Mapping Surface Soil Moisture Research Articles

Spatial-temporal constraints for surface soil moisture mapping using Sentinel-1 and Sentinel-2 data over agricultural regions

Surface soil moisture (SSM) information could have important applications in agricultural and regional water management. Remote sensing, particularly synthetic aperture radar (SAR), is an important technology for the estimation of spatial–temporal SSM over larger areas. Using Sentinel-1 and Sentinel-2 data, this research developed a general spatial–temporal constrained machine-learning-based method for surface soil moisture mapping over agricultural regions. Central to this method is the construction of spatial and temporal constraints and their implementation in machine-learning models. We first defined the spatial and temporal constraints for SSM estimation by investigating the spatial division of cultivated crop types and the temporal division of cumulative precipitation. Second, under the presumption that the SSM and associated variables are smoothly changing, we extracted the temporal difference variables from the multi-temporal remote sensing data. Finally, we incorporated two constraints as categorical features and temporal differences into a CatBoost-based model to improve surface soil moisture mapping. We verified the proposed model in a Spain study area with multi-temporal remote sensing observations. The experimental results after incorporating the spatial–temporal constraints demonstrate the efficacy of the proposed model for mapping surface soil moisture over agricultural regions, with significantly improved R2 = 0.7328, RMSE = 0.0451 vol, and MAE = 0.0351 vol This study also concluded that using multiple polarization in the machine-learning-based method could reliably and accurately estimate surface soil moisture.

A new variant of the optical trapezoid model (OPTRAM) for remote sensing of soil moisture and water bodies

Over the past few years, the Optical Trapezoid Model (OPTRAM) has been widely used as a means for high-resolution mapping of surface soil moisture using optical satellite data. In this paper, we propose a new variant of OPTRAM that can map not only soil moisture, but also water bodies such as lakes and rivers. The proposed variant was tested using laboratory experimental data as well as Landsat-8 reflectance observations. Results showed the new OPTRAM variant has greater skill than the original variant in separating land and water pixels. In addition, the new variant showed less sensitivity to the model parameters, and hence, is less user dependent. To quantitatively examine the user-dependency of the model, we analyzed OPTRAM soil moisture based on Landsat-8 satellite images in California, where we varied the model parameters in a plausible range. The correlations of the resulting maps in terms of R2 between two largely different sets of parameters were found in the range of 0.47-0.52 for the original variant and 0.67-0.76 for the new variant. Because some OPTRAM parameters can be quite uncertain, particularly in wet regions, the reduced sensitivity promises more consistent soil moisture estimates across the range of parameter choices.

Soil moisture estimates at 1 km resolution making a synergistic use of Sentinel data

