Soil Air Temperature Research Articles

TEGBed: A thermal energy harvesting testbed for batteryless internet of things

This paper presents TEGBed, a testbed designed for the evaluation and testing of batteryless devices powered using thermal energy. TEGBed offers the capability to mimic real-life temperature gradients, providing researchers with a controlled environment for testing and evaluating batteryless devices. Researchers can leverage TEGBed to accelerate their investigations, gain insights into the behaviour of batteryless devices, and drive advancements in the field of sustainable and efficient IoT technologies. The TEGBed consists of a temperature emulator for emulating temperature differences, Joule Counter, a novel hardware tool for energy and power measurement, and a framework for non-supervised operation. By using the temperature emulator, TEGBed can emulate real-life energy harvesting situations in the lab. The Joule Counter allows gauging the power output from energy harvesting units for a wide range of output power. This enables researchers to gain insights into the energy harvesting capacity and efficiency of harvesting and power management units under different scenarios. With the help of different real-world use cases, we demonstrate the capabilities and effectiveness of TEGBed in assessing thermal energy harvesters and batteryless designs. In addition, we demonstrate how TEGBed can be used for the feasibility study of harvesting energy from two different underutilized heat sources; the temperature difference between soil and air and the temperature difference between the interior and exterior of a greenhouse. Emulations with the TEGBed show that, we could harvest an average of 0.89mW power from soil-air temperature differences and 0.60mW power from greenhouse temperature differences.

Harvesting Energy From Soil-Air Temperature Differences for Batteryless IoT Devices: A Case Study

Harvesting Energy From Soil-Air Temperature Differences for Batteryless IoT Devices: A Case Study

Snow depth and the associated offset in ground temperatures in a landscape manipulated with snow-fences

Seasonal snow cover has an important impact on the difference between soil- and air temperature because of the insulation effect, and is therefore a key parameter in ecosystem models. However, it is still uncertain how specific variations in soil moisture, vegetation composition, and surface air warming, combined with snow dynamics such as compaction affect the difference between soil- and air temperature. Here, we present an analysis of 8 years (2012–2020) of snow dynamics in an Arctic ecosystem manipulation experiment (using snow fences) on Disko Island, West Greenland. We explore the snow insulation effect under different treatments (mesic tundra heath as a dry site and fen area as a wet site, snow addition from snow fences, warming using open top chambers, and shrub removal) on a plot-level scale. The snow fences significantly changed the inter-annual variation in snow depths and -phenology. The maximum annual mean snow depths were 90 cm on the control side and 122 cm on the snow addition side during all study years. Annual mean snow cover duration across 8 years was 234 days on the control side and 239 days on the snow addition side. The difference between soil- and air temperature was significantly higher on the snow addition side than on the control side of the snow fences. Based on a linear mixed-effects model, we conclude that the snow depth was the decisive factor affecting the difference between soil- and air temperature in the snow cover season (p < 0.0001). The change rate of the difference between soil- and air temperature, as a function of snow depth, was slower during the period before maximum snow depth than during the period between the day with maximum snow depth until snow ending day. During the snow-free season, the effects of the open top chambers were stronger than the effects of the shrub removal, and the combination of both contributed to the highest soil temperature in the dry site, but the warming effect of open top chambers was limited and shrub removal warmed soil temperature in the wet site. The warming effects of open top chambers and shrub removal were weakened on the snow addition side, which indicates a lagged effect of snow on soil temperature. This study quantifies important dynamics in soil-air temperature offsets linked to both snow and ecosystem changes mimicking climate change and provides a reference for future surface process simulations.

