Downscaled Soil Moisture Research Articles

Spatial downscaling of SMAP soil moisture to high resolution using machine learning over China’s Loess Plateau

Soil moisture (SM) is a critical physical parameter in land surface processes that affects the atmospheric and hydrological cycles. Soil Moisture Active Passive (SMAP) mission produces high-quality global SM data, but its low spatial resolution limits the applications at a regional or local scale. In this study, we developed a novel method to select optimal factors from 34 candidate downscaling factors for each month using three machine learning methods (Back Propagation Neural Network (BPNN), Support Vector Machine (SVM) and Random Forest (RF)) to produce monthly time series SM products at a spatial resolution of 1 km for the entire Loess Plateau in China from 2015 to 2023. 11 in-situ SM measurements distributed in Loess Plateau and precipitation data were used to evaluate the downscaling performances of the three machine learning approaches. The results show that the spatial trends of the three downscaled SM were essentially the same as the SMAP SM, with similar spatial patterns, and all three downscaled SM can provide more spatial information and texture features than SMAP SM. Among the three types of downscaled SM, the RF downscaled SM reached the highest R (0.654) and the smallest unbiased Root Mean Square Error (ubRMSE) (0.044 m3m−3), better than the BPNN and SVM downscaled SM, and most closely matched the in-situ measurements. The dynamics of SM were successfully captured by RF downscaled SM, which exhibited strong temporal consistency with the in-situ SM and responded well to precipitation events, with significant increases in SM values in the month of high precipitation and subsequent months. In conclusion, the RF model has the best downscaling effect and it can be used to provide high spatial resolution SM data for applications at a regional or local scale across the Loess Plateau.

Optimized Soil Moisture Mapping Strategies on the Tibetan Plateau Using Downscaled and Interpolated Maps as Mutual Covariates

Accurate high-resolution soil moisture maps are crucial for a better understanding of hydrological processes and energy cycles. Mapping strategies such as downscaling and interpolation have been developed to obtain high-resolution soil moisture maps from multi-source inputs. However, research on the optimization performance of integrating downscaling and interpolation, especially through the use of mutual covariates, remains unclear. In this study, we compared four methods—two standalone methods based on downscaling and interpolation strategies and two combined methods that utilize soil moisture maps as mutual covariates within each strategy—in a case study of daily soil moisture mapping at a 1 km resolution in the Tibetan Plateau. We assessed mapping performance in terms of prediction accuracy and differences in spatial coverage. The results indicated that introducing interpolated soil moisture maps into the downscaling strategy significantly improved prediction accuracy (RMSE: −5.94%, correlation coefficient: +14.02%) but was limited to localized spatial coverage (6.9% of grid cells) near in situ sites. Conversely, integrating downscaled soil moisture maps into the interpolation strategy resulted in only modest gains in prediction accuracy (RMSE: −1.07%, correlation coefficient: +1.04%), yet facilitated broader spatial coverage (40.4% of grid cells). This study highlights the critical differences between downscaling and interpolation strategies in terms of accuracy improvement and spatial coverage, providing a reference for optimizing soil moisture mapping over large areas.

Assimilation of Sentinel‐1 Backscatter to Update AquaCrop Estimates of Soil Moisture and Crop Biomass

AbstractThis study assesses the potential of regional microwave backscatter data assimilation (DA) in AquaCrop for the first time, using NASA's Land Information System. The objective is to assess whether the assimilation setup can improve surface soil moisture (SSM) and crop biomass estimates. SSM and crop biomass simulations from AquaCrop were updated using Sentinel‐1 synthetic aperture radar observations, over three regions in Europe in two separate DA experiments. The first experiment concerned updating SSM using VV‐polarized backscatter and the corrections were propagated via the model to the biomass. In the second experiment, the DA setup was extended by also updating the biomass with VH‐polarized backscatter. SSM was evaluated with local in situ data and with downscaled Soil Moisture Active Passive (SMAP) retrievals for all cropland grid cells, whereas crop biomass was compared to SMAP vegetation optical depth and the Copernicus dry matter productivity. The assimilation showed mixed results for root mean square error and Pearson's correlation, with slight overall improvements in the (anomaly) correlations of updated SSM relative to independent in situ and satellite data. By contrast, the biomass estimates obtained with backscatter DA did not agree better with reference data sets. Overall, the SSM evaluation showed that there is potential in using Sentinel‐1 backscatter for assimilation in AquaCrop, but the present setup was not able to improve crop biomass estimates. Our study reveals how the complex interaction between SSM, crop biomass and backscatter affect the impact and performance of DA, offering insight into ways to optimize DA for crop growth estimation.

