International Soil Moisture Network Research Articles

Pan-tropical daily L-band microwave land surface emissivity retrieval from GNSS-R observations

ABSTRACT The uncertainties in microwave land surface emissivity (MLSE) measurements have long limited the use of spaceborne microwave radiometer data. As an emerging observation method, Global Navigation Satellite System Reflectometry (GNSS-R) has demonstrated great potential in several land and ocean applications. In this study, a method for obtaining daily MLSE dataset in the pan-tropical region from Cyclone GNSS (CYGNSS) observations is presented and evaluated. The CYGNSS observations are first aggregated into the Equal-Area-Scalable-Earth (EASE) 2.0 36 km grid by a combined weight function of distance, time, and signal-to-noise ratio variance. Then, the method employs a pixel-by-pixel regression algorithm to conduct the daily MLSE retrieval using reference emissivity derived from the Soil Moisture Active Passive (SMAP) brightness temperature. The CYGNSS MLSE shows good agreement with SMAP MLSE, delivering an overall root-mean-square error (RMSE) of 0.022 and 0.017 for horizontal and vertical polarization, respectively, during the training set spanning the whole year of 2018. Furthermore, on the test set from January 2019 to May 2019, the RMSE values amounted to 0.030 and 0.023 for horizontal and vertical polarization, respectively. Temperature records from the International Soil Moisture Network are employed to calculate the emissivity and for in-situ validation, which yield an RMSE of 0.034 and 0.026 for the two polarizations, respectively. The proposed algorithm provides an encouraging approach to obtain accurate daily MLSE dataset for microwave remote sensing. Compared to the SMAP MLSE, the CYGNSS MLSE has a remarkable improvement of 86% in temporal resolution, greatly complementing the existing microwave emissivity datasets.

Assimilating ASCAT normalized backscatter and slope into the land surface model ISBA-A-gs using a Deep Neural Network as the observation operator: Case studies at ISMN stations in western Europe

ASCAT normalized backscatter (σ40o) and slope (σ′) contain valuable information about soil moisture and vegetation. While σ40o has been assimilated to constrain soil moisture, sometimes together with Leaf Area Index (LAI), this study is the first to assimilate σ′ directly to constrain vegetation states. Here, we assimilate σ40o and slope σ′ into the ISBA-A-gs LSM using the Simplified Extended Kalman Filter (SEKF) using a Deep Neural Network (DNN) as the observation operator. The performances of the data assimilation (DA) and open loop (OL) are evaluated against in-situ soil moisture observations from the International Soil Moisture Network (ISMN), and LAI observations from the Copernicus Global Land Service (CGLS). Given an accurate and physically plausible observation operator, along with well-defined model and observation errors, the data assimilation system should yield improved estimates of the model states. However, results show that the DA performance is neutral compared to the OL in terms of the median unbiased root mean square error (ubRMSE) and Pearson correlation coefficient (ρ) across all validation sites. In addition, an analysis of the residuals and innovations confirms that DA had limited or no impact. This poor performance is perplexing. Furthermore, given the growing interest in the use of machine-learning-based observation operators, it is essential to understand the role that the use of the DNN may be playing in this poor performance. While representativeness errors and error specification play some part, it is demonstrated that the key factor constraining the efficacy of the SEKF is the correct estimation of the Jacobians that control the degree to which the observations update the states in the SEKF. It is argued that the DNN relating model states to satellite observations must have physically-plausible and robust Jacobians for the DNN to be effective in a data assimilation framework.

Spatial Downscaling of ESA CCI Soil Moisture Data Based on Deep Learning with an Attention Mechanism

