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Accurate knowledge of soil moisture is critical for hydrological and agricultural applications such as agricultural irrigation management, drought characterization, and flood detection. Researchers have attempted to provide soil moisture using various methods and techniques. Traditionally, the amount of soil moisture was based on field measurements. On the other hand, remote sensing satellites have been widely used to provide continuous soil moisture measurements worldwide, encountering problems such as the lack of simultaneous spatial and temporal sampling rates and dependence on weather conditions. However, in recent decades, GNSS signals reflected from the Earth's surface (GNSS-R technique) have been increasingly used for soil moisture monitoring, due to the numerous advantages it offers. This paper aims to provide a comprehensive review of soil moisture retrieved by two space-based GNSS-R missions (TDS-1 and CYGNSS) to show the general past trends, gaps, and opportunities for soil moisture monitoring through GNSS-R observations. [ABSTRACT FROM AUTHOR]

Remote sensing based retrieved of soil moisture from low frequency passive microwave observations is preferred in different aspects such as better spatial coverage and more measurement compared to traditional ground-based measurements. However, due to coarse spatial resolution of the observations, their applications are limited in local to regional studies. This paper provides a framework using random forest regression to disaggregate the daily SMAP enhanced soil moisture (SPL3SMP_E) utilizing several ancillary data to overcome the spatial resolution limits and cloudiness effects. Ancillaries were acquired from sentinel-1 radar, MODIS monthly NDVI, land cover, topography, and surface soil properties. To validate the downscaled results with 1-km spatial resolution, the OZNET soil moisture measurements and sparse TDR ground soil moisture measurements were collected from Murrumbidgee catchment (Australia) and Firozabad catchment (Iran), respectively. Downscaled soil moisture product unbiased root-mean-square error (UnbRMSE) of ensemble learning demonstrated a range of 0.023 and –0.07 cm3/cm3. The produced downscaled soil moisture exhibited better local heterogeneity when compared to the coarse data and tracked the dynamics of temporal changes in soil moisture. Furthermore, cumulative distribution function (CDF) analysis showed good accuracy of downscaled soil moisture in grassland and cropland. Taken together, the findings supported usefulness of the suggested methodology in downscaling the medium- resolution SMAP soil moisture product. [ABSTRACT FROM AUTHOR]

Sea Surface Salinity (SSS) is a pre-eminent parameter in oceanology causing extreme climate and weather events such as floods and droughts. Therefore, knowledge discovery of SSS is increasingly becoming a fundamental problem in recent years. However, not only the inadequacy of in-situ SSS data in large ocean basins are hampering conduction of detailed analyses of patterning SSS variations but also conventional data-gathering techniques for SSS estimation are often too expensive and time-consuming to meet the amount of data required in SSS estimation studies. Conversely, the brand-new Soil Moisture Active-Passive (SMAP) mission could provide validated SSS data along with its main objective soil moisture retrieval. As a result, collecting a candidate data set of surface's parameters as inputs to SSS with the aid of Pearson correlation and Boruta feature selection techniques, this paper aims to study the predictive skills of machine learning approaches to estimate SMAP radiometer SSS in the Persian Gulf region from April 2015 to April 2017. Thus, four machine learning methods including Support Vector Regression (SVR), artificial neural network (ANN), random forest (RF) and gradient boosting machine (GBM) were adopted to model the SSS. Two approaches of GBM and RF provided scarcely equivalent predictions for both the calibration and validation data sets that were distinguishably substantiated by experimental results and simulations, nonetheless, slightly superior results were attained with the GBM model by correlation coefficient (r) = 0.734, root mean squared error (RMSE) = 0.906 and mean absolute error (MAE) = 0.627. The findings demonstrate promising SSS estimation from SMAP, which could provide a baseline to perceive the large-scale changes in SSS. [ABSTRACT FROM AUTHOR]