Abstract. Very high-resolution (∼10–100 m) surface soil moisture (SM) observations are important for applications in agriculture, among other purposes. This is the original goal of the S2MP (Sentinel-1/Sentinel-2-Derived Soil Moisture Product) algorithm, which was designed to retrieve surface SM at the agricultural plot scale by simultaneously using Sentinel-1 (S1) backscatter coefficients and Sentinel-2 (S2) NDVI (Normalized Difference Vegetation Index) as inputs to a neural network trained with Water Cloud Model simulations. However, for many applications, including hydrology and climate impact assessment at regional level, large maps with a high resolution (HR) of around 1 km are already a significant improvement with respect to most of the publicly available SM datasets, which have resolutions of about 25 km. In this study, the S2MP algorithm was adapted to work at 1 km resolution and extended from croplands to herbaceous vegetation types. A target resolution of 1 km also allows the evaluation of the interest in using NDVI derived from Sentinel-3 (S3) instead of S2. Two sets of SM maps at 1 km resolution were produced with S2MP over six regions of ∼104 km2 in Spain, Tunisia, North America, Australia, and the southwest and southeast regions of France for the whole year of 2019. The first set was derived from the combination of S1 and S2 data (S1 + S2 maps), while the second one was derived from the combination of S1 and S3 (S1 + S3 maps). S1 + S2 and S1 + S3 SM maps were compared to each other, to those of the 1 km resolution Copernicus Global Land Service (CGLS) SM and Soil Water Index (SWI) datasets, and to those of the Soil Moisture Active Passive (SMAP) + S1 product. The S2MP S1 + S2 and S1 + S3 SM maps are in very good agreement in terms of correlation (R≥0.9), bias (≤0.04 m3 m−3), and standard deviation of the difference (SDD≤0.03 m3 m−3) over the six domains investigated in this study. In a second step, the S1 + S3 S2MP maps were compared to the other HR maps. S1 + S3 SM maps are well correlated to the CGLS SM maps (R∼0.7–0.8), but the correlations with respect to the other HR maps (CGLS SWI and SMAP + S1) drop significantly over many areas of the six domains investigated in this study. The highest correlations between the HR maps were found over croplands and when the 1 km pixels have a very homogeneous land cover. The bias among the different maps was found to be significant over some areas of the six domains, reaching values of ±0.1 m3 m−3. The S1 + S3 maps show a lower SDD with respect to CGLS maps (≤0.06 m3 m−3) than with respect to the SMAP + S1 maps (≤0.1 m3 m−3) for all the six domains. Finally, all the HR datasets (S1 + S2, S1 + S3, CGLS, and SMAP + S1) were also compared to in situ measurements from five networks across five countries, along with coarse-resolution (CR) SM products from SMAP, SMOS, and the European Space Agency Climate Change Initiative (CCI). While all the CR and HR products show different bias and SDD, the HR products show lower correlations than the CR ones with respect to in situ measurements. The discrepancies in between the different HR datasets, except for the more simple land cover conditions (homogeneous pixels with croplands) and the lower performances with respect to in situ measurement than coarse-resolution datasets, show the remaining challenges for large-scale HR SM mapping.

Sentinel-1 and Sentinel-2 Data to Detect Irrigation Events: Riaza Irrigation District (Spain) Case Study

This paper investigates the use of high resolution (~100 m) surface soil moisture (SSM) maps to detect irrigation occurrences, in time and space. The SSM maps have been derived from time series of Copernicus Sentinel-1 (S-1) and Sentinel-2 (S-2) observations. The analysis focused on the Riaza irrigation district in the Castilla y León region (Spain), where detailed information on land use, irrigation scheduling, water withdrawal, meteorology and parcel borders is available from 2017 to 2021. The well-documented data basis has supported a solid characterization of the sources of uncertainties affecting the use of SSM to map and monitor irrigation events. The main factors affecting the irrigation detection are meteo-climatic condition, crop type, water supply and spatial and temporal resolution of Earth observation data. Results indicate that approximately three-quarters of the fields irrigated within three days of the S-1 acquisition can be detected. The specific contribution of SSM to irrigation monitoring consists of (i) an early detection, well before vegetation indexes can even detect the presence of a crop, and (ii) the identification of the irrigation event in time, which remains unfeasible for vegetation indexes. Therefore, SSM can integrate vegetation indexes to resolve the irrigation occurrences in time and space.

Comparison of bagging, boosting and stacking algorithms for surface soil moisture mapping using optical-thermal-microwave remote sensing synergies

Soil moisture information is key to irrigation water management, drought monitoring, and yield prediction. It plays a vital role in the water cycle and energy budget between the earth’s surface and atmosphere. Hence, its monitoring is crucial for both natural and anthropogenic environments. While the current remote sensing-based global SM products available at coarser resolution (3/15 km) are unsuitable for field-level operations, the most widely used microwave remote sensing suffers from model complexities and in-situ data requirements. Weather conditions limit the alternate approaches such as optical/thermal. This study aims to map surface soil moisture (SSM) at 30 m spatial resolution in a semi-arid region by fusing optical, thermal, and microwave remote sensing data using bagging, boosting, and stacking machine learning approaches. The reference data were collected using a soil moisture meter. The covariates included radar backscatter from Sentinel-1, visible, near-infrared, short-wave infrared, land surface temperature, and spectral indices derived from Landsat 8. Boruta algorithm was used for feature selection which identified radar backscatter, modified normalized difference water index, and land surface temperature as the most critical covariates impacting the SSM. The random forest (RF) showed the highest correlation coefficient (r = 0.71), and least root mean square error (RMSE = 5.17%). The cubist model had the least mean bias error (MBE = 0.21%) during independent validation. Stacking of cubist, gradient boosting machine (GBM), and RF using elastic net (ELNET) as meta-learner further reduced the MBE (0.18%) and RMSE (5.03%) during the validation. Overall, stacking multiple machine learning models improved model prediction and can be recommended to improve the digital soil moisture mapping.

