Surface soil moisture estimation at high spatial resolution by fusing synthetic aperture radar and optical remote sensing data

The upcoming NISAR Earth-observation satellite will utilize dual frequencies simultaneously, providing synthetic aperture radar (SAR) remote sensing data in both the L-band and S-band. With its ability to operate single-polarization, dual-polarization, and quad-polarization modes, NISAR will offer significant capabilities for land surface observation applications, particularly for estimating surface soil moisture (SSM) and surface roughness (Hrms). This study aims to demonstrate NISAR’s future potential in SSM and Hrms estimation by evaluating the single (SP), double (DP) and quad (QP) polarization configurations. Noisy synthetic NISAR-like data was generated using the Dubois-B model for both S- and L-bands. The use of a priori information on the soil moisture was also examined for SSM and Hrms estimations. Various neural networks (NNs) were trained using the noisy synthetic dataset. Validation was performed on noisy synthetic data, as real NISAR data is not yet available. Out of the NISAR configurations tested, the QP configuration was shown to be the most performant, with RMSE on SSM estimation of 4.2 vol.%, for QP configuration compared to 5.1 and 8.2 vol.% for SP and DP configurations when not using a priori knowledge of soil moisture conditions. RMSE on Hrms was 0.3 cm for QP configuration, compared to 0.7 and 0.6 cm for SP and DP configurations. The QP was also shown to be more capable of mitigating the effect of the incidence angle on the estimation of SSM and Hrms compared to the two other configurations. Moreover, simultaneous use of S- and L-bands enhances SSM and Hrms estimation compared to using either of these frequency bands alone in single-, dual-, or quad-polarization configurations. Furthermore, using a priori knowledge of soil moisture conditions was successful in improving the estimation precision for SSM for all NISAR configurations. Notably, for QP configuration, RMSE on SSM estimation was 3.9 vol.% and 3.2 vol.% when a priori information on SSM was considered respectively in dry to slightly wet and very wet conditions. These findings demonstrate the high potential of the future NISAR sensor for estimating SSM and Hrms.

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

Abstract. Radar remote sensing has demonstrated its applicability to the retrieval of basin-scale soil moisture. The mechanism of radar backscattering from soils is complicated and strongly influenced by surface roughness. Additionally, retrieval of soil moisture using AIEM (advanced integrated equation model)-like models is a classic example of underdetermined problem due to a lack of credible known soil roughness distributions at a regional scale. Characterization of this roughness is therefore crucial for an accurate derivation of soil moisture based on backscattering models. This study aims to simultaneously obtain surface roughness parameters (standard deviation of surface height σ and correlation length cl) along with soil moisture from multi-angular ASAR images by using a two-step retrieval scheme based on the AIEM. The method firstly used a semi-empirical relationship that relates the roughness slope, Zs (Zs = σ2/cl) and the difference in backscattering coefficient (Δσ) from two ASAR images acquired with different incidence angles. Meanwhile, by using an experimental statistical relationship between σ and cl, both these parameters can be estimated. Then, the deduced roughness parameters were used for the retrieval of soil moisture in association with the AIEM. An evaluation of the proposed method was performed in an experimental area in the middle stream of the Heihe River Basin, where the Watershed Allied Telemetry Experimental Research (WATER) was taken place. It is demonstrated that the proposed method is feasible to achieve reliable estimation of soil water content. The key challenge is the presence of vegetation cover, which significantly impacts the estimates of surface roughness and soil moisture.

