Remote Sensing Soil Moisture Research Articles

Performance of multi-source remote sensing soil moisture products over Punjab Pakistan during 2022–2023

Performance of multi-source remote sensing soil moisture products over Punjab Pakistan during 2022–2023

The Verification and Fusion Analysis of Passive Microwave Soil Moisture Products in the Three Northeastern Provinces of China

The utilization of remote sensing soil moisture products in agricultural and hydrological studies is on the rise. Conducting a regional applicability analysis of these soil moisture products is essential as a preliminary step for their effective utilization. The triple collocation (TC) method enables the estimation of the standard deviation of errors in products when true soil moisture values are unavailable. It assesses data uncertainty and mitigates the influence of product errors on fusion, thereby enhancing product accuracy significantly. In this study, the TC uncertainty error analysis was employed to integrate Soil Moisture Active Passive (SMAP), the Advanced Microwave Scanning Radiometer 2 (AMSR-2), and the European Space Agency Climate Change Initiative (ESA CCI) active (ESA CCI A) and passive (ESA CCI P) products, with ground-based measurements serving as a reference. Traditional evaluation metrics, such as the correlation coefficient (R), bias, root mean square error (RMSE), and unin situed root mean square error (ubRMSE), were employed to evaluate the accuracy of the product. The findings indicate that SMAP and ESA CCI P products demonstrate strong spatiotemporal continuity within the research area and exhibit low uncertainty across various land types. The products derived from the Advanced Microwave Scanning Radiometer 2 (AMSR-2) exhibit a high level of temporal and spatial continuity; however, there is a requirement for enhancing their accuracy. The products of ESA CCI A exhibit notable spatiotemporal disjunction, contributing significantly to their elevated level of uncertainty. After fusion with TC analysis, the correlation coefficient (R = 0.7) of the TC-2 product derived from the fusion of SMAP, AMSR-2, and ESA CCI P products is significantly higher than the correlation coefficient of the TC-1 product (R = 0.65) obtained from the fusion of SMAP, AMSR-2, and ESA CCI A products at a 95% confidence level. The integration of data can efficiently mitigate the challenges associated with spatiotemporal gaps and inaccuracies in products, offering a dependable foundation for the subsequent utilization of remote sensing products.

On the Detection of Remotely Sensed Soil Moisture Extremes

Abstract Remotely sensed soil moisture observations provide an opportunity to monitor hydrological conditions from droughts to floods. The European Space Agency’s (ESA) Climate Change Initiative has released both Combined and Passive datasets, which include multiple satellites’ measurements of soil moisture conditions since the 1980s. In this study, both volumetric soil moisture and soil moisture standardized anomalies from the U.S. Climate Reference Network (USCRN) were compared with ESA’s Combined and Passive datasets. Results from this study indicate the importance of using standardized anomalies over volumetric soil moisture conditions as satellite datasets were unable to capture the frequency of conditions observed at the extreme ends of the volumetric distribution. Overall, the Combined dataset had slightly lower measures of soil moisture anomaly errors for all regions; although these differences were not statistically significant. Both satellite datasets were able to detect the evolution from worsening to amelioration of the 2012 drought across the central United States and 2019 flood over the upper Missouri River basin. While the ESA datasets were not able to detect the magnitude of the extremes, the ESA standardized datasets were able to detect the interannual variability of extreme wet and dry day counts for most climate regions. These results suggest that remotely sensed standardized soil moisture can be included in hydrological monitoring systems and combined with in situ measures to detect the magnitude of extreme conditions. Significance Statement This study examines how well soil moisture extremes, wet or dry, can be detected from space using one of the lengthiest remotely sensed soil moisture datasets. Comparisons with high-quality station data from the U.S. Climate Reference Network revealed the satellite datasets could capture the frequency of extreme conditions important for climate monitoring, but often missed the absolute magnitudes of the extremes. Future research should focus on how to combine satellite and station data to improve the detection of extreme values important for monitoring.

