Root-zone soil moisture estimation from assimilation of downscaled Soil Moisture and Ocean Salinity data

Inversion of soil hydraulic properties from the DEnKF analysis of SMOS soil moisture over West Africa

Accurate root zone soil moisture (RZSM) estimation is essential for precision irrigation (PI) systems that seek to optimize water use efficiency. Large-scale in-situ sensors for direct measurement are costly, while existing satellites lack depth resolution for direct RZSM data. Hence, in-direct RZSM estimation methods are required. Literature illustrates that RZSM at a location is related to changing soil-water-plant characteristics. Therefore, these characteristics can provide auxiliary information on RZSM changes. By leveraging auxiliary information derived from changing soil-water-plant characteristics, this paper enables indirect RZSM estimation at non-sensor locations, effectively addressing the limitations inherent in direct RZSM measurement techniques initially discussed. Compared to existing methods, deep learning (DL) is most suitable for such data associations as they are auto-tuned to extract relative relationships from diverse big data. Among DL models, sequential models are apt for finding these relationships as all these variables are time-series sequences. The transformer neural network (TNN) is the state-of-the-art DL model for analyzing sequences. However, for in-direct RZSM estimation, the data associations within a location and multiple sensor sites with the target need to be found. Conventional TNN cannot incorporate such simultaneous multi-associations, hence, we develop a new TNN model called the hybrid TNN model, which is able to facilitate the capturing of complicated dependencies through thoughtful feature selection and engineering. First, sensor locations exhibiting analogous downscaled 1-km satellite soil moisture (SM) are identified. Next, a dynamic multilayer perceptron (D-MLP) network discerns highly correlated auxiliary-RZSM data, utilizing both ground-based and downscaled satellite data. Following this, the dual attention module identifies essential multi-associations, leveraging selected sensor and target region information. Finally, the Bayesian layer averages multi-location RZSM using the conditional probability generated based on relative relationships to yield the target location RZSM estimate. Our proposed model shows 13.066% better RZSM estimation compared to popular sequential models. The hybrid TNN RZSM estimates are used to monitor root water depletion tolerance levels for optimal PI schedules, which shows 10.846% water and 10.339% cost-saving on our selected sites. Overall the proposed model effectively demonstrates that a more accurate PI predictive algorithm saves water, improves resource conservation, and reduces irrigation costs.

<strong class="journal-contentHeaderColor">Abstract.</strong> Root-zone soil moisture (RZSM) is crucial for water resource management, drought monitoring and sub-seasonal flood climate forecast. RZSM is not directly observable from space but various model-derived RZSM products are available at the global scale and are widely used. In this paper, a comprehensive quantitative evaluation of eight RZSM products is made over the Huai river basin (HRB) in China. A direct validation is performed using observations from 58 in situ soil moisture stations from 1 April 2015 to 31 March 2020. Attention is drawn to the potential factors increasing uncertainties of model-generated RZSM, such as errors on atmospheric forcings (precipitation, air temperature), soil properties, and model parameterizations. Results indicate that the Global Land Data Assimilation System Catchment Land Surface Model (GLDAS_CLSM) performs best among all RZSM products with the highest correlation coefficient (R) and lowest unbiased root-mean square error (ubRMSE): 0.503 and 0.031 m³ m^&minus;3, respectively. All RZSM products tend to overestimate the in situ soil moisture values, except for the Soil Moisture and Ocean Salinity (SMOS) L4 product, which underestimates RZSM. The underestimated SMOS L3 SSM associated with low physical surface temperature triggers the underestimation of RZSM in SMOS L4. The RZSM overestimation by other products can be explained by the overestimation of precipitation amount, precipitation event frequency (drizzle effects) and by the underestimation of air temperature. Besides, the overestimation of the soil clay content and the underestimation of the soil sand content in different LSMs leads to larger soil moisture values. The intercomparison of the eight RZSM products shows that MERRA-2 and SMAP L4 RZSM are the most correlated with one another. These products are based on the same LSM and on the same surface meteorological forcing generated from the National Aeronautics and Space Administration (NASA) GEOS-5. In addition, model parameterizations in different LSMs vary considerably, affecting the transfer and exchange of water and heat in the vadose zone.

