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Soil organic matter (SOM) is rich in black soil area. It has a significant effect on soil health and luxuriant vegetation. The agricultural production activities can thus be guided by measurements of SOM at different periods. Most of previous work estimated SOM using reflectance spectra measurements on dried soil samples, to remove the effect of soil moisture content (SMC). However, it is time-consuming and almost conduct-prohibitive in the field. In this study, we utilized the continuous wavelet transform (CWT) on visible (VIS) and near infrared (NIR) spectra to test its effect on eliminating the SMC influence, and proposed to use the PCA-RF method coupled with CWT to predict SOM from the wet samples. Multi-scale coefficients can amplify the response of spectra to SOM in varying degrees, while improving the correlation of characteristic wavebands and minimizing the moisture interference on SOM specific wavelength. By analysing the multi-scale coefficients, we found that wavelengths ranging around the peaks of $580nm$ , $820nm$ , and especially the narrow region around $1400nm$ are highly correlated regions to SOM. Furthermore, the accuracy of SOM estimation models illustrated the effectiveness of the CWT. Results of the validation model using the dataset of wet samples on CWT scale 6 ( $R^{2}$ = 0.84, $Mse$ = 0.23%, and $RPD$ = 2.53) can be statistically equivalent to dataset of dried samples ( $R^{2}$ = 0.86, $Mse$ = 0.20%, and $RPD$ = 2.68). Combined with the PCA-RF method, SOM estimation can be perfectly performed with fewer features as input variables and has a great improved. The best prediction of validation model was on scale 6 with features extracted from 4 PCs, compared with the EPO-PLSR method ( $R^{2}$ = 0.85, $Mse$ = 0.25%, and $RPD$ = 2.54), the proposed method has a better result ( $R^{2}$ = 0.94, $Mse$ = 0.09%, and $RPD$ = 4.08).

Based on the photothermal conversion effect of doped fiber and the principle of Fiber Bragg Grating (FBG) temperature measurement, an all-fiber miniature soil moisture content sensor is developed.

The result of the comparison of satellite-based soil moisture products and in-situ data are presented. The data obtained by the SMOS and SMAP spacecraft were used. The in-situ soil moisture content was measured via a thermogravimetric approach. The absolute values of in-situ and satellite-based soil moisture have differed values on several samples. This may be due to both measurement errors and inaccuracies in the soil moisture retrieval algorithm.

Moisture is an important factor affecting crop growth in the field. It is of great practical significance to study the response of field crops to soil moisture and atmospheric relative humidity for preventing agricultural drought disaster in arid area of Northwest China. In this paper, use of farmland in Xinjiang from 2009 to 2020 the normalized difference vegetation index (NDVI) data set, the atmospheric relative humidity data set from May to October 2009-2020 of 61 meteorological stations in Xinjiang and the surface soil moisture content data set from May to October 2017 released by NASA were used to study interannual variability and correlation between NDVI, soil moisture, atmospheric mean relative humidity through correlation analysis, also the variation of NDVI, soil moisture and atmospheric relative humidity from May to October every year. The results showed that from 2009 to 2020, the interannual variation of NDVI and atmospheric relative humidity was large and both showed an upward trend, while the interannual variation of soil moisture was small and showed a slight upward trend, but it gradually decreased after 2016. NDVI was positively correlated with soil moisture and air relative humidity, but NDVI had a stronger correlation with soil moisture and was more affected by soil moisture. From May to August, the dependence of NDVI on soil water increased continuously, and began to decrease in September, and the correlation between NDVI and soil water was the least in October. The relationship between monthly NDVI and atmospheric relative humidity was consistent with soil moisture on the whole, but lower than that of soil moisture.

Cultivated strawberries are a popular fruit in the world. It is essential to understand the water management to improve the strawberry yield in the greenhouse. This work aims to build a cost-effective battery less sensor platform to clarify the relationship between root growth and water management by monitoring the soil moisture content of strawberry seedlings in pots in real-time.

