Remote Sensing for Vadose Zone Hydrology—A Synthesis from the Vantage Point

Satellite remotely sensed (RS) soil moisture (SM) is commonly assimilated to leverage land surface modeling. However, the improvement in deep soil simulation remains challenging as limited by the relative shallow penetration depth (< 5 cm). With advancements in satellite RS technologies like P ‐band sensors, retrieving deeper SM has become increasingly feasible. Here, we demonstrate the potentially added value of enriched deep soil moisture information in land surface data assimilation (DA) by using ground‐based SM observations from a dense monitoring network in the central Tibetan Plateau. Specifically, a cost function‐based multi‐layer SM DA framework is developed to optimize soil texture profiles and organic matter content. A series of DA experiments were conducted to explore the optimal assimilation and optimization depths. The results suggest that assimilation of top 20 cm SM is adequate to reasonably optimize key soil parameters for all soil layers, and thereby improves SM estimates to the depth of 40 cm. This improvement can further propagate into soil temperature profiles and surface flux (e.g., evapotranspiration) estimates. Besides, the performance of deep SM DA is found insensitive to assimilation frequency varying up to 5 days, highlighting the promise and feasibility of regional land DA with 0–20 cm SM products, which are readily accessible from future satellite missions.

ABSTRACTAccurate estimation of multi‐depth/profile soil moisture (SM) is required for sustainable water management in agriculture and hydrology. However, monitoring SM is costly and labour‐intensive, and only limited soil depths can be instrumented with soil moisture sensors. Therefore, various numerical simulation and data assimilation techniques have been used in multi‐depth soil moisture estimation. Machine learning (ML) has also gained popularity in SM estimation due to its ease of use and robustness, although proper handling of ML models also requires expertise and experience. However, the applicability of ML to estimate time series of multi‐depth SM under different land uses is mainly limited by the choice of ML models and the availability of SM data. In addition, the reliability of the trained model remains unknown when it is applied to different locations. Therefore, the objective of this study was to evaluate the widely used Long Short‐Term Memory (LSTM) model to estimate multi‐depth SM under different land covers, climates, and soils. A minimum of 10 years' daily meteorological and soil data at multiple depths were collected from six U.S. Climate Reference Network (USCRN) stations with different land covers and various climates and soils. These data were used to train the LSTM model and optimize its input parameters. Performance of the trained LSTM model was evaluated for multi‐depth SM estimation at two other “monitoring” stations with similar conditions. SM modeling at shallow depths (e.g., 5, 10 and 20 cm) was most accurate (< 10% mean absolute percent error, MAPE) with precipitation and antecedent time series of SM as inputs, while the best SM estimates at deeper depths (e.g., 50 and 100 cm) were attained with antecedent SM time series as the input. Generation of the trained LSTM model from one station to other stations emphasized on the similar soil and land cover conditions. It is hoped that this research would provide better understandings of multi‐depth SM modeling and offer new insights improving profile SM modeling accuracy for un‐instrumented sites.

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.

In large river basins where in situ data were limited or absent, satellite-based soil moisture estimates can be used to supplement ground measurements for land and water resource management solutions. Consistent soil moisture estimation can aid in monitoring droughts, forecasting floods, monitoring crop productivity, and assisting weather forecasting. Satellite-based soil moisture estimates are readily available at the global scale but are provided at spatial scales that are relatively coarse for many hydrological modeling and decision-making purposes. Soil moisture data are obtained from NASA’s soil moisture active passive (SMAP) mission radiometer as an interpolated product at 9 km gridded resolution. This study implements a soil moisture downscaling algorithm that was developed based on the relationship between daily temperature change and average soil moisture under varying vegetation conditions. It applies a look-up table using global land data assimilation system (GLDAS) soil moisture and surface temperature data, and advanced very high resolution radiometer (AVHRR) and moderate resolution imaging spectroradiometer (MODIS) normalized difference vegetation index (NDVI) and land surface temperature (LST). MODIS LST and NDVI are used to obtain downscaled soil moisture estimates. These estimates are then used to enhance the spatial resolution of soil moisture estimates from SMAP 9 km to 1 km. Soil moisture estimates at 1 km resolution are able to provide detailed information on the spatial distribution and pattern over the regions being analyzed. Higher resolution soil moisture data are needed for practical applications and modelling in large watersheds with limited in situ data, like in the Lower Mekong River Basin (LMB) in Southeast Asia. The 1 km soil moisture estimates can be applied directly to improve flood prediction and assessment as well as drought monitoring and agricultural productivity predictions for large river basins.

