Soil Temperature Monitoring Research Articles

A dataset of hourly profile soil temperature at Hailun Ecological Station in Northeast black soil region during 2010-2020

Soil temperature has a great influence on soil humification and mineralization, so it is of great significance to collect soil temperature observations at different spatial scales. Located in the core area of black soil, Hailun Agricultural Ecological Experiment Station (hereinafter referred to as Hailun Ecological Station) is a state-level long-term positioning research station of China Ecosystem Research Network. The soil temperature data of Hailun Ecological Station is of great significance for studying the soil water and heat process in black soil area. The data in this dataset are collected at Helen Ecological Station by the MILOS520 and MAWS301 automatic monitoring systems developed by VAISALA Company of Finland. We used the “Eco-meteorological Workstation” software process the observation data, and ensured the accuracy of the monitoring soil temperature data through analysis, inspection and reasonable optimization. This dataset consists of 11 data tables, namely the yearly soil temperature data of 11 years from 2010 to 2020, respectively. Each data table includes hourly soil temperature data of 0 cm, 5 cm, 10 cm, 15 cm, 20 cm, 40 cm, 60 cm and 100 cm. The publication of this dataset aims to provide scientific basis for the protection and long-term use of black soil.

Ice wedge polygon stability on steep slopes in West Greenland related to temperature and moisture dynamics of the active layer

AbstractIce wedge polygons on steep slopes have generally been described as being covered by periglacial sediments and, typically, the active layer on slopes becomes mobile during thaw periods, which can lead to solifluction. In West Greenland close to the ice margin, however, the active layer and ice wedge polygons are stable despite their occurrence on steep slopes with inclinations of ≥30°. We conducted a soil survey (including sampling for soil analyses and radiocarbon dating) in the Umimmalissuaq valley and installed a field station ~4 km east of the current ice margin to monitor soil temperature and water tension at depths of 10, 20 and 35 cm of the active layer on a steep, north‐facing slope in the middle of an ice wedge polygon from 2009 to 2015. Thawing and freezing periods lasted between 2 and 3 months and the active layer was usually completely frozen from November to April. We observed simultaneous and complete water saturation at all three depths of the active layer in one summer for 1 day. The amount of water in the active layer apparently was not enough to trigger solifluction during the summer thaw, even at slope inclinations above 30°. In addition, the dense shrub tundra absorbs most of the water during periods between thawing and freezing, which further stabilizes the slope. This process, together with the dry and continental climate caused by katabatic winds combined with no or limited frost heave, plays a crucial role in determining the stability of these slopes and can explain the presence of large‐scale stable ice wedge polygon networks in organic matter‐rich permafrost, which is about 5,000 years old. This study underlines the importance of soil hydrodynamics and local climate regime for landscape stability and differing intensities of solifluction processes in areas with strong geomorphological gradients and rising air temperatures.

Vanadium Oxide-Doped Laser-Induced Graphene Multi-Parameter Sensor to Decouple Soil Nitrogen Loss and Temperature.

Monitoring nitrogen utilization efficiency and soil temperature in agricultural systems for timely intervention is essential for crop healthwith reduced environmental pollution. Herein, this work presents a high-performance multi-parameter sensor based on vanadium oxide (VOX )-doped laser-induced graphene (LIG) foam to completely decouple nitrogen oxides (NOX ) and temperature. The highly porous 3D VOX -doped LIG foam composite is readily obtained by laser scribing vanadium sulfide (V5 S8 )-doped block copolymer and phenolic resin self-assembled films. The heterojunction formed at the LIG/VOX interface provides the sensor with enhanced response to NOX and an ultralow limit of detection of 3 ppb (theoretical estimate of 451 ppt) at room temperature. The sensor also exhibits a wide detection range, fast response/recovery, good selectivity, and stability over 16 days. Meanwhile, the sensor can accurately detect temperature over a wide linear range of 10-110°C. The encapsulation of the sensor with a soft membrane further allows for temperature sensing without being affected by NOX . The unencapsulated sensor operated at elevated temperature removes the influences of relative humidity and temperature variations for accurate NOX measurements. The capability to decouple nitrogen loss and soil temperaturepaves the way for the development of future multimodal decoupled electronics for precision agriculture and health monitoring.