Watering the plants is crucial. Soil moisture is undoubtedly a factor that must be considered about watering plants. Watering plants, according to soil moisture content, significantly affect the growth of these plants. This research will provide a solution to this. Automatic watering based on the moisture level of the soil will be developed. The system will also solve the problem experienced by apartment residents who use potted plants and have limited maintenance time with working hours. Watering the water is the key to developing plants with minimal sunlight, such as in apartments, using Arduino Uno and water sensors and soil moisture sensors that support the proposed tools included on the Internet of Things (IoT). The relay that is installed will be a watering indicator using the Fuzzy method. As a result, this developed tool can perform automatic watering at the right level, and plant owners will receive watering reports and plant conditions via notifications on their smartphones.

The increases of soil moisture content is a landslide disaster harbinger phenomenon. The attenuation constant of the radio wave increases with the soil moisture content. This study aims to realize the soil moisture content estimation by calculating the attenuation constant based on a measured received signal strength indicator (RSSI) of 920 MHz band radio waves. This paper shows the result of the preliminary experiment of the relationship between the attenuation constant of radio waves and soil moisture content.

In the framework of a project funded by the Italian Space Agency (Algoritmi-METEMW), experimental activities based on airborne multi-frequency microwave radiometric measurements and simultaneous ground-truth data collection have been carried out in Central Italy, in an agricultural area close to Florence. The aim of the project was the development of innovative algorithms for the estimate of hydrological parameters integrating active and passive microwave data. The retrieval algorithms are based on Artificial Neural Networks (ANN). IFAC multi-frequency microwave radiometers (at L, C, and X bands) have been installed on ultralight helicopters, which overflew the area in concomitance with SMAP and Sentinel-1 (S-1) image acquisitions. Comparisons of new data with past results confirmed the already stated relationships between microwave indices and soil and vegetation parameters. Soil moisture content (SMC) values estimated from L-band radiometric data are very close to those retrieved using the ANN algorithm. This result confirms the possibility of validating satellite algorithms with airborne microwave radiometers.

Sensing of soil water content is useful in many precision agriculture and resource management applications, particularly if the sensing technique permits frequent field-scale measurements at depth. To date, soil moisture sensing technologies have a trade-off between point based measurements at depth or rapid, remote measurements of water content near the surface. In this paper, we propose a non-contact thermoacoustic soil moisture sensing modality which could permit high-resolution, high-throughput mapping of water content at depth. Within, we develop an algorithm for reconstructing the speed-of-sound in soil, which is known to be highly correlated with the soil moisture content. Through verification in simulation, our algorithm demonstrates high fidelity – reconstructing speed-of-sound profiles that match well with the ground-truth.

Dielectric sensors operating in time and frequency domain can be used to determine soil moisture content. There is still a need for developing new sensors for determination of soil water content, in order to further improve measurement accuracy, lower the price or adapt the equipment for special applications. For example, many of the existing soil moisture probes do not allow accurate measurement of dielectric properties in a small volume because of their construction. The paper presents the evaluation of a seven-rod probe for an accurate determination of soil water content in a small sample volume. Firstly, digital simulations for sensitivity zone of the tested probe were performed. Next, the prototype probe was tested for two soils with various texture and moisture content in the range from air dry to near saturation. The values of dielectric permittivity (frequency domain) and apparent dielectric permittivity (time domain) were calculated from the measured $S_{11}$ parameters. The obtained data was compared with the reference Topp's equation. It was concluded that the tested probe is able to measure soil moisture in a small volume (about 8 cm 3 ) in the range of frequencies from 20 MHz to 200 MHz.

In order to reduce the working energy consumption and improve the working efficiency, this paper studies the effects of soil moisture content, soil compactness and soil adhesion on ploughing resistance. After field experiments, the effects of single factors on ploughing resistance are compared. It can be seen from the experiments that the surface compaction is higher than that of the bottom layer, With the increase of soil surface compactness, the ploughing resistance increases, and with the increase of adhesion, the ploughing resistance increases. According to the orthogonal experiment of three factors and three levels, the results show that moisture content is 17.48 % and soil compactness is 230.82 N/cm2 and the ploughing resistance is minimum when the adhesion force is 1.9 N. This study provides a theoretical reference for reducing energy consumption and improving efficiency in high-speed turnover plow operation.