Soil moisture (SM) is a critical variable affecting ecosystem carbon and water cycles and their feedback to climate change. In this study, we proposed a convolutional neural network (CNN) model embedded with a residual block and attention module, named SMNet, to spatially downscale the European Space Agency (ESA) Climate Change Initiative (CCI) SM product. In the SMNet model, a lightweight Convolutional Block Attention Module (CBAM) dual-attention mechanism was integrated to comprehensively extract the spatial and channel information from the high-resolution input remote sensing products, the reanalysis meteorological dataset, and the topographic data. The model was employed to downscale the ESA CCI SM from its original spatial resolution of 25 km to 1 km in California, USA, in the annual growing season (1 May to 30 September) from 2003 to 2021. The original ESA CCI SM data and in situ SM measurements (0–5 cm depth) from the International Soil Moisture Network were used to validate the model’s performance. The results show that compared with the original ESA CCI SM data, the downscaled SM data have comparable accuracy with a mean correlation (R) and root mean square error (RMSE) of 0.82 and 0.052 m3/m3, respectively. Moreover, the model generates reasonable spatiotemporal SM patterns with higher accuracy in the western region and relatively lower accuracy in the eastern Nevada mountainous area. In situ site validation results in the SCAN, the SNOTEL network, and the USCRN reveal that the R and RMSE are 0.62, 0.63, and 0.77, and 0.077 m3/m3, 0.093 m3/m3, and 0.078 m3/m3, respectively. The results are slightly lower than the validation results from the original ESA CCI SM data. Overall, the validation results suggest that the SMNet downscaling model proposed in this study has satisfactory performance in handling the task of soil moisture downscaling. The downscaled SM model not only preserves a high level of spatial consistency with the original ESA CCI SM model but also offers more intricate spatial variations in SM depending on the spatial resolution of model input data.

Surface soil moisture retrieval based on transfer learning using SAR data on a local scale

ABSTRACT Retrieving surface soil moisture on a local scale using Synthetic Aperture Radar (SAR) data and Deep Learning (DL) models necessitates a substantial volume of data, which may not be available in all scenarios. In this study, the application of transfer learning was introduced as a novel approach to address the scarcity of training samples for DL models in the context of soil moisture retrieval. The proposed DL model was initially trained using International Soil Moisture Network (ISMN) data, followed by a fine-tuned process on a local scale using field trip data from an agricultural area in Karaj, Iran. The proposed DL model was compared against Random Forest Regressor (RFR), Support Vector Regression (SVR), and Multi-Layer Perceptron (MLP) based on Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and R2 indicators. All models underwent hyperparameter-tunning, and their performance was evaluated using 8-fold cross-validation (CV) and various combinations of inputs. The proposed DL model outperformed other models on a local scale achieving an RMSE, MAE, and R2 of 2.42 vol%, 1.66 vol%, and 0.90, respectively. The MLP model also exhibited good performance with an RMSE, MAE, and R2 of 2.84 vol%, 2.04 vol%, and 0.88 compared to the RFR with 2.83 vol%, 2.20 vol%, and 0.86, respectively. Additionally, the SVR yielded an RMSE, MAE, and R2 of 3.71 vol%, 3.05 vol%, and 0.78. However, the RMSE, MAE, and R2 of the MLP and the proposed DL model without using transfer learning deteriorated by around 18%, 32%, and 34%, respectively.

Validation of downscaled 1‐km SMOS and SMAP soil moisture data in 2010–2021

AbstractSoil moisture (SM) is an important component for many applications in agriculture, hydrology, meteorology, and ecology. In past decades, passive/active microwave sensors onboard Earth observation satellites are utilized to obtain SM estimates from radiometer or radar observations. In this study, the Soil Moisture and Ocean Salinity (SMOS) Level 3 daily SM retrievals at 25‐km spatial resolution between 2010 and 2021 were downscaled through an apparent thermal inertia principle‐based algorithm. The 1‐km downscaled SMOS SM retrievals were validated by in situ measurements from 635 sites of 19 SM networks in the world, which were acquired from the International Soil Moisture Network and Texas Soil Observation Network. Additionally, the validation results of the SMOS SM products were compared with those of the Soil Moisture Active Passive (SMAP) global Level 2 enhanced SM products at 1‐km downscaled and original 9‐km resolution in 2015–2021. It shows that the downscaled SMOS SM data have an overall improved accuracy and outperform the coarse‐resolution 25‐km data, with a lower unbiased Root Mean Squared Error of 0.114 m3/m3 on average.