Improvement and application on the estimation model of windbreak and sand fixation function based on remote sensing soil moisture factor

Soil moisture factor is one of the important parameters in the study of wind and sand fixation functions of ecosystems. Traditional methods often use potential evaporation, rainfall, and irrigation observed by meteorological stations to estimate soil moisture, which has significant limitations in terms of spatial continuity and data availability. Based on the development of remote sensing technology in soil moisture detection, we selected four remote sen-sing indicators for soil moisture (MODIS evapotranspiration ratio method, SMAP soil moisture ratio method, visible shortwave infrared drought index method, and remote sensing humidity index method) to improve the estimation of soil moisture factor in the modified wind erosion equation model (RWEQ), and used the improved algorithm to analyze the spatiotemporal variations and driving factors of wind prevention and sand fixation services in the northwest region of Liaoning Province from 2001 to 2021. The results showed that the MODIS evapotranspiration ratio method had the highest correlation with traditional meteorological methods in calculating soil moisture. The formula obtained by fitting the two could be used to improve the calculation of soil moisture factor in the RWEQ model. From 2001 to 2021, the wind prevention and sand fixation capacity in the northwest region of Liaoning Province showed strong spatial distribution characteristics in the northern and eastern regions, while weak in the central and western regions. According to Mann-Kendall trend testing, 72.7% of the regions in northwest Liaoning Province were showing an upward trend in their ability to prevent wind and fix sand. The application of geographic detector models for driving factor analysis showed that the change in wind and sand fixation capacity was a process of multiple factors interacting with each other, greatly influenced by soil type, annual wind speed, and economic development level. Moreover, the interaction between various driving factors had a higher impact on wind and sand fixation than that of single factors. The results could improve the RWEQ model estimation and provide technical support for the long-term analysis of ecological function formation mechanisms and driving forces in the northwest region of Liaoning.

Accurate statistical seasonal streamflow forecasts developed by incorporating remote sensing soil moisture and terrestrial water storage anomaly information

Water managers need improved seasonal streamflow forecasts in drought-prone regions like the U.S. Great Plains, where the inter-annual variability in rainfall and streamflow is high and seasonal streamflow forecasting systems are not well developed. Recent research has demonstrated that in-situ soil moisture measurements can be used to produce accurate streamflow forecasts in the Great Plains where rainfall runoff is the predominant source of streamflow. However, an inadequate density of in-situ soil moisture monitoring locations makes producing such forecasts impossible in many locations. Therefore, our objective was to develop and quantify the accuracy of seasonal streamflow forecasts for rainfall-dominated watersheds using remotely sensed data. Precipitation and remotely sensed soil moisture and terrestrial water storage anomaly data were incorporated into a multiple linear regression (MLR) model to produce seasonal streamflow forecasts for five watersheds in the Great Plains. The best MLR models had Nash-Sutcliffe Efficiency (NSE) values ranging from 0.71 to 0.93, indicating good to very good model performance. In one watershed with nearby in-situ soil moisture data, the use of remotely sensed soil moisture data led to greater forecast accuracy than did the inclusion of the sparse in-situ data. Our results show that the use of remote sensing data in MLR models can provide accurate seasonal streamflow forecasts in rainfall-dominated regions which currently lack such information.

Microwave Remote Sensing of Soil Moisture

Soil moisture is an important component of the global terrestrial ecosystem and has been recognized as an Essential Climate Variable (ECV) by the Global Climate Observing System (GCOS) [...]

