Soil Moisture Measurements Research Articles

Plant water stress, not termite herbivory, causes Namibia’s fairy circles

The fairy circles of Namibia form a remarkable gap pattern in arid grassland along the Namib Desert. The origin of the fairy circles is subject to an ongoing debate. Solving the mystery of the fairy circles (FCs) requires the right timing in fieldwork after rainfall, as the newly appearing grasses complete their life cycle within only a few weeks. Here we followed the rains along the Namib between 2020 and 2022 and assessed the cause of the grass death within FCs at different time intervals after grass-triggering rainfall. To assess whether termite herbivory was the cause, we used grass excavations and observations on the roots and shoots. To test if edaphic differences may explain the grass death in FCs, we undertook infiltration measurements in 10 FC-hotspot regions. Finally, we used continuous soil-moisture measurements from the dry into the rainy seasons to examine how the newly emerging grasses affect the soil-water content in space and time. Generally, in study plots that received grass-triggering rainfall most recently, the roots of the dead grasses in FCs were in 100 % of the cases undamaged, root-shoot ratios were significantly greater, and the roots were as long or even longer as those of the surrounding matrix grasses outside of the FCs. This indicates that drought stress caused grasses in the FCs to invest resources into roots to reach the percolating water. The results also show that the cause of the grass death in fairy circles was not induced by termite herbivory. Also, we found no systematic differences in the rate of water infiltration between FCs and the matrix, hence the plant wilting cannot result from quicker percolation within FCs. However, the soil-moisture measurements indicate that the matrix grasses strongly depleted the upper soil water of the FCs after rainfall, which explains why most grasses cannot establish and quickly die in the FCs. The research shows that grass death in fairy circles occurs immediately after rainfall due to plant water stress but not due to termite activity. Our results conform with previous fieldwork, pattern analysis and theoretical modeling, suggesting that Namibia’s fairy circles are a self-organized vegetation phenomenon induced by ecohydrological feedbacks.

High-resolution soil moisture mapping using PSO-based optimized cerebellar model articulation controller (CMAC)

A good knowledge in eco-hydrological processes requires significant understanding of geospatial distribution of soil moisture (SM). However, SM monitoring remains challenging due to its large spatial variability and its dynamic time response. This study was performed to assess the performance of a particle swarm optimization (PSO)-based optimized Cerebellar Model Articulation Controller (CMAC) in generating high-resolution surface SM estimates using sentinel-2 imagery over a Mediterranean agro-ecosystem. Furthermore, the results were compared with those of PSO-based optimized group method of data handling (GMDH) as a more common data-driven method. Two different modeling approaches i.e. modeling in homogenous clusters (local approach) and modeling in entire area as an entity (global approach) were examined. Candidate predictors namely sentinel-2 spectral bands, normalized difference vegetation index (NDVI) and normalized difference water index (NDWI), digital elevation model (DEM), slope and aspect were used as the input variables to estimate SM. An intensive field survey had been done to gather in-situ SM data using a time-domain reflectometer (TDR). K-fold validation based on in-situ SM measurements demonstrated the reasonability of the SM estimation of the proposed methodology. Detecting homogeneous areas was done using genetic and particle swarm optimization algorithms. Synthesized SM product of PSO-GMDH showed a mean Normalized Root-Mean-Square Error (NRMSE) of 13.6 to 8.91 for global and local approaches in the test phase. PSO-CMAC method with an average NRMSE of 12.47 to 8.72 for global and local approaches shows the highest accuracy and outperforms the PSO-GMDH method at both local and global approaches. Overall, results revealed that clustering study area prior to running machine learning (ML) models coupled with optical satellite imagery and geophysical properties, boosts their predictive performance and can lead to more accurate mapping of SM with more heterogeneity. The results also showed that the global approach had a moderate performance in capturing the SM heterogeneity.

