Soil Moisture Measurements Research Articles

Role of Infiltration on Land–Atmosphere Feedbacks in Central Europe: Fully Coupled WRF-Hydro Simulations Evaluated with Cosmic-Ray Neutron Soil Moisture Measurements

Abstract The skill of regional climate models partly relies on their ability to represent land–atmosphere feedbacks in a realistic manner, through coupling with a land surface model. However, these models often suffer from insufficient or erroneous information on soil hydraulic parameters. In this study, the fully coupled land–atmosphere model WRF-Hydro driven with ERA5 reanalysis is employed to reproduce the regional climate over central Europe with a horizontal resolution of 4 km for the period 2017–20. Simulated soil moisture is compared with data from cosmic-ray neutron sensors (CRNSs) at three terrestrial environmental observatories. Soil hydraulic parameters from continental and global digital soil datasets (SoilGrids and EU-SoilHydroGrids), together with Campbell and van Genuchten–Mualem retention curve equations, are used to assess the role of infiltration on modeled land–atmosphere feedbacks. The percolation parameter is calibrated to better capture observed discharge amounts in the observatories. WRF-Hydro with Campbell and SoilGrids gives the lowest mean annual temperature and mean annual precipitation differences compared to the E-OBS product from European Climate Assessment and Dataset, which is achieved by reducing soil moisture in the rootzone, increasing air temperature, and decreasing precipitation through a positive soil moisture–precipitation feedback process. WRF-Hydro with van Genuchten–Mualem and EU-SoilHydroGrids best reproduces CRNS soil moisture daily variations, despite enhanced positive biases that generate a larger proportion of convective precipitation favored over wet soils and spurious discharge peaks. This study demonstrates the importance of infiltration processes to realistically reproduce land–atmosphere feedbacks. Significance Statement The ability to correctly reproduce the feedbacks between land and atmosphere is crucial for a regional climate model. This study shows that the nature of these land–atmosphere feedbacks is partly related to the description of soil infiltration, which potentially helps to improve regional climate modeling results. An updated method to disentangle the proportion of convective precipitation being favored over wet, dry, and mixed soils is provided, which sheds new light on the soil moisture–precipitation feedback mechanism.

A Digital Twin Approach for Soil Moisture Measurement with Physically Based Rendering Simulations and Machine Learning

A Digital Twin Approach for Soil Moisture Measurement with Physically Based Rendering Simulations and Machine Learning

Quantifying the potential of using Soil Moisture Active Passive (SMAP) soil moisture variability to predict subsurface water dynamics

Abstract. Advances in satellite Earth observation have opened up new opportunities for global monitoring of soil moisture (SM) at fine to medium resolution, but satellite remote sensing can only measure the near-surface soil moisture (SSM). As such, it is critically important to examine the potential of satellite SSM measurements to derive the water resource variations in deeper subsurface. This study compares the SSM variability captured by the Soil Moisture Active and Passive (SMAP) satellite and the Soil Water Index (SWI) derived from SMAP SSM with subsurface SM and groundwater (GW) dynamics simulated by a high-resolution fully integrated surface water–groundwater model over an agriculturally dominated watershed in eastern Canada across two spatial scales, namely SMAP product grid (9 km) and watershed (∼4000 km2). SMAP measurements compare well with the hydrologic simulations in terms of SSM variability at both scales. Simulated subsurface SM and GW storage show lagged and smoother characteristics relative to SMAP SSM variability with an optimal delay of ∼1 d for the 25–50 cm SM, ∼6 d for the 50–100 cm SM, and ∼11 d for the GW storage for both scales. Modeled subsurface SM dynamics agree well with the SWI derived from SMAP SSM using the classic characteristic time lengths (15 d for the 0–25 cm layer and 20 d for the 0–100 cm layer). The simulated GW storage showed a slightly delayed variation relative to the derived SWI. The quantified optimal characteristic time length Topt for SWI estimation (by matching the variations in SMAP-derived SWI and modeled root zone SM) is comparable to Topt obtained in other agricultural regions around the world. This work demonstrates SMAP SM measurements as a potentially useful aid when predicting root zone SM and GW dynamics and validating fully integrated hydrologic models across different spatial scales. This study also provides insights into the dynamics of near-surface–subsurface water interaction and the capabilities and approaches of satellite-based SM monitoring and high-resolution fully integrated hydrologic modeling.