<strong class="journal-contentHeaderColor">Abstract.</strong> Root-zone soil moisture (RZSM) is crucial for water resource management, drought monitoring and sub-seasonal flood climate forecast. RZSM is not directly observable from space but various model-derived RZSM products are available at the global scale and are widely used. In this paper, a comprehensive quantitative evaluation of eight RZSM products is made over the Huai river basin (HRB) in China. A direct validation is performed using observations from 58 in situ soil moisture stations from 1 April 2015 to 31 March 2020. Attention is drawn to the potential factors increasing uncertainties of model-generated RZSM, such as errors on atmospheric forcings (precipitation, air temperature), soil properties, and model parameterizations. Results indicate that the Global Land Data Assimilation System Catchment Land Surface Model (GLDAS_CLSM) performs best among all RZSM products with the highest correlation coefficient (R) and lowest unbiased root-mean square error (ubRMSE): 0.503 and 0.031 m³ m^&minus;3, respectively. All RZSM products tend to overestimate the in situ soil moisture values, except for the Soil Moisture and Ocean Salinity (SMOS) L4 product, which underestimates RZSM. The underestimated SMOS L3 SSM associated with low physical surface temperature triggers the underestimation of RZSM in SMOS L4. The RZSM overestimation by other products can be explained by the overestimation of precipitation amount, precipitation event frequency (drizzle effects) and by the underestimation of air temperature. Besides, the overestimation of the soil clay content and the underestimation of the soil sand content in different LSMs leads to larger soil moisture values. The intercomparison of the eight RZSM products shows that MERRA-2 and SMAP L4 RZSM are the most correlated with one another. These products are based on the same LSM and on the same surface meteorological forcing generated from the National Aeronautics and Space Administration (NASA) GEOS-5. In addition, model parameterizations in different LSMs vary considerably, affecting the transfer and exchange of water and heat in the vadose zone.

Root zone soil moisture (RZSM) serves as a crucial metric for assessing water stored in the soil. Modeling approaches are commonly employed in estimating RZSM. However, modelled RZSM often deviate from true RZSM values due to errors from model input data and parameters. Machine learning methods and data fusion techniques can enhance simulation accuracy. In this study, we conducted a comparative analysis of three methods for RZSM data fusion: random forest (RF), extended triple collocation (ETC), and Bayes Three Cornered Hat (BTCH).Soil moisture observation data from 2018 to 2022 were collected at 2121 sites across China from the China Meteorological Administration (Fig.1). Daily average data were calculated by arithmetically averaging hourly data and used in the analysis. Six RZSM datasets were utilized, including SMAP Level 4, GLDAS-NOAH2.1, GLDAS-Catchment2.2, ERA5, MERRA2, and CRSR. All these data were resampled to 0.25&amp;#176; to maintain the same spatial resolution and were arithmetically averaged as daily averages. Additionally, some parameters related to soil, climate, and vegetation were used to build a machine learning model, specifically a random forest model.&amp;#160;Fig. 1 Distribution of soil moisture sites and daily soil moisture (m3/m3) at depths ranging from 0&amp;#8211;50 cm across China during the period from 2018 to 2019To investigate the impact of different inputs on the performance of the RF method, three groups of inputs were employed. The specifics of the inputs used for the three methods are outlined in Table 1. The evaluation of the RF method results was carried out using a five-fold cross-validation approach.Model Inputs RFmodel1 NOAH, SMAP, ERA5, MERRA2, CFSR, CLSM, LAI, Soil properties, Meteorological data RFmodel2 NOAH, LAI, Soil properties, Meteorological data RFmodel3 NOAH, SMAP, ERA5, MERRA2, CFSR, CLSM BTCH NOAH, SMAP, ERA5, MERRA2, CFSR, CLSM ETC NOAH, MERRA2, CLSM &amp;#160;The boxplots show RFmodel1 performs best, emphasizing the need for comprehensive information in machine learning models. RFmodel2, superior to RFmodel3, highlights the significance of LAI, soil properties, and meteorological data in RZSM estimation. ETC and BTCH outperform individual RZSM datasets, especially in the absence of true data. The superior performance of ETC over BTCH is attributed to ETC's inputs, namely NOAH, MERRA2, and CLSM, which exhibit better accuracy compared to SMAP, ERA5, and CFSR, the inputs used by BTCH.Fig.2 Boxplots of the Pearson coefficient (R), Root Mean Square Error (RMSE), and bias between in situ root zone soil moisture (RZSM) and its estimates from the three random forest models, Bayes Three Cornered Hat (BTCH), and Extended Triple Collocation (ETC) methodsIn summary, the random forest method outperforms BTCH and ETC in the fusion of root zone soil moisture (RZSM) data, highlighting the importance of including leaf area index (LAI), soil properties, and meteorological data in the construction of the random forest model. Both BTCH and ETC demonstrate utility in enhancing RZSM estimates, making them valuable options when true data is unavailable.