Airborne GNSS-R Polarimetric Multiincidence Data Analysis for Surface Soil Moisture Estimation Over an Agricultural Site

The objective of this study is to propose a mapping of surface soil moisture ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SSM</i> ) using airborne measurements based on the GLObal Navigation Satellite System Reflectometry Instrument (GLORI), a polarimetric instrument. GNSS-R measurements were acquired at the agricultural Urgell site in Spain in July 2021. In situ measurements describing the soil moisture and roughness and the vegetation cover leaf area index were then obtained simultaneously with flight measurements. An analysis of observable copolarization (right-right) reflectivity <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${{\rm{\Gamma }}}_{RR}$</tex-math></inline-formula> and the cross-polarization (right-left) reflectivity <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\ {\Gamma }_{RL}$</tex-math></inline-formula> behaviors as a function of incidence angle is proposed, as is normalization of the reflectivity function of the incidence angle. The sensitivity of reflectivities is then proposed as a function of surface soil moisture. An empirical model with two variables, soil moisture and the normalized difference vegetation index ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">NDVI</i> ), based on the principle of the tau-omega model is then considered for the inversion of GNSS-R reflectivity <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${\Gamma }_{RL}\ $</tex-math></inline-formula> and estimation of soil moisture. This model is calibrated and validated by a threefold cross-validation approach. A mapping of SSM at 100 m resolution is created with data from the studied site and three acquired flights.

Wireless Sensor Network Informed UAV Path Planning for Soil Moisture Mapping

Adaptive and targeted allocation of mobile sensing agents, in the form of unmanned aerial vehicles (UAVs) with software defined radar (UAV-SDRadar) payloads, enable mapping of surface soil moisture in regions where <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in situ</i> wireless sensor networks (WSNs) undersample soil moisture or upscaling models perform poorly. This work presents an optimization-based UAV path planning methodology that seeks to maximize UAV flight coverage over areas where a complementing WSN yields upscaled soil moisture estimates with high uncertainty. By recursively mapping soil moisture over such areas, the combined UAV and WSN instrumentation can gradually capture the domain’s true mean soil moisture. A series of numerical simulations are presented to demonstrate the algorithm’s basic function while considering real-world and feasible operational scenarios.

Improving Spatial Representation of Soil Moisture Through the Incorporation of Single-Channel Algorithm With Different Downscaling Approaches

The use of microwave observations in the low-frequency range is a complementary tool for mapping surface soil moisture. The L-band (1–2 GHz) region is a lower frequency band of microwave radiations, and currently, only two satellite soil moisture data products are available in the L-band frequency range through the satellites Soil Moisture Active Passive (SMAP) and Soil Moisture Ocean Salinity (SMOS). Both these have almost the same spatial resolution around 36–40 km. Although SMAP also provides an enhanced soil moisture product at 9 km, still finer scale information of soil moisture is required. The present study tries to enhance coarse-scale soil moisture by incorporating the single-channel algorithm (SCA). The results obtained by SCA were used as the inputs for the downscaling algorithms instead of directly using the satellite soil moisture product. Through this study, we implement and compare three approaches: approximation of thermal inertia (ATI), triangle, and dispatch methods. The results illustrated that the downscaling algorithms perform better with the estimated SMAP soil moisture through SCA in comparison to direct SMAP soil moisture, and the thermal inertia-based approach is the best performing among the three methods.