Retrieval of Farmland Surface Soil Moisture Based on Feature Optimization and Machine Learning

Soil moisture is an important parameter affecting environmental processes such as hydrology, ecology, and climate. Synthetic aperture radar (SAR) microwave remote sensing is an important means of farmland surface soil moisture (SSM) measurement. The inversion of farmland SSM by microwave remote sensing is greatly affected by vegetation cover. To address this problem, a multisource remote sensing inversion method of farmland SSM based on feature optimization and machine learning is proposed in this paper. Six typical machine learning algorithms suitable for small sample training, including random forest, radial basis function neural network, generalized regression neural network, support vector regression, genetic algorithm–back propagation neural network, and extreme learning machine, were selected in this paper. The features extracted from Sentinel-1/2 and Radarsat-2 remote sensing data were analyzed by Pearson correlation, and those with high correlation coefficients were selected to form the optimal feature subset as the input for the subsequent machine learning models. Then, the SSM collaborative inversion models under different machine learning algorithms were constructed, and comparative experiments were set up to select the optimal prediction model. The models’ accuracy under different feature parameters were studied, and the difference in the performance between the dual-polarization SAR data and the quad-polarization SAR data in SSM inversion was explored. The experimental results showed that among the six models, the random forest model had a higher inversion accuracy, with a coefficient of determination of 0.6395 and a root mean square error of 0.0264 cm3/cm3. Meanwhile, the inversion accuracy could be greatly improved after feature optimization, and the inversion accuracy of the quad-polarization SAR data combined with optical remote sensing data, was better than that of the dual-polarization SAR data combined with optical remote sensing data.

Improvement and Performance Evaluation of a Dual-Probe Heat Pulse Distributed Temperature Sensing Method Used for Soil Moisture Estimation.

Large-scale measurements of soil moisture play a critical role in many fields, such as agriculture, hydrology, and engineering. The distributed temperature sensing (DTS) technology, based on a dual-probe heat pulse (DPHP), is a novel approach to realizing large-scale soil moisture estimation. However, the application of the method is limited by the complex optical cable layout, calculation algorithm, and lack of standardized heating strategy. In this paper, an improved DPHP-DTS method considering the soil bulk density was proposed. The measurement accuracy of the DPHP-DTS method under different heating strategies was studied in laboratory experiments, and its long-term stability in regard to the monitoring of soil moisture during natural evaporation in different soils was tested. The results show that the improved DPHP-DTS method can accurately measure the soil moisture, and the fitting algorithm can reduce the error caused by the accuracy of the DTS temperature measurement under the low-power heating strategy. Its measurement accuracy increases with the increase in the heating strength and duration. In addition, the improved DPHP-DTS method can describe soil evaporation in both sand and loess with good reliability and stability.

Implications of Watershed Management Practices on Water Availability Using Hydrus-1D Model in the Aba Gerima Watershed, Upper Blue Nile Basin, Ethiopia

The main objective of this study was to examine the implications of watershed management (WSM) on hydrological parameters in the Aba Gerima watershed in the Upper Blue Nile Basin. The Hydrus 1D model simulations were conducted in control sites and sites under WSM to estimate various components of the hydrologic cycle, using different soil physical & hydrological data under each category of experimental sites. Results were calibrated with measured soil moisture data through inverse solutions. Thus, Hydrus 1D model was found to be effective in predicting results, with R2 values of 0.73 to 0.853 and RMSE values ranging from 0.015 to 0.04. The cumulative evaporation estimated for 365 days for control sites was 37.6% higher than that of sites under WSM. Surface and bottom fluxes in the sites under WSM were 4.6% and 12.5%, respectively, higher than the control sites. This could be attributed to the increased soil water availability resulting from the implemented WSM practices in Aba Gerima, and the results of this study can be used as empirical evidence of the positive implications of WSM on water availability. Finally, WSM should be strengthened by concerned bodies and development partners in all watersheds, especially where water availability is affected by severe land degradation.