A device for measuring soil moisture in the field

RELEVANCE. When building roads, preparing foundations for high–rise buildings, information is needed about such an important parameter as the density of the soil skeleton – the ratio of the mass of soil particles to the volume of the sample of an undisturbed structure. The precise determination of this parameter is an important task, since this parameter has a nonlinear dependence on humidity. Consequently, in a narrow range of humidity changes, the density of the soil skeleton becomes maximum, which makes such a soil the most favorable for construction work. PURPOSE. To develop an autonomous device for measuring soil moisture in the field. METHODS. The dielkometric method was used to develop a device for measuring soil moisture. This is an indirect method for measuring the humidity of substances based on the dependence of the dielectric constant of these substances on their humidity. RESULTS. An autonomous device for determining soil moisture using the dielkometric method has been developed, designed for use in the field, which significantly speeds up the process of analyzing and processing measurement results, and, consequently, the process of processing information about the state of the soil. During the project, experiments were carried out to measure capacity depending on humidity and temperature at various frequencies from 100 Hz to 100 kHz in various types of soils. CONCLUSION. A device for determining soil moisture was developed and a method for determining the moisture and density of the soil skeleton by the dielkometric method was proposed, preliminary experiments were conducted. However, during the experiments, a temperature dependence was found for low frequencies, so additional temperature calibration is necessary.

Wi-Fi signal for soil moisture sensing

Measuring soil moisture is essential in various scientific and engineering disciplines. Over recent decades, numerous technologies have been employed for in situ monitoring of soil moisture. Currently, dielectric-based sensors are the most popular measurement technology and provide acceptable accuracy for various measurement purposes. However, these sensors are relatively expensive, and alternative technologies, which are cheaper, are not accurate enough for scientific purposes. Recently, the idea of using a Wi-Fi signal to measure soil moisture has been presented. Theoretically, the use of Wi-Fi technology in soil sensing follows the same concepts as the previous dielectric sensors. The main advantage of Wi-Fi technology is the possibility of providing a relatively accurate and cost-effective solution for soil moisture measurement. In this work, we try to investigate the possibility of using Wi-Fi signal characteristics for soil sensing. Therefore, a series of small-scale laboratory and field experiments were conducted to test the concept. The results of these experiments were promising, showing strong linear relationships between Wi-Fi signal properties (received signal strength indicator, RSSI) and soil water content, with R2 values ranging between 0.92 and 0.99, indicating a strong correlation. They also illustrate the possibility of using this technology to develop an inexpensive and accurate device for measuring soil moisture. However, observations from the experiments also point to problematic factors involving the hardware and software used in the measurements. It is important to control these factors in the next steps to develop a reliable measurement device.

Quantifying the Impact of Soil Moisture Sensor Measurements in Determining Green Stormwater Infrastructure Performance