Seamless downscaling of the ESA CCI soil moisture data at the daily scale with MODIS land products

Spatial downscaling has recently become a crucial process in the regional application of coarse-resolution passive microwave surface soil moisture (SSM) products. Extensive gaps in auxiliary optical/thermal infrared observation data (mainly caused by cloud cover) and gaps in coarse-resolution passive microwave SSM data lead to spatiotemporal discontinuity in downscaled SSM maps, thereby limiting their applications. An improved downscaling method for the 25-km European Space Agency (ESA) Climate Change Initiative (CCI) SSM product was proposed to obtain daily seamless downscaled SSM series at a 1-km scale. The Moderate Resolution Imaging Spectroradiometer (MODIS) Terra daily land surface temperature (LST) and normalized difference vegetation index (NDVI) products were used as auxiliary data for the downscaling process. Prior to the spatial downscaling, an annual temperature cycle model was applied to the 1-km daily daytime LST data to fill the data gaps caused by cloud cover and to derive the spatial-seamless LST (gap-filled). Subsequently, the gap-filled ESA CCI SSM was generated at the original resolution based on the relationships among the SSM, LST, and NDVI. Finally, these were utilized to obtain a seamless downscaled series SSM at 1-km spatial resolution with a value-consistent downscaling method. The proposed method was applied to data obtained for the Iberian Peninsula from January 1, 2016 to December 31, 2018. Based on the comparison with the precipitation dataset, the downscaled SSM exhibited strong temporal correlation with rainfall events. Evaluation using the in situ SSM from the REMEDHUS network highlighted the good performance of the downscaled SSM at network level with a correlation coefficient (R) of 0.820. The root-mean-square-error, unbiased root-mean-square error (ubRMSE), and bias were 0.091, 0.033, and 0.085 m3/m3, respectively. A comparison with an alternative downscaled SSM product produced by the Barcelona Expert Center, one of the soil moisture and ocean salinity mission (SMOS)–downscaled SSM datasets, also indicated that the downscaled SSM has better spatiotemporal coverage and performance in terms of R and ubRMSE with reference to the REMEDHUS network. These results confirmed that the proposed method is an efficient and convenient downscaling process that can be used to generate high-resolution SSM data without spatiotemporal gaps. The downscaled SSM data improves the accuracy of the original passive microwave SSM product in describing regional SSM variations and shows good potential for related applications at regional scale.

Measurement and zonation of soil surface moisture in arid and semi-arid regions using Landsat 8 images

Monitoring of soil surface moisture is an imperative factor in water and energy cycle. Due to the variability of soil characteristics such as topography, vegetation, and climate dynamics, this important factor varies with respect to time and place. Measuring methods can provide soil moisture information in a wide range of short intervals with reasonable accuracy. In present research, Landsat 8 satellite data with various soil moisture content estimation methods were tested. In order to evaluate the accuracy of each method, the real-field data used 80 samples of volumetric soil moisture content in suburban areas of Semnan city that were collected at the time of satellite passage of the area. Some of the indicators used in this study are normalized vegetation index, NDTI index, NDMI index, PSMI index (use full form of these indices), surface temperature, and SMSWIR index. The SMSWIR index with correlation coefficient was 0.78, and the correlation coefficient of regression model was 0.61, and RMSE was 3.69. The results of the regression model and real data were estimated to be 3.69, which are recommended for assessing surface soil moisture in arid and desert regions. Three indicators of SMSWIR index, NDTI index, and NDMI index with a small difference are not suitable indices for measuring soil moisture content in desert areas with vegetation cover. By employing multivariable regression models, soil moisture model was also prepared by using the studied indices. The findings of this research indicate that the simultaneous correlation model is superior to the surface soil moisture mapping.