Downscaling soil moisture in regions with high soil heterogeneity: the solution based on ensemble learning with sequential and parallel learner

Soil moisture plays an important role in the water and heat exchanges between the land surface and atmosphere, and it has great importance for agricultural production, ecological planning, and water resources management. Although microwave remote sensing has been widely used in large-scale soil moisture monitoring, the accuracy of the downscaled retrieval results cannot be guaranteed for regions with high vegetation coverage and high soil heterogeneity. To address these challenges, this study built soil moisture indice set based on MODIS and elevation data by calculating the Pearson correlation coefficient (R) and Maximum Information Coefficient (MIC), then constructed decision tree models (Gradient Boosting Decision Tree and Random Forest) about the indice set and low-resolution Soil Moisture Active Passive (SMAP) by using two ensemble learning methods (Bagging and Boosting). The models were applied to the high-resolution soil moisture indices in Jilin Province for the years 2017 to 2020 to generate 1 km-resolution products. In the validation process, Triple Collocation Analysis (TCA), comparison of soil moisture maps with coarse and fine resolution, and in-situ measurements in Lishu County, Tongyu County, and Jilin City were used to evaluate the differences between downscaling soil moisture results and ground observations at network, seasonal and point scales. The results were as follows: (1) The correlation coefficient (R2) calculated by the TCA method was 0.733 (GBDT_36km) > 0.649 (RF_36km), and the error variance was 0.0004 (GBDT_36km) < 0.00058 (RF_36km). (2) R at network scale was 0.798 (GBDT_SM) > 0.662 (RF_SM), RMSE was 0.040 (GBDT_SM) < 0.044 (RF_SM), the point scale R was 0.864 (GBDT_SM) > 0.833 (RF_SM), RMSE was 0.029 (GBDT_SM) < 0.039 (RF_SM). The R in four stages of the growth period was GBDT_SM > RF_SM, RMSE was GBDT_SM < RF_SM. In conclusion, the GBDT and RF models can reliably downscale soil moisture in Jilin Province, and the Boosting ensemble learning method represented by GBDT had a better estimation performance.

Spatial Downscaling of Satellite-Based Soil Moisture Products Using Machine Learning Techniques: A Review

Soil moisture (SM) is a key variable driving hydrologic, climatic, and ecological processes. Although it is highly variable, both spatially and temporally, there is limited data availability to inform about SM conditions at adequate spatial and temporal scales over large regions. Satellite SM retrievals, especially L-band microwave remote sensing, has emerged as a feasible solution to offer spatially continuous global-scale SM information. However, the coarse spatial resolution of these L-band microwave SM retrievals poses uncertainties in many regional- and local-scale SM applications which require a high amount of spatial details. Numerous studies have been conducted to develop downscaling algorithms to enhance the spatial resolution of coarse-resolution satellite-derived SM datasets. Machine Learning (ML)-based downscaling models have gained prominence recently due to their ability to capture non-linear, complex relationships between SM and its driving factors, such as vegetation, surface temperature, topography, and climatic conditions. This review paper presents a comprehensive review of the ML-based approaches used in SM downscaling. The usage of classical, ensemble, neural nets, and deep learning methods to downscale SM products and the comparison of multiple algorithms are detailed in this paper. Insights into the significance of surface ancillary variables for model accuracy and the improvements made to ML-based SM downscaling approaches are also discussed. Overall, this paper provides useful insights for future studies on developing reliable, high-spatial-resolution SM datasets using ML-based algorithms.

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.