The ability to track moisture content using soil moisture sensors in green stormwater infrastructure (GSI) systems allows us to understand the system’s water management capacity and recovery. Soil moisture sensors have been used to quantify infiltration and evapotranspiration in GSI practices both preceding, during, and following storm events. Although useful, soil-specific calibration is often needed for soil moisture sensors, as small measurement variations can result in misinterpretation of the water budget and associated GSI performance. The purpose of this research is to quantify the uncertainties that cause discrepancies between default (factory general) sensor soil moisture measurements versus calibrated sensor soil moisture measurements within a subsurface layer of GSI systems. The study uses time domain reflectometry soil moisture sensors based on the ambient soil’s dielectric properties under different soil setups in the laboratory and field. The default ‘loam’ calibration was compared to soil-specific (loamy sand) calibrations developed based on laboratory and GSI field data. The soil-specific calibration equations used a correlation between dielectric properties (real dielectric: εr, and apparent dielectric: Ka) and the volumetric water content from gravimetric samples. A paired t-test was conducted to understand any statistical significance within the datasets. Between laboratory and field calibrations, it was found that field calibration was preferred, as there was less variation in the factory general soil moisture reading compared to gravimetric soil moisture tests. Real dielectric permittivity (εr) and apparent permittivity (Ka) were explored as calibration options and were found to have very similar calibrations, with the largest differences at saturation. The εr produced a 6% difference while the Ka calibration produced a 3% difference in soil moisture measurement at saturation. Ka was chosen over εr as it provided an adequate representation of the soil and is more widely used in soil sensor technology. With the implemented field calibration, the average desaturation time of the GSI was faster by an hour, and the recovery time was quicker by a day. GSI recovery typically takes place within 1–4 days, such that an extension of a day in recovery could result in the conclusion that the system is underperforming, rather than it being the result of a limitation of the soil moisture sensors’ default calibrations.

Spatial extension of soil water regime variables derived from soil moisture values using geomorphological variables in Hungary

Accurate measurement and spatial extension of soil properties are essential in geoinformatics and precision agriculture for effective resource management, particularly irrigation planning. This study addresses the challenge of extending soil moisture data and related soil water regime variables in heterogeneous agricultural landscapes by integrating geomorphological variables (GVs) derived from high-resolution digital elevation models (DEM). In digital soil mapping, machine learning and geostatistical models often struggle with validation due to data scarcity and variability across space through many geographical regions that come from the point readings of soil properties. A different approach was developed in the form of a new methodology combining two hourly Sentek soil moisture measurements from the topsoil with DEM-derived GVs to model and extend soil water regime variables. The research was conducted on an agricultural field in a hilly area with diverse geomorphological variability. The model’s performance was validated using cross-validation techniques. The monitoring and spatial extension results indicate that GVs enhance the spatial prediction of soil moisture, capturing periodic fluctuations in the upper soil layer more effectively by using in-situ, time series soil moisture sensor readings rather than traditional, on field, one time reading approaches. We observed that certain GVs, such as the slope, both type of curvatures and the convergence, were strong predictors of soil moisture variation, enabling the model to produce more accurate irrigation recommendations for agricultural areas with similar geomorphological areas. One of the soil water regime variables was validated during the preliminary validation with mixed results. The main issue was coming from the field use and spatial scarcity of the measurements. Our approach not only provides a different method for spatially extending the current soil water regime data but also offers a framework for improving irrigation decision-making with the help of other value rates and limit related soil regime variables derived from the time series readings from the soil moisture sensors. With its variables, the model allows for forecasts of soil moisture changes, which can inform better irrigation scheduling and water resource management, all based on data from the soil monitoring sensor system.

Hydrological Modeling of Stream Drainage Basins: A Case Study on the Magyaregregy Experimental Catchment in Hungary

This paper presents the field measurements, observations, and numerical simulations conducted for a case study of the Magyaregregy experimental catchment in Hungary. Field measurements included the determination of surface runoff and infiltration intensity on an experimental plot and hydrograph measurements that assessed the ratio between surface and subsurface runoff. Soil moisture measurements both during the infiltration experiments and throughout the experimental catchments gave valuable information regarding this critical parameter. A digital terrain model and the aforementioned field measurements allowed the establishment of a numerical model using HEC-HMS 4.3 for the Magyaregregy experimental catchment process. Although the calibration process was straightforward, considerable difficulties were encountered during the model validation. While the calibration procedure gave appropriate numerical values for most calibrated parameters, it did not provide the proper initial conditions. As a possible solution, the validation period was preceded by a simulation of a relatively long time duration to gain appropriate initial conditions. Finally, the hydrological model’s validation reproduced the measured base flow, as well as the maximum values of discharges. Furthermore, the use of composite-corrected radar data for precipitation values proved to be somewhat unreliable. This supports the principle that data from remote sensing (e.g., radar data) should be used with the utmost care and deliberation as input for hydrological models.