ISSUES OF OPTIMAL SOIL MOISTURE CONTROL BASED ON DUAL-BAND SENSORS

The water content in the soil is spatially heterogeneous, which depends on climatic factors, land use, topography and properties of the soil itself. Currently, radio frequency soil moisture meters are the most common. The technical documentation of these devices indicates that the measurement error in them can reach 1 %. However, as the results of known experimental measurements show, such a result is achieved only after calibration with respect to a specific type of soil. Calculations show that in the absence of such calibration, the error can grow up to 15 %. In this regard, spectral methods of measuring soil moisture are more advantageous. The wellknown results of the conducted studies show that in this method the main interfering factor is the content of organic substances in the soil and taking into account only the phosphorus content in the soil makes it possible to achieve a measurement error of 6.5 %. This circumstance emphasizes the prospects of exploring additional ways to increase the efficiency of spectral methods for measuring the moisture content in the soil. The question of the optimal construction of a soil moisture monitoring network based on contact dualband optical soil moisture sensors operating in the NIR and SWIR ranges is formulated and solved. It is shown that the use of the usual averaging of the measurement results obtained on two ranges of the sensor when measuring with a constant frequency can lead to an inaccurate result due to differences in the dynamic ranges of optical signals at the detector inputs caused by the non-identity of the humidification conditions of the sub-field. The possibility of choosing an adaptive measurement mode is shown when sub-stages with a large dynamic range of humidity changes should be measured with a higher frequency, proportional to the specified dynamic range, which is equivalent to the implementation of an adaptive measurement mode.

Exotic Grasses Reduce Infiltration and Moisture Availability in a Temperate Oak Savanna.

Biological invasions represent one of the most urgent conservation challenges. Oregon white oak (Quercus garryana) savannas, a complex of grassland and transitional forest, are especially sensitive to these invasions. These ecosystems have been severely degraded and fragmented over the past century and are being encroached by conifers, and oak seedlings are failing to emerge from the understory at many locations. Understanding competitive interactions between Oregon white oak and associated native and exotic vegetation would provide insight into forest-grassland dynamics and the role of exotic grasses in the decline of native species, the processes that maintain temperate savanna ecosystems, and the role of soil water uptake by individual savanna species in contributing to overall species assemblages. In this study, we quantified the soil moisture budget for invaded and uninvaded oak-associated ecosystems. From February to October 2007 we used a split paired plot experiment in Duncan, British Columbia, Canada to measure soil moisture on treatment sites where exotic grasses were removed with herbicide and control plots where they were not, using three depths (5, 20, and 35 or 50 cm) in the soil profile. Our results show that the plots that contained exotic vegetation had a faster rate of soil drying following precipitation events at the 5 cm depth than plots with the predominantly native species. We attribute this difference to the capacity of exotic vegetation to exploit soil moisture more rapidly than native vegetation at times of the year when native vegetation cannot. These results provide insight into one mechanism by which exotic grasses affect associated native plants and could help guide restoration efforts.

Ground Hyper-Spectral Remote-Sensing Monitoring of Wheat Water Stress during Different Growing Stages

Monitoring agricultural drought via ground hyper-spectral remote sensing has always been a hot topic in the fields of agriculture and meteorology. In this study, a greenhouse experiment was conducted on wheat subjected to water stress during its different growth stages, namely tillering, jointing, and milk maturity. An instrument (HOBO ware PRO) used to continuously measure soil moisture was employed to measure the soil water content (SWC). An analytical spectral device (ASD) was utilized to obtain the spectral curve of wheat subject to different water treatment methods. The canopy temperature was obtained using thermal infrared sensors (METER SI-400). The relationships between the SWC, wheat drought stage, canopy temperature, and spectral response characteristics were elucidated. The results showed that the significant differences in spectral characteristics were due to water stress during the different growth stages of wheat. Red-edge parameters of red-valley position (RVP) and red-edge position (REP) both changed by 21 nm for the tillering-stage drought and the jointing-stage drought; however, the RVP and REP values for the milk maturity stage drought and the treatment under no water stress changed by 2 nm. Further, it was proved that the red-edge blue-shift phenomenon was affected not only by the different wheat growth processes, but also by the water stress at different growth stages. Red-edge reflectance clearly reflects wheat water stress at different growth stages. From SWC and canopy temperature analysis results, SWC and canopy temperature had a significant difference between wheat drought at different growth stages, and the canopy temperature at the jointing stage drought had the strongest change. The water index (WI) based on eleven vegetation water indexes exhibited a good performance for distinguishing wheat water stress at different growth stages. In conclusion, ground-based hyperspectral remote sensing can provide a large amount of high temporal and spectral resolution data on vegetation and its surrounding environment, making it an important technical tool for wheat drought monitoring, which has a great significance on the monitoring and early warning of wheat drought, reducing drought-related yield losses, and ensuring food security.