Comparison of two satellite-based soil moisture reconstruction algorithms: A case study in the state of Oklahoma, USA

Soil moisture plays a critical role in regional water cycles and is one of the key indicators for agricultural droughts. Satellite-based microwave radiometers have been the main instrument for mapping surface soil moisture at regional to global scales. However, known issues still exist that lead to data gaps in microwave-based remotely sensed soil moisture products. Multiple gap-filling approaches have been recently developed to generate spatially continuous satellite-based soil moisture. However, there are few inter-comparisons of such approaches despite their importance. In this study, we present a comparison between a triangular feature space-based (Tri) model and a machine learning (ML) based random forest (RF) model for seamless reconstruction in the European Space Agency’s Essential Climate Variables Soil Moisture product (ECV SM) over Oklahoma, USA. Five variables are implemented in the models: the normalized difference vegetation index (NDVI), daytime land surface temperature (LST), nighttime LST, daily average LST, and diurnal LST (ΔLST), and different combinations of these five variables are examined. The reconstructed soil moisture is validated against the original ECV SM and local in-situ measurements. The RF model achieved precise and outstanding performance (R2 = 0.95, RMSE = 0.02 m3/m3, bias = 0.07%) in comparison to the original ECV SM. Comparatively, the Tri and RF models revealed equivalent performance in fitting the in-situ measurements, and both effectively alleviated the bias from the ECV SM. Specifically, Tri, which used daytime LST and NDVI to establish a soil moisture estimation model, displayed the highest correlation coefficient as well as the smallest error (R = 0.620, RMSE = 0.080 m3/m3, ubRMSE = 0.038 m3/m3) for the in-situ measurements. Moreover, the assessments for sub-regions proved the consistency and robustness of both the Tri and RF models. Missing sub-regional training samples are unlikely to degrade performance of the models as long as there are sufficient valid samples in the whole region. The results of this study highlight the promising potential of Tri and RF models in efficiently filling the gaps in the ECV SM and can provide a reference for future studies focused on satellite-based soil moisture gap-filling algorithm selections.

Assessment of Multi-Scale SMOS and SMAP Soil Moisture Products across the Iberian Peninsula

In the last decade, technological advances led to the launch of two satellite missions dedicated to measure the Earth’s surface soil moisture (SSM): the ESA’s Soil Moisture and Ocean Salinity (SMOS) launched in 2009, and the NASA’s Soil Moisture Active Passive (SMAP) launched in 2015. The two satellites have an L-band microwave radiometer on-board to measure the Earth’s surface emission. These measurements (brightness temperatures TB) are then used to generate global maps of SSM every three days with a spatial resolution of about 30–40 km and a target accuracy of 0.04 m3/m3. To meet local applications needs, different approaches have been proposed to spatially disaggregate SMOS and SMAP TB or their SSM products. They rely on synergies between multi-sensor observations and are built upon different physical assumptions. In this study, temporal and spatial characteristics of six operational SSM products derived from SMOS and SMAP are assessed in order to diagnose their distinct features, and the rationale behind them. The study is focused on the Iberian Peninsula and covers the period from April 2015 to December 2017. A temporal inter-comparison analysis is carried out using in situ SSM data from the Soil Moisture Measurements Station Network of the University of Salamanca (REMEDHUS) to evaluate the impact of the spatial scale of the different products (1, 3, 9, 25, and 36 km), and their correspondence in terms of temporal dynamics. A spatial analysis is conducted for the whole Iberian Peninsula with emphasis on the added-value that the enhanced resolution products provide based on the microwave-optical (SMOS/ERA5/MODIS) or the active–passive microwave (SMAP/Sentinel-1) sensor fusion. Our results show overall agreement among time series of the products regardless their spatial scale when compared to in situ measurements. Still, higher spatial resolutions would be needed to capture local features such as small irrigated areas that are not dominant at the 1-km pixel scale. The degree to which spatial features are resolved by the enhanced resolution products depend on the multi-sensor synergies employed (at TB or soil moisture level), and on the nature of the fine-scale information used. The largest disparities between these products occur in forested areas, which may be related to the reduced sensitivity of high-resolution active microwave and optical data to soil properties under dense vegetation.