A soil-air temperature model to determine the start of season phenology of deciduous forests

Forest ecosystems play a major role in sequestering atmospheric carbon dioxide, which can help offset the detrimental effects of anthropogenic carbon emissions. However, climate change has and will continue to affect the phenology of forest ecosystems’ carbon uptake, changing both the “carbon uptake transition date” - when forests shift from being a net carbon source to sink - and the “green-up date” reflecting the onset of bud burst. Previous studies have shown that a forest's carbon uptake transition date correlates to the date when soil temperature warms enough to surpass mean annual air temperature (soil-air temperature model). However, we still don't know if this simple relationship holds across different sites or over longer time periods. In this study, we explore the relationship between climate and both types of phenological transition dates using over 200 site years of data between 1997 and 2022. Using flux tower data from 18 sites across North America and Europe, we derive three potential carbon uptake transition dates corresponding to the dates when 10%, 25%, and 50% of seasonal net ecosystem exchange (NEE) amplitude is reached. Using PhenoCam data, we then derive three potential green-up dates corresponding to when 10%, 25%, and 50% of total seasonal green chromatic coordinate (GCC) is reached (the greenness model). We evaluate our model estimates using concordance coefficients, a metric of agreement between two measures, to determine which process, carbon uptake or budburst, is best predicted by the soil-air temperature model and to what extent. We find that variation in phenological relationships can be attributed to different regional and bioclimatic groups, highlighting potential biome-specific strengths and limitations of the soil-air temperature model. This model offers a simple approach to better understand phenological transitions and identify potential and limitations for a simple universal SOS prediction approach in deciduous forests.

No-tillage with straw mulching promotes wheat production via regulating soil drying-wetting status and reducing soil-air temperature variation at arid regions

Straw returning is a technical measure of crop production widely adopted in arid and water-deficient areas. However, the mechanism by which straw returning drives spatio-temporal changes in soil moisture and temperature to improve crop productivity remains unclear. So, a three-year field experiment was conducted with four various straw returning treatments for wheat production: 1) no-tillage with straw mulching (NTSM), 2) no-tillage with straw standing (NTSS), 3) conventional tillage with straw incorporation (CTS), and 4) conventional tillage without straw returning (CT, control). The spatio-temporal variations of soil moisture content and soil temperature were assessed. It will provide the basis for selecting the appropriate way of straw returning to increase wheat yield and clarifying its hydrothermal regulation mechanism. NTSM and NTSS were effective in maintaining soil moisture and regulating soil temperature, and had a greater effect on grain increase, than CTS and CT. Meanwhile, NTSM had a relatively higher yield than NTSS through better regulation of soil hydrothermal characteristics. NTSM had higher soil moisture content in the 0–120 cm depth by 9.8–10.2%, 6.7–8.2%, 6.5–10.2%, and 6.9–7.4% at sowing to jointing, jointing to booting, booting to early-filling, and early-filling to maturing stages of wheat, respectively. NTSM decreased evapotranspiration modulus coefficient (ETMC) and evapotranspiration intensity (ETI) at jointing to early-filling stage of wheat, but increased both indexes from early-filing to maturing stage of wheat, which is helpful to adjust the contradiction of soil moisture demand for wheat in different growth stages. Additionally, NTSM had 9.8–10.2%, 6.7–8.2%, 6.5–10.2%, and 6.9–7.4% lower soil temperature in the 5–25 cm depth than CT, at the four aforementioned wheat growth stages, respectively. The difference of soil temperature was mainly reflected at the 5–15 cm depth by analyzing the difference on soil temperature in various soil depths. The treatment on NTSM had little variation and stabilized soil temperature, according to the difference between air and average soil temperature of 5–15 cm depth. NTSM allowed wheat to grow a suitable thermal environment across their growth period, which is a significant regulatory mechanism for wheat to maintain better growth and development. Thereby, NTSM boosted wheat grain yield by 18.6–27.3% as compared to CT. Collectively, the NTSM treatment had better effect on regulating soil drying-wetting status and reducing soil-air temperature variation, which could be used as a recommended measure to improve wheat productivity at arid conditions.

Spatial Estimates of Soil Moisture for Understanding Ecological Potential and Risk: A Case Study for Arid and Semi-Arid Ecosystems