Development of semi-automatic recalibration system and curve-fit models for smart soil moisture sensor

Reliable measurement of soil moisture is one of the primary requirements for agriculture, irrigation management, and the study of soil–water dynamics. To improve the accuracy of a smart soil moisture sensor beyond the manufacturer's calibration, there is a need for soil-specific recalibration and improved sensor models. This paper discusses the development of a semi-automatic virtual instrumentation system integrated with a standard gravimetric method for soil-specific recalibration of a relatively new, low-cost capacitive soil moisture sensor, SoilWatch 10, in loamy–sandy soil. Using dual sensor with confidence-weighted averaging sensor fusion method, variance in the raw sensor readings is minimized. Further, the pre-processed recalibration data is used to develop linear, polynomial, and optimized 3-layer FFBP-NN (feedforward backpropagation neural network) direct and inverse models of the sensor. Performance evaluation of the models indicate that goodness of fit and accuracy of the FBNN models is the highest, followed by the polynomial and linear models.

Global Evaluation of SMAP/Sentinel-1 Soil Moisture Products

SMAP/Sentinel-1 soil moisture is the latest SMAP (Soil Moisture Active Passive) product derived from synergistic utilization of the radiometry observations of SMAP and radar backscattering data of Sentinel-1. This product is the first and only global soil moisture (SM) map at 1 km and 3 km spatial resolutions. In this paper, we evaluated the SMAP/Sentinel-1 SM product from different viewpoints to better understand its quality, advantages, and likely limitations. A comparative analysis of this product and in situ measurements, for the time period March 2015 to January 2022, from 35 dense and sparse SM networks and 561 stations distributed around the world was carried out. We examined the effects of land cover, vegetation fraction, water bodies, urban areas, soil characteristics, and seasonal climatic conditions on the performance of active–passive SMAP/Sentinel-1 in estimating the SM. We also compared the performance metrics of enhanced SMAP (9 km) and SMAP/Sentinel-1 products (3 km) to analyze the effects of the active–passive disaggregation algorithm on various features of the SMAP SM maps. Results showed satisfactory agreement between SMAP/Sentinel-1 and in situ SM measurements for most sites (r values between 0.19 and 0.95 and ub-RMSE between 0.03 and 0.17), especially for dense sites without representativeness errors. Thanks to the vegetation effect correction applied in the active–passive algorithm, the SMAP/Sentinel-1 product had the highest correlation with the reference data in grasslands and croplands. Results also showed that the accuracy of the SMAP/Sentinel-1 SM product in different networks is independent of the presence of water bodies, urban areas, and soil types.

Designing Sensor Node to Measure Soil Moisture Used in IoT Network

Together with the Industry 4.0 revolution, the IoT networks have been continuously growing, accompanied by the invention and improvement of communication protocols and sensor terminals. New sensor nodes in the IoT systems need to adapt to the protocol and optimize the built-in data collection and measurement capabilities. In this paper, we present a new development direction for the IoT network sensor node, namely the use of a capacitive sensor principle to research and develop the sensor node to measure soil moisture. Simulation has been carried out to evaluate the advantages and disadvantages of the method and to propose the applicability of sensor nodes in IoT systems.

Local-scale evaluation of the simulated interactions between energy, water and vegetation in ISBA, ORCHIDEE and a diagnostic model

Abstract. The processes involved in the exchange of water, energy and carbon in terrestrial ecosystems are strongly intertwined. To accurately represent the terrestrial biosphere in land surface models (LSMs), the intrinsic coupling between these processes is required. Soil moisture and leaf area index (LAI) are two key variables at the nexus of water, energy and vegetation. Here, we evaluated two prognostic LSMs (ISBA and ORCHIDEE) and a diagnostic model (based on the LSA SAF, Satellite Application Facility for Land Surface Analysis, algorithms) in their ability to simulate the latent heat flux (LE) and gross primary production (GPP) coherently and their interactions through LAI and soil moisture. The models were validated using in situ eddy covariance observations, soil moisture measurements and remote-sensing-based LAI. It was found that the diagnostic model performed consistently well, regardless of land cover, whereas important shortcomings of the prognostic models were revealed for herbaceous and dry sites. Despite their different architecture and parametrization, ISBA and ORCHIDEE shared some key weaknesses. In both models, LE and GPP were found to be oversensitive to drought stress. Though the simulated soil water dynamics could be improved, this was not the main cause of errors in the surface fluxes. Instead, these errors were strongly correlated to errors in LAI. The simulated phenological cycle in ISBA and ORCHIDEE was delayed compared to observations and failed to capture the observed seasonal variability. The feedback mechanism between GPP and LAI (i.e. the biomass allocation scheme) was identified as a key element to improve the intricate coupling between energy, water and vegetation in LSMs.