Soil Temperature (ST) data, obtained from either field works or satellite imagery, has frequently been studied for Soil Moisture (SM) estimation. However, a combination of ST data at different depths and soil surface temperature, i.e., Surface Radiometric Temperature (SRT) or Land Surface Temperature (LST), has not yet been well investigated for accurate SM prediction. In this study, an empirical model was first developed to estimate SM at 5 cm Depth (SM5D) over areas with no or sparse vegetation cover using the field SRT and field ST data at 5 cm Depth (ST5D). A Root Mean Square Error (RMSE) and a correlation coefficient (r) of 0.037 m3 m−3 and 0.8 were obtained using this model, respectively. Then, the SRT was substituted by the LST obtained from Landsat thermal bands and ST5D was estimated using the ST data collected at the nearest weather station to the study area by developing a regression equation. The second model demonstrated an RMSE and r of 0.035 m3 m−3 and 0.71, respectively. Overall, it was concluded that the proposed models had high potential for SM estimation using the ST data at different depths collected in the field or acquired by optical satellites. [ABSTRACT FROM AUTHOR]

In this paper, the applicability of the recently developed compact polarimetric decomposition and inversion algorithm to estimate soil moisture under low agricultural vegetation cover is investigated using simulated L-band compact polarimetric synthetic aperture radar (PolSAR) data. The surface scattering component is separated from the volume component of the vegetation through a model-based compact polarimetric decomposition (m-α) under the assumption of randomly orientated vegetation volume and reflection symmetry. The extracted surface scattering component is compared with two physics-based, low frequency surface scattering models such as extended Bragg (X-Bragg) and polarimetric two scale model (PTSM) in order to invert soil moisture for corresponding model- and data-derived surface scattering mechanism parameter αs. In addition to the parameter αs from m-α decomposition, the applicability of other scattering mechanism parameters, such as δ (relative phase) and χ (degree of circularity) from m-δ and m-χ decompositions are also investigated for their suitability to invert soil moisture. The algorithm is applied on a time series of simulated L-band compact polarimetric E-SAR data from the AgriSAR'2006 campaign over the Görmin test site in Northern Germany. The compact PolSAR-derived soil moisture is validated against in situ time-domain reflectometry (TDR) measurements. Including various growth stages of three different crop types, the estimated soil moisture values indicate an overall root mean square error (RMSE) of 9-12 and 9-15 vol.% using the X-Bragg model and the PTSM, respectively. The inversion rate for vegetation covered soils ranges from 5% to 40% including all phenological stages of the crops and different soil moisture conditions (range from 4 to 34 vol.%). The time series of soil moisture inversion results using compact polarimetry reveal that the developed algorithm is less sensitive to wet soils under growing agriculture crops due to less sensitivity of scattering mechanism parameters αs and χ for εs > 20. Thus, further developments and investigations are needed to invert soil moisture for compact PolSAR data with high inversion rates and consistently less RMSE (<5 vol.%) over the various crop growing season. [ABSTRACT FROM AUTHOR]

The retrieval of soil moisture from passive microwave remote-sensing data is presently one of the most effective methods for monitoring soil moisture. However, the spatial resolution of passive microwave soil moisture products is generally low; thus, existing soil moisture products should be downscaled in order to obtain more accurate soil moisture data. In this study, we explore the theoretical feasibility of applying the spectral downscaling method to the soil moisture in order to generate high spatial resolution soil moisture based on both Moderate Resolution Imaging Spectroradiometer and Fengyun-3B (FY3B) data. We analyse the spectral characteristics of soil moisture images covering the east-central of the Tibetan Plateau which have different spatial resolutions. The spectral analysis reveals that the spectral downscaling method is reliable in theory for downscaling soil moisture. So, we developed one spectral downscaling method for deriving the high spatial resolution (1 km) soil moister data from the FY3B data (25 km). Our results were compared with the ground truth measurements from 15 selected experimental days in 16 different sites. The average coefficient of determination (R2) of the spectral downscaling increased nearly doubled than that of the original FY3B soil moisture product. The spectral downscaled soil moister data were successfully applied to examine the water exchange between the land and atmosphere in the study regions. The spectral downscaling approach could be an efficient and effective method to improve the spatial resolution of current microwave soil moisture images. [ABSTRACT FROM AUTHOR]