Resource-Efficient FPGA Architecture for Real-Time RFI Mitigation in Interferometric Radiometers.

Interferometric radiometers operating at L-band, such as ESA's SMOS mission, enable crucial Earth observations providing high-resolution measurements of soil moisture, ocean salinity, and other geophysical parameters. However, the increasing electromagnetic spectrum utilization has led to significant Radio Frequency Interference (RFI) challenges, particularly critical given the sensors' fine temperature resolution requirements of less than 1 K. This work presents the hardware implementation of an advanced RFI detection and mitigation algorithm specifically designed for interferometric radiometers, targeting future L-band missions. The implementation processes 1-bit quantized signals at 57.69375 MHz from multiple receivers, employing time-frequency analysis and polarimetric detection techniques while optimizing Field Programmable Gate Array (FPGA) resource utilization. Novel optimization strategies include overclocked processing cores operating at 230.775 MHz, efficient resource sharing through operation serialization, and strategic memory management. The system achieves real-time processing capabilities while maintaining detection probabilities above 63% with false alarm rates below 1% for typical interference scenarios. Performance validation using synthetic datasets demonstrates robust operation across various RFI conditions, making this implementation suitable as part of the RFI detection and mitigation efforts for future interferometric radiometer missions beyond SMOS.

Landscape-level variation in spring leaf phenology is driven by precipitation seasonality in the Mexican red oak Quercus castanea

Abstract Background and Aims Water availability is one of the essential factors that determine the distribution of plant species, as well as their ecological strategies. The study of leaf phenology, in conjunction with other leaf traits of an ecological nature, such as functional traits, makes it possible to determine the life history strategies of plant species and their variation along environmental gradients, which in turn influences the demographic rates of populations. In the present study, we analyzed the effect of water availability at the landscape scale on spring leaf phenology and foliar traits such as leaf mass per area (LMA) and leaf thickness (LT) in the oak species Quercus castanea from a tropical latitude in central-western Mexico. Methods Six sites were selected in the Cuitzeo basin, Michoacán, across a water availability gradient, ranging from 766 mm to 1145 mm of mean annual precipitation. Leaf samples were collected from 10 adult trees at each site and LT and LMA were estimated. Leaf phenology was monitored for each tree every two weeks between March and July for two consecutive years, 2021 and 2022, alongside soil moisture measurements. Temperature and precipitation variables for the two study years were obtained from meteorological stations and long-term bioclimatic variables from the Worldclim database. Key Results Significant spatial and temporal variation in leaf phenology was observed. Earlier leaf development and shorter development times were observed with increased soil moisture in March and April, and with higher precipitation in October of the previous year. Also, sites with long-term higher precipitation seasonality and with lower precipitation of the warmest quarter showed longer development times. A positive association between development times and leaf thickness was also observed. Conclusions : Quercus castanea shows a brevideciduous leaf phenology but with significant variation among populations, reflecting spatiotemporal mosaics of environmental and genetic variation and in covariation with leaf functional traits such as leaf thickness.

Interpretable Quality Control of Sparsely Distributed Environmental Sensor Networks Using Graph Neural Networks