Passive microwave remote sensing data and measured soil moisture content in AMSR-E (Advanced Microwave scanning Radiometer - Earth Observing System) had been adopted for hilly region of Central Sichuan province from 2006 to 2010. Using Tbv6.9, Tbv10.7, Tbv18.7 and Tbh36.5 as input factors, A GRNN neural network was established to retrieve soil water content, and the correlation coefficient between the simulated value and the spatial and temporal sequence of measured soil water was calculated. The results showed that the GRNN neural network's correlation coefficient inversion of soil moisture and the measured soil moisture is relatively higher than the one of soil moisture retrieved by the equation inversion of standard atmospheric transmission and the measured soil moisture. Thus using AMSR-E data based on GRNN neural network model to simulate the soil moisture is feasible.

This talk focuses on the innovative sensors using electromagnetic sensing principles and sensor systems suitable for various applications in agriculture, industry and defense. Several new materials and techniques have been applied to improve sensor characteristics and to increase sensor system performances. Our presented sensors include metamaterial-based sensors and waveguide sensors for both liquid and solid material characterizations, radio frequency sensor systems for plant and soil moisture content measurements, and ground penetrating radar (GPR) for classifying buried objects. The sensor structures and system designs for some of these sensors will be given in details. Also, some applications of our innovative sensor systems will be demonstrated.

The collaborative research project “ALGORITHMS” (20192021) between the Italian Space Agency (ASI) and the Institute of Applied Physics of the National Research Council of Italy (IFAC–CNR) aims to develop innovative algorithms to estimate the main hydrological parameters (e.g. soil moisture content, vegetation properties, snow water equivalent). The proposed algorithms combine Synthetic Aperture Radar (SAR), multispectral and hyperspectral satellite data with in-situ measurements. First results are presented based on retrieval analyses and surveys over the test sites in northern and central Italy, using exceptionally long, consistent and multi-polarized C- and X-band SAR time series from the Copernicus Sentinel-1 and ASI’s COSMO-SkyMed missions, as well as Copernicus Sentinel2 high resolution multi-spectral imagery.

GNSS-Reflectometry is a new type of remote sensing technology for soil moisture retrieval. The popularity of GNSS-R can be explained by its plenty of signal sources, all weathers and 24-hours conditions and so on. Up until now, various GNSS-R soil moisture retrieval methods have been reported. However, it still faces some problems due to the uncertainty of the experimental environment, complex modeling, and retrieval process. To investigate the behavior of GNSS-R soil moisture retrieval process, we utilized the Random Forest algorithm that is a popular ensemble machine learning method in computer subject, which just emerged now for the classification of remote sensing and geographic data. In this paper, the variable importance (VI) was evaluated. It showed that the input variable SNR is much more sensitive to the obtained permittivity with the importance of a minimum of 40%. Moreover, the variable theta showed the least importance (below 10%) in the GNSS-R soil moisture retrieval. In some extreme cases (changing the parameter of the algorithm), the importance of theta shows nearly zero means it is not sensitive to the obtained permittivity and soil moisture content. This paper provides some experimental insights into the behavior of the GNSS-R soil moisture retrieval procedure.

Drought will affect ecological environment and cause economic losses. Guangdong Province has been suffering from seasonal drought for a long time. Traditional drought assessment mainly relies on site-scale soil moisture content data or meteorological-related data. The spatial representativeness of this drought assessment method based on single point is poor. The high temporal and spatial resolution of remote sensing images is more conducive to our assessment of long-term and large-scale drought changes. Soil moisture and dryness can affect the growth of vegetation. In vegetation-covered areas, indicators reflecting vegetation status can also be used for drought assessment to a certain extent. We calculated Vegetation Condition Index (VCI) using an 8-day composite Moderate Resolution Imaging Spectroradiometer (MODIS) from 2003 to 2017. The spatial and temporal distribution of drought in Guangdong Province in recent years was analyzed based on VCI. The following main conclusions are obtained: 1) the results of remote sensing drought assessment by using VCI are consistent with the actual situation. The annual average VCI in Guangdong Province in 2005, 2006 and 2010 was lower than in other years. According to the meteorological data of Guangdong Province, severe drought occurred from late 2004 to early 2005, from late 2005 to early 2006 and from late 2010 to early 2011. 2) On a long-term trend, the drought situation tends to decrease year by year in Guangdong. Compared with other areas in Guangdong, drought occurrence in eastern Guangdong is less frequent and milder. 3) Spring drought and autumn-winter drought of different severity usually occur in Guangdong Province. Spring drought is usually the most serious in Western Guangdong and the lightest in eastern Guangdong.