This study aims to preliminarily validate two newly developed temporal parameter-based surface soil moisture (SSM) retrieval models, namely the mid-morning model and daytime model, using both microwave satellite soil moisture product and in situ SSM measurements over a well-organized soil moisture network named REd de MEDición de la HUmedad del Suelo (REMEDHUS) in Spain. Ground SSMmeasurements and geostationary satellite observations were primarily implemented to obtain the model coefficients for the two SSM retrieval models for each cloud-free day. Thesemodel coefficients were subsequently used to estimate SSM using the Meteosat Second Generation products over the study area. Preliminary verification using both a satellite product and in situ SSMmeasurements demonstrated that SSMvariation can be well detected by both SSM retrieval models. Specifically, a generally similar accuracy (coefficient of determination R²: 0.419-0.379, root mean square error: 0.046-0.051m³m-3, Bias: -0.020 to -0.025m³m-3) was found for the mid-morningmodel and the daytimemodel with the microwave missions based climate change initiative SSM product, respectively. Moreover, except for the comparable R² (0.614-0.675), a better accuracy (Bias: 0.032-0.044 m³m-3, RMSE: 0.043-0.050 m³m-3) are achieved for the daytime model and the mid-morning model with network SSM measurements, respectively. These results indicate that the daytime model exhibited generally comparable or better accuracy than that of the mid-morning model over the study area. This study has strengthened the feasibility of using multi-temporal information derived from the geostationary satellites to estimate SSM in future research. [ABSTRACT FROM AUTHOR]

The main objective of this study is to combine remote-sensing and artificial intelligence (AI) approaches to estimate surface soil moisture (SM) at 100 m spatial and daily temporal resolution. The two main variables used in the Triangle method, that is, land-surface temperature (LST) and vegetation cover, were downscaled and calculated at 100 m spatial resolution. LSTs were downscaled applying the Wavelet-Artificial Intelligence Fusion Approach (WAIFA) on Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat imageries. Vegetation fractions were also estimated at 100 m spatial resolution using linear spectral un-mixing and Wavelet–AI models. Vegetation indices (VIs) were replaced with the vegetation fractions obtained from sub-pixel classification in theTs–VI triangle space. The downscaled data were then used for calculating the evaporative fraction (EF), temperature-vegetation-dryness index (TVDI), vegetation temperature condition index (VTCI), and temperature-vegetation index (TVX) at 100 m spatial resolution. Thereafter, surface SM modelling was performed using a combination of Particle Swarm Optimization with Adaptive Neuro Fuzzy Inference System (PSO-ANFIS) and Support Vector Regression (PSO-SVR) modelling approaches. Results showed that the best input data set to estimate SM includes EF, TVDI,Ts,Fvegetation,Fsoil, temperature (T), precipitation at timet(Pt, Pt –1,Pt –2), and irrigation (I). It was also confirmed that PSO-SVR outperformed the PSO-ANFIS modelling approach and could estimate SM with a coefficient of determination (R2) of 0.93 and a root mean square error (RMSE) of 1.29 at 100 spatial resolution. Range of error was limited between −2.64% and 2.8%. It was also shown that the method proposed by Tang et al., (2010) improved the final SM estimations. [ABSTRACT FROM PUBLISHER]

