Retrieval Of Soil Moisture Content Research Articles

STUDIES AND APPLICATION OF REMOTE SENSING RETRIEVAL METHOD OF SOIL MOISTURE CONTENT IN LAND PARCEL UNITS IN IRRIGATION AREA

Abstract. Soil moisture is one of the important hydrological elements. Obtaining soil moisture accurately and effectively is of great significance for water resource management in irrigation area. During the process of soil moisture content retrieval with multiremote sensing data, multi- remote sensing data always brings multi-spatial scale problems which results in inconformity of soil moisture content retrieved by remote sensing in different spatial scale. In addition, agricultural water use management has suitable spatial scale of soil moisture information so as to satisfy the demands of dynamic management of water use and water demand in certain unit. We have proposed to use land parcel unit as the minimum unit to do soil moisture content research in agricultural water using area, according to soil characteristics, vegetation coverage characteristics in underlying layer, and hydrological characteristic into the basis of study unit division. We have proposed division method of land parcel units. Based on multi thermal infrared and near infrared remote sensing data, we calculate the ndvi and tvdi index and make a statistical model between the tvdi index and soil moisture of ground monitoring station. Then we move forward to study soil moisture remote sensing retrieval method on land parcel unit scale. And the method has been applied in Hetao irrigation area. Results show that compared with pixel scale the soil moisture content in land parcel unit scale has displayed stronger correlation with true value. Hence, remote sensing retrieval method of soil moisture content in land parcel unit scale has shown good applicability in Hetao irrigation area. We converted the research unit into the scale of land parcel unit. Using the land parcel units with unified crops and soil attributes as the research units more complies with the characteristics of agricultural water areas, avoids the problems such as decomposition of mixed pixels and excessive dependence on high-resolution data caused by the research units of pixels, and doesn’t involve compromises in the spatial scale and simulating precision like the grid simulation. When the application needs are met, the production efficiency of products can also be improved at a certain degree.

Quantitative Retrieval of Soil Moisture Content in the Upper Reaches of the Minjiang River

Quantitative Retrieval of Soil Moisture Content in the Upper Reaches of the Minjiang River

A Novel Hybrid Method for the Correction of the Theoretical Model Inversion in Bio/Geophysical Parameter Estimation

This paper presents a novel hybrid method to the estimation of bio/geophysical parameters, which models and corrects deviations from correct target values when theoretical electromagnetic models are used for the inversion process. The proposed hybrid method integrates theoretical models with empirical observations associated to a few field reference samples. This is achieved based on two steps. In the first step, deviations between estimations obtained by a theoretical model and empirical observations are initially computed. Then, deviations associated to unlabeled samples (for which reference measures are not existing) are characterized based on two different strategies: 1) the global deviation bias strategy (which assumes that the deviations of samples are constant within the input space); and 2) the local deviation bias strategy (which assumes that the deviations of samples are variable within different portions of the input space). In the second step, the theoretical model estimates of unlabeled samples are corrected based on the estimated deviations. The experimental analysis carried out in the context of soil moisture content retrieval from microwave remotely sensed data confirms the effectiveness of the proposed hybrid estimation method.

From Point to Pixel Scale: An Upscaling Approach for In Situ Soil Moisture Measurements

Core Ideas Soil moisture can be remotely sensed with active radar. A new method allows spatial upscaling of in situ and remotely sensed data. A machine learning based method is used for soil moisture retrieval. Given the high spatial variability of soil moisture content (SMC), direct comparison and integration of observations from different sources and measurement scales is becoming a major challenge. We have developed a spatial upscaling method for SMC that enables the direct combination of in situ measurements and remotely sensed data. The approach is based on the fact that spatial soil moisture patterns are related to ancillary features like topography, land cover, and soil type. This study used in situ data from a well‐equipped research site in the northern Italian Alps. One of the main goals was to enable the use of these data for the validation of the NASA Soil Moisture Active Passive (SMAP) products. Dealing with medium‐ to coarse‐resolution satellite imagery, especially in mountain areas, requires compensating for different measurement scales. The study approach was assessed based on Envisat advanced synthetic aperture radar (ASAR) data, which were resampled to reproduce the spatial scale of the SMAP data. Results show that the representativeness of in situ data, with respect to the 3‐ by 3‐km SMAP pixel scale, can be improved significantly—direct correlation between SMC and satellite backscatter was improved from R = 0.05 to 0.28; furthermore, the error of the estimated SMC was improved from RMSE = 0.12 to 0.03 m3 m−3. This leads to more accurate reference data, which can help to improve the retrieval of SMC from remotely sensed imagery.