Changes in the ground surface temperature in permafrost regions along the Qinghai–Tibet engineering corridor from 1900 to 2014: A modified assessment of CMIP6

Numerous studies were published in the last two decades to evaluate and project the permafrost changes in its thermal state, mainly based on the soil temperature datasets from the Coupled Model Intercomparison Project (CMIP), and discuss the impacts of permafrost changes on regional hydrological, ecological and climatic systems and even carbon cycles. However, limited monitored soil temperature data are available to validate the CMIP outputs, resulting in the over-projection of future permafrost changes in CMIP3 and CMIP5. Moreover, future permafrost changes in CMIP6, particularly over the Qinghai–Tibet Plateau (QTP), where permafrost covers more than 40% of its territory, are still unknown. To address this gap, we evaluated and calibrated the monthly ground surface temperature (GST; 5 cm below the ground surface), which was often used as the upper boundary to simulate and project permafrost changes derived from 19 CMIP6 Earth System Models (ESMs) against in situ measurements over the QTP. We generated the monthly GST series from 1900 to 2014 for five sites based on the linear calibration models and validated them through the three other sites using the same calibration methods. Results showed that all of the ESMs could capture the dynamics of in situ GST with high correlations (r > 0.90). However, large errors were detected with a broad range of centred root-mean-square errors (1.14–4.98 °C). The Top 5 model ensembles (MME5) performed better than most individual ESMs and averaged multi-model ensembles (MME19). The calibrated GST performed better than the GST obtained from MME5. Both annual and seasonal GSTs exhibited warming trends with an average annual rate of 0.04 °C per decade in the annual GST. The average seasonal warming rate was highest in winter and spring and lowest in summer. This reconstructed GST data series could be used to simulate the long-term permafrost temperature over the QTP.

Genesis and preservation of patterned ground in permafrost non-affected soils – Central Apennines, Italy

Several research have been focused on the polygenetic origin of patterned ground soils, ascribing to the freeze-thaw cycles the driving mechanism in the sorted features genesis. Therefore, this type of soils may be considered strictly associated with permafrost-affected areas because freezing and thawing of soil are generally related to the seasonal frost and/or the occurrence of the active layer. Since permafrost can reach mid-latitudes depending on local microclimatic conditions, the Majella Massif (central Apennines, Italy) gained interest because of the presence of periglacial landforms (rock glaciers, kettle holes, and patterned ground), although no direct observations and measurements indicating the occurrence of permafrost have been reported. This work was aimed to determine the genesis and preservation of patterned ground on the Majella Massif through monitoring of soil and air temperatures and derived parameters, and soil characteristics.Results suggested that permafrost was absent at a depth <2 m. However, this did not exclude the presence of deep and sporadic permafrost, as a relict form of a shallower permafrost present in ancient climatic phases. Although during these phases permafrost-driven processes were probably responsible for the formation of the patterned ground, nowadays the preservation and reorganization of this periglacial form is driven by phenomena including superficial freeze-thaw cycles and snow redistribution due to wind. Specifically, the preservation of the miniature patterned ground has been ascribed to shallower phenomena related to the water-holding capacity of the soil and to freeze-thaw cycles of the upper soil layers, which resulted in differential frost heave and segregation of coarse fragments and fines.