<strong class="journal-contentHeaderColor">Abstract.</strong> Soil moisture is a key variable in monitoring climate and an important component of the hydrological, carbon, and energy cycles. Satellite products ameliorate the sparsity of field measurements but are inherently limited to observing the near-surface layer, while water available in the unobserved root zone controls critical processes like plant water uptake and evapotranspiration. A variety of approaches exists for modelling root-zone soil moisture (RZSM), including approximating it from surface layer observations. While the number of available RZSM datasets is growing, they usually do not contain estimates of their uncertainty. In this paper we derive a long-term RZSM dataset (2002&ndash;2020) from the Copernicus Climate Change Service (C3S) surface soil moisture (SSM) COMBINED product via the exponential filter (EF) method. We identify the optimal value of the method&rsquo;s model parameter <em>T</em> , which controls the level of smoothing and delaying applied to the surface observations, by maximizing the correlation of RZSM estimates with field measurements from the International Soil Moisture Network (ISMN). Optimized T-parameter values were calculated for four soil depth layers (0&ndash;10 cm, 10&ndash;40 cm, 40&ndash;100 cm, and 100&ndash;200 cm) and used to calculate a global RZSM dataset. The quality of this dataset is then globally evaluated against RZSM estimates of the ERA5-Land reanalysis. Results of the product comparison show satisfactory skill in all four layers with median Pearson correlation ranging from 0.54 in the topmost to 0.28 in the deepest soil layer. Temporally-dynamic product uncertainties for each of the RZSM product layers are estimated by applying standard uncertainty propagation to SSM input data and by estimating structural uncertainties of the EF method from ISMN ground reference measurements taken at the surface and in varying depths. Uncertainty estimates were found to exhibit both realistic absolute magnitudes as well as temporal variations. The product described here is, to our best knowledge, the first global, long-term, uncertainty-characterized, and purely observation-based product for RZSM estimates up to 2 m depth.

Multi-objective calibration of a hydrologic model using spatially distributed remotely sensed/in-situ soil moisture

In this paper, L-band microwave observations were assimilated using the ensemble Kalman filter to improve root-zone soil moisture (RZSM) estimates from a coupled soil vegetation atmosphere transfer (SVAT)-vegetation model linked to a forward microwave model. Simultaneous state-parameter updates were performed by assimilating both synthetic and field observations during a growing season of sweet corn every three days, matching the temporal coverage of observations from the Soil Moisture and Ocean Salinity and Soil Moisture Active Passive missions. The sensitivities of parameters to the states were investigated using the information-theoretic measure of conditional entropy. Among the soil parameters, the pore-size index (λ) was the most sensitive to brightness temperatures ( <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">T</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">B</sub> ) during the early and midgrowth stages, while porosity (φ) was the most sensitive to <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">T</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">B</sub> during the reproductive stage. In the microwave model, the soil roughness parameters, root mean square (RMS) height ( <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">r</i> ), and correlation length ( <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">l</i> ) were the most sensitive during the early and mid stages, while the vegetation regression parameter ( <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">b</i> ) was the most sensitive during the reproductive stage. In the synthetic experiment, assimilation of <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">T</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">B</sub> provided RMS error reductions in RZSM estimates of 70% compared to open loop estimates. Minimal variations in performance were observed across different stages of the season during the synthetic experiment. However, when field observations of <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">T</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">B</sub> were assimilated, significant differences in RZSM estimates were observed during different growth stages. Maximum RMS difference (RMSD) reductions in RZSM estimates of 33.3% were observed compared to open loop estimates during the early stages, while improvements of 4.8% and 16.7% were observed in the mid- and reproductive stages, respectively. Further analyses of assimilation with field observations also suggest some improvements in the SVAT model are needed for moisture transport immediately following the precipitation/irrigation events. In the microwave model, the linear vegetation formulation for estimating canopy opacity, parameterized by <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">b</i> , was inadequate in capturing the complexities in <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">T</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">B</sub> during stages of high vegetative and reproductive growth rates.

