Ground Measurements Of Soil Moisture Research Articles

DOWNSCALING OF SOIL MOISTURE PRODUCT OF SMAP

Abstract. Soil moisture as a variable parameter of the Earth's surface plays a very important role in many applications such as meteorology, climatology, water resources management and hydrology. Therefore, access to soil surface moisture product with high spatial resolution is very important. Due to the lack of access to soil moisture information with high spatial resolution, the main goal in this article is to downscale the existing soil moisture products and improve their spatial resolution into 1 square kilometer. For this purpose, two methods based on regression and neural network have been used for downscaling the 3 km soil moisture products of SMAP satellite. To this end, other available satellite data and products including various combinations of land surface temperature (LST), normalized difference vegetation index (NDVI), brightness temperature in different polarizations of (TBH and TBV) passive microwave sensor data, digital elevation model (DEM) and short-wavelength infrared (SWIR) data of MODIS and Sentinel 3 have been used. In this study, two regions in the north and south of Iran, Golestan and Fars provinces, have been examined, due to the lack of ground measurements of soil moisture, the SMAP product with the resolution of 1 km which has been already downscaled by exploiting the Sentinel-1 radar data, was used to evaluate the results. The evaluation results in Golestan and Fars provinces showed the correlation coefficient of 0.82 to 0.93 and 0.72 to 0.77, respectively, and the average percentage of absolute error in both regression and neural network methods was less than 21 to 30 and 42 to 46 percent.

Spatial–temporal modeling of root zone soil moisture dynamics in a vineyard using machine learning and remote sensing

High-resolution spatial–temporal root zone soil moisture (RZSM) information collected at different scales is useful for a variety of agricultural, hydrologic, and climate applications. RZSM can be estimated using remote sensing, empirical equations, or process-based simulation models. Machine learning (ML) approaches for evaluating RZSM across numerous spatial–temporal scales are less generalizable than process-based models. However, data-driven ML approaches offer a unique opportunity to develop complex models of soil moisture without making assumptions about the processes governing soil water dynamics in a given study region. In this study, comparisons were made between two models, pySEBAL and EFSOIL, which were based on evaporation fraction (EF) and soil properties, and a data-driven model based on the Random Forest (RF) ensemble algorithm. These approaches were evaluated to demonstrate their capabilities for RZSM estimation. The EF obtained from Landsat images was used after validation with eddy covariance measurements as the major input to all three models, along with other meteorological and soil physical properties. The RF model was trained using in situ soil moisture data from Time Domain Reflectometry (TDR) sensors installed in a vineyard from 2018 to 2020. The predictor variables comprised of meteorological, soil properties, EF, and a vegetation index. The results reveal that there was a strong correlation between the in situ measured soil moisture and the RF predicted soil moisture at all sensor locations. Due to the complexity of the physical processes involved in soil water flow, the empirical models pySEBAL and EFSOIL were unable to reliably predict RZSM values at all monitored locations. The high RZSM predicted by pySEBAL demonstrated the presence of possible bias in the model’s algorithm used to estimate soil moisture. We also demonstrated that ML based on the RF algorithm may be used to predict spatially distributed RZSM when a few soil moisture ground measurements are combined with remote sensing to produce soil moisture maps.

Analysis of L-Band SAR Data for Soil Moisture Estimations over Agricultural Areas in the Tropics

The main objective of this study is to analyze the potential use of L-band radar data for the estimation of soil moisture over tropical agricultural areas under dense vegetation cover conditions. Ten radar images were acquired using the Phased Array Synthetic Aperture Radar/Advanced Land Observing Satellite (PALSAR/ALOS)-2 sensor over the Berambadi watershed (south India), between June and October of 2018. Simultaneous ground measurements of soil moisture, soil roughness, and leaf area index (LAI) were also recorded. The sensitivity of PALSAR observations to variations in soil moisture has been reported by several authors, and is confirmed in the present study, even for the case of very dense crops. The radar signals are simulated using five different radar backscattering models (physical and semi-empirical), over bare soil, and over areas with various types of crop cover (turmeric, marigold, and sorghum). When the semi-empirical water cloud model (WCM) is parameterized as a function of the LAI, to account for the vegetation’s contribution to the backscattered signal, it can provide relatively accurate estimations of soil moisture in turmeric and marigold fields, but has certain limitations when applied to sorghum fields. Observed limitations highlight the need to expand the analysis beyond the LAI by including additional vegetation parameters in order to take into account volume scattering in the L-band backscattered radar signal for accurate soil moisture estimation.