Soil temperature and moisture (soil-climate) affect plant growth and microbial metabolism, providing a mechanistic link between climate and growing conditions. However, spatially explicit soil-climate estimates that can inform management and research are lacking. We developed a framework to estimate spatiotemporal-varying soil moisture (monthly, annual, and seasonal) and temperature-moisture regimes as gridded surfaces by enhancing the Newhall simulation model. Importantly, our approach allows for the substitution of data and parameters, such as climate, snowmelt, soil properties, alternative potential evapotranspiration equations and air-soil temperature offsets. We applied the model across the western United States using monthly climate averages (1981–2010). The resulting data are intended to help improve conservation and habitat management, including but not limited to increasing the understanding of vegetation patterns (restoration effectiveness), the spread of invasive species and wildfire risk. The demonstrated modeled results had significant correlations with vegetation patterns—for example, soil moisture variables predicted sagebrush (R2 = 0.51), annual herbaceous plant cover (R2 = 0.687), exposed soil (R2 = 0.656) and fire occurrence (R2 = 0.343). Using our framework, we have the flexibility to assess dynamic climate conditions (historical, contemporary or projected) that could improve the knowledge of changing spatiotemporal biotic patterns and be applied to other geographic regions.

Effect of atmospheric temperature on underground radon: A laboratory experiment

The effect of atmospheric temperature on underground radon flow was investigated in a customized climate-controlled laboratory (CCL) system, which enabled us to isolate the impact of ambient atmospheric temperature variations on underground radon transport. The soil thermal gradients that developed, following atmospheric warming, acted as the driving force for the diffusive radon flow, resulting in a decrease in the radon concentration along the experimental column setup at a rate of ∼70 Bq∙m−3 per oC∙m−1 (∼0.4% of the radon concentration). When the ambient temperature decreased, compared to the soil temperature, an air-soil temperature difference developed along the column, which acted as a driving force for radon to flow along the column and promptly increased the radon concentration at a rate of ∼140 Bq∙m−3 per oC (∼0.8% of the radon concentration). The overall radon concentration changes under the experimental conditions were up to 30%. The changes in the molecular diffusion coefficient in the experimental temperature range were ∼7%, with thermal diffusion as a possible additional mechanism contributing to radon transport due to temperature. The cyclic changes in ambient temperature in the forced conditions experiments were found to be directly correlated with underground radon oscillations. The same frequency for ambient temperature and radon concentration, along the experimental column in low frequency warming-cooling cycles (i.e., 4–8 days), was found. This good correlation was lost at higher frequencies (two days or more), due to the asymmetrical response of radon to atmospheric warming and cooling. The results of this study explain some of the field observations in underground radon monitoring.

Three-Dimensional Dynamic Variations of Ground/Air Surface Temperatures and Their Correlation with Large-Scale Circulation Indexes in Southwest China (1980–2019)

Air/soil temperatures play important roles in land–atmosphere interactions. The three-dimensional (temporal, spatial, and vertical) variations of maximum, mean, and minimum ground soil temperature at 0 cm (GSTx, GSTm, and GSTn, respectively), surface air temperature at 2 m (SATx, SATm, and SATn, respectively), and soil–air temperature difference (SATDx, SATDm, and SATDn, respectively) and their potential linkages with large-scale indexes in Southwest China during 1980–2019 were analyzed. Variations of GST and SAT at the majority of stations (pixels) exhibited significant (p ≤ 0.05) warming, albeit at different rates; consequently, SATD exhibited different variation. Moreover, the period of GST, SAT, and SATD was similar in intra-annual and interannual oscillation but was different in interdecadal oscillation. The variation rate of GST, SAT, and SATD exhibited significant (p ≤ 0.05) correlation with elevation, but with different variation gradient. Notably, asymmetric variation of SATDx (downward trend) and of SATDn (upward trend) with elevation was found at elevations &gt;3 km. Wavelet coherence showed that the Atlantic Multidecadal Oscillation is the dominant factor affecting GST and SAT, whereas the Pacific Decadal Oscillation and the North Atlantic Oscillation make the greatest contributions to SATD. It was found that GST, SAT, and SATD exhibit different variations under the effects of global warming, the driving mechanism of which requires further study.