Generation of spatially complete and daily continuous surface soil moisture of high spatial resolution

Surface soil moisture (SSM), as a vital variable for water and heat exchanges between the land surface and the atmosphere, is essential for agricultural production and drought monitoring, and serves as an important boundary condition for atmospheric models. The spatial resolution of soil moisture products from microwave remote sensing is relatively coarse (e.g., ~40 km × 40 km), whereas SSM of higher spatiotemporal resolutions (e.g., 1 km × 1 km and daily continuous) is more useful in water resources management. In this study, first, to improve the spatiotemporal completeness of SSM estimates, we downscaled land surface temperature (LST) output from the China Meteorological Administration Land Data Assimilation System (CLDAS, 0.0625° × 0.0625°) using a data fusion approach and MODIS LST acquired on clear-sky days to generate spatially complete and temporally continuous LST maps across the North China Plain. Second, spatially complete and daily continuous 1 km × 1 km SSM was generated based on random forest models combined with quality LST maps, normalized difference vegetation index (NDVI), surface albedo, precipitation, soil texture, SSM background fields from the European Space Agency Soil Moisture Climate Change Initiative (CCI, 0.25° × 0.25°) and CLDAS land surface model (LSM) SSM output (0.0625° × 0.0625°) to be downscaled, and in situ SSM measurements. Third, the importance of different input variables to the downscaled SSM was quantified. Compared with the original CCI and CLDAS SSM, both the accuracy and spatial resolution of the downscaled SSM were largely improved, in terms of a bias (root mean square error) of −0.001 cm3/cm3 (0.041 cm3/cm3) and a correlation coefficient of 0.72. These results are generally comparable and even better than those in published studies, with our SSM maps featuring spatiotemporal completeness and relatively high spatial resolution. The downscaled SSM maps are valuable for monitoring agricultural drought and optimizing irrigation scheduling, bridging the gaps between microwave-based and LSM-based SSM estimates of coarse spatial resolution and thermal infrared-based LST at a 1 km × 1 km resolution.

The Assimilation of Remote Sensing-Derived Soil Moisture Data into a Hydrological Model for the Mahanadi Basin, India

Accurate knowledge of the spatio-temporal variation in soil moisture provides insight into larger-scale hydrological processes and can, therefore, help in improving hydrological predictions. The strength of remote sensing for mapping surface soil moisture is well proven. In addition, data assimilation offers the opportunity to combine the advantages of modelling with those of remote sensing data to achieve higher accuracy and continuous improvement in hydrological forecasts. In this study, Advanced Microwave Scanning Radiometer for Earth observation science soil moisture product was assimilated into Variable Infiltration Capacity (VIC) hydrological model using Kalman filter data assimilation technique. Further, the updated multilayer spatio-temporal soil moisture distributions across the Mahanadi Basin, India, were simulated using the hydrological model. The VIC model was set up and parameterized using field-observed and remote sensing-derived data. Based on the sensitivity analysis of the model, the ‘four-parameter’ (Tmax, Tmin, Prec, and WS) meteorological forcing scenario was selected as the operational scenario. The output fluxes obtained from VIC were routed to simulate discharge at five stations for the calibration and validation of the model. With R2 and model efficiency values close to 0.95 and 0.99, respectively, the model was proven to be suitable for simulating the hydrological responses of the basin. Soil moisture was assimilated in the top soil layer of the model using the Kalman filter approach, and the multilayer soil moisture regime was generated using the modelling approach. The validation of soil moisture (assimilated) products proves that these products are better than remote sensing and traditionally modelled soil moisture products, in both spatial and temporal domains in terms of availability and accuracy.