Leaf area index (LAI) and actual evapotranspiration (ETa) from satellite observations were used to estimate simultaneously the soil hydraulic parameters of four soil layers down to 60 cm depth using the combined soil water atmosphere plant and genetic algorithm (SWAP–GA) model. This inverse model assimilates the remotely sensed LAI and/or ETa by searching for the most appropriate sets of soil hydraulic parameters that could minimize the difference between the observed and simulated LAI (LAIsim) or simulated ETa (ETasim). The simulated soil moisture estimates derived from soil hydraulic parameters were validated using values obtained from soil moisture sensors installed in the field. Results showed that the soil hydraulic parameters derived from LAI alone yielded good estimations of soil moisture at 3 cm depth; LAI and ETa in combination at 12 cm depth, and ETa alone at 28 cm depth. There appeared to be no match with measurement at 60 cm depth. Additional information would therefore be needed to better estimate soil hydraulic parameters at greater depths. Despite this inability of satellite data alone to provide reliable estimates of soil moisture at the lowest depth, derivation of soil hydraulic parameters using remote sensing methods remains a promising area for research with significant application potential. This is especially the case in areas of water management for agriculture and in forecasting of floods or drought on the regional scale.

Today, the most popular approaches in agricultural forecasting leverage process-based crop models, crop monitoring data, and/or remote sensing imagery. Individually, each of these tools has its own unique advantages but is, nonetheless, limited in prediction accuracy, precision, or both. In this study we integrate in situ and remote sensing (RS) soil moisture observations with APSIM model through sequential data assimilation to evaluate the improvement in model predictions of downstream state variables across 5 experimental sites in the U.S Midwest. Four RS data products and in-situ observations spanning 19 site-years were used through two data assimilation approaches namely Ensemble Kalman Filter (EnKF) and Generalized Ensemble Filter (GEF) to constrain model states at observed time steps and estimate joint background and observation error matrices. Then, the assimilation’s impact on estimates of soil moisture, yield, NDVI, tile drainage, and nitrate leaching was assessed across all site-years. When assimilating in situ observations, the accuracy of soil moisture forecasts in the assimilation layers was improved by reducing RMSE by an average of 17 % for 10 cm and ~28 % for 20 cm depth soil layer across all site-years. These changes also led to improved simulation of soil moisture in deeper soil layers by an average of 12 %. Although crop yield was improved by an average of 23 %, the greatest improvement in yield accuracy was demonstrated in site-years with higher water stress, where assimilation served to increase available soil water for crop uptake. Alternatively, estimates of annual tile drainage and nitrate leaching were not well constrained across the study sites. Trends in drainage constraint suggest the importance of including additional data constraint such as evapotranspiration. The assimilation of RS soil moisture showed weaker constraint of downstream model state variables when compared to the assimilation of in situ soil moisture. The median reduction in soil moisture RMSE for observed soil layers was lower, on average, by a factor of 5. However, crop yield estimates were still improved overall with a median RMSE reduction of 17.2 %. Crop yield prediction was improved when assimilating both in-situ and remote sensing soil moisture observations and there is strong evidence that yield improvement was higher when under water-stressed conditions. Comparisons of system performance across different combinations of remote sensing data products indicated the importance of high temporal resolution and accurate observation uncertainty estimates when assimilating surface soil moisture observations.

Understanding the key factors that influence soil moisture estimation using the unscented weighted ensemble Kalman filter

Sequential assimilation of soil moisture and streamflow data in a conceptual rainfall–runoff model

The complex interactions between soil moisture and precipitation are difficult to observe, and consequently there is a lack of consensus as to the sign, strength, and location of these interactions. Inconsistency between soil moisture–precipitation interaction studies can be attributed to a multitude of factors, including the difficulty of demonstrating causal relationships, dataset differences, and precipitation autocorrelation. The purpose of this study is to explore these potential confounding factors and determine which are most important for consideration when assessing statistical coupling between soil moisture and precipitation. Soil moisture is assessed via three remote sensing datasets: the Advanced Microwave Scanning Radiometer for Earth Observing System, the Tropical Rainfall Measuring Mission Microwave Imager, and the Essential Climate Variable Soil Moisture. Estimates of soil moisture are coupled with afternoon thunderstorm events identified by the Thunderstorm Observation by Radar (ThOR) algorithm, and dry soil or wet soil preferences for convection initiation are determined for over 16 000 thunderstorm events between 2005 and 2007. Differences in soil moisture datasets were found to have the largest impact with regard to determining wet or dry soil preferences. Precipitation autocorrelation is prevalent in the data; however, precipitation autocorrelation did not influence the results with regard to dry or wet soil preferences. Consideration of the convective environment (i.e., weakly or synoptically forced) did result in significant differences in wet/dry soil preference, but only for certain soil moisture datasets. The results suggest that observation-driven soil moisture–precipitation interaction studies should both consider the convective environment and implement multiple soil moisture datasets to assure robust results.

Soil moisture estimation from inverse modeling using multiple criteria functions

Abstract. Soil moisture plays a critical role in land surface processes and as such there has been a recent increase in the number and resolution of satellite soil moisture observations and the development of land surface process models with ever increasing resolution. Despite these developments, validation and calibration of these products has been limited because of a lack of observations on corresponding scales. A recently developed mobile soil moisture monitoring platform, known as the rover, offers opportunities to overcome this scale issue. This paper describes methods, results and testing of soil moisture estimates produced using rover surveys on a range of scales that are commensurate with model and satellite retrievals. Our investigation involved static cosmic-ray neutron sensors and rover surveys across both broad (36 × 36 km at 9 km resolution) and intensive (10 × 10 km at 1 km resolution) scales in a cropping district in the Mallee region of Victoria, Australia. We describe approaches for converting rover survey neutron counts to soil moisture and discuss the factors controlling soil moisture variability. We use independent gravimetric and modelled soil moisture estimates collected across both space and time to validate rover soil moisture products. Measurements revealed that temporal patterns in soil moisture were preserved through time and regression modelling approaches were utilised to produce time series of property-scale soil moisture which may also have applications in calibration and validation studies or local farm management. Intensive-scale rover surveys produced reliable soil moisture estimates at 1 km resolution while broad-scale surveys produced soil moisture estimates at 9 km resolution. We conclude that the multiscale soil moisture products produced in this study are well suited to future analysis of satellite soil moisture retrievals and finer-scale soil moisture models.