Potential of a two-component polarimetric decomposition at C-band for soil moisture retrieval over agricultural fields

This study proposes a two-component (surface and volume) C-band polarimetric decomposition to retrieve soil moisture over agricultural fields covered by different crop types. RADARSAT-2 data analysis shows that vegetation attenuation on the surface scattering component is stronger for the narrow-leaf (wheat) than broad-leaf crops (canola, corn and soybean). Thus, the vegetation attenuation factor is integrated into the proposed C-band polarimetric decomposition to better account for soil contribution over the whole crop phenological season. After removing the volume scattering component from the full coherency matrix, the surface scattering component was simulated by the Oh soil scattering model, instead of conventional Bragg or X-Bragg model, which is not physically valid at C-band due to limited roughness constraint condition. The proposed retrieval algorithm was applied to the RADARSAT-2 time series acquired during the Soil Moisture Active Passive Validation Experiment in 2012 (SMAPVEX12). Results indicate that during the crop growth, the volume scattering power of wheat shows weak temporal variation, while it increases for corn, soybean and canola. The sensitivity of the ground scattering component to soil moisture is enhanced due to the removal of the volume scattering from the full signature. The retrieved soil moisture was validated using the soil moisture ground measurements during the SMAPVEX12. The validations indicate correlation coefficients from 0.63 to 0.76, and RMSEs from 0.058 to 0.074 m3/m3 for the entire phenological period of SMAPVEX12 campaign. The negligible dihedral scattering component (1.6–9.2%) at C-band greatly reduces the complexity of the soil moisture retrieval from the ground component.

Sentinel 1 Radar Data Correlation WITH GROUND Measurements of Soil Moisture and Temperature

Sentinel 1 Radar Data Correlation WITH GROUND Measurements of Soil Moisture and Temperature

Estimation of Soil Moisture Index Using Multi-Temporal Sentinel-1 Images over Poyang Lake Ungauged Zone

The C-band radar instruments onboard the two-satellite GMES Sentinel-1 constellation provide global measurements with short revisit time (about six days) and medium spatial resolution (5 × 20 m), which are appropriate for watershed scale hydrological applications. This paper aims to explore the potential of Sentinel-1 for estimating surface soil moisture using a multi-temporal approach. To this end, a linear mixed effects (LME) model was developed over Poyang Lake ungauged zone, using time series Sentinel 1A and 1B images and soil moisture ground measurements from 15 automatic observation sites. The model assumed a linear relationship that varied with both time and space between soil moisture and backscattering coefficient (SM- σ 0 ). Results showed that three LME models developed with different polarized σ 0 images all meet the European Space Agency (ESA) accuracy requirement for GMES soil moisture product (≤5% in volume), with the vertical transmit and vertical receive (VV) polarized model achieving the best performance. However, the SM- σ 0 relationship was found to depend strongly on space, making it difficult to predict absolute soil moisture for each grid. Therefore, a relative soil moisture index was then proposed to correct for site effect. When compared with those of the linear fixed effects model, the soil moisture indices predicted by the LME model captured the temporal dynamics of measured soil moisture better, with the overall R2 and cross-validated R2 being 0.68 and 0.64, respectively. These results indicate that the LME model can be effectively applied to estimate soil moisture from multi-temporal Sentinel-1 images, which is useful for monitoring flood and drought disasters, and for improving stream flow prediction over ungauged zones.

An Operational In Situ Soil Moisture & Soil Temperature Monitoring Network for West Wales, UK: The WSMN Network.

This paper describes a soil moisture dataset that has been collecting ground measurements of soil moisture, soil temperature and related parameters for west Wales, United Kingdom. Already acquired in situ data have been archived to the autonomous Wales Soil Moisture Network (WSMN) since its foundation in July 2011. The sites from which measurements are being collected represent a range of conditions typical of the Welsh environment, with climate ranging from oceanic to temperate and a range of the most typical land use/cover types found in Wales. At present, WSMN consists of a total of nine monitoring sites across the area with a concentration of sites in three sub-areas around the region of Aberystwyth located in Mid-Wales. The dataset of composed of 0–5 (or 0–10) cm soil moisture, soil temperature, precipitation, and other ancillary data. WSMN data are provided openly to the public via the International Soil Moisture Network (ISMN) platform. At present, WSMN is also rapidly expanding thanks to funding obtained recently which allows more monitoring sites to be added to the network to the wider community interested in using its data.