Soil Temperature Prediction Based on 1D-CNN-MLP Neural Network Model

Highlights To predict soil temperature, a new deep learning model called 1D-CNN-MLP is proposed, which has higher accuracy or faster convergence compared with MLP or LSTM. Convolutional neural network part in the model could extract and calculate transmission of soil temperature. Using the non-sequential data of several soil temperature layers combined with the model, we can predict other temperature layers. The model can greatly reduce the difficulty and cost of soil temperature measurement. Abstract. Soil temperature plays an important role in agriculture. In order to achieve cost reduction in the sensor arrangement when monitoring soil temperature, a novel model called 1D-CNN-MLP (One dimensional convolutional neural network-Multilayer perceptron) was proposed for soil temperature prediction. Meteorological data and soil temperature data on different soil layers collected for the 2018~2021 period from a weather station in Yangling, China, were used for calculation in our work. Our model was evaluated using statistical measures of MSE (Mean Square error). The model parameters with high operation efficiency and high accuracy are obtained, and the training result records much lower error than MLP (multilayer perceptron) and faster convergence than LSTM (long short-term memory) with an MSE of 0.288 x 10&amp;-3. The 1D-CNN (One-dimensional convolutional neural network) part of the model is used to reveal and extrapolate the law of how soil temperature propagates in different soil layers. In the case where only three layers of soil temperature data are known, the characteristic temperature layer depths of 10 cm, 15 cm, and 40 cm, are selected to place sensors and obtain the best prediction effect of soil temperature at different depths of 5 to 160 cm with a RMSE (Root mean squared error) of 1.988?. The model may help users with improved and economical soil temperature prediction and control, thus boosting crop yield. Ultimately, we found the model has a relatively poor performance in the accuracy of deep soil temperature prediction when only three layers of soil temperature data are known, and it is suggested that the model can be further optimized in terms of kernel parameter setting, data composition, and the variation law of deep soil temperature. Keywords: 1D-CNN, MLP, Soil temperature prediction.

The Design of Soil Temperature and Humidity Monitoring Systems with IoT-Based LoRa Technology

Soil temperature and humidity are important factors in affecting the condition of agricultural sector, which has an impact on the quality and quantity of the production. Lack of information on the condition of agricultural soil is one of the causes in productivity deficiency in the process of agricultural cultivation. The application of technology in the field of agriculture is expected to be able to reduce various adverse effects of agricultural soil conditions. One of which is by periodic monitoring, such as the temperature and humidity of agricultural soil. This research aims to design LoRa technology to be used as a data transmission medium for monitoring soil temperature and humidity by applying a system that is based on the Blynk application, which will make the users easier to monitor the system remotely. The temperature sensor was able to acquire data with 98.37% accuracy and the soil humidity sensor was able to acquire data with 91.63% accuracy. The changes in LoRa transmission parameters for monitoring data have an effect on the quality of its performance. The experimental results with Bandwidth variation (BW) from 31.25 kHz, 62.50 kHz, 125 kHz, 250 kHz, and 500 kHz at a distance of 15m, the best SNR and RSSI values were obtained for BW 31.25 kHz with values of 5.42 dB and -104.90 dBm. Whereas, the best ToA is obtained with a BW of 500 kHz with a value of 27.50 ms. While, the experimental result with the variation of Coding Rate (CR) from CR 4/5, 4/6, 4/7, and 4/8 at a distance of 15m, the best SNR and RSSI values were obtained CR 4/8 with values of 4.10 dB and -106.40 dBm and he best ToA was obtained CR 4/5 with a value of 112.70 ms. In testing by using variation Spreading Factor (SF) from SF7, SF9, and SF12, the higher the SF value used, the wider the range of area data communication will be. Configuration SF7 and SF9 were only able to reach a distance of 25m, while SF12 was able to reach a distance of 35m.

Spatial downscaling of satellite soil moisture products based on apparent thermal inertia: Considering the effect of vegetation condition