Soil moisture is a key component of the water and energy balance of the Earth's surface, and as such, is important to many Earth science disciplines. Soil moisture has been identified as a parameter of significant potential for improving the accuracy of large-scale land surface-atmosphere interaction models. Because of high spatial variability, accurate estimates of surface soil moisture are often difficult to make by conventional ground measuring techniques, especially at large spatial scales. Since satellite remote sensing observations are already a spatially averaged value, they are ideally suited for measuring many land surface parameters such as soil moisture. Passive microwave remote sensing presents significant potential for providing regular spatially representative estimates of surface soil moisture at global scales. But, while the optimum wavelength for soil moisture sensing is in the L-band (1.4 GHz or /spl lambda/ = 21 cm), such a sensor has yet to be deployed operationally. However, new and improved microwave retrieval techniques that maximize the information that can be obtained from less optimum sensors, such as C-band and even X-band, are being developed. Progress from one such study is presented, along with preliminary results from several validation studies. It is currently planned to develop a 20+ year retrospective global database of surface soil moisture, to be made available through the Goddard Space Flight Center Distributed Active Archive Center (DAAC).

High-resolution surface and rootzone soil moisture over US cropland: A novel framework assimilating multi-source remote sensing data, machine learning, and the Layered Green and Ampt Infiltration with Redistribution model

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

The observation of spatially distributed soil moisture fields is an essential component for a large range of hydrological, climate, and agricultural applications. While direct measurements are expensive and limited to small spatial domains, the inversion of airborne and satellite L-band radiometer data has shown the potential to provide spatial estimates of near surface soil moisture from the local up to the global scale. When using L-band radiometer observations for soil moisture retrieval, a major limitation is the attenuation of the microwave signal by the vegetation, hampering the signal inversion and thereby making spatially distributed plant information necessary. Usually vegetation types are considered with a vegetation type specific global parameterization, e.g., for leaf area index (LAI). Within this study we evaluate and address the effect of spatially varying LAI on high spatial resolution (pixel size 50 m) airborne L-band brightness temperature of crop canopies that are usually regarded homogeneous. To account for within field variations of LAI we used airborne imaging spectrometer data (pixel size 1.5 m) to empirically create maps of LAI using spectral greenness vegetation indices. We found clear (R2<0.90) functional relationships between spatially varying L-band brightness temperature and LAI variations within crop canopies that in literature are usually assumed homogeneous. Very good (R2 = 0.93) near surface soil moisture estimates were achieved using multi-variate regression and adding plant specific spectral information to the independent variable set for final soil moisture retrieval. The study shows that a multi-sensor campaign using airborne L-band radiometer and imaging spectrometers provide a powerful data set for monitoring patterns of near surface soil moisture and vegetation canopy at the field scale with high accuracy.

Estimation of surface soil moisture (SSM) is fundamental for hydrological monitoring and agricultural management, particularly in regions affected by recurrent droughts such as central–northeastern Argentina. Data assimilation techniques provide a robust approach to estimating SSM variability by integrating numerical soil models—i.e., land surface models—with observations from satellite-based remote sensors. Satellite missions based on L-band microwave sensors, such as NASA’s SMAP and ESA’s SMOS, provide global SSM retrievals, yet these observations often exhibit systematic biases arising from instrument noise, indirect measurements (i.e., observational operator), and temporal and spatial heterogeneities. Within data assimilation techniques, the ensemble Kalman filter (EnKF) has been widely employed for SSM estimation. This algorithm assumes unbiased observations; thus, bias correction becomes necessary to ensure optimal assimilation. In this study, we evaluate three off-line observational bias-correction techniques within a land data assimilation framework based on the Noah-MP v4.0.1 land surface model and an EnKF. The assessment focuses on the 2022 dry season over the endorheic Pampas region. We introduce a bias correction approach to mitigate sampling errors in cumulative distribution function (CDF) matching: (i) the climatological statistics are computed using homogeneous soil texture pixels within the bin, and (ii) a 45-day moving temporal sampling window is used to give a smoother evolution of the CDF. During dry periods, we empirically demonstrate that soil moisture probability density functions are statistically distinguishable across different soil textures within the bin. Furthermore the monthly-fixed statistics exhibited jumps during the dry season. This approach is compared with the standard CDF matching and the normal deviate scaling. These three off-line bias correction techniques are applied to correct SMAP and SMOS satellite retrievals prior to data assimilation. We show that this improved statistical sampling for CDF matching has a non-negligible impact on the SSM estimates resulting from EnKF, particularly during dry periods. The corrected sampling of CDF matching shows better alignment and stronger correlation with the time series of the independent in-situ soil moisture measurements. Overall, the study emphasizes the need for context-aware bias-correction techniques to enhance SSM data assimilation in regions with strong seasonal precipitation variations. Moreover, SSM estimations influence deeper model layers through vertical propagation of the information. These results motivate future work exploring how surface corrections might lead to enhanced subsurface estimates.