Data assimilation using support vector machines and ensemble Kalman filter for multi-layer soil moisture prediction

Hybrid data assimilation (DA) is a method seeing more use in recent hydrology and water resources research. In this study, a DA method coupled with the support vector machines (SVMs) and the ensemble Kalman filter (EnKF) technology was used for the prediction of soil moisture in different soil layers: 0–5 cm, 30 cm, 50 cm, 100 cm, 200 cm, and 300 cm. The SVM methodology was first used to train the ground measurements of soil moisture and meteorological parameters from the Meilin study area, in East China, to construct soil moisture statistical prediction models. Subsequent observations and their statistics were used for predictions, with two approaches: the SVM predictor and the SVM-EnKF model made by coupling the SVM model with the EnKF technique using the DA method. Validation results showed that the proposed SVM-EnKF model can improve the prediction results of soil moisture in different layers, from the surface to the root zone.

Soil Moisture Estimation Under Tropical Forests Using UHF Radar Polarimetry

In this paper, we report on the performance of a semiempirical algorithm for the retrieval of soil moisture (SM) under dense tropical forests using ultrahigh frequency (UHF) polarimetric synthetic aperture radar (SAR) data. The algorithm is a simplification of a 3-D coherent model of forest canopy based on the distorted Born approximation (DBA). The simplified model reduces the number of parameters and preserves the three dominant scattering mechanisms of volume, volume-surface, and surface for three polarized backscattering coefficients, i.e., σ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">HH</sub> , σ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">HV</sub> , and σ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">VV</sub> , at UHF frequencies. The inversion process uses the Levenberg-Marquardt nonlinear least squares method to estimate the three model parameters: vegetation aboveground biomass, integrated SM up to a certain depth, and surface roughness. The performance of the inversion process is examined by first using simulation data where the initial values of the inversion process vary randomly and then using airborne UHF SAR data acquired in Costa Rica over La Selva Biological Station. The results with simulated data show that the inversion process is not significantly sensitive to initial values considering they are in the range of ±50% of the true value. A root-mean-square error (RMSE) of less than 4% can be achieved in retrieving the SM. The use of an alternate inversion approach without initial conditions using a genetic algorithm is less efficient (> 120 times longer time) and produces larger error with simulated data (RMSE = 11%) than the Levenberg-Marquardt estimation method. The inversion model simultaneously produces a biomass and SM distribution at 100-m spatial resolution. The RMSE of biomass estimation is 38 Mg/ha (15% relative error) when compared with 28 field plots. Over the plots where SM ground measurements are available, but not at the exact same day as the radar flight occurred, the total volumetric RMSE is 13.6%. However, only two ground measurements were very close to the flight day (three days apart), and for those, the SM estimate has about 3% absolute volumetric error. At the P-band, the SM sensing depth is inversely correlated with the SM allowing to map the spatial variations of SM close to the average root zone or hydrological active horizon of soils in tropical ecosystems.

Soil moisture derivation using triangle method in the lighvan watershed, north western Iran

Soil moisture determination plays a major role in water and crop management. In this research, the accuracy of the Triangle Method (TM) was evaluated to predict surface soil moisture content using Moderate Resolution Imaging Spectro-radiometer (MODIS) satellite images with 1km resolution. The investigation was carried out within an area of 76 km2 in Lighavn watershed in East Azerbaijan, North West of Iran. The analysis was based on ground measurements of soil moisture at 225 points (45 pixels) across the catchment on 2 different days. Ground measurements from the first day were used to train the models, while those from the second day were used for validation. A range of polynomial regressions from 1st to 4th orders were established between the ground measured soil moisture and MODIS Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST). The best results were obtained for the 4th order polynomial of the TM with the efficiency error (ER) and adjusted determination coefficient (R2adj) criterions, respectively, equal to 11.0% and 0.63 for calibration and 15.9% and 0.60 for validation stage. Therefore, the TM was found to provide reliable estimates of soil moisture, without the need for prior information of the soil surface roughness or the vegetation type and water content.