The disaggregation based on physical and theoretical scale change (DISPATCH) algorithm was developed to improve the spatial resolution of Soil Moisture and Ocean Salinity (SMOS) soil moisture (SM) using 1 km resolution Moderate resolution Imaging Spectroradiometer (MODIS) data. The main objective of this paper is firstly, to adapt the DISPATCH algorithm to the 100 m resolution Landsat data and secondly, to determine an optimal intermediate spatial resolution (ISR) between the original (40 km) SMOS resolution and the targeted 100 m resolution. It is found that the ISR (set to 1 km, 3 km and 5 km) impacts the accuracy in the sequentially downscaled 100 m resolution SM depending on both the SM heterogeneity present within the spatial extent considered, and the gap between the low to high resolution ratio.

Estimation of root zone soil moisture using passive microwave remote sensing: A case study for rice and wheat crops for three states in the Indo-Gangetic basin

Real surface soil moisture retrieved from the Soil Moisture and Ocean Salinity (SMOS) satellite is downscaled and assimilated in a fully integrated hydrological and soil‐vegetation‐atmosphere transfer (MIKE SHE SW‐ET) model using a bias aware ensemble transform Kalman filter (Bias‐ETKF). Satellite‐derived soil moisture assimilation in a catchment scale model is typically restricted by two challenges: (1) passive microwave is too coarse for direct assimilation and (2) the data tend to be biased. The solution proposed in this study is to disaggregate the SMOS bias using a higher resolution land cover classification map that was derived from Landsat thermal images. Using known correlations between SMOS bias and vegetation type, the assimilation filter is adapted to calculate biases online, using an initial bias estimate. Real SMOS‐derived soil moisture is assimilated in a precalibrated catchment model in Denmark. The objective is to determine if any additional gains can be achieved by SMOS surface soil moisture assimilation beyond the optimized model. A series of assimilation experiments were designed to (1) determine how effectively soil moisture corrections propagate downward in the soil column, (2) compare the efficacy of in situ versus SMOS assimilation, and (3) determine how soil moisture assimilation affects fluxes and discharge in the catchment. We find that assimilation of SMOS improved R 2 soil moisture correlations in the upper 5 cm compared to a network of 30 in situ sensors for most land cover classes. Assimilation also brought modest gains in R 2 at 25 cm depth but slightly degraded the correlation at 50 cm depth. Assimilation overcorrected discharge peaks.