Estimation of Soil Moisture in Mountain Areas Using SVR Technique Applied to Multiscale Active Radar Images at C-Band

This paper presents an approach for retrieval of soil moisture content (SMC) from different satellite sensors with a focus on mountain areas. The novelties of the paper are: the extension of an already developed method to coarse resolution data (150 m) in mountain environment with high land heterogeneity, with only VV polarization and the proper selection of input features. During the result analysis, several algorithm characteristics were clearly identified: 1) the performances showed to be strongly related to input features such as topography and vegetation indices; 2) the algorithm needs a training phase; 3) the averaging window needs to be proper selected to take into account both the speckle noise and the characteristics of the area under investigation; and 4) the algorithm, being data driven, can be considered as site dependent. The experimental analysis is carried out on images acquired over the Südtirol/Alto Adige Province in Italy during 2010-2011 from the RADARSAT2 and Envisat ASAR in Wide Swath mode. SMC maps were compared with spatially distributed ground measurements, resulting in a root mean squared error (RMSE) value ranging from 0.045 to 0.07 m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> /m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> . Concerning the multiscale analysis, the results indicated that RADARSAT2 maps are able to detect the spatial heterogeneity and soil moisture dynamics at local scale, while ASAR WS SMC maps are able to identify mainly the two main classes of pasture and meadows. When these estimates are compared with SMC values from meteorological stations a RMSE value of 0.10 m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> /m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> for both satellites indicated a reduced capability to follow the temporal dynamics.

Comparison between SAR Soil Moisture Estimates and Hydrological Model Simulations over the Scrivia Test Site

In this paper, the results of a comparison between the soil moisture content (SMC) estimated from C-band SAR, the SMC simulated by a hydrological model, and the SMC measured on ground are presented. The study was carried out in an agricultural test site located in North-west Italy, in the Scrivia river basin. The hydrological model used for the simulations consists of a one-layer soil water balance model, which was found to be able to partially reproduce the soil moisture variability, retaining at the same time simplicity and effectiveness in describing the topsoil. SMC estimates were derived from the application of a retrieval algorithm, based on an Artificial Neural Network approach, to a time series of ENVISAT/ASAR images acquired over the Scrivia test site. The core of the algorithm was represented by a set of ANNs able to deal with the different SAR configurations in terms of polarizations and available ancillary data. In case of crop covered soils, the effect of vegetation was accounted for using NDVI information, or, if available, for the cross-polarized channel. The algorithm results showed some ability in retrieving SMC with RMSE generally &lt;0.04 m3/m3 and very low bias (i.e., &lt;0.01 m3/m3), except for the case of VV polarized SAR images: in this case, the obtained RMSE was somewhat higher than 0.04 m3/m3 (≤0.058 m3/m3). The algorithm was implemented within the framework of an ESA project concerning the development of an operative algorithm for the SMC retrieval from Sentinel-1 data. The algorithm should take into account the GMES requirements of SMC accuracy (≤5% in volume), spatial resolution (≤1 km) and timeliness (3 h from observation). The SMC estimated by the SAR algorithm, the SMC estimated by the hydrological model, and the SMC measured on ground were found to be in good agreement. The hydrological model simulations were performed at two soil depths: 30 and 5 cm and showed that the 30 cm simulations indicated, as expected, SMC values higher than the satellites estimates, with RMSE higher than 0.08 m3/m3. In contrast, in the 5-cm simulations, the agreement between hydrological simulations, satellite estimates and ground measurements could be considered satisfactory, at least in this preliminary comparison, showing a RMSE ranging from 0.054 m3/m3 to 0.051 m3/m3 for comparison with ground measurements and SAR estimates, respectively.