Evaluating the NASA MERRA-2 climate reanalysis and ESA CCI satellite remote sensing soil moisture over the contiguous United States

ABSTRACT Accurate large-scale soil moisture (SM) retrievals using the daily NASA MERRA-2’ climate reanalysis and ESA’ Climate Change Initiative (CCI) remote sensing datasets are compromised by temporal and spatial ambiguities. To address this deficiency, we assess the accuracy of the above-mentioned datasets against the Soil Climate Analysis Network (SCAN) in-situ measurements at nine sites across the contiguous United States (CONUS) during 2014–2016. The sites are selected to represent different climate regions over the CONUS. SM dynamics from NASA MERRA-2 and ESA CCI are compared with those of SCAN, and an SM dataset is developed based on a spatiotemporal analysis. Our results show that the NASA MERRA-2 and ESA CCI SM datasets have different accuracies at the nine SCAN sites in different seasons. The MERRA-2 and CCI SM datasets have the highest accuracy at sites with the lowest number of extreme events, indicating that both datasets cannot robustly capture extremum soil moisture values. The highest (lowest) agreement between the MERRA-2/CCI and SCAN SM data is observed in April (February) with the nine-site average unbiased root-mean-square-difference (ubRMSD) of 0.0638 cm3/cm3 (0.0914 cm3/cm3). In all the nine sites, the CCI SM data are more accurate than those of MERRA-2 in April–October. The CCI SM data shows greater accuracy in the sites with lower SM values and/or higher SM variability. MERRA-2 and CCI SM datasets show higher and lower accuracy at the sites with pasture and agricultural vegetation, respectively. Finally, a new SM dataset is created by using the more accurate SM data from MERRA-2 and CCI in each site and season.

A Pixel-Scale Measurement Method of Soil Moisture Using Ground-Penetrating Radar

Ground validation of remote sensing soil moisture requires ground measurements corresponding to the pixel scale. To date, there is still a lack of simple, fast and reasonable methods for soil moisture measurement at pixel scale between point measurements and remote sensing observations. In this study, a measurement method of soil moisture using ground-penetrating radar (GPR) was proposed for pixel scale. We used a PulseEKKOTM PRO GPR system with 250 MHz antennas to measure soil moisture by Fixed Offset (FO) method in four 30 × 30 m2 plots chosen from the desert steppe. This study used a random combination method to analyze soil moisture measurements acquired by different numbers of GPR survey lines. The results showed that two survey lines of GPR would be sufficient under confidence level of 90% with the relative error of 7%, and four survey lines of GPR would be eligible under confidence level of 95% with the relative error of 5% for each plot. GPR measurement can reproduce the spatial distribution of soil moisture with higher resolution and smaller error, especially when two and four survey lines are designed in cross shape and grid shape, respectively. The method was applied to ground validation for the soil moisture from Landsat 8, showing the advantages of stable relative errors, less contingency and reliable evaluation when compared to point measurements. This method is fast and convenient and not limited to a certain pixel, and thus largely benefits the scale matching of remote sensing results and field measurements in ground validation.

A Long-term Consistent Artificial Intelligence and Remote Sensing-based Soil Moisture Dataset

The Consistent Artificial Intelligence (AI)-based Soil Moisture (CASM) dataset is a global, consistent, and long-term, remote sensing soil moisture (SM) dataset created using machine learning. It is based on the NASA Soil Moisture Active Passive (SMAP) satellite mission SM data and is aimed at extrapolating SMAP-like quality SM back in time using previous satellite microwave platforms. CASM represents SM in the top soil layer, and it is defined on a global 25 km EASE-2 grid and for 2002–2020 with a 3-day temporal resolution. The seasonal cycle is removed for the neural network training to ensure its skill is targeted at predicting SM extremes. CASM comparison to 367 global in-situ SM monitoring sites shows a SMAP-like median correlation of 0.66. Additionally, the SM product uncertainty was assessed, and both aleatoric and epistemic uncertainties were estimated and included in the dataset. CASM dataset can be used to study a wide range of hydrological, carbon cycle, and energy processes since only a consistent long-term dataset allows assessing changes in water availability and water stress.