Performance of thermoelectric conversion device with power management module based on shallow soil-air temperature difference

The development of wireless sensor technology has provided an alternative research direction for monitoring of the Internet of Things. However, the practical application of wireless sensor technology is currently constrained by power supply requirements and the rather laborious task of replacing or maintaining the sensor batteries. In this paper, a wireless node power supply system based on the temperature difference between the shallow soil-air was designed. The distinguishing features of this device include application of a gravity heat pipe, thermoelectric generators (TEGs), copper heat fins, and radiators to absorb, transfer and convert the thermal energy. The study measured the performance of the proposed device in an experimental outdoor platform, the results show that in the 8 h monitoring of a day, the temperature difference of thermoelectric device ranged from 12.96k to 24.69k, the peak voltage of the device was 722.13 mV and the peak power was 3.62 mW when the proposed device was at the optimal external load. Furthermore a power management system was designed to increase the output voltage and eventually store it in the supercapacitor. In conclusion, The system provides an effective mechanism for solving the energy supply problem of wireless sensor.

Simulation of Heat and Water Transport on Different Tree Canopies: A Finite Element Approach

Heat and water transport modeling is a widely explored topic in micro-meteorology, agriculture, and forestry. One of the most popular models is the Simultaneous Heat and Water (SHAW) model, which includes partial differential equations (PDEs) for air-soil temperature and humidity, but with a priori discretized PDE for the foliage temperature in each canopy layer; it is solved using the finite difference method and the canopy shape is defined as a simple rule of proportionality of total quantities such as the total leaf area index. This work proposes a novel canopy shape characterization based on Weibull distribution, providing a continuous vertical shape function capable of fitting any tree species. This allows formulating a fully continuous SHAW-derived model, which is numerically solved by a finite element approach of P1 Lagrange type. For this novel approach, several numerical experiments were carried out to understand how the shape of well distinguishable canopies influences heat and water transport.

Response of abiotic soil CO2 flux to the difference in air-soil temperature in a desert

Abiotic soil CO2 flux (Fa) has been observed in drylands worldwide, while its generation mechanism is not determined. The remarkable features for the occurrence time of Fa, that the positive Fa (CO2 emission from soil) and negative Fa (CO2 absorption by soil) generally occurred in the daytime and nighttime, respectively, suggested that the generation of Fa is related to temperature. Previous studies have focused on the sole effect of soil temperature (Ts) or air temperature (Ta) on Fa, but very little is regarding to the overall response of Fa to both Ta and Ts in the air-soil system. In the Mu Us Desert, China, we continuously measured the hourly Fa, Ts, Ta and the difference between Ta and Ts (Ta-Ts) at 4-day intervals from June to October 2016. During this period, soil abiotically absorbed atmospheric CO2 at a rate of 0.32 ± 0.19 g m−2 d−1. Fa was not correlated with Ts and was weakly correlated with Ta, however, it was strongly and positively correlated with Ta-Ts. A quadratic model adequately fitted the relationship between Fa and Ta-Ts, and similar relationships were also observed between positive Fa and Ta-Ts and between negative Fa and Ta-Ts. The results of Fa measurement highlight that desert soils can absorb CO2 from the atmosphere through abiotic processes. The relationships of Fa with Ts, Ta and Ta-Ts indicate that, abiotic CO2 exchange between soil and atmosphere depends on air-soil temperature gradient in desert. Furthermore, the thermal convection driven by Ta-Ts may play a major role in the generation of nocturnal negative Fa. Our findings provide a new possible perspective for revealing the reasons of Fa generation in drylands.

No Tillage With Plastic Re-mulching Maintains High Maize Productivity via Regulating Hydrothermal Effects in an Arid Region.

Plastic is a valuable mulching measure for increasing crop productivity in arid environments; however, little is known about the main mechanism by which this valuable technology actuates spatial–temporal changes in soil hydrothermal effect. So a 3-year field experiment was conducted to optimize soil hydrothermal effect of maize field with three plastic mulched management treatments: (1) no tillage with plastic re-mulching (NM), (2) reduced tillage with plastic mulching (RM), and (3) conventional tillage with annual new plastic mulching (CM). The results showed that NM treatment increased soil water content by 6.6–8.4% from maize sowing to seedling stage, than did CM, and it created a good soil moisture environment for sowing of maize. Also, NM had greater soil water content by 4.8–5.6% from maize silking to early-filling stage than had CM, and it made up for the abundant demand of soil moisture for the vigorous growth of maize filling stage. The NM treatment increased water consumption (WC) before maize big-flare stage, decreased WC from big-flare to early-filling stage, and increased WC after early-filling stage. So NM treatment effectively coordinated water demand contradiction of maize at entire growing season. NM decreased soil accumulated temperature (SAT) by 7.0–13.0% at maize sowing to early-filling stage than did CM, but NM had little influence on the SAT during filling stage. In particular, the treatment on NM had smaller absolute values of air–soil temperature differences than RM and CM treatments during maize filling stage, indicating that NM treatment maintains the relative stability of soil temperature for ensuring grain filling of maize. The NM treatment allowed the maize to grow in a suitable hydrothermal status and still maintained high yield. In addition, NM treatment obtained higher net income and rate of return by 6.4–11.0% and 44.1–54.5%, respectively, than did CM, because NM treatment mainly decreased the input costs for plastic and machine operations. Therefore, the NM treatment can be recommended as a promising technique to overcome simultaneous heat stress and water shortage in arid environments.