Utility of thermal remote sensing for evaluation of a high‐resolution weather model in a city

AbstractProgress in high‐resolution numerical weather prediction (NWP) for urban areas will require new modelling approaches and extensive evaluation. Here, we exploit land surface temperature (LST) data from Landsat‐8 to assess 100 m resolution NWP for London (UK) on four cloud‐free days. The LST observations are directional radiometric temperatures with non‐negligible uncertainties. We consider the challenges of informative comparison between the Landsat LST and the NWP scheme's internal characterisation of the complete surface temperature. The LST spatial coverage allows large‐scale observation–model differences to be explored. In one case, obvious spatial artifacts in the NWP surface temperature are observed relative to the Landsat LST. These are found to be related to the NWP's initial method of downscaling of soil moisture using soil properties. Updated model runs have higher spatial correlation between model and Landsat LST. In cases where meteorological conditions favour the formation of horizontal convective rolls, warmer air temperatures associated with updraughts in the mixed layer extend inappropriately to the urban surface. This manifests as warm stripes in the model surface temperature that are not present in the Landsat LST. NWP–Landsat LST differences are larger in more built‐up areas on days nearer summer solstice. This is largely attributed to urban thermal anisotropy, as Landsat preferentially views warmer urban surfaces, whereas the model LST represents all surfaces. We evaluate two approaches to quantify this sampling effect, but further work is needed to fully constrain it and facilitate more informative model evaluation.

Watershed scale soil moisture estimation model using machine learning and remote sensing in a data-scarce context

Soil moisture content can be used to predict drought impact on agricultural yield better than precipitation. Remote sensing is viable source of soil moisture data in instrument-scarce areas. However, space-based soil moisture estimates lack suitability for daily and high-resolution agricultural, hydrological, and environmental applications. This study aimed to assess the potential of the random forest machine learning technique to enhance the spatial resolution of remote soil moisture products from the SMAP satellite. Models were built using random forest for spatial downscaling of SMAP-L3-E, then visually and statistically evaluated for disaggregation quality. The impact of topography, soil properties, and precipitation on the downscaled soil moisture was examined. The relationship between downscaled soil moisture and in-situ soil moisture was analyzed. The results indicate that the proposed method demonstrated spatial and hydrological coherence, along with a satisfactory downscaling quality. Statistical validation indicated suitable generalization error for scientific and practical use (RMSE &lt; 0.05 cm3 cm-3). Random forest effectively achieved spatial downscaling of SMAP-L3-E in the study area. Principal component and spatial analysis revealed dependence of downscaled soil moisture on elevation, soil organic carbon content, clay content, and saturated hydraulic conductivity, mainly under near-saturation conditions. Regarding validation against in-situ data, downscaled soil moisture explained in-situ soil moisture well under low soil water content ( = 0.624). Downscaling performance deteriorates for water contents between 0.40 to 0.50 cm3 cm-3, suggesting inadequacy under near saturation conditions at a daily temporal frequency. However, coarser temporal aggregations (7 to 10 days) yielded an average 0.98 correlation coefficient, regardless of saturation conditions. These results could potentially be applied in irrigation planning, soil physics studies and hydrology monitoring, to forecasting the occurrence of droughts, leaching of contaminants, surface runoff modeling, carbon cycle studies, soil's capacity to store and provide nutrients.

A novel land surface temperature reconstruction method and its application for downscaling surface soil moisture with machine learning

Downscaling of soil moisture data is important for high resolution hydrological modeling. Most downscaling studies in the literature have used spatially discontinuous land surface temperature (LST) maps as the main auxiliary parameter, which limits the creation of continuous soil moisture maps. The number of studies on soil moisture downscaling with machine learning that use gapless LST maps is limited. With this motivation, a hybrid reconstruction method has been proposed in this study to practically obtain continuous LST maps, which are then used to produce high resolution surface soil moisture (SSM) datasets. The proposed method is shown to have high mean performance with R2 and RMSE values of 0.94 and 1.84°K, respectively, for the period between 2019 and 2022. The developed reconstructed LST maps were then used to downscale original 9 km spatial resolution soil moisture datasets of SMAP L3 and SMAP L4 with Random Forest (RF) machine learning algorithm. The RF model were run with four different rainfall datasets, and the MSWEP rainfall dataset was found to produce the best results. The use of antecedent rainfall values as input variables in machine learning models has been shown to improve the performance of the models R2 0.76 to 0.93. The accuracy of the downscaled data was later evaluated for Western Anatolia Basins (WAB) in Türkiye with 31 in-situ stations. The downscaled SMAP L4 had good average statistical indicators R (0.815 ± 0.1), RMSE (0.09 ± 0.047 cm3/cm3), and ubRMSE (0.058 ± 0.025 cm3/cm3). Downscaled SMAP L3 was also validated with in-situ observations with satisfactory R (0.79 ± 0.074), RMSE (0.09 ± 0.043 cm3/cm3), and ubRMSE (0.06 ± 0.026 cm3/cm3) statistics. Furthermore, the performance of the downscaled SMAP L3 was also cross validated with SMAP + Sentinel 1 (L2) dataset between 2019 and 2022. The mean statistics of R (0.761 ± 0.11) and Root Mean Squared Difference (RMSD) (0.05 ± 0.014 cm3/cm3) between downscaled SMAP L3 and L2 data revealed that the new reconstruction method of LST used in the RF model for downscaling of soil moisture performed well to obtain high resolution soil moisture datasets. The proposed technique also overcame the difficulties associated with coastal regions where data was masked for quality considerations, by not only enhancing overall spatial resolution but also filling these data gaps and giving a complete SSM coverage.