We investigated the correlation between backscatter and soil moisture considering precipitation and crop effects using dual polarized Sentinel-1A data. The analyzed data consist of a time-series of 38 Sentinel-1A GRD images acquired on a 12-days repeat cycle from July 2017 to October 2018 over Sapone in Burkina Faso. We show that the temporal change of backscatter corresponds to the soil moisture content rather than crops.

Data assimilation is always considered as the reliable method to obtain space-time continuous soil moisture content, which is of great significance for regional water resource management. Effective observation data input plays an important role in maintaining steady output of assimilation error and improving the capacity of data assimilation. During the process of data assimilation with remote sensing for observation on regional scale, it is hard to guarantee the completeness of regional observation data. Sometimes, some parts of research region have no remote sensing observation, which leads that region of the modelling and data assimilation has no coordinate with region of remote sensing observation. In the part of region without remote sensing observation (lacking-observation region), data assimilation can’t be performed like in the part of region with remote sensing observation (observation region) because there no observation data to input but modelling data to the data assimilation system. And then it may influence the precision of data assimilation results in lacking-observation region. Thereby, we should promote the completeness of regional observation data and maybe fill the remote sensing images with some mathematical and statistical methods, one of which is that extend the differences(model errors) between modelling results and remote sensing observation data in the observation region to the lacking-observation region. This research used geostatistical methods to extend model errors from observation region to lacking-observation region. Then we calculate data assimilation results in lacking-observation region combined with land surface modelling results in lacking-observation region, thus to obtain the complete data assimilation results in whole research region. It’s shown that this method can truly improve data assimilation capacity in the overall research region. Meanwhile, this method can improve the data assimilation effectiveness in current period and also the subsequent period. It shows this method has active effects on maintaining the continuous and steady data assimilation result. Thus, this method can be an effective means to improve regional data assimilation capacity when lack of regional observation data.

Soil moisture content is an important hydrological parameter, which is of great significance for drought evaluation. In this paper, soil moisture simulated from three NLDAS-based Advanced Land Surface Models including Community Land Model version 4.0 (CLM4.0), Noah LSM with Multiphysics options (Noah-MP) and Catchment LSM-Fortuna 2.5(CLSM-F2.5) are used to study the trend in the past 35 years (1980-2014) over the conterminous US (CONUS). The water content of the soil surface layer (0-10cm) and root layer (0-100cm) is the focus of this study. The Mann-Kendall mutation test, and Theil-Sen approach are used to analyze the trend of soil moisture. The results from CLM4.0 are different from those from CLSM-F2.5 and Noah-MP. The change of surface soil water content is similar to that of root soil water content, and the surface soil water was affected by the meteorological conditions. The trend of soil moisture in autumn is quite different from that in the other three seasons over CONUS, especially in the central plain area.

Soil image samples were collected and calculated image gray level, the probability distribution of soil image gray level was analyzed. A mathematical model was constructed with the average gray-value of soil image as input and soil moisture as output. Measurement of soil moisture was calculated based on Monte Carlo method. Comparison of measured values and actual values of soil water content, the results show that: 1) Using Monte Carlo method measure soil water content that reduces the analysis of complex nonlinear model, reduces the calculation of some additional first-order or high-order sensitivity coefficients, and reduces the amount of calculation. 2) The accuracy of measurement value depends on Monte Carlo simulation times, and the more number of Monte Carlo simulations, the more accuracy of the soil moisture content measurement values, which makes the very small error between the measurement values and the actual values of soil moisture content.