Gap‐Filled Multivariate Observations of Global Land–Climate Interactions

AbstractThe volume of Earth system observations has grown massively in recent decades. However, multivariate or multisource analyses at the interface of atmosphere and land are still hampered by the sparsity of ground measurements and the abundance of missing values in satellite observations. This can hinder robust multivariate analysis and introduce biases in trends. Nevertheless, gap‐filling is often done univariately, which can obscure physical dependencies. Here, we apply the new multivariate gap‐filling framework CLIMate data gapFILL (CLIMFILL). CLIMFILL combines state‐of‐the‐art spatial interpolation with an iterative approach accounting for dependencies across multiple incomplete variables. CLIMFILL is applied to a set of remotely sensed and in situ observations over land that are central to observing land–atmosphere interactions and extreme events. The resulting gridded monthly time series covers 1995–2020 globally with gap‐free maps of nine variables: surface layer soil moisture from European Space Agency (ESA)‐Climate Change Initiative (CCI), land surface temperature and diurnal temperature range from Moderate‐resolution Imaging Spectroradiometer, precipitation from GPM, terrestrial water storage from GRACE, ESA‐CCI burned area, and snow cover fraction as well as 2‐m temperature and precipitation from CRU. Time series of anomalies are reconstructed better compared to state‐of‐the‐art interpolation. The gap‐filled data set shows high correlations with ERA5‐Land, and soil moisture estimates compare favorably to in situ observations from the International Soil Moisture Network. Soil moisture drying trends in ESA‐CCI only agree with the reanalysis product ERA5‐Land trends after gap‐filling. We furthermore showcase that key features of droughts and heatwaves in major fire seasons are well represented. The data set can serve as a step toward the fusion of multivariate multisource observations.

A cross-resolution transfer learning approach for soil moisture retrieval from Sentinel-1 using limited training samples

Synthetic Aperture Radar (SAR) data is increasingly popular as a data source for global near-surface soil moisture mapping, but large-scale applications are still challenging due to the complex scattering process and the cumbersome data preprocessing. The emergence of deep learning methods has allowed advances in the remote sensing of large-scale surface parameters, but its application in SAR soil moisture retrieval has suffered from the availability of ground soil moisture measurements. Accordingly, this study proposed a cross-resolution transfer learning framework, with the assumption that sophisticated models for different spatial resolutions share a similar model architecture and trainable parameters. A robust high-resolution model can thus be trained with fewer samples by using coarse models. Accordingly, 25 deep learning models were pre-trained taking ∼387,000 Soil Moisture Active Passive (SMAP) Level-3 9 km enhanced passive soil moisture measurements as the truth, with an average validation RMSE of 0.03 m3/m3. They were then transferred to finer grids of 0.1–1 km using a small number of in-situ samples. A total of ∼190,000 daily soil moisture measurements from the international soil moisture network (ISMN) were used to evaluate the proposed framework in three scenarios. The results show that 1) 5000–6000 random samples are sufficient to achieve a target RMSE of 0.06 m3/m3; 2) training samples from a short period (2 or 4 months for Sentinel-1) of 2021 resulted in an overall RMSE of ∼0.068 m3/m3 in an independent period of 2016–2020; 3) the transfer learning also improved the retrieval accuracy (10–30% in relative) over areas without ground samples used for training but failed to yield an acceptable accuracy over mountainous areas. The promising results from this study confirmed the effectiveness of using “pre-trained models + scenario specific models” for regional to global soil moisture retrieval from Sentinel-1.

Ensemble of optimised machine learning algorithms for predicting surface soil moisture content at a global scale

Abstract. Accurate information on surface soil moisture (SSM) content at a global scale under different climatic conditions is important for hydrological and climatological applications. Machine-learning-based systematic integration of in situ hydrological measurements, complex environmental and climate data, and satellite observation facilitate the generation of reliable data products to monitor and analyse the exchange of water, energy, and carbon in the Earth system at a proper space–time resolution. This study investigates the estimation of daily SSM using 8 optimised machine learning (ML) algorithms and 10 ensemble models (constructed via model bootstrap aggregating techniques and five-fold cross-validation). The algorithmic implementations were trained and tested using International Soil Moisture Network (ISMN) data collected from 1722 stations distributed across the world. The result showed that the K-neighbours Regressor (KNR) had the lowest root-mean-square error (0.0379 cm3 cm−3) on the “test_random” set (for testing the performance of randomly split data during training), the Random Forest Regressor (RFR) had the lowest RMSE (0.0599 cm3 cm−3) on the “test_temporal” set (for testing the performance on the period that was not used in training), and AdaBoost (AB) had the lowest RMSE (0.0786 cm3 cm−3) on the “test_independent-stations” set (for testing the performance on the stations that were not used in training). Independent evaluation on novel stations across different climate zones was conducted. For the optimised ML algorithms, the median RMSE values were below 0.1 cm3 cm−3. GradientBoosting (GB), Multi-layer Perceptron Regressor (MLPR), Stochastic Gradient Descent Regressor (SGDR), and RFR achieved a median r score of 0.6 in 12, 11, 9, and 9 climate zones, respectively, out of 15 climate zones. The performance of ensemble models improved significantly, with the median RMSE value below 0.075 cm3 cm−3 for all climate zones. All voting regressors achieved r scores of above 0.6 in 13 climate zones; BSh (hot semi-arid climate) and BWh (hot desert climate) were the exceptions because of the sparse distribution of training stations. The metric evaluation showed that ensemble models can improve the performance of single ML algorithms and achieve more stable results. Based on the results computed for three different test sets, the ensemble model with KNR, RFR and Extreme Gradient Boosting (XB) performed the best. Overall, our investigation shows that ensemble machine learning algorithms have a greater capability with respect to predicting SSM compared with the optimised or base ML algorithms; this indicates their huge potential applicability in estimating water cycle budgets, managing irrigation, and predicting crop yields.