The Soil Moisture Active Passive Validation Experiment 2012 was conducted as a pre-launch validation campaign for the Soil Moisture Active Passive mission over 6 weeks in June and July 2012. During this campaign, the Passive Active L-Band System (PALS) was flown at a low altitude, providing radar and radiometer measurements that were contained within a single agricultural field. The campaign domain consisted of 55 agricultural fields, where soil moisture was measured coincident to the PALS flight times and measurements of vegetation volumetric water content (VWC) and leaf area index (LAI) were measured weekly. The low-altitude flights allowed for the comparison between measured VWC and LAI for 11 fields to radar parameters derived from the radar backscatter. Only the correlation between the HV backscatter and the soybean VWC was considered strong (|r| > 0.7). All other correlations between the radar parameters and the VWC (or LAI) were moderate (0.3 < |r| < 0.7) or weak (|r|< 0.3). The established relationships between radar parameters and VWC were used in a forward radiation transfer model to estimate H-pol brightness temperature. It was found that the RMSE between the brightness temperatures estimated using the measured VWC was lowest when using the relationship between VWC and LAI (3.9 K for soybeans, 6.8 K for spring wheat, and 9.3 K when all crop data are combined). Despite a lower correlation, the RMSE associated with using the radar vegetation index relationship with VWC was less than when HV was used (7.9 K) for soybeans, which would result in an error in soil moisture estimation of just over 4%. The RMSEs for all other VWC and radar parameter relationships were greater than 10 K. [ABSTRACT FROM AUTHOR]

This study focuses on developing a new method of surface soil moisture estimation over wheat fields using Environmental Satellite Advanced Synthetic Aperture Radar (Envisat ASAR) and Landsat Thematic Mapper (TM) data. The Michigan Microwave Canopy Scattering (MIMICS) model was used to simulate wheat canopy backscattering coefficients from experiment plots at incidence angles of 23° (IS2) and 43.9° (IS7). Based on simulated data, the scattering characteristics of a wheat canopy were first investigated in order to derive an optimal configuration of polarization (HH) and incidence angle (IS2) for soil moisture estimation. Then a parametric model was developed to describe wheat canopy backscattering at the optimal configuration. In addition, direct backscattering and two-way transmissivity of wheat crowns were derived from the TM normalized difference vegetation index (NDVI); direct ground backscattering was derived from surface soil moisture and TM NDVI; and backscattering from double scattering was derived from total backscattering. A semi-empirical model for soil moisture estimation was derived from the parametric model. Coefficients in the semi-empirical model were obtained using a calibration approach based on measured soil moisture, ASAR, and TM data. A validation of the model was performed over the experimental area. In this study, the root mean square error (RMSE) for the estimated soil moisture was 0.041 m3 m−3, and the correlation coefficient between the measured and estimated soil moisture was 0.84. The experimental results indicate that the semi-empirical model could improve soil moisture estimation compared to an empirical model. [ABSTRACT FROM AUTHOR]

Acquiring information on the spatio-temporal variability of soil moisture is of key importance in extending our capability to understand the Earth system’s physical processes, and is also required in many practical applications. Earth observation (EO) provides a promising avenue to observe the distribution of soil moisture at different observational scales, with a number of products distributed at present operationally. Validation of such products at a range of climate and environmental conditions across continents is a fundamental step related to their practical use. Variousin situsoil moisture ground observational networks have been established globally providing suitable data for evaluating the accuracy of EO-based soil moisture products. This study aimed at evaluating the accuracy of soil moisture estimates provided from the Soil Moisture and Ocean Salinity Mission (SMOS) global operational product at test sites from the REMEDHUS International Soil Moisture Network (ISMN) in Spain. For this purpose, validated observations fromin situground observations acquired nearly concurrent to SMOS overpass were utilized. Overall, results showed a generally reasonable agreement between the SMOS product and thein situsoil moisture measurements in the 0–5 cm soil moisture layer (root mean square error (RMSE) = 0.116 m3m−3). An improvement in product accuracy for the overall comparison was shown when days of high radio frequency interference were filtered out (RMSE = 0.110 m3m−3). Seasonal analysis showed highest agreement during autumn, followed by summer, winter, and spring seasons. A systematic soil moisture underestimation was also found for the overall comparison and during the four seasons. Overall, the result provides supportive evidence of the potential value of this operational product for meso-scale studies and practical applications. [ABSTRACT FROM AUTHOR]