A Performance Analysis of Soil Dielectric Models over Organic Soils in Alaska for Passive Microwave Remote Sensing of Soil Moisture

Passive microwave remote sensing of soil moisture (SM) requires a physically based dielectric model that quantitatively converts the volumetric SM into the soil bulk dielectric constant. Mironov 2009 is the dielectric model used in the operational SM retrieval algorithms of the NASA Soil Moisture Active Passive (SMAP) and the ESA Soil Moisture and Ocean Salinity (SMOS) missions. However, Mironov 2009 suffers a challenge in deriving SM over organic soils, as it does not account for the impact of soil organic matter (SOM) on the soil bulk dielectric constant. To this end, we presented a comparative performance analysis of nine advanced soil dielectric models over organic soil in Alaska, four of which incorporate SOM. In the framework of the SMAP single-channel algorithm at vertical polarization (SCA-V), SM retrievals from different dielectric models were derived using an iterative optimization scheme. The skills of the different dielectric models over organic soils were reflected by the performance of their respective SM retrievals, which was measured by four conventional statistical metrics, calculated by comparing satellite-based SM time series with in-situ benchmarks. Overall, SM retrievals of organic-soil-based dielectric models tended to overestimate, while those from mineral-soil-based models displayed dry biases. All the models showed comparable values of unbiased root-mean-square error (ubRMSE) and Pearson Correlation (R), but Mironov 2019 exhibited a slight but consistent edge over the others. An integrated consideration of the model inputs, the physical basis, and the validated accuracy indicated that the separate use of Mironov 2009 and Mironov 2019 in the SMAP SCA-V for mineral soils (SOM <15%) and organic soils (SOM ≥15%) would be the preferred option.

A comprehensive assessment of in situ and remote sensing soil moisture data assimilation in the APSIM model for improving agricultural forecasting across the US Midwest

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

Soil moisture monitoring by downscaling of remote sensing products using LST/VI space derived from MODIS products

Abstract Soil moisture (SM) has an important role in the earth's water cycle and is a key variable in water resources management. Considering the critical state of water resources in the Urmia Lake basin, northwest Iran, this study examined the potential for utilizing a variety of remote sensing data and products, in conjunction with a promising downscaling method, to monitor soil moisture with a reasonable spatial and temporal resolution, as a novel and effective tool for agricultural and water resource management. Accordingly, remote sensing products of surface soil moisture were scaled to MODIS's image scale (∼1 km) using the UCLA downscaling method and Temperature, Vegetation, Drought Index (TVDI) values obtained from the scattering space method. Results showed that the LPRM, ESA-CCI, and GLDAS downscaled images had the highest inverse correlation with the TVDI (best results) accordingly equal to −0.600, −0.787, and −0.630. Also, based on the evaluation of the obtained results with the ground stations data, the LPRM and the ESA-CCI downscaled images had the best statistical indices values accordingly in 2010 and 2014 that confirm the promising application of remote sensing soil moisture data (rLPRM (2010) = 0.92, MAELPRM (2010) = 4.14%, RMSELPRM (2010) = 6.39% and rESA-CCI (2014) = 0.7, MAEESA-CCI (2014) = 2.23%, RMSEESA-CCI (2014) = 2.59%).

A Spatial Downscaling Method for Remote Sensing Soil Moisture Based on Random Forest Considering Soil Moisture Memory and Mass Conservation

Remote sensing soil moisture (SM) has been widely used in various earth science studies and applications, but their low resolution limits their usage and downscaling of them is needed. In this study, we proposed a spatial downscaling method for SM based on random forest considering soil moisture memory and mass conservation to improve downscaling performance. The lagged SM was added as a predictor to represent soil moisture memory, in addition to the regular predictors in previous downscaling studies. The Soil Moisture Active Passive (SMAP) SM data of the Pearl River Basin were used to test our downscaling method. The results show that the downscaling model obtained good performance on the test set (R2 = 0.848, ubRMSE = 0.034 m3/m3 and Bias = 0.008 m3/m3). The spatial and temporal performance of the RF downscaling model can be improved by adding lagged SM variables. Downscaled data obtained can retain the information of the original SMAP SM data well and show more spatial details, and mass conservation correction is considered to be useful to eliminate systematic bias of the downscaling model. Downscaled SM achieved acceptable performance in in situ validation, though it was inevitably limited by the performance of the original SMAP data. The proposed downscaling method can serve as a powerful tool for the development of high-resolution SM information.