Abstract Environmental sensor networks play a crucial role in monitoring key parameters essential for understanding Earth’s systems. To ensure the reliability and accuracy of collected data, effective quality control (QC) measures are essential. Conventional QC methods struggle to handle the complexity of environmental data. Conversely, advanced techniques such as neural networks, are typically not designed to process data from sensor networks with irregular spatial distribution. In this study, we focus on anomaly detection in environmental sensor networks using graph neural networks, which can represent sensor network structures as graphs. We investigate its performance on two datasets with distinct dynamics and resolution: commercial microwave link (CML) signal levels used for rainfall estimation and SoilNet soil moisture measurements. To evaluate the benefits of incorporating neighboring sensor information for anomaly detection, we compare two models: graph convolution network (GCN) and a graph-less baseline: long short-term memory (LSTM). Our robust evaluation through 5-fold cross-validation demonstrates the superiority of the GCN models. For CML, the mean area under receiver operating characteristic curve for the GCN was 0.941 compared to 0.885 for the baseline-LSTM, and for SoilNet, it was 0.858 for GCN and 0.816 for the baseline-LSTM. Visual inspection of CML time series revealed that the GCN proficiently classified anomalies and remained resilient against rain-induced events often misidentified by the baseline-LSTM. However, for SoilNet, the advantage of GCN was less pronounced, likely due to inconsistent and less precise labeling. Through interpretable model analysis, we demonstrate how feature attributions vividly illustrate the significance of neighboring sensor data, particularly in distinguishing between anomalies and expected changes in signal level in the time series.

Health monitoring: a pathway to improved sustainable drainage system maintenance

ABSTRACT Sustainable drainage systems (SuDSs) have gained popularity, however, guidance for monitoring and maintaining SuDS components remains limited, especially considering their long-term performance in changing environmental conditions. This study begins to address this gap by developing a proof-of-concept model for monitoring infiltration trench (IT) ‘health’. In this study, ‘health’ refers to the physical condition, performance, and overall well-being of the IT. A physical model, constructed following UK SuDS manual guidelines, serves as a testing ground to evaluate IT performance under various maintenance scenarios. The physical model was instrumented to deliver a system for health evaluation. By identifying distinct maintenance parameters, we assess the IT health in terms of infiltration and attenuation/storage volume. The two parameters of ‘leaf build-up’ (surface condition) and ‘sediment build-up’ (subsurface condition) were used as indicative health parameters. The IT instrumentation was able to quantify the adverse effect of sediment build-up on both storage volume and infiltration time. After the sediment was added, the average peak attenuation volume decreased by 40% under a low flow rate and by 20% under a high flow rate. Additionally, the average infiltration time dropped by 26% for both high and low flow rates. Results suggest soil moisture measurements can indicate when IT maintenance is needed.

Experimental Design: Implementation of IoT-based drip irrigation to enhance the productivity of Cilembu sweet potato (Ipomoea batatas) cultivation

Effective irrigation methods are crucial for meeting plant water needs while preventing excessive evaporation, soil dryness, and water shortages. Conventional irrigation methods often result in inefficient water distribution, leading to a reduced agricultural productivity. This study aimed to enhance water use efficiency and improve crop yield by employing Internet of Things (IoT)-based drip irrigation systems. The system regulates water supply and monitor environmental conditions using low-power sensor nodes that measure soil moisture, air temperature, humidity, and light intensity. Field experiments were conducted to ascertain optimal water requirements by adjusting irrigation durations to 15, 30, 60, and 120 minutes and modifying water application based on soil moisture levels below 70%. Irrigation scheduling was performed at 6 am and 6 pm. The implementation of IoT-based drip irrigation in Cilembu sweet potato field resulted in a production yield of 44 tons/ha, marking a 45% productivity compared to conventional irrigation methods. Furthermore, water savings of 65% were achieved through the application of IoT-controlled drip irrigation. These results demonstrate the potential of IoT-based systems in optimizing water use and enhancing agricultural productivity, making it a valuable approach for sustainable farming practices.

Forest plant indicator values for moisture reflect atmospheric vapour pressure deficit rather than soil water content.

Soil moisture shapes ecological patterns and processes, but it is difficult to continuously measure soil moisture variability across the landscape. To overcome these limitations, soil moisture is often bioindicated using community-weighted means of the Ellenberg indicator values of vascular plant species. However, the ecology and distribution of plant species reflect soil water supply as well as atmospheric water demand. Therefore, we hypothesized that Ellenberg moisture values can also reflect atmospheric water demand expressed as a vapour pressure deficit (VPD). To test this hypothesis, we disentangled the relationships among soil water content, atmospheric vapour pressure deficit, and Ellenberg moisture values in the understory plant communities of temperate broadleaved forests in central Europe. Ellenberg moisture values reflected atmospheric VPD rather than soil water content consistently across local, landscape, and regional spatial scales, regardless of vegetation plot size, depth as well as method of soil moisture measurement. Using in situ microclimate measurements, we discovered that forest plant indicator values for moisture reflect an atmospheric VPD rather than soil water content. Many ecological patterns and processes correlated with Ellenberg moisture values and previously attributed to soil water supply are thus more likely driven by atmospheric water demand.