Due to large footprints of remotely sensed microwave brightness temperatures, accuracy of microwave observations in areas of large surface heterogeneity has always been a technological challenge. Microwave observations in areas dominated by waterbodies typically exhibit observed brightness temperature several tens of kelvins lower than areas having no surface water. The non-linearity between brightness temperature and other geophysical quantities such as soil moisture makes the accuracy of microwave observations a critical element for accurate estimation of these quantities. In retrieving soil moisture estimates, an error of 1 K in remotely sensed microwave brightness temperatures results in about 0.5–1% error in volumetric soil moisture. Large uncertainties in the observed brightness temperatures make such observations unusable in areas of large brightness temperature contrast. In this article, we discuss a deconvolution method to improve accuracy using the overlap in the adjacent microwave observations. We have shown that the method results in improved accuracy of 40% in brightness temperature estimation in regions of high brightness temperature contrast. [ABSTRACT FROM PUBLISHER]

The feasibility of measuring changes in surface soil moisture content with differential interferometric synthetic aperture radar (DInSAR) has received little attention in comparison with other active microwave techniques. In this study, multi-polarization C- and L-band DInSAR is explored as a potential tool for the measurement of changes in surface soil moisture in agricultural areas. Using 10 ascending phased array L-band SAR (PALSAR) scenes acquired by the Japanese Advanced Land Observing Satellite (ALOS) and 12 descending advanced SAR (ASAR) scenes acquired by the European ENVISAT satellite between July 2007 and November 2009, a series of 27 differential interferograms covering a common study area over southern Ireland were generated to investigate whether small-scale changes in phase are linked to measured soil moisture changes. Comparisons of observed mean surface displacement andin situmean soil moisture change show that C-band cross-polarization pairs displayed the highest correlation coefficients over both the barley (correlation coefficient,r = 0.51,p = 0.04)- and potato crop (r = 0.81,p = 0.003)-covered fields. Current results support the hypothesis that a soil moisture phase contribution exists within differential interferograms covering agricultural areas. [ABSTRACT FROM PUBLISHER]

Spatially distributed air temperature is desired for various scientific studies, including climatalogical, hydrological, agricultural, environmental and ecological studies. In this study, empirical models with regard to land cover and spatial scale were introduced and compared to estimate air temperature from satellite-derived land surface temperature and other environmental parameters. Aqua MODIS (Moderate Resolution Imaging Spectroradiometer) data and meteorological data obtained throughout 2005 in the Yangtze River Delta were adopted to develop statistical algorithms of air temperature. Four empirical regression models with different forms and different independent variables resulted in errors ranging from 2.20°C to 2.34°C. Considering the different relationships between air temperature and land surface temperature for different land types, these four models were evaluated and the most proper equation for each land-cover type was determined. The model containing these selected equations gave a slightly improved mean absolute error (MAE) of 2.18°C. Then the spatial scale effect of this empirical model was analysed with observed air temperature and spatially averaged land surface characteristics. The result shows that the estimation error of air temperature tends to be lower as spatial window size increases, suggesting that the model performances are improved by spatially averaging land surface characteristics. Comprehensively considering the accuracy and computational demand, 5 × 5 pixel size is the most favourable window size for estimating air temperature. The validation of the empirical model at 5 × 5 pixel size shows that it achieves an MAE of 1.98°C and an R 2 of 0.9215. This satisfactory result indicates that this approach is proper for estimating air temperature, and spatial window size is an important factor that should be considered when calculating air temperature. It is expected that better accuracy will be achieved if the different weights of pixels at different distances can be set according to high-density micro-meteorological data. [ABSTRACT FROM AUTHOR]