Many applications such as irrigation management and flood predictions require soil moisture (SM) estimates at fine-resolutions. Soil moisture estimates of current satellite missions are normally at coarse spatial resolutions, such as 25–36 km for Soil Moisture and Ocean Salinity (SMOS) SM data. Many spatial downscaling approaches have been developed to get finer resolution SM. Apparent thermal inertia (ATI), as a reliable approximation of thermal inertia (TI) that could be derived from daily fine-resolution optical/thermal remote sensing (RS) data, has been testified to be highly related to SM. However, the empirical relationship between ATI and SM varies with vegetation cover density. The main purpose of this study is to develop an ATI-based SM downscaling method that is free of vegetation cover effects. The study was conducted in Naqu areas in the central Tibetan Plateau (TP) in southwest China, where spatially intensive in-situ SM measurements were routinely obtained. European Space Agency’s Climate Change Initiative (ESA CCI) soil moisture combined product (version 4.2) was used for this study. The fine-resolution (1 km) ATI data were derived from the Collection 6 MODerate resolution Imaging Spectroradiometer (MODIS) products. A modified quantile-quantile (Q-Q) adjustment method was applied to correct the bias of the CCI SM data before downscaling. A non-linear regression model between bias-corrected CCI SM and fine resolution ATI was built to disaggregate the bias-corrected SM to spatial resolution of 1 km. In this model, Enhanced Vegetation Index (EVI) was introduced to globally and quantitatively adjust the vegetation effect in the relationship between SM and ATI. The bias-corrected CCI SM and the downscaled 1 km resolution SM were evaluated using the in-situ SM measurements obtained from the Multi-scale Soil Moisture and Temperature Monitoring Network on the central TP (CTP-SMTMN) in the study area. The results indicate that the proposed modified Q-Q adjustment method effectively reduced the bias of the original CCI SM data (bias = 0 m3/m3). The downscaled 1 km resolution SM shows fine scale spatial variability of soil moisture and preserves the accuracy (R = 0.552) and temporal variability of the bias-corrected CCI SM. The accuracies of the downscaled SM over areas with different vegetation cover show quite good consistency. The uncertainties of the proposed SM spatial downscaling method were comprehensively analyzed. The proposed ATI-based SM downscaling algorithm is applicable to generating representative and consistent high spatial resolution SM over areas with different vegetation cover density.

Effects of plastic film mulching on the spatiotemporal distribution of soil water, temperature, and photosynthetic active radiation in a cotton field.

Plastic film mulching (PFM) affects the spatiotemporal distribution of soil moisture and temperature, which in turn affects cotton growth and the spatiotemporal distribution of canopy photosynthetically active radiation (PAR). Due to the spatial heterogeneity of soil moisture, temperature and limited monitoring methods, the issues such as relatively few sampling points and long sampling intervals in most existing studies prevent the accurate quantification of spatiotemporal changes in moisture and temperature along soil profile. To investigate the effects of PFM on spatiotemporal changes in soil moisture, temperature, and canopy PAR in cotton fields, two field trials of plastic film-mulched (M) and nonmulched (NM) cultivations were performed in 2018 and 2019. The grid method was used for the soil information continuous monitoring and multiple-time fixed-site canopy PAR monitoring during the duration of cotton growth. Two-year field trial data showed that, M cultivation increased soil moisture by approximately 13.6%–25% and increased temperature by 2–4 °C in the 0–50 cm soil layer before the first irrigation (June 20) and by 1–2 °C in the 70–110 cm soil layer, compared with NM cultivation. In addition, the temperature difference between the two treatments gradually decreased with the increase in irrigation and air temperature. The M treatment reached the peak PAR interception rate 10 days earlier than the NM treatment. In 2018 and 2019, the PAR peak value under the M treatment was 4.62% and 1.8% higher than that under the NM treatment, respectively, but the PAR interception rate was decreased rapidly in the late growth stage. Overall, PFM had an effect on soil moisture retention during the whole growth period and greatly increased the soil temperature before budding stage, thus promoted the early growth of cotton. Considering this, we suggest that the irrigation quota and frequency could be appropriately decreased in the case of plastic film mulching cultivation. For nonmulching cultivation, the irrigation quota and frequency should be increased, and it is necessary to take measures to improve the soil temperature before middle July.