In this paper, we explore the use of the Least Squares Support Vector Machines (LS-SVM) technique for modeling vegetation surface parameters inversion. The methodology has been applied to two sets of data to retrieve soil moisture content and vegetation water content from simulated microwave data. Based on the SMAP mission, we simulated the backscattering coefficients and the brightness temperature of vegetation surface by combining active and passive sensor at the frequencies of 1.26 and 1.41GHz, respectively, at incidence angles of 30, 40 degree. To assess the effectiveness of the method, the Gaussian distributed noise are added. A set of preliminary numerical results is provided in order to assess the performance of the proposed technique. This study demonstrates the great potential of LS-SVM in the estimation of soil moisture from microwave remotely sensed data.

In order to reduce damages caused by landslide disasters, it is important for residents to quickly take refuge from the disasters. Therefore, it is necessary to prepare the environment that the residents are able to decide appropriate timing for their evacuation. Our research group has proposed and developed the Grass-root Information Distribution System for monitoring landslide disasters using MANET (Mobile Ad-hoc NETwork) connected with mobile terminals, for the purpose of capturing landslide disasters information in the local area, delivering the information in the local MANET, and sharing the information among the local area residents. In our developed system, we have also constructed and operated the fixed type monitoring systems with the solar power supply in the dangerous places where the landslide disasters occurred in the past. By use of the infrared camera, the residents can monitor the dangerous places during night and day. To automatically detect landslide danger from the camera images, this study applied deep learning to classify the images to several danger levels. Beside, this study developed the landslide monitoring techniques using gas sensors and soil moisture content sensors. This report shows the overview of the monitoring techniques and observation results obtained from the camera images and the sensors.

The accurate forecast information of soil moisture of farmland can help improve the accuracy of irrigation forecasting and improve the efficiency of farmland irrigation water. In this paper, Based on the meteorological data of the past years, the function relationship between the reference of crop evapotranspiration in adjacent time periods was established to forecast the soil water content for the winter wheat experiment data of Huoquan Irrigation Experimental Station in Shanxi Province for 2015–2016. The forecasting result shows that the soil moisture prediction obtained high fitting precisions to the measured soil moisture data, with the correlation coefficient (R2) of 0.7841, the index of agreement of 0.92, the absolute prediction error of 0.812 and the relative prediction error of 0.051; and according to the standard 20%,15% and 10% of the allowable error, the accuracy rate of precision is 100%, 100% and 90% respectively which reaches the precision level of A. It is more reliable to use the method of predicting the soil water content by seting up the function of reference crop evapotranspiration in adjacent periods.

An ample amount of water quantity is vital for a plant to grow. People are unable to water the plants when they go on vacations or regularly fail to remember to water plants, which results into damaging the plants. Giving water to plants is one of the most significant practice and overall a labor demanding work. Automatic watering systems lessen the responsibility of watering the plants when there is a requirement. The two major part of watering method is to know when and how much to water the plants. In this paper, a system is implemented such that it will sense the soil moisture content of the plant and turn the motor ON to water the plant when needed. This system makes the plant more self-reliant by watering itself.

Shallow groundwater has a decisive impact on land surface temperature (LST) and soil moisture (SM). In the present paper relationship between shallow groundwater, SM and LST was studied. For this purpose, the groundwater level and soil moisture were measured in 59 and 39 locations respectively in the southwest of Iran, during June 2016, Simultaneous with the overpass of a Landsat 8 satellite from the study site. After necessary image processing the LST was retrieved from the Landsat image using the split window algorithm. Then relationship between retrieved LST and different field observation were studied. Results show that there is a significant relationship between the groundwater depth and SM with LST. These results indicate that shallow groundwater depth and soil moisture content could be estimated and mapped using the retrieved LST from the satellite imagery.