Role of time variant and invariant terms on soil moisture spatio‐temporal variability in a subhumid coastal wetland

AbstractThe knowledge of soil moisture spatio‐temporal variability is highly relevant for water resources management. This paper reports an analysis of the spatial–temporal variability of soil moisture data for a small to medium‐scale soil‐sensors network in a coastal wetland of southwestern Spain. Measurements were taken from five sites located in the Doñana National Park over the time‐period of one hydrological year from September 2017 to September 2018. The total area of the soil‐sensors network shows an extension about 25 × 3 km. Soil moisture data was separated into time invariant (the temporal mean of the whole period at each site) and time‐variant terms (the deviations of soil moisture from the mean, or anomalies). The time‐invariant component was generally the main contributor to the total spatial variance of soil moisture and it was mostly controlled by the groundwater levels in the area. Nevertheless, the time variant terms have a huge effect on soil moisture variability in very dry states. Characteristic convex time‐dependent patterns for this field site were found between spatially averaged soil moisture and its variability. This information could be used for the up and downscaling of soil moisture from satellite data. Those patterns of relation between spatial mean and variability of soil moisture were only affected by heavy rainfalls giving rise to hysteretic behaviour. This study shows that even though groundwater level is a time‐variant variable, it significantly affects soil moisture's time‐variant but also time‐invariant terms due to the different average groundwater level depths at the different sites.

Spatial downscaling of SMAP radiometer soil moisture using radar data: Application of machine learning to the SMAPEx and SMAPVEX campaigns

This study developed a random forest approach for downscaling the coarse-resolution (36 km) soil moisture measured by The National Aeronautics and Space Administration (NASA) Soil Moisture Active Passive (SMAP) mission to 1 km spatial resolution, utilizing airborne remotely sensed data (radar backscatter and radiometer retrieved soil moisture), vegetation characteristics (normalized difference vegetation index), soil properties, topography, and ground soil moisture measurements from before the launch of SMAP for training a random forest model. The 36 km SMAP soil moisture product was then downscaled by the trained model to 1 km resolution using the information from SMAP. The downscaled soil moisture was evaluated using airborne retrieved soil moisture observations and ground soil moisture measurements. Considering the airborne retrieved soil moisture as a reference, the results demonstrated that the proposed random forest model could downscale the SMAP radiometer product to 1 km resolution with a correlation coefficient of 0.97, unbiased Root Mean Square Error of 0.048 m3 m−3 and bias of 0.016 m3 m−3. Accordingly, the downscaled soil moisture captured the spatial and temporal heterogeneity and demonstrated the potential of the proposed machine learning model for soil moisture downscaling.

TRIMS LST: a daily 1 km all-weather land surface temperature dataset for China's landmass and surrounding areas (2000–2022)