An algorithm for generating soil moisture and snow depth maps from microwave spaceborne radiometers: HydroAlgo

Abstract. A systematic and timely monitoring of land surface parameters that affect the hydrological cycle at local and global scales is of primary importance in obtaining a better understanding of geophysical processes and in managing environmental resources as well as natural disasters. Soil moisture and snow water equivalent are two quantities that play a major role in these applications. In this paper an algorithm for hydrological purposes (called hereinafter HydroAlgo), which is able to generate maps of snow depth (SD) and soil moisture content (SMC) from AMSR-E data, has been developed and implemented within the framework of the JAXA ADEOS-II/AMSR-E and GCOM/AMSR-2 programs, as well as of a project of the Italian Space Agency that is devoted to civil protection from floods and landslides. As auxiliary output, the algorithm also generates maps of vegetation biomass (VB). An initial phase of pre-processing includes the improvement of spatial resolution, as well as masking for urban areas, water bodies, and dense vegetation. The algorithm was then split into two branches, the first of which focused on the retrieval of SMC and the second, on SD. Both parameters were retrieved using Artificial Neural Network (ANN) methods. The algorithm was calibrated using a wide set of experimental data collected on three sites: Mongolia and Australia (for SMC), and Siberia (for SD), integrated with model simulations. These results were then validated by comparing the algorithm outputs with experimental data collected on two additional sites: a part of a watershed in Northern Italy, and a large portion of Scandinavia. An additional test of the algorithm was also performed on a large scale, and included sites characterized by differing climatic and meteorological conditions.

The AMSL LST algorithm validated for the Xinjiang Autonomous Region in China

The split-window land surface brightness temperature (LSTb) algorithm of Coll and Caselles (1994) is one of the first approaches to estimate LSTb applied for large surface areas. In this article, we describe a calibrated and validated version of the Coll and Caselles (1994) algorithm applied for the retrieval of land surface air temperature (LSTa) – equivalent to standard WMO (World Meteorological Organization) temperature measurements – for the province of Xinjiang (PR of China). Locally received MODIS (Moderate Resolution Imaging Spectroradiometer) imagery (Fukang receiving station) is used as the input data stream for the so-called AMSL (Aqua MODIS SWA LSTa) algorithm. The objective to develop this algorithm is that it is an input for a distributed hydrological model as well as a soil moisture content retrieval algorithm. In the Xinjiang province with an abundance of arid to semi-arid regions, a highly continental climate, irrigated crop fields and mountain ranges of 6000 m and higher, one typically deals with the spatio-temporally complex conditions, making a high-accuracy retrieval of LSTa quite a challenge. The calibration and validation of the AMSL LSTa product (LSTa,amsl) – using the Jackknife method – is performed using LSTa measurements (LSTa,tmb) from 49 meteorological stations managed by the Tarim Meteorological Bureau (TMB). These stations are distributed relatively homogeneously over the province. The TMB stations’ temperature data are split into 40 calibration LSTa,tmb data sets and 9 validation LSTa,tmb data sets. We can observe that when validated, the LSTa,amsl versus LSTa,tmb validation relationship elicits a high correlation, a slope very close to 1 and an intercept very close to 0. The validated LSTa,amsl estimates demonstrate an estimation accuracy of 0.5 K. The results presented in this article suggest that the LSTa,amsl product is suitable to estimate the land surface air temperature spatio-temporal fields for the arid and semi-arid regions of the Xinjiang province accurately.