Spatial and temporal soil moisture estimation from RADARSAT-2 imagery over Flevoland, The Netherlands

► Assess the ability of RADARSAT-2 for soil moisture retrieval over Flevoland. ► Evaluate both spatial and temporal soil moisture retrieval techniques. ► Circumvent surface roughness field measurements. ► Spatial and temporal retrievals have accuracy around 4 vol% for medium rough soils. ► Temporal retrieval performs better for smoother fields covered by stubbles. Both spatial and temporal soil moisture dynamics have a large impact on hydrologic processes in agricultural catchments. As acquiring ground measurements of soil moisture is labor intensive, it is often limited in space (to a few locations) and time (to a small number of campaigns). Remote sensing offers a useful alternative, as it allows for observing both across time and space. This study analyses the potential to retrieve spatial and temporal soil moisture information from a series of RADARSAT-2 HH- and VV-polarized C-band (5.405 GHz) backscatter observations over a large number of (nearly) bare soil agricultural fields in Flevoland, The Netherlands, acquired in the framework of AgriSAR 2009. The RADARSAT-2 backscatter observations are found to be strongly correlated with continuous soil moisture measurements recorded at two agricultural sites during the 2009 growing season, as well as with spatial soil moisture measurements conducted during three intensive field campaigns in August and September 2009. Furthermore, two different soil moisture retrieval approaches have been evaluated: a physically-based modeling approach and change detection technique. The physically-based approach makes use of the Integral Equation Model with input of effective surface roughness parameters that are updated every acquisition for each field. The soil moisture retrieval accuracy of this technique is calculated based on leave-one-out cross-validation, yielding an accuracy (RMSE) about 4 vol% for fields with medium surface roughness at both polarization schemes. For smoother fields covered by stubbles, the retrieval accuracy slightly deteriorates. The change detection technique relies on a normalization of the backscatter between dry and wet reference soil moisture measurements, yielding accuracies about 4 vol% both for fields characterized by medium roughness and stubble conditions. This study confirms the large potential of RADARSAT-2 data for the retrieval of spatial and temporal soil moisture dynamics.

Relating TRMM precipitation radar land surface backscatter response to soil moisture in the Southern United States

Soil moisture is an important variable in the hydrological cycle and plays a vital role in agronomy, meteorology, and hydrology. It regulates the exchange of water and heat between land surface and atmosphere and thus plays an important role in the development of weather patterns. It is difficult to obtain a comprehensive spatio-temporal map of soil moisture because of expensive installation of soil moisture measuring instruments. In this paper, a model to estimate soil moisture (ms) using Tropical Rainfall Measuring Mission Precipitation Radar (TRMMPR) backscatter (σ°) and Normalized Difference Vegetation Index (NDVI) is developed for the Southern United States. Soil moisture data from Soil and Climate Analysis Network (SCAN) stations is used to calibrate and validate the model. The estimated values of ms compare well with the ground measurements of soil moisture. The model works well for various landcovers but works best for low density vegetated areas (closed shrubland). All the soil moisture estimates in this landcover have an absolute error of less than 8%. The model performance deteriorates with increase in vegetation density (crops and forest). Overall, the model performance is satisfactory for all landcover types with RMSE less than 6.3% and absolute error of 10% or less for 90% of the estimates. Estimation of soil moisture over a large area with low error provides another use of TRMMPR data.

Evaluation of the SMOS L-MEB passive microwave soil moisture retrieval algorithm

Soil moisture will be mapped globally by the European Soil Moisture and Ocean Salinity (SMOS) mission to be launched in 2009. The expected soil moisture accuracy will be 4.0 %v/v. The core component of the SMOS soil moisture retrieval algorithm is the L-band Microwave Emission of the Biosphere (L-MEB) model which simulates the microwave emission at L-band from the soil–vegetation layer. The model parameters have been calibrated with data acquired by tower mounted radiometer studies in Europe and the United States, with a typical footprint size of approximately 10 m. In this study, aircraft L-band data acquired during the National Airborne Field Experiment (NAFE) intensive campaign held in South-eastern Australia in 2005 are used to perform the first evaluation of the L-MEB model and its proposed parameterization when applied to coarser footprints (62.5 m). The model could be evaluated across large areas including a wide range of land surface conditions, typical of the Australian environment. Soil moisture was retrieved from the aircraft brightness temperatures using L-MEB and ground measured ancillary data (soil temperature, soil texture, vegetation water content and surface roughness) and subsequently evaluated against ground measurements of soil moisture. The retrieval accuracy when using the L-MEB ‘default’ set of model parameters was found to be better than 4.0 %v/v only over grassland covered sites. Over crops the model was found to underestimate soil moisture by up to 32 %v/v. After site specific calibration of the vegetation and roughness parameters, the retrieval accuracy was found to be equal or better than 4.8 %v/v for crops and grasslands at 62.5-m resolution. It is suggested that the proposed value of roughness parameter H R for crops is too low, and that variability of H R with soil moisture must be taken into consideration to obtain accurate retrievals at these scales. The analysis presented here is a crucial step towards validating the application of L-MEB for soil moisture retrieval from satellite observations in an operational context.