Intercomparison of recent microwave satellite soil moisture products on European ecoregions

Several satellite microwave-based soil moisture (SM) products have become available in recent years, making possible to produce different datasets at continental and/or a global scale. Numerous variables, including geographic heterogeneity, weather and climate, and land cover, have an impact on these products' accuracy. In this study, we account for all these variables by using the ecoregions as the scale of analysis and conducting a thorough comparison of satellite products with ground-based SM measurements. The whole Europe has been selected as study area, considering it as the aggregation of several ecoregions, each of them classified as an homogeneous zones, in terms of climate, vegetation and potentially investigable soil cover. Selected SM satellite data includes: i) the National Aeronautics and Space Administration (NASA) SMAP L4 V5; ii) the European Space Agency (ESA) SMOS-IC V2.0; iii) the H115 and H116 SM products in time-series format generated by analyzing data collected by the Advanced Scatterometer (ASCAT) aboard MetOp satellites; iv) the CGLS SSM 1 km obtained by the radar onboard ESA’s SENTINEL-1 platforms; and v) the European Space Agency’s Climate Change Initiative for Soil Moisture (ESA CCI SM) “COMBINED” ESA CCI SM v06.1 product, created by merging satellite-based passive/active microwave measurements. Such a variety, in terms of technologies and main features, of publicly available online SM products (in their up-to-date version), allowed for a comprehensive intercomparison against in situ measurements of the International Soil Moisture Network (ISMN), that spreads across all of Europe within the above-mentioned terrestrial ecoregion, using four different metrics, i.e., r, bias, ubRMSE and r/rANOM.Overall the intercomparison has underlined how SMAP L4 and ESA CCI show the best performance regardless of the considered metric. ASCAT has achieved best performances among non-modeled/blended products, while SMOS-IC has showed slightly better performance than the CGLS SSM 1 km. Using ecoregions allowed to further characterize the differences of each satellite products and identify the areas where all these products are more reliable (e.g., group (1) made by ecoregions as the Cantabrian Mixed forests or the Balkan Mixed forests; or group 2 made by ecoregions which belong to Mediterranean area) or less performing (e.g., the Central European mixed forests and the Pontic steppe). Hence, these classes have been further analyzed exploiting the ERA5-Land data, in terms of the SM, air temperature, precipitation, evaporation and Leaf area index. The research has provided helpful insights into how the performance of particular SM satellite products changes based on both the characteristics of the product under consideration and the area under investigation (i.e., one ecoregion compared to another).

Uncertainty estimation for a new exponential-filter-based long-term root-zone soil moisture dataset from Copernicus Climate Change Service (C3S) surface observations

Abstract. 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 exist 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 (2002–2020) 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's model parameter T, 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 (0–10, 10–40, 40–100, and 100–200 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 the 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 in the EF method from ISMN ground reference measurements taken at the surface and at varying depths. Uncertainty estimates were found to exhibit both realistic absolute magnitudes and temporal variations. The product described here is, to the best of our knowledge, the first global, long-term, uncertainty-characterized, and purely observation-based product for RZSM estimates up to 2 m depth.