&amp;lt;p&amp;gt;Merging data from different instruments is required to construct long time data records of soil moisture (SM). This is the goal of projects such as the ESA Climate Change Initiative (CCI) for SM (Gruber et al., 2019), which uses both active and passive microwave sensors. Currently, the GLDAS v2.1 model is used as reference to re-scale active and passive time series by matching their Cumulative Density Function (CDF) to that of the model. Removing the dependency on models is important, in particular for data assimilation applications into hydrological or climate models, and it has been proposed (Van der Schalie et al., 2018) to use L-band data from one of the two instruments specifically designed to measure SM, ESA Soil Moisture and Ocean Salinity (SMOS) and NASA Soil Moisture Active Passive (SMAP) satellites, as reference to re-scale other time series.&amp;lt;br&amp;gt;To investigate this approach, AMSR-2 SM time series obtained from C1-, C2- and X-band observations using LPRM (Land Parameter Retrieval Model) were re-scaled by CDF-matching (Brocca et al., 2011) using different SMAP and SMOS official (SMAP L2 V005, SMOS L3 V300, SMOS NRT V100&amp;amp;V200) and research (SMOS IC V103) SM products as well as the SMAP and SMOS LPRM v6 SM data used by the ESA CCI. The time series re-scaled using L-band remote sensing data were compared to those re-scaled using GLDAS and were evaluated against in situ measurements at several hundred sites retrieved from the International Soil Moisture Network (Dorigo et al., 2011). The results were analyzed as a function of the land cover class and the Koppen-Geiger climate classification.&amp;lt;br&amp;gt;Overall, AMSR-2 time series re-scaled using SMAP L2, SMAP LPRM and SMOS IC data sets as reference gave the best correlations with respect to in situ measurements, similar to those obtained by the time series re-scaled using GLDAS and slightly better than those of the original AMSR-2 time series. These results imply that different SMAP and SMOS products could actually be used to replace GLDAS as reference for the re-scaling of other sensors time series within the ESA CCI. However, one must bear in mind that this study is limited to the re-scaling of AMSR-2 data at a few hundred sites.&amp;lt;br&amp;gt;For a more detailed assessment of the L-band data set to be used for a global re-scaling, it is necessary to investigate other effects such as the spatial coverage or the time series length. SMAP spatial coverage is better than that of SMOS in regions affected by radio frequency interference. In contrast, the length of SMAP time series can be too short to capture the long term SM variability for climate applications in some regions. The CDF of SMOS time series computed from the date of SMAP launch is significantly different to those of the full length SMOS time series in some regions of the Globe. Possible ways of using a coherent SMAP/SMOS L-band data set will be discussed.&amp;lt;/p&amp;gt;

Abstract. The increasing frequency of drought events has expanded the research interest in drought monitoring. In this regard, remote sensing is a useful tool to globally mapping the agricultural drought. While this type of drought is directly linked to the availability of root zone soil moisture (RZSM) for plants growth, current satellite soil moisture observations only characterize the water content of the surface soil layer (0–5 cm). In this study, two soil moisture-based agricultural drought indices were obtained at a weekly rate from June 2010 to December 2016, using RZSM estimations at 1 km from the Soil Moisture and Ocean Salinity (SMOS) satellite, instead of surface soil moisture (SSM). The RZSM was estimated by applying the Soil Water Index (SWI) model to the SMOS SSM. The Soil Moisture Agricultural Drought Index (SMADI) and the Soil Water Deficit Index (SWDI) were assessed over the Castilla y León region (Spain) at 1 km spatial resolution. They were compared with the Atmospheric Water Deficit (AWD) and the Crop Moisture Index (CMI), both computed at different weather stations distributed over the study area. The level of agreement was analyzed through statistical correlation. Results showed that the use of RZSM does not influence the characterization of drought, both for SMADI and SWDI.

Particle Filter-based assimilation algorithms for improved estimation of root-zone soil moisture under dynamic vegetation conditions

An Ensemble Kalman Filter (EnKF)-based assimilation algorithm was implemented to estimate root-zone soil moisture (RZSM) using a Soil-Vegetation-Atmosphere Transfer (SVAT) model during a complete growing season of corn in Central Mexico. Synthetic and field soil moisture (SM) observations and NASA SMAP SM retrievals were used to understand the effect of vertically spatial updates and uncertainties in meteorological forcings on RZSM estimates. Assimilation of RZSM every 3 days using SM observations at 4 depths lowered the averaged standard deviation (ASD) and the root mean square error (RMSE) by 60 % and 50 %, respectively, compared to the open-loop ASD. The assimilation of synthetic SM at the top 0-5 cm obtained RZSM closer to observations compared to THEXMEX-18 SM measurements and SMAP SM retrievals. Differences between EnKF estimates and SM observations and SMAP SM retrievals are mainly due to misrepresentation of vegetation conditions. The results improved SM estimates up to 10-cm depth using SMAP SM retrievals; however, additional studies are needed to improve SM at deeper layers. The implemented methodology can estimate SM at the top 10 cm of the soil every 3 days to mitigate the impact of the climate change on agricultural production over rainfed areas, particularly in developing countries.