Abstract. Land surface temperature (LST) is a key variable within Earth's climate system and a necessary input parameter required by numerous land–atmosphere models. It can be directly retrieved from satellite thermal infrared (TIR) observations, which contain many invalid pixels mainly caused by cloud contamination. To investigate the spatial and temporal variations in LST in China, long-term, high-quality, and spatiotemporally continuous LST datasets (i.e., all-weather LST) are urgently needed. Fusing satellite TIR LST and reanalysis datasets is a viable route to obtain long time-series all-weather LSTs. Among satellite TIR LSTs, the MODIS LST is the most commonly used, and a few corresponding all-weather LST products have been reported recently. However, the publicly reported all-weather LSTs were not available during the temporal gaps of MODIS between 2000 and 2002. In this study, we generated a daily (four observations per day) 1 km all-weather LST dataset for China's landmass and surrounding areas, the Thermal and Reanalysis Integrating Moderate-resolution Spatial-seamless (TRIMS) LST, which begins on the first day of the new millennium (1 January 2000). We used the enhanced reanalysis and thermal infrared remote sensing merging (E-RTM) method to generate the TRIMS LST dataset with the temporal gaps being filled, which had not been achieved by the original RTM method. Specifically, we developed two novel approaches, i.e., the random-forest-based spatiotemporal merging (RFSTM) approach and the time-sequential LST-based reconstruction (TSETR) approach, respectively, to produce Terra/MODIS-based and Aqua/MODIS-based TRIMS LSTs during the temporal gaps. We also conducted a thorough evaluation of the TRIMS LST. A comparison with the Global Land Data Assimilation System (GLDAS) and ERA5-Land LST demonstrates that the TRIMS LST has similar spatial patterns but a higher image quality, more spatial details, and no evident spatial discontinuities. The results outside the temporal gap show consistent comparisons of the TRIMS LST with the MODIS LST and the Advanced Along-Track Scanning Radiometer (AATSR) LST, with a mean bias deviation (MBD) of 0.09/0.37 K and a standard deviation of bias (SD) of 1.45/1.55 K. Validation based on the in situ LST at 19 ground sites indicates that the TRIMS LST has a mean bias error (MBE) ranging from −2.26 to 1.73 K and a root mean square error (RMSE) ranging from 0.80 to 3.68 K. There is no significant difference between the clear-sky and cloudy conditions. For the temporal gap, it is observed that RFSTM and TSETR perform similarly to the original RTM method. Additionally, the differences between Aqua and Terra remain stable throughout the temporal gap. The TRIMS LST has already been used by scientific communities in various applications such as soil moisture downscaling, evapotranspiration estimation, and urban heat island modeling. The TRIMS LST is freely and conveniently available at https://doi.org/10.11888/Meteoro.tpdc.271252 (Zhou et al., 2021).

A Spatial Downscaling Framework for SMAP Soil Moisture Based on Stacking Strategy

Soil moisture (SM) data can provide guidance for decision-makers in fields such as drought monitoring and irrigation management. Soil Moisture Active Passive (SMAP) satellite offers sufficient spatial resolution for global-scale applications, but its utility is limited in regional areas due to its lower spatial resolution. To address this issue, this study proposed a downscaling framework based on the Stacking strategy. The framework integrated extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), and categorical boosting (CatBoost) to generate 1 km resolution SM data using 15 high-resolution factors derived from multi-source datasets. In particular, to test the influence of terrain partitioning on downscaling results, Anhui Province, which has diverse terrain features, was selected as the study area. The results indicated that the performance of the three base models varied, and the developed Stacking strategy maximized the potential of each model with encouraging downscaling results. Specifically, we found that: (1) The Stacking model achieved the highest accuracy in all regions, and the performance order of the base models was: XGBoost &gt; CatBoost &gt; LightGBM. (2) Compared with the measured SM at 87 sites, the downscaled SM outperformed other 1 km SM products as well as the downscaled SM without partitioning, with an average ubRMSE of 0.040 m3/m3. (3) The downscaled SM responded positively to rainfall events and mitigated the systematic bias of SMAP. It also preserved the spatial trend of the original SMAP, with higher levels in the humid region and relatively lower levels in the semi-humid region. Overall, this study provided a new strategy for soil moisture downscaling and revealed some interesting findings related to the effectiveness of the Stacking model and the impact of terrain partitioning on downscaling accuracy.