Spatial and temporal monitoring of drought conditions using the satellite rainfall estimates and remote sensing optical and thermal measurements

• Synergistic use of remote sensing rainfall, optical, and thermal measurements. • Extracting long-term drought index in an acceptable spatial and temporal coverage. • Investigating the PERSIANN-CDR derived drought indices using gauge measurements. Drought is an important natural disaster that causes devastating impacts on the ecosystem, livestock, environment, and society. So far, various remote-sensing methods have been developed to estimate drought conditions, each of which has advantages and restrictions. This study aims to monitor the real-time drought indices at the field scales via the integration of various earth observations. Our proposed method consists of two steps. In the first step, the relationships between long-term standardized precipitation indices (SPI) derived from PERSIANN-CDR rainfall data and two drought-dependent parameters derived from MODIS products, including normalized NDVI and soil-air temperature gradient, are obtained at the spatial resolution of PERSIANN-CDR grid (approximately 25 km). As the next step, the corresponding relationships are applied to estimate the drought index maps at the spatial resolution of MODIS products (1 km). Numerous analyses are carried out to evaluate the proposed method. The results revealed that, from various drought indices, including SPIs of different timescales (1, 3, 6, and 12-months), SPI-3 and SPI-6 are more appropriate to the proposed method in terms of correlation with temperature and vegetation parameters. The findings also demonstrate the competency of the proposed method in estimating SPI indices with average RMSE 0.67 and the average correlation coefficient of 0.74.

Radiatively driven NH3 release from agricultural field during wintertime slack season

A chemical ionization mass spectrometer (CIMS) was deployed to measure ambient ammonia (NH3) with a time-resolution of ~5 min. Unexpectedly high levels of NH3, peaking at 15.5 ppbv and averaging at 2.1 ± 1.9 ppbv, were observed near an agricultural field in Nanjing, China, during wintertime, a slack season for farming. Re-partitioning from aerosols can only explain a small portion of this NH3 due to the considerably low air temperature, whereas the major source was emission from soil, as indicated by significant positive correlations of the NH3 concentration with the ground surface temperature, solar radiation intensity, and particularly the surface-air temperature difference. The dissolved/adsorbed NH3 and NH4NO3 in soil likely originated from conversion of nitrogen-containing organic matter by microorganisms through ammonification and nitrification. The upward transfer of NH3 released from the soil was facilitated by convective mixing driven by solar radiation, which produced the required high soil-air temperature gradient. This winter release of NH3 from the soil is not captured by commonly used long-term, passive-sampler based methods, giving rise to an incorrect assumption that during winter, a slack season for farming, soil emits no NH3 and so this source has remained unresolved by typical emission inventory models. Our results indicate that contrary to the common assumption, agricultural background emissions are a non-negligible source of NH3 even in wintertime, which may exert a significant impact on regional air pollution formations. Hence, a refined NH3 emission inventory is critically needed to account for the haze events in wintertime northern China.