Soil moisture content retrieval based on apparent thermal inertia for Xinjiang province in China

In the arid to semi-arid regions of north-western China, soil moisture is the main hydrological driver for vegetation growth. With the launch of the Moderate Resolution Imaging Spectroradiometer (MODIS), the local MODIS reception capacity and the strong pressure on water resources in the province, the detection and mapping of soil moisture content (SMC) has become a major issue for the regional water management authorities of the province.In this article, we apply the apparent thermal inertia approach to quantify SMC in the soils of the province of Xinjiang using locally received MODIS data. We report on SMC mapping for the entire province for the year 2005.For the estimation, diurnal land surface temperature (LST (Tσ)), LST difference (ΔTσ) and broadband albedo (α0) were applied to determine the space–time variability of SMC. The retrieval of SMC was based on the rationale that high apparent thermal inertia (Iτ) values correspond to high SMCs and low Iτ values correspond to the minimal ones. To enable the application of the technique, a new classification of soil texture was established based on an existing Chinese soil type classification.Typically only topsoil surface moisture content is retrieved with thermal remote sensing (RS). However, SMC retrieval for a 1 m soil profile was performed by applying a semi-empirical modelling approach. The model uses a two-layer water balance equation, and its SMC (θg) input is based on its linear relationship with the soil moisture saturation index (θsi) at time t.For validation purposes, the automatic weather station and time domain reflectometry (TDR) monitoring network included eight sites in the province, including the Mosuowan and Tianshan snow sites and the Turpan, Bayangburk, Kuerle, Yinsu, Alagan and Yiganbujima TDR sites for which data for the year of 2005 were acquired by the Xinjiang Meteorological Bureau (XMB), the Tarim Management Bureau (TMB) and the Xinjiang Institute for Ecology and Geography (XIEG).When time series of SMC determined by using Iτ are compared with the measurements at the different validation sites, regression curve slopes of the validation relationships vary between 0.499 and 0.922. The R2 values vary between 0.25 and 0.83. The minimum and maximum root mean square errors (RMSEs) are 0.001 and 0.028, respectively. Results suggest that apparent thermal inertia application is quite suitable for θg retrieval of a 1 m soil moisture profile in an arid to semi-arid region. The Aqua MODIS 10-day mean soil moisture product is proven to deliver quantitatively correct SMC imagery representing seasonal changes quite realistically.

Polarimetric RADARSAT-2 imagery for soil moisture retrieval in alpine areas

In this work, the polarimetric capability of RADARSAT-2 images is exploited in the aim of soil moisture content retrieval in Alpine meadows and pastures. Three feature extraction methods are investigated: the simple polarimetric intensity and phase processing, the H/A/α polarimetric decomposition, and the Independent Component Analysis (ICA). The features extracted according to these strategies were assessed for their capability to improve the soil moisture estimation by considering both quantitative performance on a set of reference samples and qualitative analysis of the corresponding output soil moisture content maps. The method proposed for the soil moisture estimation was based on the Support Vector Regression technique combined with an innovative multi-objective model selection strategy. The results indicated that the use of polarimetric features such as HH and HV channels improved the estimation of soil moisture content in the investigated mountain area, especially because the HV channel was able to disentangle the vegetation effect on the radar signal. From the preliminary results presented in this paper, the use of the H/A/α polarimetric decomposition and the ICA technique seem to not determine a significant improvement in the soil moisture estimation.

A new semi-empirical model for soil moisture content retrieval by ASAR and TM data in vegetation-covered areas