Characteristics of Soil Temperature, Humidity, and Salinity on Bird Island within Qinghai Lake Basin, China

The temperature, moisture, and salt content of soil in alpine regions are sensitive to changes in climatic factors and are important indicators of ecosystem functions. In this study, we collected soil moisture, temperature and electrical conductivity data at different depths at a sampling site on Bird Island in Qinghai Lake during winter using a continuous soil temperature, moisture and salt content monitoring system and analyzed their variations and influential factors. The variation in soil moisture showed an obvious ‘V-shaped’ pattern from 00:00 to 23:00 and an upward trend with soil layer depth. From 00:00 to 23:00, the overall soil temperature data fitted a ‘unimodal’ curve and showed a clear and continuous upward trend with soil layer depth at a rate of 0.684 (p &lt; 0.001). Soil electrical conductivity data also exhibited a distinct ‘V-shaped’ pattern from 00:00 to 23:00 and a continuous increase with increasing soil depth. The correlation between soil temperature, moisture, and conductivity and the spatial distribution of five climate factors indicated that climate factors accounted for 53.6% of the changes in soil temperature, moisture, and salinity. Climate factors showed a significant positive correlation with soil temperature, moisture, and conductivity (p &lt; 0.001), and air temperature was the most important factor influencing soil temperature and soil moisture changes, whereas wind direction was the most important factor influencing soil conductivity. (Wind direction and wind speed affect soil evapotranspiration, and then affect soil moisture and solute transport process). The results of this preliminary study reveal the characteristics associated with soil temperature, moisture, and salinity changes in winter within the wetlands of Bird Island on Qinghai Lake in the context of climate change, and they can be used as valuable reference data in further studies investigating associated changes in ecosystem functions.

Modeling the Temperature Field in Frozen Soil under Buildings in the City of Salekhard Taking into Account Temperature Monitoring

Most residential buildings and capital structures in the permafrost zone are constructed on the principle of maintaining the frozen state of the foundation soils. The changing climate and the increasing anthropogenic impact on the environment lead to changes in the boundaries of permafrost. These changes are especially relevant in the areas of piling foundations of residential buildings and other engineering structures located in the northern regions since they can lead to serious accidents caused by the degradation of permafrost and decrease the bearing capacity of the soil in such areas. Therefore, organization of temperature monitoring and forecasting of temperature changes in the soil under the buildings is an actual problem. To solve this problem, we use computer simulation methods of three-dimensional nonstationary thermal fields in the soil in combination with real-time monitoring of the temperature of the soil in thermometric wells. The developed approach is verified by using the temperature monitoring data for a specific residential building in the city of Salekhard. Comparison of the results of numerical calculations with experimental data showed good agreement. Using the developed computer software, nonstationary temperature fields under this building are obtained and, on this basis, the bearing capacities of all piles are calculated and a forecast of their changes in the future is given. To avoid decreasing the bearing capacity of piles it is necessary to prevent the degradation of permafrost and to supply the thermal stabilization of the soil. The proposed approach, based on a combination of the soil temperature monitoring and computer modeling methods, can be used to improve geotechnical monitoring methods.

Multifunctional multilevel moisture sensor

Controlling the moisture level is especially important when irrigating various crops to obtain a resistant, stable harvest and save water resources. Over moistening of the soil adversely affects the soil structure and can lead to salinization, waterlogging, and acidification. In addition, the formation of surface water runoff from the fields causes depletion of nutrients, oxygen starvation, and, ultimately, soil degradation. The degradation of fields (areas of the field) leads to the withdrawal of the field from crop rotation (exploitation), a reduction in acreage, the need for agrotechnical measures, reclamation, and, as a result, high financial costs. Currently applied irrigation technologies lead to the withdrawal of up to 50% of irrigated land. Therefore, the technology of differentiated irrigation with constant control over the condition of the soil and plants will help eliminate these negative consequences while increasing the yield and reducing the consumption of water, electricity, and labor costs. For the implementation of this technology, sensors are needed to control the moisture and temperature of the soil at different depths and perform operational control of the water supply to the field. The implementation of a constant connection between the field and the plant with the water supply system will allow creating digital irrigation map and forming of a database about the irrigated field underlying the information and created advisory system. The system for the continuous monitoring of soil moisture and temperature at different depths is based on IT technology with data transmission via radio communication channels. It uses platforms for data transmission via such communication channels as LoRaWAN (highly economical wireless communication interface from ICBCom), GSM (Groupe Spécial Mobile - the global standard for digital mobile cellular communication), and the Internet.