Soil moisture at 30 m from multiple satellite datasets fused by random forest

As a key variable used to characterize the climate process between the land surface and atmosphere, the surface soil moisture (SM) plays an irreplaceable role in the fields of hydrology, meteorology and agriculture. However, the relatively coarse spatial resolution of SM products currently limit the application in water resource management at the field scale. In this study, we proposed a high-spatial-resolution SM retrieval framework based on the random forest algorithm (RF-SM) to integrate in situ SM dataset from in the International Soil Moisture Network (ISMN), Landsat 8 optical and thermal observations, soil properties from SoilGrids V2.0, meteorological variables from the fifth generation of the European ReAnalysis (ERA5) dataset and four coarse-scale SM products including the Soil Moisture Active/Passive (SMAP), the Soil Moisture and Ocean Salinity (SMOS), European Space Agency Climate Change Initiative (ESA CCI) and U.S. National Climate Assessment Land Data Assimilation System (NCA-LDAS). Compared to three other machine learning (ML) algorithms [extremely randomized trees (Extra-Trees), gradient boosting regression tree (GBRT) and extreme gradient boosting (XGBoost)], the random forest (RF) algorithm exhibited the best performance against a subset of 100 validation sites with a Kling–Gupta efficiency (KGE) of 0.69 and root-mean-square-error (RMSE) of 0.063 m3/m3. In terms of different land cover types and typical sites, RF-SM also showed a better accuracy than any of the individual SM product. Finally, the retrieval framework was applied to map the 30-m resolution SM spatial distributions in five substudy areas in the U.S. The results suggest that it is feasible to retrieve accurate SM at a 30-m spatial resolution from multiple satellite datasets based on the RF algorithm, which has important practical significance for agricultural drought monitoring at the field scale.

The Characterization of the Vertical Distribution of Surface Soil Moisture Using ISMN Multilayer In Situ Data and Their Comparison with SMOS and SMAP Soil Moisture Products

In this paper, we investigated the vertical distribution characteristics of surface soil moisture based on ISMN (International Soil Moisture Network) multilayer in situ data (5, 10, and 20 cm; 2, 4, and 8 in) and performed comparisons between the in situ data and four microwave satellite remote sensing products (SMOS L2, SMOS-IC, SMAP L2, and SMAP L4). The results showed that the mean soil moisture difference between layers can be −0.042~−0.024 (for the centimeter group)/−0.067~−0.044 (for the inch group) m3/m3 in negative terms and 0.020~0.028 (for the centimeter group)/0.036~0.040 (for the inch group) m3/m3 in positive terms. The surface soil moisture was found to have very significant stratification characteristics, and the interlayer difference was close to or beyond the SMOS and SMAP 0.04 m3/m3 nominal retrieval accuracy. Comparisons revealed that the satellite retrievals had a higher correlation with the field measurements of 5 cm/2 in, and SMAP L4 had the smallest difference with the in situ data. The mean difference caused by using 10 cm/4 in and 20 cm/8 in in situ data instead of the 5 cm/2 in data could be about −0.019~−0.018/−0.18~−0.015 m3/m3 and −0.026~−0.023/−0.043~−0.039 m3/m3, respectively, meaning that there would be a potential depth mismatch in the data validation.

Assessment of 9 km SMAP soil moisture: Evidence of narrowing the gap between satellite retrievals and model-based reanalysis

A number of global surface soil moisture (SM) datasets have been retrieved from the L-band frequency Soil Moisture Active Passive (SMAP) and the Soil Moisture and Ocean Salinity (SMOS) missions to study the terrestrial water, energy, and carbon cycles. This paper presents the performance of the recently developed 9 km global SMAP product (hereafter SMAP-INRAE-BORDEAUX, SMAP-IB9). The product retrieves SM from the 9 km SMAP radiometric products using the forward model (L-MEB, L-band Microwave Emission of the Biosphere) of SMOS INRA-CESBIO (SMOS-IC) and SMOS L2 algorithms. We inter-compared SMAP-IB9 with two other products with a similar grid resolution (∼10 km): the SMAP Enhanced Level-3 SM dataset (SMAP-E) and the enhanced global dataset for the land component of the fifth generation of European reanalysis (ERA5-Land) with the main objective of assessing the discrepancy in accuracy between remotely sensed and model SM datasets. We found that ERA5-Land and SMAP-IB9 SM had the overall highest correlations (R = 0.62(±0.15) for ERA5-Land vs. 0.60(±0.17) for SMAP-IB9 and 0.50(±0.15) for SMAP-E) by comparing with the International Soil Moisture Network (ISMN) in-situ measurements from 22 networks. ERA5-Land showed better performances in the forest areas where SMAP-IB9 and SMAP-E still showed high potential in detecting the time variations of the observed SM, particularly in terms of median correlation values (0.62(±0.18) for SMAP-IB9 vs. 0.66(±0.16) for ERA5-and). The discrepancy in R between satellite and model SM products that were reported in some past studies has decreased to statistically insignificant levels over time. For instance, in the non-forest areas, we found that the latest versions of the SMAP SM products (SMAP-E and SMAP-IB9) had relatively comparable performances with ERA5-Land with regard to median ubRMSE (0.07(±0.02) m3/m3 for both SMAP-E and ERA5-Land) and R (0.59(±0.16) for SMAP-IB9 vs. 0.61(±0.15) for ERA5-Land), respectively.