Improving soil moisture assimilation efficiency via model calibration using SMAP surface soil moisture climatology information

To minimize systematic differences between soil moisture (SM) time series derived from remote sensing (RS) and land surface model (LSM), RS-based SM climatology information is typically discarded during land data assimilation (DA). However, recent studies have demonstrated that SM climatology estimates provided by L-band microwave RS retrievals can significantly outperform comparable estimates derived from LSMs. Consequently, neglecting a RS-based SM climatology may lead to degraded SM spatial patterns generated by land DA. Here, we propose a climatology-optimized SM DA framework, which first calibrates LSM parameters to leverage the RS SM climatology information. Next, multi-sensor RS SM retrievals are assimilated into the calibrated LSM using Ensemble Kalman Filter (EnKF). This framework is demonstrated using the Variable Infiltration Capacity (VIC) model and RS SM retrievals derived from the L-band Soil Moisture Active Passive (SMAP) and C-band Advanced SCATterometer (ASCAT) sensors. Here, both SMAP and ASCAT SM retrievals are assimilated into the VIC model after calibrating VIC to match spatial variations captured in the SMAP SM climatology. The DA SM results are validated using in-situ SM observations derived from 820 stations. Results show that SMAP-based SM climatology calibration directly improves the quality of SM spatial patterns estimated by VIC. In addition, the SMAP-based calibration also benefits our model-error representation and thereby yields better Kalman gains that improve temporal SM DA accuracy. Relative to typical DA approaches, this newly proposed DA framework improves both spatial (0.26 versus 0.51 (−)) and temporal correlations (0.48 versus 0.52 (−)) versus in-situ SM observations. In addition, SM improvements are effectively propagated into improved streamflow estimates – leading to an average increase of Nash and Sutcliffe coefficient from 0.74 to 0.76 (−). Overall, we demonstrate that RS SM climatology information is valuable for land DA and our climatology-optimized framework successfully retains such information to the benefit of land DA performance.

Irrigation characterization improved by the direct use of SMAP soil moisture anomalies within a data assimilation system

Prior soil moisture data assimilation (DA) efforts to incorporate human management features such as agricultural irrigation has only shown limited success. This is partly due to the fact that observational rescaling approaches for bias correction used in soil moisture DA systems are less effective when unmodeled processes such as irrigation are the dominant source of systematic biases. In this article, we demonstrate an alternative approach, i.e. anomaly correction for overcoming this limitation. Unlike the rescaling approaches, the proposed method does not scale remote sensing soil moisture retrievals to the model climatology, but it extracts the temporal variability information from the retrievals. The study demonstrates this approach through the assimilation of soil moisture retrievals from the Soil Moisture Active Passive mission into the Noah land surface model. The results demonstrate that DA using the anomaly correction method can better capture the effect of irrigation on soil moisture in agricultural areas while providing comparable performance to the DA integrations using rescaling approaches in non-irrigated areas. These findings emphasize the need to reduce inconsistencies between remote sensing and the models so that assimilation methods can employ information from remote sensing more directly to develop representations of unmodeled processes such as irrigation.