Retrieval of land surface variables and atmospheric variables over land from passive microwave remote-sensing data sets has been a challenge for many years. A lot of progress has been made in these quests such as using cloud-resolving models and data assimilation. Data assimilation allows the integration of observations (including observation errors) into imperfect models, thereby yielding more improved model forecasts. In this work, a coupled data assimilation framework (CDAF) is proposed and applied to predict the evolution of land surface and atmospheric conditions. CDAF comprises a coupling of two data assimilation schemes, namely a land data assimilation scheme (LDAS) and an ice microphysics data assimilation scheme (IMDAS). This system has been developed and evaluated using data for the Tibetan Plateau. In this framework, both low-frequency and high-frequency passive microwave brightness temperatures (T Bs) are assimilated. Low-frequency T Bs are assimilated in the LDAS subsystem and used to obtain land surface conditions, which are subsequently used as improved initial conditions together with high-frequency T Bs and assimilated in the IMDAS subsystem to obtain atmospheric conditions. The retrieved land surface variables and integrated atmospheric variables are demonstrated to show good agreement with observed land and atmosphere conditions such as those derived from point measurements of temperature and soil moisture (using the Soil Moisture and Temperature Measurement System (SMTMS)), sonde, Advanced Infrared Sounder (AIRS) and Global Precipitation Climatology Project (GPCP) products. The distribution of integrated cloud liquid water and cloud ice is shown to follow the observed cloud distribution over the study area. It is shown that by using IMDAS with modifications to account for precipitation and a good description of land surface emission, it is possible to obtain precipitation information of high fidelity over the land surface. Retrieved integrated water vapour using IMDAS shows correspondence with ‘corrected’ AIRS total precipitable water product. It is also shown that the relative humidity profile obtained from IMDAS agrees with the corresponding sonde profile. From the simulations, it is clear that by using the CDAF, there is marked improvement in the forecast conditions compared with the non-assimilation scenario for all of the variables considered. Comparisons with observed land surface conditions and inferences of atmosphere state from the Geostationally Operational Environmental Satellite Series 9 (GOES-9) InfraRed Channel 1 (IR1) brightness temperatures and the GCPC's cumulative daily precipitation indicate that the CDAF is able to generate reliable forecasts that agree with observation-derived products. [ABSTRACT FROM AUTHOR]

A data assimilation (DA) methodology that uses two state-of-the-art techniques, relevance vector machines (RVMs) and support vector machines (SVMs), is applied to retrieve surface (0–6 cm) soil moisture content (SMC) and SMC at a depth of 30 cm. RVMs and SVMs are known for their robustness, efficiency and sparseness and provide a statistically sound approach to solve inverse problems and thus to build statistical models. Here, we build a statistical model that produces acceptable estimations of SMC by using inexpensive and readily available data. The study area for this research is the Walnut Creek watershed in Ames, south-central Iowa, USA. The data were obtained from Soil Moisture Experiments 2002 (SMEX02) conducted at Ames, Iowa. The DA methodology combines remotely sensed inputs with field measurements, crop physiological characteristics, soil temperature, soil water-holding capacity and meteorological data to build a two-step model to estimate SMC using both techniques, i.e. RVMs and SVMs. First, the RVM is used to build a model that retrieves surface (0–6 cm) SMC. This information serves as a boundary condition for the second step of this model, which estimates SMC at a depth of 30 cm. An exactly similar routine is followed with an SVM for estimation of surface (0–6 cm) SMC and SMC at a depth of 30 cm. The results from the RVM and SVM models are compared and statistics show that RVMs perform better (root mean square error (RMSE) =  0.014 m3 m−3) when compared with SVMs (RMSE =  0.017 m3 m−3) with a reduced computational complexity and more suitable real-time implementation. Cross-validation techniques are used to optimize the model. Bootstrapping is used to check over/under-fitting and uncertainty in model estimates. Computations show good agreement with the actual SMC measurements with coefficients of determination (R 2) for RVM equal to 0.92 and for SVM equal to 0.88. Statistics indicate a good model generalization capability with indexes of agreement (IoAs) for RVM equal to 0.97 and for SVM equal to 0.96. [ABSTRACT FROM AUTHOR]