A novel decision level data fusion algorithm for soil moisture content estimation is proposed in this paper. Firstly, individual estimations are determined, respectively, from the inversion of the Integral Equation Model (IEM) for Sentinel-l and from the Temperature Vegetation Dryness Index (TVDI) for LANDSAT-8. Then, a feature level fusion of these methods is performed using an Artificial Neural Network (ANN). Finally, all estimations including the feature level fusion estimation are fused at the decision level using a novel weights based estimation. The area of interest for this study is Blackwell Farms, Guildford, United Kingdom and datasets were taken on 17/11/2017 for both Landsat-8 and Sentinel-1. Estimation from the proposed decision level fusion method produces a Root Mean Square Error RMSE (1.090%) which is lower than RMSE of the individual estimations of each sensor as well as that of the feature level fusion estimation.

Soil moisture content is an essential climate variable that is operationally delivered at low resolution (e.g. 36-9 km) by earth observation missions, such as ESA/SMOS, NASA/SMAP and EUMETSAT/ASCAT. However numerous land applications would benefit from the availability of soil moisture maps at higher resolution. For this reason, there is a large research effort to develop soil moisture products at higher resolution using, for instance, data acquired by the new ESA's Sentinel missions. The objective of this study is twofold. First, it presents the validation status of a pre-operational soil moisture product derived from Sentinel-1 at 1 km resolution. Second, it assesses the possibility of integrating Sentinel-2 data and additional ancillary information, such as parcel borders and high resolution soil texture maps, in order to obtain soil moisture maps at "field scale" resolution, i.e. similar to 0.1 km Case studies concerning agricultural sites located in Europe are presented.

Traditional agriculture is transforming into smart agriculture due to the prominence of the Internet of Things (IoT). Low-cost and low-power are the key factors to make any IoT network useful and acceptable to the farmers. In this paper, we have proposed a low-power, low-cost IoT network for smart agriculture. For monitoring the soil moisture content, we have used an in-house developed sensor. In the proposed network, the IITH mote is used as a sink and sensor node which provides low-power communication. We have evaluated our network with state of the art networks, proposed for agriculture monitoring. Power and cost are the two metrics used for evaluation of these networks. Results show that the proposed network consumes less power and has on average 83% prolonged lifetime at a lower cost compared to previously proposed network in the agriculture field.

An algorithm was developed using existing empirical model for determining soil moisture content of bare soil. Phased array L-Band synthetic aperture radar (PALSAR) data was first classified for different land cover such that; water, urban, tall vegetation, short vegetation and bare soil. The objective was to determine the soil pixel from study area by masking the other class. An inversion technique based on empirical model was derived and applied on bare soil pixel to determine the soil moisture. Finally a Graphical User Interface (GUI) was developed to retrieve soil moisture for bare soil area along with the classified map of the study area.

Pavement life span is often affected by the amount of voids in the base and subgrade soils, especially the soil moisture content. Ground Penetrating Radar (GPR) is one of the desirable techniques to indirectly measure the in-situ soil moisture content through electrical properties of soils. The inversion using transmission line matrix method from GPR data is applied for converting moisture content of the soils. Laboratory and field tests proved satisfactory results.

The freeze-thaw circulating water and soil are the two important factors of soil compression strength The study based on Different moisture content and freeze-thaw cycles, In order to study the influence of freeze-thaw cycles on the damage characteristics of loess with different moisture content of the loess. Take samples from Shanxi Yangqu No. 1 tunnel face as the research object. Do unconfined compressive strength test on condition of different moisture content and different freeze-thaw cycles, analysis the effect of moisture content and The freeze-thaw cycle on unsaturated loess strength damage, and has carried on the fitting formula. The results is same soil moisture content under the freeze-thaw cycle of experience, the uniaxial compressive strength decreased with the times of freezing and thawing cycles. Hope this paper can provide construction reference in loess distributed area of yangqu even in shanxi.

Mapping the soil water content in large area is an essential process in various fields such as, agriculture, ecology, civil engineering, as well as flood and water resource management. Most of the available methods to gauge soil moisture content are tedious and expensive. Water content of the soil will affect the soil electric properties. In GPR system, the soil electrical properties will affect the received reflected wave. Detailed information about Ground Penetrating Radar (GPR) reflected wave propagation is potentially useful for extracting the soil water content. Extraction model of Soil Water Content Information based on Least Square Method for GPR System is proposed in this paper. Theoretical and numerical studies on the proposed method have been performed and the results show that the proposed method is useful to extract information about volumetric water content and thickness of topsoil from the GPR data.