Sub-metre mapping of surface soil moisture in proglacial valleys of the tropical Andes using a multispectral unmanned aerial vehicle

Surface soil moisture is a critical but often neglected component of the hydrologic budget. Within mountain environments, surface soil moisture is highly heterogeneous and challenging to measure. Point measurements are often poorly representative of larger areas, while satellite pixels are generally too coarse in these topographically varied landscapes. In the Cordillera Blanca, Peru, rapid glacier recession is impacting downstream water supply in timing, quantity and quality. Recent research has shown that these proglacial valleys provide vital ecosystem services and store considerable amounts of water within the groundwater systems. However, the spatial and temporal variability of soil water storage is poorly understood. In this tropical Andean setting, we use an unmanned aerial vehicle (UAV) with multispectral (visible, near infrared, thermal infrared) sensors to map sections of two proglacial valleys in the Cordillera Blanca, Peru at sub-metre resolution. We use the Temperature Vegetation Dryness Index (TVDI) (Sandholt et al., 2002), and apply it for the first time to UAV borne imagery to estimate absolute surface soil moisture through calibration with in-scene field measurements. Resulting surface soil moisture maps have 50 cm spatial resolution with an R2 value of 0.55 and 0.76 for the two study sites, Pachacoto and Llanganuco respectively. We analyse the multispectral orthomosaics and soil moisture maps to improve our understanding of the controls on spatial variability in surface soil moisture within the proglacial valleys of the Cordillera Blanca. The maps permit us to identify both important groundwater spring sources and areas where domestic grazing impacts observed soil moisture estimates. The high resolution observations provided by the UAV facilitate a greater understanding of fine scale heterogeneity within these environments.

Triangle Space-Based Surface Soil Moisture Estimation by the Synergistic Use of &lt;inline-formula&gt; &lt;tex-math notation="LaTeX"&gt;$In\ Situ$ &lt;/tex-math&gt; &lt;/inline-formula&gt; Measurements and Optical/Thermal Infrared Remote Sensing: An Alternative to Conventional Validations

Together with the continuous development of passive microwave surface soil moisture (SSM) products from newly launched satellites, it is necessary to perform reliable validations to assess their accuracy. With this aim, a new “bottom-up” validation approach is proposed based on the synergistic use of in situ SSM measurements from the soil moisture measurement station network (REMEDHUS) of the University of Salamanca, Salamanca, Spain, and optical/thermal infrared observations over 18 cloud-free days from Landsat-8. An SSM estimation method using the boundary information from the land surface temperature and normalized difference vegetation index triangle space was developed for regional SSM mapping. The retrieved SSM reached a relatively good performance (mean $\text{R}^{2}$ and root-mean-squared error of 0.64 and 0.033 $\text{m}^{3}/\text{m}^{3}$ , respectively). Then, the regional SSM was aggregated into the grid-cell scale of the Advanced Microwave Scanning Radiometer 2 (AMSR2) L3 high-resolution (0.1° /10 km) soil moisture product to validate it both at network and grid-cell levels. At the network level, the derived regional SSM showed a good agreement with the averaged in situ measurements over the network ( $R = 0.731$ ). However, at the grid-cell level, small variations were observed for the cells over the network between the estimates and the AMSR2 product, with negative biases (−0.044 to $-0.090~\text{m}^{3}/\text{m}^{3}$ ) and positive correlations ( $R > 0.25$ ) for most cells. In addition, it is shown that the descending product has a slightly better performance than does the ascending one. The preliminary assessments suggested that the proposed method provides new insights into the validation of passive microwave soil moisture products, while avoiding the common issue related to the big disparity in spatial scales between satellite observation and in situ measurements.

A Spatially Consistent Downscaling Approach for SMOS Using an Adaptive Moving Window

The European Space Agency (ESA)'s Soil Moisture and Ocean Salinity (SMOS) is the first spaceborne mission using L-band radiometry to monitor the Earth's global surface soil moisture (SM). After more than 7 years in orbit, many studies have contributed to improve the quality and applicability of SMOS-derived SM maps. In this research, a novel downscaling algorithm for SMOS is proposed to obtain high-resolution (HR) SM maps at 1 km (L4), from the ∼40 km native resolution of the instrument. This algorithm introduces the concept of a shape adaptive moving window as an improvement of the current semi-empirical downscaling approach at SMOS Barcelona Expert Center, based on the “universal triangle”. Its inputs are as follows: the SMOS SM (L3 at ∼40 km spatial resolution), the vertical and the horizontal SMOS brightness temperatures (L1C at ∼40 km), and the HR normalized difference vegetation index and land surface temperature from optical-based sensors. The proposed method has the following advantages: HR SM maps are obtained while maintaining the dynamic range from the original L3 product; energy is conserved, because differences between aggregated L4 and L3 SM maps are negligible; and it can be applied to continental areas, even when they integrate different climates. A comparison of SMOS L3 and L4 products with in situ data for networks allocated in Spain and Australia shows good agreement in terms of correlation and root mean square error. The proposed method is shown to capture 1-km SM spatial variability while preserving the quality of SMOS at its native resolution.