Soil moisture estimation from Sentinel-1 interferometric observations over arid regions

We present an experimental methodology based on interferometric synthetic aperture radar (InSAR) time series analysis that can provide surface (top 5 cm) soil moisture (SSM) estimations over sandy arid soils. A co-registered Single Look Complex (SLC) SAR stack as well as meteorological information are required as input of the proposed methodological workflow. The proposed methodology consists of five steps. In the first step, meteorological data are exploited to identify the snow/ice free SAR acquisition related to lowest SSM level. In the second step, interferometric (coherence and phase closure) observables over a spatial grid are calculated. In the third step, for each grid cell the ordering of SAR acquisitions according to the SSM is performed. The sorting is performed by starting from the SAR acquisition related to lowest SSM level and by exploiting coherence and backscattering information. In the fourth step, for each grid cell the coherence values due to SSM variations are calculated by exploiting the ordering of SAR acquisitions according to the SSM. In the fifth step, SSM is estimated by inverting an analytical interferometric model using coherence values due to SSM variations and phase closure information. The proposed method estimates SSM over arid regions by ordering SAR acquisitions based on SSM, by calculating coherence values due to SSM variations and by inverting an interferometric model.A case study over an arid region in California/Arizona is presented. The proposed workflow was applied in Sentinel-1 (C-band) VV-polarized InSAR observations. The estimated SSM results were assessed with independent SSM observations from a station of the International Soil Moisture Network (ISMN) (RMSE: 0.029 m3/m3 R: 0.78) and with ERA5-Land reanalysis model data (RMSE: 0.049 m3/m3 R: 0.62). In summary, the proposed methodology was able to provide accurate SSM estimations at high spatial resolution (∼250 m). A discussion of the benefits and the limitations of the proposed methodology highlighted the potential of interferometric observables for SSM estimation over arid regions.

Improvement of CYGNSS soil moisture retrieval model considering water and surface temperature

The key to retrieving soil moisture (SM) with spaceborne Global Navigation Satellite System-Reflectometry (GNSS-R) data is to correct the effects of soil surface temperature (SST), roughness, water, and vegetation. In this study, Cyclone Global Navigation Satellite System (CYGNSS) data are used to calculate surface reflectivity and surface roughness is characterized based on the statistical moments of Delay-Doppler Maps (DDM). After removing open water, a multiple linear regression model is created to retrieve SM by combining the SST and vegetation optical depth (VOD) parameters provided by Soil Moisture Active Passive (SMAP) data. With a correlation coefficient of 0.815 and a root-mean-square error of 0.066 cm3cm−3, an experimental analysis reveals decent consistency between the CYGNSS SM and the referenced SMAP SM. In addition, a time-series analysis between the CYGNSS SM and the International Soil Moisture Network (ISMN) referenced SM data shows a good correlation. Since surface reflectivity is significantly affected by water and because there is a coupling relationship between SST and SM, the differences of CYGNSS SM when the two factors are simultaneously considered or ignored are also analyzed. The experimental results show that after removing water and incorporating SST into the linear regression model, the accuracy of CYGNSS SM has been improved significantly, with the root mean square error, mean absolute error, and Bias increasing by 6%, 7%, and 11.3%, respectively. This study demonstrates the necessity of considering water and SST in SM retrieval and provides a novel approach for SM retrieval using high accuracy and high spatial and temporal resolution data.

Evaluation of Remote Sensing and Reanalysis Products for Global Soil Moisture Characteristics