An IoT-based Intelligent Irrigation and Weather Forecasting System

Introduction:: The most crucial ingredient in agriculture is water. The amount of water that plants require must be provided to them. However, growers alternate between giving their plants more water than they truly need and giving them less and partly because they become overwatered due to meteorological circumstances like unexpected rainfall. Methods:: We employ an IoT-based intelligent irrigation system to get around this problem. It includes a centrifugal pump, a motor driver board, and a soil moisture sensor with YL69 probes. When the soil moisture level drops, the pump automatically delivers water to the plants with minimal human involvement. The electrical conductivity theory is how the sensor for soil moisture functions. A DHT11 sensor and a barometer, which provide information on the local temperature, humidity, and atmospheric pressure, are both parts of the weather monitoring system with the help of this, farmers can forecast the local weather and plan their irrigation accordingly. Results:: In this patent study, the thing speak API enables us to continually monitor information from a computer or mobile device, and the ESP8266 module links the complete system to the internet. Through this approach, water waste is reduced, and irrigation efficiency is increased while crop health and quality are preserved. Conclusion:: Overall, this research demonstrated how the Internet of Things-based intelligent irrigation systems may enhance agricultural water management. By combining soil moisture monitoring, weather monitoring, and autonomous management, we may develop irrigation techniques that are more precise, effective and patent leading to higher crop yields and sustainable agricultural practices.

Fine-scale surficial soil moisture mapping using UAS-based L-band remote sensing in a mixed oak-grassland landscape

Soil moisture maps provide quantitative information that, along with climate and energy balance, is critical to integrate with hydrologic processes for characterizing landscape conditions. However, soil moisture maps are difficult to produce for natural landscapes because of vegetation cover and complex topography. Satellite-based L-band microwave sensors are commonly used to develop spatial soil moisture data products, but most existing L-band satellites provide only coarse scale (one to tens of kilometers grid size), information that is unsuitable for measuring soil moisture variation at hillslope or watershed-scales. L-band sensors are typically deployed on satellite platforms and aircraft but have been too large to deploy on small uncrewed aircraft systems (UAS). There is a need for greater spatial resolution and development of effective measures of soil moisture across a variety of natural vegetation types. To address these challenges, a novel UAS-based L-band radiometer system was evaluated that has recently been tested in agricultural settings. In this study, L-band UAS was used to map soil moisture at 3–50-m (m) resolution in a 13 square kilometer (km2) mixed grassland-forested landscape in Sonoma County, California. The results represent the first application of this technology in a natural landscape with complex topography and vegetation. The L-band inversion of the radiative transfer model produced soil moisture maps with an average unbiased root mean squared error (ubRMSE) of 0.07 m3/m3 and a bias of 0.02 m3/m3. Improved fine-scale soil moisture maps developed using UAS-based systems may be used to help inform wildfire risk, improve hydrologic models, streamflow forecasting, and early detection of landslides.

A Low Cost Capacitive Soil Moisture Measurement and Datalogger System

Water resources, which are indispensable for agriculture, industry and domestic use, are under threat today. In order to use water and soil resources efficiently and sustainably, it has become an important need to develop new systems and methods that can detect the moisture content of the soil. With the developing technology and different techniques, the moisture content of the soil began to be measured more accurately, reliably and quickly. One of these techniques is measuring soil moisture with high-precision and low-cost capacitive sensors. In this study, an example system for measuring and recording soil moisture was designed using the Arduino microcontroller board and capacitive sensors. In the study conducted in a climate-controlled greenhouse, a very high negative correlation was found between sensor and gravimetric measurement values. The regression relationship between sensor values and gravimetric measurement values was examined and R2 values between 85.6% and 95.0% were obtained.