Soil Moisture Measuring Techniques and Factors Affecting the Moisture Dynamics: A Comprehensive Review

The amount of surface soil moisture (SSM) is a crucial ecohydrological natural resource that regulates important land surface processes. It affects critical land–atmospheric phenomena, including the division of energy and water (infiltration, runoff, and evaporation), that impacts the effectiveness of agricultural output (sensible and latent heat fluxes and surface air temperature). Despite its significance, there are several difficulties in making precise measurements, monitoring, and interpreting SSM at high spatial and temporal resolutions. The current study critically reviews the methods and procedures for calculating SSM and the variables influencing measurement accuracy and applicability under different fields, climates, and operational conditions. For laboratory and field measurements, this study divides SSM estimate strategies into (i) direct and (ii) indirect procedures. The accuracy and applicability of a technique depends on the environment and the resources at hand. Comparative research is geographically restricted, although precise and economical—direct measuring techniques like the gravimetric method are time-consuming and destructive. In contrast, indirect methods are more expensive and do not produce measurements at the spatial scale but produce precise data on a temporal scale. While measuring SSM across more significant regions, ground-penetrating radar and remote sensing methods are susceptible to errors caused by overlapping data and atmospheric factors. On the other hand, soft computing techniques like machine/deep learning are quite handy for estimating SSM without any technical or laborious procedures. We determine that factors, e.g., topography, soil type, vegetation, climate change, groundwater level, depth of soil, etc., primarily influence the SSM measurements. Different techniques have been put into practice for various practical situations, although comparisons between them are not available frequently in publications. Each method offers a unique set of potential advantages and disadvantages. The most accurate way of identifying the best soil moisture technique is the value selection method (VSM). The neutron probe is preferable to the FDR or TDR sensor for measuring soil moisture. Remote sensing techniques have filled the need for large-scale, highly spatiotemporal soil moisture monitoring. Through self-learning capabilities in data-scarce areas, machine/deep learning approaches facilitate soil moisture measurement and prediction.

Accuracy calibration and evaluation of capacitance-based soil moisture sensors for a variety of soil properties

Accurate measurement of soil moisture (θ) is key to hydrology and agriculture research. Soil moisture sensor technology is the predominant method for measuring θ, and such measurements are used as a standard for evaluating results from remote sensing and data assimilation. Therefore, improving the θ measurement accuracy of soil moisture sensors is of great significance. This study used the capacitance-based soil moisture sensor (5TM, Decagon Devices, Inc.) as an example to illustrate the necessity of calibration. The 5TM soil moisture sensor calculates θ by measuring the dielectric constant (ε) of the soil medium, and ε is affected by soil properties (texture, salinity, soil organic matter, etc.). Consequently, a common calibration model (CCM) was developed to calibrate θ from the 5TM sensor by incorporating the soil properties using soil samples collected from 17 sites in 13 provinces in China at 4 different depths. First, the θ change experiments were conducted in the laboratory for each soil sample. Second, a linear calibration model (LCM) was applied to calibrate the 5TM measured soil moisture (θ5TM) based on “true” soil moisture (θtrue) assessed through the gravimetric method. The results indicated (1) high correlation coefficient (R=0.95) was found for θ5TM and θtrue, but with a high root mean square error (RMSE) of 0.051 m3m−3, and a more significant underestimation with increasing θ; (2) LCM calibration results (θLCM) showed a higher R (0.99) and a lower RMSE (0.017 m3m−3). Finally, the CCM was established through relating the LCM coefficients (aLCMandbLCM) and soil properties based on multiple regression, with RMSE of 0.126 m3m−3 and 0.023 m3m−3 for aCCMandbCCM respectively. The CCM calibrated result (θCCM) showed an RMSE of 0.02 m3m−3 and R of 0.98. CCM can almost replace LCM in terms of similar accuracy. In this study, a CCM for soil moisture sensors is proposed, which provides a new approach for soil moisture sensor calibration.