Estimations of the surface soil moisture and distributions play a key role in the ecological, environmental, and topographical investigations for intertidal mudflats that are characteristically wet and periodically submerged by sea water. However, existing techniques have limitations of measurement. Along with the three-dimensional (3D) point cloud, long-range terrestrial laser scanners (TLSs) can simultaneously record the co-located intensity value of each echo that internally contains physical reflectance characteristics of the scanned point. Most long-range TLSs emit near-infrared lasers that can be strongly absorbed by the water. Thus, the intensity values of the areas with high water moisture are theoretically smaller than those of the regions with low moisture. In this study, the intensity data of long-range TLSs are corrected for the incidence angle and distance effects and the corrected intensity data are utilized to quantitatively estimate the surface soil moisture of intertidal mudflats. A case study is conducted for a mudflat in Chongming Island, Shanghai, China, using a long-range TLS (Riegl VZ-4000) that can measure distance up to 4000 m with a near-infrared wavelength of 1550 nm. Results show that compared with traditional techniques (e.g., gravimetric measurements) the corrected intensity data of long-range TLSs are a very effective data source for a quick, accurate, and detailed estimation of surface soil moisture for large-area mudflats. The estimation accuracy is approximately 97%. Additionally, by combining the point cloud of the mudflats the 3D distribution of the moisture can be accurately mapped to potentially analyze the intrinsic association between moisture and topography, vegetation coverage, and habitation of creatures in mudflats.

Soil moisture is a significant environmental factor that influences both the water and energy balance at the land-atmosphere interface. Therefore, proper assessment of the spatial and temporal distribution of soil moisture is crucial for many hydrological applications such as weather forecasting, agricultural water resource management and drought monitoring. This study involves the assimilation of Soil Moisture Active Passive (SMAP) soil moisture dataset within a land surface model and the evaluation of its performance in precise estimation of soil moisture by comparing the statistics with respect to standard European Space Agency&amp;#8217;s Climate Change Initiative (ESA-CCI) soil moisture dataset. The Ensemble Kalman Filter technique has been used for assimilating SMAP soil moisture data using Noah-MP land surface model within NASA Land Information System (LIS) framework. The data assimilation (DA) framework includes Cumulative Distribution Function (CDF) matching for bias correction and twenty ensembles per tile. Meteorological forcings for the simulations have been taken from MERRA2 and IMD. Improvement or degradation due to DA has been analyzed in terms of the difference in anomaly correlation between open loop (OL) and DA soil moisture outputs with respect to the ESA-CCI soil moisture dataset over the entire Indian domain. The DA result shows improvement over larger areas in the case of MERRA2 forced simulations than IMD+MERRA2. The seasonal impact of DA in terms of the differences in DA and OL simulated soil moisture shows less variability in summer than winter. The results are validated with in-situ soil moisture datasets. Overall, the study shows that data assimilation is giving better results than open loop LSM simulation, which can be used for improved estimation of other water balance components.