Automatic Vegetable Watering System Using Fuzzy Logic with Integration of Soil Moisture, Rain Sensors, and RTC

Conventional vegetable watering often presents challenges, particularly in ensuring that plants receive adequate water without excessive manual intervention. This research proposes a solution in the form of an automatic watering system using fuzzy logic, which integrates soil moisture sensors, rain sensors, and an RTC (Real-Time Clock) for scheduling. The system is designed to replace manual watering methods with an automated process, thus improving the efficiency and effectiveness of vegetable cultivation. The developed device uses a soil moisture sensor to monitor soil conditions, a rain sensor to detect rainfall, and an RTC to determine the optimal watering times. The Arduino Uno acts as the main controller that activates the water pump via a relay driver based on data received from the sensors. Test results show that the system operates according to the established criteria, with a satisfactory accuracy level. The system successfully waters the plants at 07:00 WIB and 15:00 WIB, based on dry soil conditions and no rain. The trials showed that the device has an average soil moisture measurement error of 5%, and a time discrepancy of about 22 seconds on the RTC module. Each 1% increase in soil moisture requires approximately 1 second of watering duration. Watering times are adjusted to prevent the plants from drying out or dying, with a soil moisture threshold of below 40% set as the condition for requiring watering.

Climatological Evaluation of Three Assimilation and Reanalysis Datasets on Soil Moisture over the Tibetan Plateau

Soil moisture is critical in the linkage between the land and atmosphere of energy and water exchange, especially over the Tibetan Plateau (TP). However, due to the lack of in situ plateau soil moisture measurements, the reanalyzed and assimilated data are the major supplements for TP climate research. Based on observations from 1992 to 2013, this study provides a comprehensive evaluation of three sets of assimilation and reanalysis products (GLDAS, ERA5-Land, and MERRA-2) on the climatic mean and variability of soil moisture over the Tibetan Plateau (TPSM). For the climatic mean, GLDAS captures the spatial distribution and annual cycle of TPSM better than other datasets in terms of lower spatial RMSE (0.07 m3×m-3) and bias (0.06 m3×m-3). In terms of the climatic variability of TPSM, the multi-data average (MDA) highlights its advantages in reducing the bias relative to any single data product. MDA describes the TPSM anomalies more stably and accurately in terms of temporal trend and variation (r = 0.94), as well as the dipole spatial pattern in EOF1. When considering both the climatic mean and spatial variability, the performance of MDA is more accurate and balanced than that of a single data product. This study overcomes the deficiency of limited time and space in previous evaluations of TPSM and indicates that multi-data averaging may be a more effective approach in the climate investigation of TPSM.

Main technical requirements for radiometric system for remote soil moisture meter

A dual-polarization L-band microwave radiometer was developed and tested for remote soil moisture measurement. The tests were conducted to study the possible integration of soil moisture and temperature data obtained by the radiometer into the precision farming system. The purpose of this work is to clarify and substantiate the main technical parameters of a new microwave radiometer for determining moisture content portraits of large areas of agricultural land, based on the analysis of the results of flight tests of dual-polarization microwave radiometric remote soil moisture indicator, which were conducted using a quadrocopter in different regions of the Russian Federation. Crop yields are determined by moisture content in the productive layer of soil. To obtain data on soil moisture at different depths it is necessary to use multi-frequency radiometers. So, the use of a multi-frequency microwave radiometer with wavelengths of 21, 11 and 5.5 cm was justified. The mass of such radiometer should not exceed 5 kg. and dimensions not more than 360x360x60 mm. so that it could be used as a carrier of small class UAV with payload not more than 5 kg. The second sensitivity should be no worse than 0.5 degrees. Continuous operation time from the built-in battery is not less than 3 hours.