Artificial neural network-assisted glacier forefield soil temperature retrieval from temperature measurements

Soil temperature is one of the most important glacio-meteorological parameters that play a critical role in glacier energy and mass balance dynamics, surface hydrological processes, and glacier-atmosphere interaction. However, the availability of the data is acutely scarce in the Himalayan glaciated region. In this study, we applied artificial neural network (ANN) models for the prediction of soil temperature of glacial forefield region of the Pindari Glacier (Central Himalaya). Three-layer feed-forward ANN models were developed and tested for estimating multi-depth soil temperatures using concurrent and antecedent air-soil temperature data for one complete annual cycle as inputs for the models. Models with different combinations of input variables were tested, and best sets of variables were selected based on the prediction accuracy. Rigorous statistics were further employed to compare the performances of different models. High concurrence was obtained between ANN-estimated and measured soil and air temperatures as evident by various correlation coefficients and error ranges. In a boarder perspective, our results point toward the applicability of developed ANN models to provide robust soil temperature prediction for the glacial forefield regions of the Central Himalaya.

Contrasting climate-based approaches and building simulations for the investigation of Earth-to-air heat exchanger (EAHE) cooling sensitivity to building dimensions and future climate scenarios in North America

Evolving cooling needs have promoted the examination of passive low energy cooling solutions such as Earth-to-air heat exchangers (EAHE). These systems heat or cool passing air based on the seasonal air-soil temperature differences, therefore exhibiting a natural correlation between system performance and climate. Key performance indicators (KPIs) of EAHE cooling potential generated from climatic indicators were compared to building simulation results, for a multitude of system and building configurations, baseline cooling thresholds, and future climate scenarios to identify sensitivity of these indices useful for pre-design evaluations. The analysis was conducted for nine representative locations in North America. Using weather files representative of future climate scenarios, the potential for EAHE cooling was shown to persist in cooler climates in contrast to cooling dominated regions like Miami and Phoenix whose use of the EAHE was not effective. In terms of building and system design, shading and control natural ventilation (CNV) were identified as the ideal additions to promote EAHE cooling. Mean soil temperature was also identified as a possible metric for estimating EAHE cooling as cooler soil temperatures expectedly perform better. The comparison between the two approaches demonstrated climate indicators consistently reflected the behaviour predicted by building simulation despite changes to building dimensions. As a result, climate indicators have been shown to be effective pre-design tools in estimating EAHE cooling potential. These can be used by stakeholders evaluating the benefit of EAHEs in providing sustainable cooling in various climates and future climate conditions.

An ignored key link in greenhouse effect: Soil and soil CO2 slow heat loss

The ever-increasing atmospheric CO2 concentration is a key driver of modern global warming. However, the low heat capacity of atmosphere and strong convection processes in the troposphere both limit heat retention. Given the higher heat capacity and CO2 concentration in soil compared to the atmosphere, the direct contributions of soil to the greenhouse effect may be significant. By experimentally manipulating CO2 concentrations both in the soil and the atmosphere, we demonstrated that the soil-retained heat and the slower soil heat transmission decrease the amount of heat energy leaking from the earth. Furthermore, the soil air temperature was affected by soil CO2 concentration, with the highest value recorded at 7500 ppm CO2. This study indicates that soil and soil CO2, together with atmospheric CO2, play a crucial role in the greenhouse effect. The spatial and temporal heterogeneity of soils and soil CO2 should be further investigated, given their potentially significant influence on global climate change.

The “breathing spots” in karst areas—the sites of advective exchange of gases between soils and adjacent underground cavities

Microclimatic processes were studied in the soil of the well-defined “breathing spot” named Půlhodina (BSP) in the Hranice Karst (Czech Republic). The results have indicated that air is exchanged between the external atmosphere and a supposed underground cavity under air-buoyancy control. Upward airflow ventilation mode (UAF mode) and downward airflow ventilation mode (DAF mode) were distinguished. The virtual temperature of switching between both modes was about 16.5 °C. At the UAF mode, the underground air is rising from the cavity through the BSP, warms soil, and carries out the underground CO2. At the DAF mode, the external air is sucked into the cavity through the BSP soil, promoting the advective flux of the soil-respired CO2 deeper into the cavity. Whereas the warming of the soil is a dominant process in the UAF mode, the CO2 advective flux into the cavity is the main process in the DAF mode. The simplified mathematical model was derived as a function of the external air temperature and the soil air temperature to simulate the time evolution of CO2 concentration in the BSP. Despite simplifying assumptions, this model showed a good agreement with the collected data. The advective CO2 flux from a breathing spot soil may significantly contribute to the total CO2 flux into the adjacent underground cavity. Its total values may reach 4 × 10−3 mol s−1 or even more, based on the actual CO2 concentration, the exchanged airflow magnitude (both controlled by external temperature), and the breathing spot area. This conclusion is important for karsologists studying the cave CO2 budget, CO2 sources, and the mechanisms of CO2 transport into caves.