RTM-Based Downscaling of Medium Resolution Soil Moisture Using Sentinel-1 Data Over Agricultural Fields

RTM-Based Downscaling of Medium Resolution Soil Moisture Using Sentinel-1 Data Over Agricultural Fields

Improved downscaling of microwave-based surface soil moisture over a typical subtropical monsoon region

Microwave remote sensing has great potential for capturing surface soil moisture (SSM) but suffers from its coarse spatial resolution. In this study, we combined the disaggregation based on physical and theoretical change (DISPATCH) and random forest (RF) approaches to downscale microwave-based SSM in the Yangtze River Delta region, China. Results showed that downscaled SSM by DISPATCH approach was superior to that by RF approach with Pearson correlation coefficient (R), unbiased root mean square error (ubRMSE), and the mean bias (Bias) of 0.778, 0.022 m3 m-3 and 0.061 m3 m-3, respectively. However, the DISPATCH approach was inapplicable in pixels with weak link between SSM and evapotranspiration, while the RF approach did not have such restriction. Therefore, the DISPATCH approach was supplemented with the RF approach to downscale the SSM for the entire study area. In addition, this downscaling process was conducted for the four different seasons and for the entire study period to test whether considering seasonality was necessary in the downscaling. The downscaled SSM was the best in autumn with R = 0.843, ubRMSE = 0.022 m3 m-3, and Bias = 0.048 m3 m-3, followed by summer and spring, and poorest in winter. However, the seasonally downscaled SSM performed better than that downscaled for the entire period. Moreover, over the entire study period, land surface temperature, land cover heterogeneity, soil texture, and water area percentage exerted significant influences on errors. However, contribution order of these environmental factors on errors varied with seasons. This study would be helpful to widen DISPATCH applicability in the semi-humid/humid region and maximize the potential of various data in generating high accurate and spatiotemporally continuous SSM estimates.

Spatial Downscaling of Soil Moisture Based on Fusion Methods in Complex Terrains

Large-area soil moisture (SM) data with high resolution and precision are the foundation for the research and application of hydrological and meteorological models, water resource evaluation, agricultural management, and warning of geological disasters. It is still challenging to downscale SM products in complex terrains that require fine spatial details. In this study, SM data from the Soil Moisture Active and Passive (SMAP) satellite were downscaled from 36 to 1 km in the summer and autumn of 2017 in Sichuan Province, China. Genetic-algorithm-optimized backpropagation (GABP) neural network, random forest, and convolutional neural network were applied. A fusion model between SM and longitude, latitude, elevation, slope, aspect, land-cover type, land surface temperature, normalized difference vegetation index, enhanced vegetation index, evapotranspiration, day sequence, and AM/PM was established. After downscaling, the in situ information was fused through a geographical analysis combined with a spatial interpolation to improve the quality of the downscaled SM. The comparative results show that in complex terrains, the GABP neural network better captures the soil moisture variations in both time and space domains. The GDA_Kriging method is able to merge in situ information in the downscaled SM while simultaneously maintaining the dynamic range and spatial details.

Assimilating Soil Moisture Information to Improve the Performance of SWAT Hydrological Model

The present work aims to highlight the possibility of improving model performance by assimilating soil moisture information in the calibration and validation process. The Soil and Water Assessment Tool (SWAT) within QGIS, i.e., QSWAT, was used to simulate the hydrological processes within the test basin, i.e., Vosvozis River Basin (VRB) in NE Greece. The model calibration and validation were conducted via SWAT-CUP for a four-year period from 2019 to 2022, in three different ways, i.e., using the traditional calibration process with river flow measurements, using satellite-based soil moisture only in the calibration, and finally incorporating satellite-based soil moisture datasets and calibrating using simultaneously flow and soil moisture information. All modeling approaches used the same set of input data related to topography, land cover, and soil information. This study utilized the recently released global scale daily downscaled soil moisture at 1 km from the Soil Moisture Active Passive (SMAP) mission to generate soil moisture datasets. Two performance indicators were evaluated: Nash Sutcliffe (NS) and coefficient of determination (R2). Results showed that QSWAT successfully simulated river flow in VRB with NS = 0.61 and R2 = 0.69 for the calibration process using river flow measurements at the outlet of VRB. However, comparing satellite-based soil moisture, NS and R2 were considerably lower with an average derived from the 19 subbasins (NS = 0.55, R2 = 0.66), indicating lower performance related to the simulation of soil moisture regime. Subsequently, introducing satellite-derived soil moisture as an additional parameter in the calibration process along with flow improved the acquired average soil moisture results of the 19 subbasins (NS = 0.85, R2 = 0.91), while preserving the satisfactory performance related to flow simulation (NS = 0.57, R2 = 0.66). Our work thus demonstrates how assimilating available satellite-derived soil moisture information into the SWAT model may offer considerable improvement in the description of soil moisture conditions, keeping the satisfactory performance in flow simulation.