Soil moisture (SM) exists at the land-atmosphere interface and serves as a key driving variable that affects global water balance and vegetation growth. Its importance in climate and earth system studies necessitates a comprehensive evaluation and comparison of mainstream global remote sensing/reanalysis SM products. In this study, we conducted a thorough verification of ten global remote sensing/reanalysis SM products: SMAP DCA, SMAP SCA-H, SMAP SCA-V, SMAP-IB, SMOS IC, SMOS L3, LPRM_C1, LPRM_C2, LPRM_X, and ERA5-Land. The verification was based on ground observation data from the International SM Network (ISMN), considering both static factors (such as climate zone, land cover type, and soil type) and dynamic factors (including SM, leaf area index, and land surface temperature). Our goal was to assess the accuracy and applicability of these products. We analyzed the spatial and temporal distribution characteristics of global SM and discussed the vegetation effect on SM products. Additionally, we examined the global high-frequency fluctuations in the SMAP L-VOD product, along with their correlation with the normalized difference vegetation index, leaf area index, and vegetation water content. Our findings revealed that product quality was higher in regions located in tropical and arid zones, closed shrubs, loose rocky soil, and gray soil with low soil moisture, low leaf area index, and high average land surface temperature. Among the evaluated products, SMAP-IB, SMAP DCA, SMAP SCA-H, SMAP SCA-V, and ERA5-Land consistently performed better, demonstrating a good ability to capture the spatial and temporal variations in SM and showing a correlation of approximately 0.60 with ISMN. SMOS IC and SMOS L3 followed in performance, while LPRM_C1, LPRM_C2, and LPRM_X exhibited relatively poor results in SM inversion. These findings serve as a valuable reference for improving satellite/reanalysis SM products and conducting global-scale SM studies.

Generation of global 1 km daily soil moisture product from 2000 to 2020 using ensemble learning

Abstract. Motivated by the lack of long-term global soil moisture products with both high spatial and temporal resolutions, a global 1 km daily spatiotemporally continuous soil moisture product (GLASS SM) was generated from 2000 to 2020 using an ensemble learning model (eXtreme Gradient Boosting – XGBoost). The model was developed by integrating multiple datasets, including albedo, land surface temperature, and leaf area index products from the Global Land Surface Satellite (GLASS) product suite, as well as the European reanalysis (ERA5-Land) soil moisture product, in situ soil moisture dataset from the International Soil Moisture Network (ISMN), and auxiliary datasets (Multi-Error-Removed Improved-Terrain (MERIT) DEM and Global gridded soil information (SoilGrids)). Given the relatively large-scale differences between point-scale in situ measurements and other datasets, the triple collocation (TC) method was adopted to select the representative soil moisture stations and their measurements for creating the training samples. To fully evaluate the model performance, three validation strategies were explored: random, site independent, and year independent. Results showed that although the XGBoost model achieved the highest accuracy on the random test samples, it was clearly a result of model overfitting. Meanwhile, training the model with representative stations selected by the TC method could considerably improve its performance for site- or year-independent test samples. The overall validation accuracy of the model trained using representative stations on the site-independent test samples, which was least likely to be overfitted, was a correlation coefficient (R) of 0.715 and root mean square error (RMSE) of 0.079 m3 m−3. Moreover, compared to the model developed without station filtering, the validation accuracies of the model trained with representative stations improved significantly for most stations, with the median R and unbiased RMSE (ubRMSE) of the model for each station increasing from 0.64 to 0.74 and decreasing from 0.055 to 0.052 m3 m−3, respectively. Further validation of the GLASS SM product across four independent soil moisture networks revealed its ability to capture the temporal dynamics of measured soil moisture (R=0.69–0.89; ubRMSE = 0.033–0.048 m3 m−3). Lastly, the intercomparison between the GLASS SM product and two global microwave soil moisture datasets – the 1 km Soil Moisture Active Passive/Sentinel-1 L2 Radiometer/Radar soil moisture product and the European Space Agency Climate Change Initiative combined soil moisture product at 0.25∘ – indicated that the derived product maintained a more complete spatial coverage and exhibited high spatiotemporal consistency with those two soil moisture products. The annual average GLASS SM dataset from 2000 to 2020 can be freely downloaded from https://doi.org/10.5281/zenodo.7172664 (Zhang et al., 2022a), and the complete product at daily scale is available at http://glass.umd.edu/soil_moisture/ (last access: 12 May 2023).

Reconstruction of Global Long-Term Gap-Free Daily Surface Soil Moisture from 2002 to 2020 Based on a Pixel-Wise Machine Learning Method