Active microwave and passive optical remote sensing data have demonstrated their respective advantages in inversion of surface soil moisture content. A new semi-empirical model is presented for soil moisture content retrieval in vegetation-covered areas, using ENVISAT-ASAR and LANDSAT-TM data collaboratively. Derivation of the algorithm is based on simplification of the Michigan Microwave Canopy Scattering Model (MIMICS). In the model, the ground surface is divided into a canopy layer and a soil layer, and empirical relationships simulated among vegetation water mass Wc, the backscatter coefficient σpq1, the bidirectional scattering coefficient σpq2 and the extinction coefficient τp. The key input parameters of the semi-empirical model are reduced to only the leaf area index (LAI), which can be easily inverted by the optical model PROSAIL, allowing coupling of the microwave and optical models to be achieved. Also, vegetation RMS height (Sveg) is introduced to correct for the radar-shadow effect caused by over-laying vegetation. Analysis of the parameter sensitivity of the semi-empirical model showed that when the regional Leaf Area Index is small (LAI⩽3), the model is more applicable. Soil moisture distribution in the study area was mapped using the semi-empirical model and field ground measurements used for model validation. This showed that, after correction of the radar-shadow effect, the average relative error (Er) between ground-measured and semi-empirical model-derived estimates of soil moisture decreased from 17.6% to 10.4%, while the RMS reduced from 0.055 to 0.031 g cm−3. The accuracy of soil moisture estimates from the semi-empirical model is much better than for the MIMICS model (Er = 22.7%, RMS = 0.068 g cm−3), showing that the semi-empirical model is efficient at obtaining regional surface soil moisture contents when LAI is small.

Comparison of vegetation water contents derived from shortwave-infrared and passive-microwave sensors over central Iowa

Retrieval of soil moisture content using the vertical and horizontal polarizations of multiple frequency bands on microwave sensors can provide an estimate of vegetation water content (VWC). Another approach is to use foliar-water indices based on the absorption at shortwave-infrared wavelengths by liquid water in the leaves to determine canopy water content, which is then related to VWC. An example of these indices is the normalized difference infrared index (NDII), which was found to be linearly related to canopy water content using various datasets, including data from the Soil Moisture Experiments 2002 and 2005 in central Iowa. Here we compared independent estimates of VWC from WindSat to Moderate resolution Imaging Spectroradiometer (MODIS) NDII over central Iowa from 2003 to 2005. Results showed that there was a linear relationship between the MODIS and WindSat estimates of VWC, although WindSat-retrieved VWC was greater than MODIS-retrieved VWC. WindSat and MODIS have different satellite overpass times and in most climates we expect VWC to vary over a day due to transpiration and plant water stress. However, a sensitivity analysis indicated that the diurnal variation of VWC should not have a significant effect on retrievals of VWC by either method. The results of this study indicated that soil moisture retrievals from microwave sensors may be improved using VWC from optical sensors determined by foliar-water indices and classifications of land cover type.

Errors in soil moisture content estimates induced by uncertainties in the effective soil dielectric constant

Soil moisture content (SMC) is an important variable in the hydrological cycle. Sensors have been developed to enable continuous monitoring of SMC (remote sensing from satellite and devices for in situ measurements), many of which are based on measurement of the soil dielectric properties from which SMC is inferred. Most SMC estimation techniques depend on the relationship between SMC and its dielectric properties, but the lack of a universal effective soil dielectric model introduces an additional source of error on SMC estimates. This work compares nine models of this relationship, and quantifies errors due to uncertainties in the relation between water content and dielectric properties. Errors of around 4% are reported with maximum figures at relatively low water content. Inconsistency between these models and the inconsistency in the effect of soil texture on SMC retrieval from satellite‐based sensors is also evaluated.

Remote sensing of vegetation water content from equivalent water thickness using satellite imagery