Monitoring Andean high altitude wetlands in central Chile with seasonal optical data: A comparison between Worldview-2 and Sentinel-2 imagery

In the Maipo watershed, situated in central Chile, mining activities are impacting high altitude Andean wetlands through the consumption and exploitation of water and land. As wetlands are vulnerable and particularly susceptible to changes of water supply, alterations and modifications in the hydrological regime have direct effects on their ecophysiological condition and vegetation cover. The aim of this study was to evaluate the potential of Worldview-2 and Sentinel-2 sensors to identify and map Andean wetlands through the use of the one-class classifier Bias support vector machines (BSVM), and then to estimate soil moisture content of the identified wetlands during snow-free summer using partial least square regression.The results obtained in this research showed that the combination of remote sensing data and a small sample of ground reference measurements enables to map Andean high altitude wetlands with high accuracies. BSVM was capable to classify the meadow areas with an overall accuracy of over ∼78% for both sensors. Our results also indicate that it is feasible to map surface soil moisture with optical remote sensing data and simple regression approaches in the examined environment. Surface soil moisture estimates reached r2 values of up to 0.58, and normalized mean square errors of 19% using Sentinel-2 data, while Worldview-2 estimates resulted in non-satisfying results. The presented approach is particularly valuable for monitoring high-mountain wetland areas with limited accessibility such as in the Andes.

A surface soil moisture mapping service at national (Italian) scale based on Sentinel-1 data

This paper presents MULESME, a software designed for the systematic mapping of surface soil moisture using Sentinel-1 SAR data. MULESME implements a multi-temporal algorithm that uses time series of Sentinel-1 data and ancillary data, such as a plant water content map, as inputs. A secondary software module generates the plant water content map from optical data provided by Landsat-8, or Sentinel-2, or MODIS. Each output of MULESME includes another map showing the level of uncertainty of the soil moisture estimates. MULESME was tested by using both synthetic and actual data. The results of the tests showed that root mean square error is in the range between 0.03 m3/m3 (synthetic data) and 0.06 m3/m3 (actual data) for bare soil. The accuracy decreases in the presence of vegetation (root mean square in the range 0.08–0.12 m3/m3), as expected.

A Robust Coinversion Model for Soil Moisture Retrieval From Multisensor Data

Optical remote sensing has been widely used to estimate soil moisture. However, modeling soil moisture dynamics across a large area based on remotely sensed optical data still poses a problem because of its spatial discontinuity due to cloud contamination. This study proposes a multisensor strategy for better mapping surface soil moisture on a daily basis at a regional scale. The basic idea is to decompose the surface soil moisture at any location into two terms, namely, baseline value in an observed period and daily variation, and to estimate for each term differently. For a certain day of interest, the corresponding 16-day composite of Moderate Resolution Imaging Spectroradiometer (MODIS) data is used to estimate the soil moisture baseline values across space, and the Advanced Microwave Scanning Radiometer for EOS (AMSR-E) data are employed to estimate the daily variations. The proposed model was applied to produce daily surface soil moisture maps at a 1-km resolution for the fairly large study area of Xinjiang, China, regardless of the local weather conditions. It was found that the integrated use of MODIS and AMSR-E data was able to achieve significantly higher accuracy in surface soil moisture estimation (with a root-mean-square error of 3.99% in May and 4.43% in August, 2009) than the approaches based on either data alone could. The proposed model is expected to perform well for mapping surface soil moisture in other arid areas after the required parameters are calibrated with the local field data.