Global, long-term, gap-free, high quality soil moisture products are extremely important for hydrological monitoring and climate change research. However, soil moisture products produced from satellite observations have data gaps due to the limited capabilities of satellite orbit/swath and retrieval algorithms, which limit the regional and global applications of soil moisture data in hydrology and agriculture studies. To solve this problem, we proposed a gap-filling method to reconstruct a global gap-free surface soil moisture product by applying the machine learning (Random Forest) algorithm on a pixel-by-pixel basis, taking into account the nonlinear relationship between surface soil moisture and the related surface environmental variables. The gap-filling method was applied to the NN-SM surface soil moisture product, which has a fraction of data gaps of around 50% globally on a multi-year average. A global daily gap-free surface soil moisture dataset from 2002 to 2020 was then generated. The reconstructed values of several sub-regions after manually eliminating the original values were cross-verified with the original data, and this clearly demonstrated the reliability of the reconstruction method with the correlation coefficient (R) ranging between 0.770 and 0.918, the Root Mean Square Error (RMSE) between 0.057 and 0.082 m3/m3, the unbiased Root Mean Square Error (ubRMSE) between 0.053 and 0.081 m3/m3, and Bias between −0.012 and 0.008 m3/m3. The accuracy of the reconstructed surface soil moisture dataset was evaluated using in situ observations of surface soil moisture at 12 sites from the International Soil Moisture Network (ISMN) and the Long-Term Agroecosystem Research (LTAR) network, and the results showed good accuracy in terms of R (0.610), RMSE (0.067 m3/m3), ubRMSE (0.045 m3/m3) and Bias (0.031 m3/m3). Overall, the reconstructed surface soil moisture dataset retained the characteristics of the NN-SM product, such as high accuracy and good spatiotemporal pattern. However, with the advantage of continuous spatiotemporal coverage, it is more suitable for further applications in the analysis of global surface soil moisture trends, land surface hydrological processes, and land-atmosphere energy and water exchanges, etc.

A 21-year dataset (2000–2020) of gap-free global daily surface soil moisture at 1-km grid resolution

Global soil moisture estimates from current satellite missions are suffering from inherent discontinuous observations and coarse spatial resolution, which limit applications especially at the fine spatial scale. This study developed a dataset of global gap-free surface soil moisture (SSM) at daily 1-km resolution from 2000 to 2020. This is achieved based on the European Space Agency - Climate Change Initiative (ESA-CCI) SSM combined product at 0.25° resolution. Firstly, an operational gap-filling method was developed to fill the missing data in the ESA-CCI SSM product using SSM of the ERA5 reanalysis dataset. Random Forest algorithm was then adopted to disaggregate the coarse-resolution SSM to 1-km, with the help of International Soil Moisture Network in-situ observations and other optical remote sensing datasets. The generated 1-km SSM product had good accuracy, with a high correlation coefficent (0.89) and a low unbiased Root Mean Square Error (0.045 m3/m3) by cross-validation. To the best of our knowledge, this is currently the only long-term global gap-free 1-km soil moisture dataset by far.

Assessment of five SMAP soil moisture products using ISMN ground-based measurements over varied environmental conditions

Since the launch of Soil Moisture Active Passive (SMAP) mission in 2015, several advanced global surface soil moisture (SM) products derived from different retrieval schemes have been released. However, the factors contributing to the performance differences of these SMAP products have not been fully investigated. This study tries to fill the gap by assessing and intercomparing five SMAP products, namely, the single channel algorithm V/H-pol (SCA-V/H), dual channel algorithm (DCA), multi-temporal dual channel algorithm (MT-DCA) and the new SMAP-INRAE-BORDEAUX (SMAP-IB), using the International Soil Moisture Network (ISMN) ground measurements worldwide during 2016–2020. After reviewing the retrieval algorithms, the impact of five potential perturbing factors on the skills of all products is reported; these factors including mean surface soil temperature (MSST), surface roughness (log(SR)), vegetation density (characterized by vegetation optical depth (VOD)), mean surface soil wetness (MSSM) and soil organic matter (SOM). Results shows that the SMAP-IB product has the highest overall R value of 0.75 with ISMN ground SM, and the smallest unbiased root mean square difference (ubRMSD) values same as SCA-V and DCA (∼0.058 m3/m3). The performance of all five products in terms of R and ubRMSD rapidly degrades over the regions with high VOD, rough surface and low MSST. In contradiction with the fact that product performance degrades with increasing roughness, the ubRMSD of all five products increases with decreasing log(SR) on a relatively smooth surface (log(SR) < 6) with larger growth rates for SMAP-IB, SCA-H and MT-DCA. Regarding characterizing the temporal variations of in situ SM anomalies (Ranom), Ranom increases as the ground becomes wet for all products within a certain range (MSSM < 0.3 m3/m3). In particular, SMAP-IB and MT-DCA present higher Ranom values than the other three products under wet soil surfaces (MSSM > 0.3 m3/m3). The two metrics R and Ranom decrease significantly with increasing SOM for SMAP-IB compared to the other products, and SMAP-IB obtains the lowest values for both metrics when SOM > 20%. These findings are expected to provide useful information for appropriate product selection and improvement.