Estimation of soil moisture patterns in mountain grasslands by means of SAR RADARSAT2 images and hydrological modeling

The NASA Soil Moisture Active Passive (SMAP) mission Level‐4 Soil Moisture (L.4_SM) product provides global, 3‐hourly, 9‐km resolution estimates of surface (0–5 cm) and root zone (0–100 cm) soil moisture with a mean latency of ~2.5 days. The underlying L4_SM algorithm assimilates SMAP radiometer brightness temperature (Tb) observations into the NASA Catchment land surface model using a spatially distributed ensemble Kalman filter. In Version 4 of the L4_SM modeling system the upward recharge of surface soil moisture from below under nonequilibrium conditions was reduced, resulting in less bias and improved dynamic range of L4_SM surface soil moisture compared to earlier versions. This change and additional technical modifications to the system reduce the mean and standard deviation of the observation‐minus‐forecast Tb residuals and overall soil moisture analysis increments while maintaining the skill of the L4_SM soil moisture estimates versus independent in situ measurements; the average, bias‐adjusted root‐mean‐square error in Version 4 is 0.039 m3/m3 for surface and 0.026 m3/m3 for root zone soil moisture. Moreover, the coverage of assimilated SMAP observations in Version 4 is near global owing to the use of additional satellite Tb records for algorithm calibration. L4_SM soil moisture uncertainty estimates are biased low (by 0.01–0.02 m3/m3) against actual errors (computed versus in situ measurements). L4_SM runoff estimates, an additional product of the L4_SM algorithm, are biased low (by 35 mm/year) against streamflow measurements. Compared to Version 3, bias in Version 4 is reduced by 46% for surface soil moisture uncertainty estimates and by 33% for runoff estimates.

A method to retrieve surface soil moisture (SM), at global scale, from L-Band telemetry of SMOS satellite using artificial Neural Networks (NNs) is presented. The NNs are trained using in-situ SM measurements as reference data, and SMOS Level-3 Temperature Brightness (TB) values and other auxiliary information, like MODIS NDVI, soil texture, and Skin Temperature from ECMWF as input. The retrieval is done in three steps. First multiple NN s, one per available in-situ site, are trained. Then a “representative” reference SM dataset is defined by examining the statistical relationships which link measurements from individual insitu SM sites and the input data. Finally, this representative reference SM data set is used to train an artificial NN using SMOS TBs and other inputs over the period 2011–2014. The resulting NN is in turn applied to 2017 SMOS TBs and others input data to retrieve SM at a global scale. The NN predicted SM is compared against SMOS Level 2 SM products as well as ECMWF forecast and is found to well capture the temporal and spatial variability of SM.

&amp;lt;p&amp;gt;Soil moisture content is an important hydrological and climatic variable with applications in a wide range of domains. The high spatial variability of soil moisture cannot be well captured from conventional point-based in-situ measurements. Remote sensing offers a feasible way to observe spatial pattern of soil moisture from regional to global scales. Microwave remote sensing has long been used to estimate Surface Soil Moisture Content (SSMC) at lower spatial resolutions (&amp;gt;1km), but few accurate options exist in the higher spatial resolution (&amp;lt;1km) domain. This study explores the capabilities of deep learning in the high-resolution domain of remotely sensed SSMC by using a Convolutional Neural Network (CNN) to estimate SSMC from Sentinel-1 acquired Synthetic Aperture Radar (SAR) imagery. The developed model incorporates additional SSMC predictors such as Normalized Difference Vegetation Index (NDVI), temperature, precipitation, and soil type to yield a more accurate estimation than traditional empirical formulas that focus solely on the conversion of backscatter signals to relative soil moisture. This also makes the developed model less sensitive to site-specific conditions and increases the model applicability outside the training domain. The model is developed and tested with in-situ soil moisture measurements in Denmark from a dense network maintained by HOBE (Danish Hydrological Observatory). The unique advantage of the developed model is its transferability across climate zones, which has been historically absent in many prior models. This would open up opportunities for high-resolution soil moisture mapping through remote sensing in areas with relatively few soil moisture gauges. A reliable high-resolution soil moisture platform at good temporal resolution would allow for more precise erosion modelling, flood forecasting, drought monitoring, and precision agriculture.&amp;lt;/p&amp;gt;

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.