IOT BASED SMART SOIL HEALTH MANAGEMENT SYSTEM WITH REMOTE MONITORING AND CONTROL SYSTEM

Soil situation monitoring sensors enable farmers to accumulate statistics about rainfall, temperature, and other metrics over time to tune tendencies and predict irrigation needs. Farmers will keep time that’s normally spend manually checking crop water ranges due to the fact sensors can supply them real-time facts right away on nearly any on-line device. Sensors enable for higher water conservation whilst making sure there isn’t over or under watering of vegetation. Smart soil moisture, humidity, temperature and PH monitoring and analysis. Auto-pump on-off device in fields, with excessive fast SDST Relay with low water stage cut-off machine for long-life of the water pumps. This whole project is done in very low cost keeping in mind the current financial condition of farmers of INDIA.

A Wireless Underground Sensor Network Field Pilot for Agriculture and Ecology: Soil Moisture Mapping Using Signal Attenuation.

Wireless Underground Sensor Networks (WUSNs) that collect geospatial in situ sensor data are a backbone of internet-of-things (IoT) applications for agriculture and terrestrial ecology. In this paper, we first show how WUSNs can operate reliably under field conditions year-round and at the same time be used for determining and mapping soil conditions from the buried sensor nodes. We demonstrate the design and deployment of a 23-node WUSN installed at an agricultural field site that covers an area with a 530 m radius. The WUSN has continuously operated since September 2019, enabling real-time monitoring of soil volumetric water content (VWC), soil temperature (ST), and soil electrical conductivity. Secondly, we present data collected over a nine-month period across three seasons. We evaluate the performance of a deep learning algorithm in predicting soil VWC using various combinations of the received signal strength (RSSI) from each buried wireless node, above-ground pathloss, the distance between wireless node and receive antenna (D), ST, air temperature (AT), relative humidity (RH), and precipitation as input parameters to the model. The AT, RH, and precipitation were obtained from a nearby weather station. We find that a model with RSSI, D, AT, ST, and RH as inputs was able to predict soil VWC with an R2 of 0.82 for test datasets, with a Root Mean Square Error of ±0.012 (m3/m3). Hence, a combination of deep learning and other easily available soil and climatic parameters can be a viable candidate for replacing expensive soil VWC sensors in WUSNs.

Triple Probe Heat Pulse (TPHP) Soil Moisture Content and Temperature Monitoring Digital System Using Nanomaterials based Sensor Elements for Precision Agriculture

I discuss the TPHP soil moisture content and temperature measuring digital system using Indium doped Tin Oxide nanopowder (ITO) electric heater and Nickel manganite (NiMn2O4) nanoceramic powder thermistor as a temperature sensor fabrication, characterization, calibration, and testing. The NiMn2O4 nanoceramic powder is synthesized using the wet chemical method and the ITO nanopowder-based heater is fabricated by a simple fill and press method. The heater, temperature sensor, Atmega328 microcontroller with its software, stainless steel cylindrical tubes, and 3D-printed package are used to fabricate the device. The distance between the heater and the temperature sensor is set to 0.006 m and heat pulse duration is set to 240 s. The heater consumes 3.587W electric power. The temperature sensor can measure the soil temperature up to 50°C. The system is capable of measuring volumetric soil moisture content from 3% to 37% efficiently with a strong correlation coefficient of 0.997 with respect to the standard. The test result shows that there is less than a 2.8% error which is the smallest among the category and the sensitivity of the sensor at volumetric soil moisture content ( θ v ) = 0.28 m 3 m − 3 becomes 3.14 ∘ C per m 3 m − 3 . The developed system is simple to fabricate and operate highly precise, low cost, low power and light in weight.