Evaluation of SMOS, SMAP, AMSR2 and FY-3C soil moisture products over China

Microwave remote sensing can provide long-term near-surface soil moisture data on regional and global scales. Conducting standardized authenticity tests is critical to the effective use of observed data products in models, data assimilation, and various terminal scenarios. Global Land Data Assimilation System (GLDAS) soil moisture data were used as a reference for comparative analysis, and triple collocation analysis was used to validate data from four mainstream passive microwave remote sensing soil moisture products: Soil Moisture and Ocean Salinity (SMOS), Soil Moisture Active and Passive (SMAP), Global Change Observation Mission–Water using the Advanced Microwave Scanning Radiometer 2 (AMSR2) instrument, and Fengyun-3C (FY-3C). The effects of topography, land cover, and meteorological factors on the accuracy of soil moisture observation data were determined. The results show that SMAP had the best overall performance and AMSR2 the worst. Passive microwave detection technology can accurately capture soil moisture data in areas at high altitude with uniform terrain, particularly if the underlying surface is soil, and in areas with low average temperatures and little precipitation, such as the Qinghai–Tibet Plateau. FY-3C performed in the middle of the group and was relatively optimal in northeast China but showed poor data integrity. Variation in accuracy between products, together with other factors identified in the study, provides a baseline reference for the improvement of the retrieval algorithm, and the research results provide a quantitative basis for developing better use of passive microwave soil moisture products.

Assimilation of SMAP Products for Improving Streamflow Simulations over Tropical Climate Region—Is Spatial Information More Important Than Temporal Information?

Streamflow is one of the key variables in the hydrological cycle. Simulation and forecasting of streamflow are challenging tasks for hydrologists, especially in sparsely gauged areas. Coarse spatial resolution remote sensing soil moisture products (equal to or larger than 9 km) are often assimilated into hydrological models to improve streamflow simulation in large catchments. This study uses the Ensemble Kalman Filter (EnKF) technique to assimilate SMAP soil moisture products at the coarse spatial resolution of 9 km (SMAP 9 km), and downscaled SMAP soil moisture product at the higher spatial resolution of 1 km (SMAP 1 km), into the Soil and Water Assessment Tool (SWAT) to investigate the usefulness of different spatial and temporal resolutions of remotely sensed soil moisture products in streamflow simulation and forecasting. The experiment was set up for eight catchments across the tropical climate of Vietnam, with varying catchment areas from 267 to 6430 km2 during the period 2017–2019. We comprehensively evaluated the EnKF-based SWAT model in simulating streamflow at low, average, and high flow. Our results indicated that high-spatial resolution of downscaled SMAP 1 km is more beneficial in the data assimilation framework in aiding the accuracy of streamflow simulation, as compared to that of SMAP 9 km, especially for the small catchments. Our analysis on the impact of observation resolution also indicates that the improvement in the streamflow simulation with data assimilation is more significant at catchments where downscaled SMAP 1 km has fewer missing observations. This study is helpful for adding more understanding of performances of soil moisture data assimilation based hydrological modelling over the tropical climate region, and exhibits the potential use of remote sensing data assimilation in hydrology.

Calibration and Validation of SWAT Model by Using Hydrological Remote Sensing Observables in the Lake Chad Basin

Model calibration and validation are challenging in poorly gauged basins. We developed and applied a new approach to calibrate hydrological models using distributed geospatial remote sensing data. The Soil and Water Assessment Tool (SWAT) model was calibrated using only twelve months of remote sensing data on actual evapotranspiration (ETa) geospatially distributed in the 37 sub-basins of the Lake Chad Basin in Africa. Global sensitivity analysis was conducted to identify influential model parameters by applying the Sequential Uncertainty Fitting Algorithm–version 2 (SUFI-2), included in the SWAT-Calibration and Uncertainty Program (SWAT-CUP). This procedure is designed to deal with spatially variable parameters and estimates either multiplicative or additive corrections applicable to the entire model domain, which limits the number of unknowns while preserving spatial variability. The sensitivity analysis led us to identify fifteen influential parameters, which were selected for calibration. The optimized parameters gave the best model performance on the basis of the high Nash–Sutcliffe Efficiency (NSE), Kling–Gupta Efficiency (KGE), and determination coefficient (R2). Four sets of remote sensing ETa data products were applied in model calibration, i.e., ETMonitor, GLEAM, SSEBop, and WaPOR. Overall, the new approach of using remote sensing ETa for a limited period of time was robust and gave a very good performance, with R2 > 0.9, NSE > 0.8, and KGE > 0.75 applying to the SWAT ETa vs. the ETMonitor ETa and GLEAM ETa. The ETMonitor ETa was finally adopted for further model applications. The calibrated SWAT model was then validated during 2010–2015 against remote sensing data on total water storage change (TWSC) with acceptable performance, i.e., R2 = 0.57 and NSE = 0.55, and remote sensing soil moisture data with R2 and NSE greater than 0.85.