Detection of springtime near-surface soil thaw events in mid-latitude areas is important for understanding the near-surface earth surface system. Radar backscatter time series, such as QuikSCAT Ku-band, are valuable for surface thaw detection in permafrost areas, but their usefulness in mid-latitudes remains unverified. In order to validate the application of radar backscatter time series for surface thaw detection in mid-latitude areas, we propose a new multi-step method based on the significant signature of radar backscatter time series when springtime soil thaw events happen. The method mainly focuses on the estimated geographical boundary of thaw events and the detection of the primary thaw date; the duration of the freeze–thaw transition period is a lesser focus. The method is applied to soil freeze–thaw conditions in a mid-latitude area – northern China and Mongolia – including humid land and dryland, and achieves a reliability of R 2 = 0.678 ± 0.021 (P < 0.01) by comparison with ground truth measurements. The results indicate that elevation/temperature and soil moisture conditions are the key drivers of the timing of springtime soil thaw events. The success of our study demonstrates the potential for our method to be applied in mid-latitude areas, and widens the application potential of radar backscatter time series beyond high north and permafrost studies. [ABSTRACT FROM AUTHOR]

Soil moisture is an important indicator to describe soil conditions, and can also provide information on crop water stress and yield estimation. The combination of vegetation index (VI) and land surface temperature (LST) can provide useful information on estimation soil moisture status at regional scale. In this paper, the Huang-huai-hai (HHH) plain, an important food production area in China was selected as the study area. The potential of Temperature-Vegetation Dryness Index (TVDI) from Moderate Resolution Imaging Spectroradiometer (MODIS) data in assessing soil moisture was investigated in this region. The 16-day composite MODIS Vegetation Index product (MOD13A2) and 8-day composite MODIS temperature product (MOD11A2) were used to calculate the TVDI. Correlation and regression analysis was carried out to relate the TVDI against in-situ soil moisture measurements data during the main growth stages of winter wheat/summer maize. The results show that a significantly negative relationship exists between the TVDI and in-situ measurements at different soil depths, but the relationship at 10-20 cm depth (R2 = 0.43) is the closest. The spatial and temporal patterns in the TVDI were also analysed. The temporal evolution of the retrieved soil moisture was consistent with crop phenological development, and the spatial distribution of retrieved soil moisture accorded with the distribution of precipitation during the whole crop growing seasons. The TVDI index was shown to be feasible for monitoring the surface soil moisture dynamically during the crop growing seasons in the HHH plain. [ABSTRACT FROM AUTHOR]

Crop variables were estimated using ground-based multi-temporal, multi-angle and co-polarized scatterometer X-band data at 9.89 GHz. An outdoor crop-bed was prepared to observe the scatterometer response at VV and HH polarizations in the incidence angle range from 20° to 70°. Soil moisture (SM) and crop variables, including leaf area index (LAI), leaf water area index (LWAI) and plant height (PH), of a kidney bean crop at different stages of growth were measured. The aim of this study was to estimate the crop variables and to determine how the variables interact with microwaves. We found that LAI correlated better than LWAI with the microwave backscattering coefficient σ0 (at 9.89 GHz) and the correlation with PH was lower, at the most suitable incidence angle of 40°. The value of σ0 increased with an increase in LAI at both polarizations. The crop variables retrieved by inversion of the cloud model and a polarization-based model were compared with another model based on least squares optimization. The results are promising for the estimation of crop variables, particularly LAI. [ABSTRACT FROM AUTHOR]

In this letter, the performance of newly developed drought indices, the perpendicular drought index (PDI) and modified perpendicular drought index (MPDI), are further explored for regional surface dryness monitoring to provide clear guidance on appropriate implementation of these indices over different eco-systems through in-depth analysis of their advantages and constraints. Spatio-temporal patterns of surface drought derived by MODerate Resolution Imaging Spectroradiometer (MODIS)-based PDI and MPDI are compared against field-measured soil moisture (SM), rainfall, and regional hydrological conditions. Results indicate that there are significant negative correlations between the PDI, the MPDI, and mean 0-20 cm SM content and rainfall. The PDI and the MPDI provide similar results at the early stage of vegetation growth, but a greater agreement between the drought information extracted by the MPDI and field measurements is observed for vegetated surfaces where the PDI fails. Therefore, it is recommended that PDI be used for bare soil applications, since it does not require calculation of additional information such as the fraction of vegetation which may contain some uncertainties, but the MPDI should be used for vegetated regions. [ABSTRACT FROM AUTHOR]