Low-Power, Flexible Sensor Arrays with Solderless Board-to-Board Connectors for Monitoring Soil Deformation and Temperature.

Landslides are a global and frequent natural hazard, affecting many communities and infrastructure networks. Technological solutions are needed for long-term, large-scale condition monitoring of infrastructure earthworks or natural slopes. However, current instruments for slope stability monitoring are often costly, require a complex installation process and/or data processing schemes, or have poor resolution. Wireless sensor networks comprising low-power, low-cost sensors have been shown to be a crucial part of landslide early warning systems. Here, we present the development of a novel sensing approach that uses linear arrays of three-axis accelerometers for monitoring changes in sensor inclination, and thus the surrounding soil’s deformation. By combining these deformation measurements with depth-resolved temperature measurements, we can link our data to subsurface thermal–hydrological regimes where relevant. In this research, we present a configuration of cascaded I2C sensors that (i) have ultra-low power consumption and (ii) enable an adjustable probe length. From an electromechanical perspective, we developed a novel board-to-board connection method that enables narrow, semi-flexible sensor arrays and a streamlined assembly process. The low-cost connection method relies on a specific FR4 printed circuit board design that allows board-to-board press fitting without using electromechanical components or solder connections. The sensor assembly is placed in a thin, semi-flexible tube (inner diameter 6.35 mm) that is filled with an epoxy compound. The resulting sensor probe is connected to an AA-battery-powered data logger with wireless connectivity. We characterize the system’s electromechanical properties and investigate the accuracy of deformation measurements. Our experiments, performed with probes up to 1.8 m long, demonstrate long-term connector stability, as well as probe mechanical flexibility. Furthermore, our accuracy analysis indicates that deformation measurements can be performed with a 0.390 mm resolution and a 95% confidence interval of ±0.73 mm per meter of probe length. This research shows the suitability of low-cost accelerometer arrays for distributed soil stability monitoring. In comparison with emerging low-cost measurements of surface displacement, our approach provides depth-resolved deformation, which can inform about shallow sliding surfaces.

Water-conscious management strategies reduce per-yield irrigation and soil emissions of CO2, N2O, and NO in high-temperature forage cropping systems

Agricultural soils are important sources of greenhouse gases carbon dioxide (CO2) and nitrous oxide (N2O), as well as nitric oxide (NO), a precursor to tropospheric ozone. Management approaches that constrain these emissions can limit future warming and improve regional air quality, especially in high-temperature agroecosystems where soil emissions are high. Subsurface drip irrigation is a promising management solution that can limit emissions via targeted rhizosphere access to water and nitrogenous fertilizers. In complementary field studies in southern California, we compared per-yield irrigation and soil emissions in surface- and drip-irrigated field plots growing alfalfa (Medicago sativa L.) and sudangrass (Sorghum bicolor ssp. Sudanese), two forage crops with differing fertilizer requirements. For each study, we monitored soil temperature, moisture, and emission responses to irrigation in both spring and summer using a custom automated chamber array that recorded measurements every 30 minutes. We found that, compared to furrow irrigation, drip irrigation in sudangrass increased hay yield by 6% and per-yield soil CO2 emissions by 9% while it decreased irrigation demand by 49%, N2O emissions by 59%, and NO by 49%. In alfalfa, drip irrigation increased yield by 7% while decreasing irrigation by 1%, per-yield soil CO2 emissions by 59%, N2O by 38%, and NO by 20%. In both crops, differences between irrigation types were strongest in summer months, when high temperatures produced large pulses of N2O and NO in sudangrass and CO2 in alfalfa following flood irrigation relative to small pulses following drip irrigation. As agriculture intensifies in warmer climates, implementation of subsurface drip irrigation can help reduce the emission of soil emissions that affect Earth’s climate and regional air quality.