Satellite soil moisture estimates have received increasing attention over the past decade. This paper examines the applicability of estimating soil moisture states and soil hydraulic parameters through two particle filter (PF) methods: The PF with commonly used sampling importance resampling (PF-SIR) and the PF with recently developed Markov chain Monte Carlo sampling (PF-MCMC) methods. In a synthetic experiment, the potential of assimilating remotely sensed near-surface soil moisture measurements into a 1-D mechanistic soil water model (HYDRUS-1D) using both the PF-SIR and PF-MCMC algorithms is analyzed. The effects of satellite temporal resolution and accuracy, soil type, and ensemble size on the assimilation of soil moisture are analyzed. In a real data experiment, we first validate the Advanced Microwave Scanning Radiometer--Earth Observing System (AMSR-E) soil moisture products in the Oklahoma Little Washita Watershed. Aside from rescaling the remotely sensed soil moisture, a bias correction algorithm is implemented to correct the deep soil moisture estimate. Both the ascending and descending AMSR-E soil moisture data are assimilated into the HYDRUS-1D model. The synthetic assimilation results indicated that, whereas both updating schemes showed the ability to correct the soil moisture state and estimate hydraulic parameters, the PF-MCMC scheme is consistently more accurate than PR-SIR. For real data case, the quality of remotely sensed soil moisture impacts the benefits of their assimilation into the model. The PF-MCMC scheme brought marginal gains than the open-loop simulation in RMSE at both surface and root-zone soil layer, whereas the PF-SIR scheme degraded the open-loop simulation.

Future soil moisture (SM) estimation is a practically useful task for eco-hydrologists, agriculturists, and stakeholders in environment health monitoring to generate comprehensive understanding of hydro-physical and soil dynamic system. This paper demonstrates the capability of a hybridised long short-term memory (LSTM) predictive framework to emulate SM under global warming scenarios. The proposed model is developed by integrating Boruta-random forest (BRF) feature selection and capturing significant antecedent memory of SM behaviour were applied to estimate the future SM using Coupled Model Intercomparison Phase-5 (CMIP5) repository. The BRF is adapted to extract pertinent features in hydro-meteorological variables intrinsically related to SM, and therefore, is used to construct a hybridised deep learning (i.e., BRF-LSTM) model. To establish the viability of deep learning model for SM estimation until 2100, five stations closely matched to the global climate model grid are selected in Australia's Murray Darling Basin. The performance skill of BRF-LSTM model is compared against standalone models (i.e., LSTM, SVR, and MARS). The results showed that the hybrid deep learning model (i.e., BRF-LSTM) with a feature selection capability could significantly outperform the standalone models for both warming simulations. The proposed hybrid model also demonstrated superiority in SM estimation with over 95% of all predictive errors lying below 0.02 mm, and low relative root means square error (≈ 1.06% for RCP4.5 and ≈ 1.888% for RCP8.5) to outperform all the benchmark models. This study demonstrates the capability of LSTM algorithm coupled with BRF feature selection to simulate future soil moisture under climate change, and so, can be successfully implemented in hydrology, agriculture, soil use management and environmental management.

The observation could be used to reduce the model uncertainties with data assimilation. If the observation cannot cover the whole model area due to spatial availability or instrument ability, how to do data assimilation at locations not covered by observation? Two commonly used strategies were firstly described: One is covariance localization (CL); the other is observation localization (OL). Compared with CL, OL is easy to parallelize and more efficient for large-scale analysis. This paper evaluated OL in soil moisture profile characterizations, in which the geostatistical semivariogram was used to fit the spatial correlated characteristics of synthetic L-Band microwave brightness temperature measurement. The fitted semivariogram model and the local ensemble transform Kalman filter algorithm are combined together to weight and assimilate the observations within a local region surrounding the grid cell of land surface model to be analyzed. Six scenarios were compared: 1_Obs with one nearest observation assimilated, 5_Obs with no more than five nearest local observations assimilated, and 9_Obs with no more than nine nearest local observations assimilated. The scenarios with no more than 16, 25, and 36 local observations were also compared. From the results we can conclude that more local observations involved in assimilation will improve estimations with an upper bound of 9 observations in this case. This study demonstrates the potentials of geostatistical correlation representation in OL to improve data assimilation of catchment scale soil moisture using synthetic L-band microwave brightness temperature, which cannot cover the study area fully in space due to vegetation effects.

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.