The use of radar backscattering signals for measuring soil moisture and surface roughness

In this article, the possible use of synthetic-aperture radar (SAR), for measuring the moisture content and surface roughness is analyzed. Data have been acquired on the Melarchez subwatershed during the Orgeval '89 campaign from March to December 1989. Radar back-scattering measurements were provided by the French airborne scatterometer ERASME. Simultaneous ground measurements of soil moisture and roughness, leaf-area index, and water content of the canopy were conducted on 12 test fields. Using radar configurations close that of the ERS-1 SAR, the results clearly indicate that radar data cannot be easily converted into moisture estimates at field scale over a variety of bare soil, essentially due to the effect of roughness on radar response. Nevertheless, mean soil-moisture values measured within bare soil fields over the whole subwatershed, show a decrease in soil-surface moisture from March to October. The same feature is clearly obtained by averaged radar measurements over all bare-soil fields, indicating that ERS-1 could be used for monitoring the moisture state on a regional scale. Furthermore, the great variability of radar backscattering and radar-slope data, for a given soil practice, for example, ploughing or sowing, and for the same date of measurement over bare soil, suggests that, under natural field conditions, backscattering from a soil surface may not be sufficient to explain and understand the radar response and that volume scattering may have to be accounted for as well. Finally, concerning soil roughness, the standard deviation of surface-height slope discriminates the roughness states, which apparently is a pertinent parameter for analyzing the variation of the backscattering coefficient with incidence angle.

Push broom microwave radiometer observations of surface soil moisture in Monsoon '90

The push broom microwave radiometer (PBMR) was flown on six flights of the NASA C‐130 to map the surface soil moisture over the U.S. Department of Agriculture's Agricultural Research Service Walnut Gulch experimental watershed in southeastern Arizona. The PBMR operates at a wavelength of 21 cm and has four horizontally polarized beams which cover a swath of 1.2 times the aircraft altitude. By flying a series of parallel flight lines it was possible to map the microwave brightness temperature (TB), and thus the soil moisture, over a large area. In this case the area was approximately 8 by 20 km. The moisture conditions ranged from very dry, &lt;2% by volume, to quite wet, &gt;15%, after a heavy rain. The rain amounts ranged from less than 10 mm to more than 50 mm over the area mapped with the PBMR. With the PBMR we were able to observe the spatial variations of the rain amounts and the temporal variation as the soil dried. The TB values were registered to a Universal Transverse Mercator grid so that they could be compared to the rain gage readings and to the ground measurements of soil moisture in the 0‐ to 5‐cm layer. The decreases in TB were well correlated with the rainfall amounts, R2 = 0.9, and the comparison of Tg with soil moisture was also good with an R2 of about 0.8. For the latter, there was some dependence of the relation on location, which may be due to soil or vegetation variations over the area mapped. The application of these data to runoff forecasts and flux estimates will be discussed.

Monte Carlo Simulation of the Effect of Soil Moisture Variation on the Microwave Emission from Soils

In this paper, results of a Monte Carlo simulation of the effect of noise on the relationship between the microwave emissivity of soil and its moisture content are presented. It is found that whenever the magnitude of the noise for the independent variable, in this case the soil moisture, is increased, both the slope of the regression and the correlation coefficient decrease. In particular, when the noise has a magnitude equivalent to a coefficient of variation of 0.25, the slope and correlation coefficient are in good agreement with those obtained from the data of a 21-cm airborne microwave radiometer which was flown over a test site in Hand County, South Dakota. The comparison was made using a linear relationship to determine the estimated emissivity from the ground measurements of soil moisture. The linear relationship was derived from a radiative transfer model calculation of the microwave emissivities using realistic soil-moisture profiles. The effect of surface roughness was included in the relationship, and the variability of the surface roughness was also simulated by a Monte Carlo technique.