scale measurement for surface parameters limits the regional surface soil moisture (SSM) estimation using synthetic aperture radar (SAR). Moreover, the coarse resolution of soil moisture products generated by existing methods, which fuse SAR and passive microwave products, cannot fully satisfy the requirement of specific regional applications. To solve this problem, an SAR-optical data fusion method for soil moisture estimation (SOFSME) based on a cascade neural network is proposed in this study. SOFSME obtains surface parameters from historical soil moisture images and related environmental images to estimate a SSM image with high resolution at large scale from Sentinel-1A C-band SAR data. Validation experiments in single and multiple land-use type areas showed that the SOFSME performed best on bare soil areas with a median root mean square error of 0.0203. The median universal image quality index of estimated soil moisture image was 0.1454, which was better for single cropland areas than multi-land-use type areas. The Pearson correlation coefficient showed a median value of 0.7645 in both experiments. These results showed that the SOFSME had high accuracy, availability, and stability in regional soil moisture estimation. Compared with existing methods, the SOFSME can provide high-quality soil moisture images and does not directly depend on field measurement data. Thus, the proposed SOFSME method is of great value for high-resolution soil moisture estimation in more regional applications.

Abstract. Soil moisture is key for understanding soil–plant–atmosphere interactions. We provide a soil moisture pattern recognition framework to increase the spatial resolution and fill gaps of the ESA-CCI (European Space Agency Climate Change Initiative v4.5) soil moisture dataset, which contains &gt; 40 years of satellite soil moisture global grids with a spatial resolution of ∼ 27 km. We use terrain parameters coupled with bioclimatic and soil type information to predict finer-grained (i.e., downscaled) satellite soil moisture. We assess the impact of terrain parameters on the prediction accuracy by cross-validating downscaled soil moisture with and without the support of bioclimatic and soil type information. The outcome is a dataset of gap-free global mean annual soil moisture predictions and associated prediction variances for 28 years (1991–2018) across 15 km grids. We use independent in situ records from the International Soil Moisture Network (ISMN, 987 stations) and in situ precipitation records (171 additional stations) only for evaluating the new dataset. Cross-validated correlation between observed and predicted soil moisture values varies from r= 0.69 to r= 0.87 with root mean squared errors (RMSEs, m3 m−3) around 0.03 and 0.04. Our soil moisture predictions improve (a) the correlation with the ISMN (when compared with the original ESA-CCI dataset) from r= 0.30 (RMSE = 0.09, unbiased RMSE (ubRMSE) = 0.37) to r= 0.66 (RMSE = 0.05, ubRMSE = 0.18) and (b) the correlation with local precipitation records across boreal (from r= &lt; 0.3 up to r= 0.49) or tropical areas (from r= &lt; 0.3 to r= 0.46) which are currently poorly represented in the ISMN. Temporal trends show a decline of global annual soil moisture using (a) data from the ISMN (-1.5[-1.8,-1.24] %), (b) associated locations from the original ESA-CCI dataset (-0.87[-1.54,-0.17] %), (c) associated locations from predictions based on terrain parameters (-0.85[-1.01,-0.49] %), and (d) associated locations from predictions including bioclimatic and soil type information (-0.68[-0.91,-0.45] %). We provide a new soil moisture dataset that has no gaps and higher granularity together with validation methods and a modeling approach that can be applied worldwide (Guevara et al., 2020, https://doi.org/10.4211/hs.9f981ae4e68b4f529cdd7a5c9013e27e).

Corn is a sustainability crop commodity that has the potential to contribute to the growth and progress of the agricultural industry. The objective of this research is to examine the various factors that impact maize production on dry and wet soils, compare the relationship between maize yield and income on dry and wet lands, and assess the sustainability of maize in the Mekikis village of Purwoasri district. Descriptive and statistical analyzes (double regression analysis), enterprise feasibility analysis, and differential tests of averages (independent sample t-test) comprise the method of analysis in this study. Nonproportional stratified random sampling was utilized to obtain a sample size of 64 respondents, 32 of whom were wetland farmers and 32 of whom were dry land farmers. The attributes of the farmers who participated in the research encompassed gender, age, most recent educational attainment, primary occupation, family count, land area ownership, and land area. The results of the double linear regression analysis indicate that both the land area and labor force significantly impact maize production on moist soil. The impact of various factors such as land area, seeds, inorganic fertilisers, and pesticides on maize production in arid soil is substantial. The results of the independent sample t-test indicate a statistically significant difference between the yield and income of maize grown in moist and dry soils. specifically, the profit on maize is Rs. 1,624,218,099.94, while the yield on maize in dry soils is 1,899.87 per kilogramme. The usability analysis of maize indicates that the R/C ratio differs when the soil is moist versus when it is dry.

A preliminary study toward consistent soil moisture products from the Advanced Microwave Scanning Radiometer 2 (AMSR2) is presented. Its predecessor, the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E), has provided Earth scientists with a consistent and continuous global soil moisture dataset. A major challenge remains to achieve synergy between these soil moisture datasets, which is hampered by the lack of an overlapping observation period of the sensors. Here, observations of the multifrequency microwave radiometer on board the Tropical Rainfall Measuring Mission (TRMM) satellite were used to improve consistency between AMSR-E and AMSR2. Several scenarios to achieve synergy between the AMSR-E and AMSR2 soil moisture products were evaluated. The novel soil moisture retrievals from C-band observations, a frequency band that is lacking on board the TRMM satellite, are also presented. A global comparison of soil moisture retrievals against ERA-Interim soil moisture demonstrates the need for an intercalibration procedure. Several different scenarios based on filtering were tested, and the impact on the soil moisture retrievals was evaluated against two independent reference soil moisture datasets (reanalysis and in situ soil moisture) that cover both individual observation periods of the AMSR-E and AMSR2 sensors. Results show a high degree of consistency between both satellite products and two independent reference products for the soil moisture products retrieved from X-band observations. Care should be taken in the interpretation of the presented soil moisture products, and future research is needed to further align the AMSR2 and AMSR-E sensor calibrations.