Soil Moisture Content (SMC) plays an important role in different environmental studies. In this study, to assess SMC, a method based on reflectance values in red and NIR bands from HJ-1B is introduced. This model divides SNIR-R into three separate regions based on the pixels NDVI values and fits three different regression equations to each region. All the achieved results showed that as NDVI values get larger, the accuracy of the proposed models decreases. Furthermore, in this study, the model was applied for assessment of SMC. It was concluded that satellite estimated SMC is highly correlated with field measured data.

Soil moisture measurements are important for disciplines such as hydrology, climatology and agriculture. GNSS Reflectometry (GNSS-R) technique has been demonstrated to be an effective tool to sense soil moisture content (SMC). Besides airborne observations, great efforts have also been made concerning ground-based measurements. However, those methods are either based on too many hypotheses or depend on specially-made receivers. This paper proposes a new interference method which can extract parameters related to SMC from the raw SNR data routinely collected by geodetic GPS receivers. During long-term observation, the surface soil moisture variations can be ideally tracked by the effective reflector height, which decreases along with the increasing SMC. This method can also be applied to the retrieval of the surface topography around the GPS stations.

In this paper, microwave imaging as a tool for estimation of soil moisture content is presented. Two experimental setups employing microwave antennas as transmitter and probing sensors are discussed.

In order to design holographic and impulse GPRs, as well as metal detector (MD) sensors for humanitarian demining in the Donbass conflict zone, we have compiled a listing of the AP and AT mines that have been confirmed in use in Donbass, and their dimensions and construction. Just as importantly, we have attempted, from existing literature (since it is an active war zone), to characterize the specific soils in this region in order to understand the physical property contrasts between mine casings/components and the soils in which they may be buried, as well as the propagation characteristics of radar signals in these soils. We conclude that MD should be generally effective for locating Donbass mines, and GPRs operating near the frequency of 2GHz should provide sufficient penetration for detection and, simultaneously, sufficient resolution to assist with discrimination of mines from clutter. As is common in GPR applications, the main limiting factor may be soil moisture content, so operations may be restricted to dry periods or seasons. Field measurements are scheduled to confirm the values from the literature.

Retrievals of surface soil moisture (SSM) from remotely sensed satellites have become important in agriculture, meteorology. However, any models have been presented for different satellite imagery, which are complex with data processing cumbersome and inconvenient. This paper introduces the one model of CBERS-02B and two models of Landsat TM image. Comparing and analyzing the accuracy of the models from CBERS-02B satellite imagery. The results showed that the overall accuracy of SSM from CBERS-02B's average soil moisture content reached 91.26%. It is higher than the accuracy of the other models of Landsat TM. These results demonstrate that the model can effectively calculate the SSMs for CBERS-02B satellite imagery.

There are few regulatory restrictions involving the use of fully autonomous unmanned aerial systems in unpopulated, farming areas of Australia. The combination of a fully autonomous aerial and ground systems would provide efficient and cost effective retrieval of soil and vegetation data for use in precision agriculture. The aerial system will survey the site and collect spectral imagery to analyse plant density, stress and nutrition. The ground sensors will collect soil moisture content readings throughout the site. The data from both systems will be collated at a central base station. The base station will also provide housing and interface with the aerial system.

We present a method able to retrieve the soil moisture content and the surface roughness of a moderately vegetated soil in a possibly hilly area, from a fully polarimetric SAR dataset. The estimation procedure is based on the prediction of the second order statistics of the scattered field provided by the Slope-Corrected Polarimetric Two-Scale Model combined with a two-component scattering model. The performance of the estimation method is assessed by comparing obtained retrieval results to “in situ” measurements. To this aim, data from AGRISAR 2006 campaign are employed. Keywords—SAR Polarimetry; soil moisture retrieval