Vegetation water content (VWC) is one of the most important parameters for the successful retrieval of soil moisture content from microwave data. Normalized Difference Infrared Index (NDII) is a widely-used index to remotely sense Equivalent Water Thickness (EWT) of leaves and canopies; however, the amount of water in the foliage is a small part of total VWC. Sites of corn (Zea mays), soybean (Glycine max), and deciduous hardwood woodlands were sampled to estimate EWT and VWC during the Soil Moisture Experiment 2005 (SMEX05) near Ames, Iowa, USA. Using a time series of Landsat 5 Thematic Mapper, Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Advanced Wide Field Sensor (AWiFS) imagery, NDII was related to EWT with R2 of 0.85; there were no significant differences among land-cover types. Furthermore, EWT was linearly related to VWC with R2 of 0.87 for corn and 0.48 for soybeans, with a significantly larger slope for corn. The 2005 land-cover classification product from the USDA National Agricultural Statistics Service had an overall accuracy of 92% and was used to spatially distribute VWC over the landscape. SMEX05 VWC versus NDII regressions were compared with the regressions from the Soil Moisture Experiment 2002 (SMEX02), which was conducted in the same study area. No significant difference was found between years for corn (P=0.13), whereas there was a significant difference for soybean (P=0.04). Allometric relationships relate the size of one part of a plant to the sizes of other parts, and may be the result from the requirements of structural support or material transport. Relationships between NDII and VWC are indirect, NDII is related to canopy EWT, which in turn is allometrically related to VWC.

A new parameterization of the effective temperature for L band radiometry

An accurate value of the effective temperature is critical for soil emissivity retrieval, and hence soil moisture content retrieval, from passive microwave observations. Computation of the effective temperature needs fine profile measurements of soil temperature and soil moisture. The availability of a two year long data set of these surface variables from SMOSREX (Surface Monitoring Of the Soil Reservoir EXperiment) makes it possible to study the effective temperature at the seasonal to interannual scale. This study shows that present parameterizations do not adequately describe the seasonal variations in sensing depth. Therefore, a new parameterization is proposed that is stable at the seasonal to interannual scales while retaining simplicity.

Quantitative Retrieval of Soil Moisture Content and Surface Roughness From Multipolarized Radar Observations of Bare Soil Surfaces

A semiempirical polarimetric backscattering model for bare soil surfaces is inverted directly to retrieve both the volumetric soil moisture content M/sub v/ and the rms surface height s from multipolarized radar observations. The rms surface height s and the moisture content M/sub v/ can be read from inversion diagrams using the measurements of the cross-polarized backscattering coefficient /spl sigma//sub vh//sup 0/ and the copolarized ratio p(=/spl sigma//sub hh//sup 0///spl sigma//sub vv//sup 0/). Otherwise, the surface parameters can be estimated simply by solving two equations (/spl sigma//sub vh//sup 0/ and p) in two unknowns (M/sub v/ and s). The inversion technique has been applied to the polarimetric backscattering coefficients measured by ground-based polarimetric scatterometers and the Jet Propulsion Laboratory airborne synthetic aperture radar. A good agreement was observed between the values of surface parameters (the rms height s, roughness parameter ks, and the volumetric soil moisture content M/sub v/) estimated by the inversion technique and those measured in situ.

Measuring soil moisture with imaging radars

An empirical algorithm for the retrieval of soil moisture content and surface root mean square (RMS) height from remotely sensed radar data was developed using scatterometer data. The algorithm is optimized for bare surfaces and requires two copolarized channels at a frequency between 1.5 and 11 GHz. It gives best results for kh/spl les/2.5, /spl mu//sub /spl upsi///spl les/35%, and /spl theta//spl ges/30/spl deg/. Omitting the usually weaker hv-polarized returns makes the algorithm less sensitive to system cross-talk and system noise, simplifies the calibration process and adds robustness to the algorithm in the presence of vegetation. However, inversion results indicate that significant amounts of vegetation (NDVI>0.4) cause the algorithm to underestimate soil moisture and overestimate RMS height. A simple criteria based on the /spl sigma//sub hv//sup 0///spl sigma//sub vv//sup 0/ ratio is developed to select the areas where the inversion is not impaired by the vegetation. The inversion accuracy is assessed on the original scatterometer data sets but also on several SAR data sets by comparing the derived soil moisture values with in-situ measurements collected over a variety of scenes between 1991 and 1994. Both spaceborne (SIR-C) and airborne (AIRSAR) data are used in the test. Over this large sample of conditions, the RMS error in the soil moisture estimate is found to be less than 4.2% soil moisture.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>