Straw and plastic management regulate air-soil temperature amplitude and wetting-drying alternation in soil to promote intercrop productivity in arid regions

Soil coverage with straw and plastic mulch is a valuable and useful strategy for enhancing crop production in arid regions. However, a better understanding of the mechanism by which this innovative practice drives spatial and temporal variation in soil temperature and water content and improves crop production could guide cropping system optimization. A three-year field experiment was performed with four wheat-maize strip-intercropping treatments: 1) no-tillage with 25−30 cm tall wheat straw standing in wheat strips and residual plastic mulching in maize strips (NTS), 2) no-tillage with 25−30 cm tall wheat straw mulching in wheat strips and residual plastic mulching in maize strips (NTM), 3) conventional tillage with 25−30 cm tall wheat straw incorporation in wheat strips and annual new plastic mulching in maize strips (CTS), and 4) conventional tillage without wheat straw retention in wheat strips and annual new plastic mulching in maize strips (CT). The temporal and spatial variation in soil temperature and soil water content was assessed. NTM had the greatest soil water retention, and increased soil water content in the 0−120 cm soil depth by 6.4, 5.2, and 5.4 % at the wheat independent growing period, intercrops co-growing period, and maize independent growing period, respectively, compared with CT. In the 0−15 cm soil depth, compared with CT, NTS decreased soil temperature by 0.73, 1.18, and 0.85 °C, NTM decreased it by 1.35, 1.95, and 1.38 °C, during the three aforementioned crop growing periods. With the intercropping treatment, soil temperature in the 0−15 cm soil depth of maize strips was higher than that of wheat strips with NTS by 1.13, 3.67, and 1.44 °C, and with NTM higher by 1.32, 2.97, and 1.75 °C, especially higher as 1.38, 4.00,and 1.68 °C with CT, during the three aforementioned crop growing periods. According to the value of difference between air and soil temperatures, NTM maintained soil heat in the low temperature season and reduced soil temperature in the high temperature season. These allowed the intercrops to grow in a collaborative state with the NTM treatment during their growing period, it is an important regulation mechanism for growth and development of intercropped wheat and maize. NTM improved total grain yield of wheat plus maize by 14.9 % in comparison to CT. The optimized soil temperature and increased soil moisture for the intercrops’ strips with NTM indicates that the integrated set of practices in this treatment can be used as a superior technique to overcome simultaneous water shortage and heat stress in arid irrigated areas of northwestern China.

Soil Moisture Monitoring of the Plant Root Zone by Using Phenology as Context in Remote Sensing

In this study, the phenological behavior and energy balance of plants are used as a sensory mechanism for root-zone soil moisture monitoring using both in-situ and satellite remote sensing data. The commonly used in-situ measurements are not feasible for mapping soil moisture at large-scale agricultural areas. Local direct root-zone soil moisture measurements cannot be reliably interpolated owing to the high spatial variability of soil structure and the vegetative content. Remote sensing methods are negatively affected by vegetation coverage and density regarding penetration and backscattering characteristics. In order to overcome these limitations, we propose a root-zone soil moisture estimation method utilizing a context-aware data clustering process, which can be applied prior to any statistical analysis, for empirical evaluation of data. In this aspect, the crops’ phenological stages and soil–air temperature differences are defined as the two contexts for data clustering. Parameters such as canopy–air temperature difference, land surface temperature, and solar radiation with respect to plant energy and water processes are used for the analysis. The proposed model is utilized using piecewise linear regression of data obtained from 16 rainfed wheat parcels distributed across Turkey, under different climatic and topographic conditions. It is shown that the proposed context-aware data clustering process enables the nonlinear plant behavior to be analyzed linearly. The correlation value of the whole season increased from 21% to a range between 78% and 95% for different clusters. The outliers became relevant and the parameters became significant after the proposed context-aware data clustering.