Substrate and Topsoil Impact on Soil Water and Soil Temperature in Arctic Diamond Mine Reclamation

Soil properties in the Arctic are insufficiently explored and documented, particularly extensive monitoring of soil water and soil temperature over a period of time. Soil water and soil temperature are critical for understanding land surface and atmosphere interactions and are considered key factors for revegetation during mine reclamation. This study assessed how substrate and topsoil influenced soil temperature and soil water content at a reclaimed diamond mine in the Northwest Territories of Canada. Three substrates (crushed rock, processed kimberlite, and lake sediment) with and without topsoil were used. Mean air temperature changed little from year to year, although summer temperature showed a slightly increasing trend. Both annual and summer precipitation sharply declined over time. Soil water was influenced more by substrate than by placing 10 cm of topsoil on it. Processed kimberlite had greater water retention characteristics and water content than lake sediment and crushed rock substrates (significantly). Surface soil water content was lower with than without topsoil, suggesting that 10 cm of topsoil was not enough to influence it. Soil temperatures were not influenced by either substrate or topsoil. This study suggests processed kimberlite could be used as a substrate component for water and temperature management during reclamation of this extreme environment.

Variability in Soil Moisture by Natural and Artificial Snow: A Case Study in Mt. Balwang Area, Gangwon-do, South Korea

Soil moisture is an important variable for understanding hydrological processes, and the year-round monitoring of soil moisture and temperature reflect the variations induced by snow cover and its melt. Herein, we monitored the soil moisture and temperature in high (two sites) and low (two sites) elevation regions with groundwater sampling near the Mt. Balwang area in Gangwon-do, South Korea from Sep 2020 to May 2021. This study aims to investigate the temporal and spatial variations in soil moisture and temperature due to snow (natural and artificial snow) and its melt. A ski resort has been operating in this area and has been producing artificial snow during winter periods; thus, the spring snowmelt comprises both natural and artificial snow. The effect of soil freezing and thawing, wind conditions, vegetation covers, the timing and intensity of snow cover and snowmelt were differed in the monitoring sites. The high elevation sites 1 and 2 exhibit the relatively longer and consistent snow cover than the low elevation sites. Particularly, site 2 show late (May 8) snow melting even this site is in south slope of the Mt. Balwang. The relatively steady and moist soil layers at sites 1, 2, and 3 during the warm period can be considered as influential points to groundwater recharge. Moreover, the differences between the mean δ18O (−9.89‰) of the artificial snow layers and other samples were low: in the order of surface water (0.04‰) &amp;gt;groundwater (−0.66 and −1.01‰) &amp;gt;natural snow (1.34 and −3.80‰). This indicates that the imprint of artificial snow derived from surface water and with decreasing amount of natural snow around the Mt. Balwang region, the results support the assumption that the potential influence of artificial snowmelt on groundwater quality. This study helps to understand the snow dynamic and its influence on the hydrological processes in this region by combining the hydro-chemical and isotopic analysis.

An Open-Source, Durable, and Low-Cost Alternative to Commercially Available Soil Temperature Data Loggers.

Soil temperatures play an important role in determining the distribution and function of organisms. However, soil temperature is decoupled from air temperature and varies widely in space. Characterizing and predicting soil temperature requires large and expensive networks of data loggers. We developed an open-source soil temperature data logger and created online resources to ensure our design was accessible. We tested data loggers constructed by students, with little prior electronics experience, in the lab, and in the field in Alaska. The do-it-yourself (DIY) data logger was comparably accurate to a commercial system with a mean absolute error of 2% from −20–0 °C and 1% from 0–20 °C. They captured accurate soil temperature data and performed reliably in the field with less than 10% failing in the first year of deployment. The DIY loggers were ~1.7–7 times less expensive than commercial systems. This work has the potential to increase the spatial resolution of soil temperature monitoring and serve as a powerful educational tool. The DIY soil temperature data logger will reduce data collection costs and improve our understanding of species distributions and ecological processes. It also provides an educational resource to enhance STEM, accessibility, inclusivity, and engagement.