Double-scale analysis on the detectability of irrigation signals from remote sensing soil moisture over an area with complex topography in central Italy

Despite a detailed knowledge of the spatial-temporal dynamics of irrigation being necessary to optimize the agricultural production without exacerbating the pressure exercised on the water resource, such information is still often lacking worldwide. In this study, a double-scale analysis on the detectability of the irrigation occurrence over an area in central Italy through remote sensing soil moisture is proposed; the period of interest is a 3-year time span from 2017 to 2019. The detectability of district- or sub-district-scale irrigation signals through remotely sensed soil moisture data is investigated at two different spatial resolutions: 1 km and plot scale. Three soil moisture products sampled at 1 km resolution are evaluated: a DISPATCH (DISaggregation based on Physical And Theoretical scale CHange) downscaled version of SMAP (Soil Moisture Active Passive) and two Sentinel-1-derived products, namely the 1 km version delivered by Copernicus and a plot-scale-born version developed by THEIA and aggregated at 1 km. The THEIA Sentinel-1 product aggregated at 100 m is used in the plot-scale analysis. Over the study area, the irrigation extent is determined by the fragmentation of the agricultural fields and the complex topography, making the adoption of plot-scale data necessary. Satisfactory results are obtained by comparing maps of irrigated areas at 100 m spatial resolution produced through the k-means clustering algorithm with ground-truth data, since the method fails only once out of seven in properly reproducing the irrigated or non-irrigated conditions occurred over four pilot agricultural fields.

Quantifying Uncertainties in Passive Microwave Remote Sensing of Soil Moisture via a Bayesian Probabilistic Inversion Method

Improving the accuracy of remotely sensed soil moisture (SM) is a challenging and popular topic. Quantifying the uncertainty in the SM inversion process and enhancing the confidence of SM retrieval are promising ways to address this challenge but have received little attention. We present a Bayesian probabilistic inversion algorithm that can simultaneously retrieve SM, surface roughness, and vegetation optical depth data and quantify the uncertainty in the inversion. The proposed algorithm is evaluated using airborne polarimetric L-band multibeam radiometer (PLMR) observations. We use three combinations, 3-angular observations at V-polarization (3CV), 3-angular observations at H-polarization (3CH) and 6-channel observations (6CA), to identify the optimal configuration for SM retrieval by taking advantage of the PLMR’s dual-polarization and multiple angles. Uncertainties are quantified by introducing multiple uncertainty quantification metrics into Bayesian posterior distributions of SM retrievals. The estimates are validated against multiscale ground-based measurements, including manual measurements and wireless sensor network (WSN) measurements, and the spatial representativeness of the ground-based reference regarding the validation of pixel-scale SM retrievals is discussed. The 6CA attempt yields the best SM estimates (correlation coefficient (R) ≥ 0.864, root mean square error (RMSE) ≤0.04 m3/m3, and unbiased RMSE (unRMSE) ≤ 0.035 m3/m3), while the 3CH attempt yields the lowest uncertainty. Additionally, dense manual measurements are more representative than sparsely distributed WSN measurements. Overall, combining dual-polarized observations yields the best SM estimates but introduces additional uncertainty. This study highlights uncertainties quantification in SM inversion and thus provides confidence in SM inversion, facilitating improved SM retrieval algorithms.