Previous studies to determine the utility of satellite remote sensing for soil moisture monitoring have concentrated on field scale assessment. The European Remote Sensing satellites (ERS-1 and ERS-2) Synthetic Aperture Radar (SAR) instruments have a spatial resolution of less than 30 m in azimuth and 26 m in range, and potentially offer a considerably finer scale of soil moisture assessment. A series of soil moisture measurements were taken within a 15 ha bare field on the Essex (UK) coast at times to coincide with ERS-1 overpasses in the autumn and winter of 1995-96. This dataset has been used to investigate the ability of ERS-1 SAR backscatter response to detect spatial variation in soil moisture within the field. Analysis of variograms of SAR backscatter show that spatial variations in the ERS SAR data can be detected within the field and that spatial dependency increases when the soil is wet. Field measurements of soil moisture do not reflect these findings in quite the same way; during periods of low soil moisture content, the spatial variation of soil moisture cannot be easily characterized by the variograms. When the field is wet, spatial autocorrelation is more evident in the variograms. Regression analysis of paired observations of measured soil moisture and ERS SAR backscatter demonstrated that no significant relationship could be obtained for each day. However, when paired data for different dates were combined, a significant relationship between SAR backscatter and soil moisture could be obtained. The results suggest, therefore, that for this case study, soil moisture can be predicted at the field scale but not at the within-field/pixel scale. [ABSTRACT FROM AUTHOR]

Priroda microwave radiometer satellite observations were acquired and analysed in conjunction with soil moisture and other important ground and meteorological observations. Priroda was a remote sensing module on the MIR Space Station. The experiment was a component of the Southern Great Plains 1997 (SGP97) hydrology experiment. Problems with the MIR platform during the experiment resulted in a very limited set of data. Priroda sensor performance and some analyses of soil moisture conditions were conducted. These results provide important information for utilizing Priroda data and for soil moisture algorithm development. [ABSTRACT FROM AUTHOR]

This paper presents some additional theoretical and experimental results on the problem of estimating the soil depth to which soil moisture can be directly measured by VHF radiometers. The experimental work was implemented in August 1992 at the Research Farm of the Institute of Hydrotechnique and Amelioration, Sofia, Bulgaria. The soil thermal microwave emission was measured by an L-band Dicke-type radiometer at 1.65GHz at off-nadir angle from 20 to 60. It was found that the value obtained for the effective penetration depth depends on the definition used in the analysis: the definition of penetration depth is based on soil thermal emissivity or soil reflectivity. Further it was found that the depth to which the soil moisture can be directly measured exhibits a negligible dependence on polarization (horizontal, vertical) as well as on off-nadir observation angle. For the purpose of soil brightness temperature and soil reflectivity computing, a fast and robust method and algorithm were proposed which are based on Kong's coherent soil model. The experimental measurements were used to verify the model fitting to experimental conditions and results. [ABSTRACT FROM AUTHOR]

The scaling-multiscaling properties of passive microwave remotely sensed soil moisture images from the Washita '92 experiment are investigated. Analysis of moments of the soil moisture shows a log-log linearity of moments with spatial scale and the concavity of corresponding slopes with the order of moments. These properties suggest a deviation from simple scaling and the possible presence of multiscaling. By decomposing the soil moisture images into large-scale and small-scale fluctuation parts using wavelet transforms, it is shown that the small-scale fluctuation features obey a simple scaling law. The implications for such a decomposition and the coupling between the large-scale and small-scale features are discussed. [ABSTRACT FROM AUTHOR]