Two-step fusion method for generating 1 km seamless multi-layer soil moisture with high accuracy in the Qinghai-Tibet plateau

Current remote sensing techniques fail to observe and generate large scale multi-layer soil moisture (SM) due to the inherent features of the satellite sensors. The lack of comprehensive understanding of multi-layer SM hinders the sustainable development of agriculture, hydrology, and food security. In order to overcome the depth barrier of traditional SM assimilation and downscaling methods, we developed a Two-step Multi-layer SM Downscaling (TMSMD) framework by fusing multi-source remotely sensed, reanalysis, and in-situ data through both machine learning and state-of-the-art deep learning models to generate multi-layer SM. The produced multi-layer SM was characterized by high resolution (1 km), high spatio-temporal continuity (cloud-free and daily), and high accuracy (i.e., 3H data). Firstly, the coarse resolution SMAP SM was downscaled to 1 km spatial resolution using LightGBM to weaken the effects of scale mismatch issue and provide high-resolution input for the subsequent calibration. Results indicated that the downscaled SMAP SM remained high consistency with the original SMAP SM product. With the high-resolution inputs, we calibrated the downscaled SMAP SM using multi-layer in-situ SM through state-of-the-art attention-based LSTM. Results demonstrated that the average PCC, RMSE, ubRMSE, and MAE were improved by 22.3 %, 50.7 %, 26.2 %, and 56.7 % compared to SMAP L4 SM while 38.5 %, 52.1 %, 29.5 %, and 58.7 % compared to downscaled SMAP SM. Further spatio-temporal and comparative analysis confirmed that the multi-layer SM produced by the TMSMD framework had excellent performance in capturing the spatial and temporal dynamics. In conclude, the proposed TMSMD framework successfully generated 3H multi-layer SM data and is promising for accurate assessment and monitoring in agriculture, water resources, and environmental domains.

Impact of crop types and irrigation on soil moisture downscaling in water-stressed cropland regions

Soil moisture (SM) is one of the determining variables of natural ecosystems and human managed regions. The downscaling of coarse spatial-resolution SM data is an important way to obtain high-spatial-resolution data and conduct precise agricultural management. However, the spatial heterogeneity of agricultural management makes it challenging to improve the accuracy of SM data downscaling in croplands. Previously, natural index was considered, such as meteorology, crop growth, topography and soil texture variables, but less so have human agricultural management index. We integrated important agricultural management index (crop type and irrigation) into random forest (RF) model for a downscaling study involving Global Land Data Assimilation System (GLDAS) SM products (from 0.25° to 1 km). The integration of crop type and irrigation improved the downscaling accuracy (R-values of 0.724 and 0.749, respectively) and increased the time series consistency (the Euclidean distance (ED) were reduced by 51% and 20%, respectively) for different soil layers (0–10 cm and 10–40 cm). The best correlations were found for winter wheat and summer maize (both had R-values >0.780). The relative importance of the crop type and irrigation in SM downscaling reached 15% ∼ 18%, behind only to the effects of topography (35%) and meteorology (19% ∼ 22%). This study emphasizes the important impact of human activities on soil moisture in croplands.

Building Trust in Earth Science Findings through Data Traceability and Results Explainability

To trust findings in computational science, scientists need workflows that trace the data provenance and support results explainability. As workflows become more complex, tracing data provenance and explaining results become harder to achieve. In this paper, we propose a computational environment that automatically creates a workflow execution's record trail and invisibly attaches it to the workflow's output, enabling data traceability and results explainability. Our solution transforms existing container technology, includes tools for automatically annotating provenance metadata, and allows effective movement of data and metadata across the workflow execution. We demonstrate the capabilities of our environment with the study of SOMOSPIE, an earth science workflow. Through a suite of machine learning modeling techniques, this workflow predicts soil moisture values from the 27 km resolution satellite data down to higher resolutions necessary for policy making and precision agriculture. By running the workflow in our environment, we can identify the causes of different accuracy measurements for predicted soil moisture values in different resolutions of the input data and link different results to different machine learning methods used during the soil moisture downscaling, all without requiring scientists to know aspects of workflow design and implementation.