Application of Soil Moisture Active Passive (SMAP) Satellite Data in Seismic Response Assessment

The proven relationship between soil moisture and seismic ground response highlights the need for a tool to track the Earth’s surface soil moisture before and after seismic events. This paper introduces the application of Soil Moisture Active Passive (SMAP) satellite data for global soil moisture measurement during earthquakes and consequent events. An approach is presented to study areas that experienced high level of increase in soil moisture during eleven earthquakes. Two ancillary datasets, Global Precipitation Measurement (GPM) and Global Land Data Assimilation (GLDAS), were used to isolate areas that had an earthquake-induced increase in soil moisture from those that were due to hydrological processes. SMAP-based soil moisture changes were synthesized with seismic records developed by the United States Geological Survey (USGS), mapped ground failures in reconnaissance reports, and surface changes marked by Synthetic Aperture Radar (SAR)-based damage proxy maps. In the majority of the target earthquakes, including Croatia 2020, Greece 2020, Indonesia 2018, Taiwan 2016, Ecuador 2016, and Nepal 2015, a relationship between the SMAP soil moisture estimates and seismic events was evident. For these events, the earthquake-induced soil moisture response occurred in liquefaction-prone seismic zones. The New Zealand 2016 event was the only study region for which there was a clear inconsistency between ΔSMSMAP and the seismic records. The promising relationship between soil moisture changes and ground deformations indicates that SMAP would be a useful data resource for geotechnical earthquake engineering applications and reconnaissance efforts.

How much is enough in watering plants? State-of-the-art in irrigation control: Advances, challenges, and opportunities with respect to precision irrigation

With a rapidly expanding global population placing an ever growing demand on freshwater resources, an increased focus on irrigation techniques tailored to the specific needs of plant appears as one solution to minimize overall freshwater consumption. Precision irrigation methods seek to realize an acceptable compromise between yield and irrigation water consumption through control of the timing and quantity of water supplied to plants. The goal is to maintain the water content of the soil, achieve specific water use efficiency with regard to yield or maintain the physiological response of the plant to water stress within predetermined limits. Reliance on soil moisture measurements to establish irrigation water demand inadequately addresses heterogenous distribution of water in soil. Growing research interest is observed detailing the determination of plant water status directly from physiological responses. This paper reviews irrigation control approaches based on different plant water status assessment techniques. A distinct focus is made on application scale of the discussed control approaches, an aspect that has not been considered intensively enough in previous discussions of irrigation control approaches. A discussion of the observed strengths and shortcomings and technological advances supporting the various methods used to quantify plant water status extends the review. Emerging trends that are likely to have an impact on plant water status determination and optimal timing and quantification of irrigation water requirements are integrated to show latest results. A peek into the future of precision irrigation foresees greater reliance on plant-based signals, both in characterization of the control variable, namely the plant water status, and in generation of controller outputs in terms of quantity and timing.

Land surface temperature assimilation into a soil moisture-temperature model for retrieving farm-scale root zone soil moisture

• Data are assimilated into soil moisture and temperature modules of HYDRUS-1D. • For SSM retrieval, assimilation of SSM data is superior to LST assimilation. • For retrieval of RZSM, LST assimilation is better than SSM assimilation. • Assimilation of LANDSAT8-LST data leads to a more accurate RZSM retrieval. • Assimilation of MODIS-LST results in more efficient estimation of soil parameters. Thermal infrared remote sensing have been extensively applied to estimate global- or regional- extent surface soil moisture. Meanwhile, potentials of the remotely sensed data for farm-scale retrieval of root zone soil moisture (RZSM) as well as estimation of soil hydraulic parameters, have been rarely investigated. Using Ensemble Kalman Filter, we propose a new methodology to assimilate land surface temperature (LST) of both MODIS and LANDSAT-8, into the soil temperature module of HYDRUS-1D model. The main objectives are to estimate soil hydraulic parameters and to retrieve RZSM with high spatiotemporal resolution independent of any in-situ measurements of soil temperature or moisture. However, we consider some modeling scenarios by which we assimilate in-situ measurements of soil moisture into the soil moisture module of the HYDRUS-1D model to provide a reference to compare with results of the LST assimilation scenarios. We apply the proposed methodology to a farm located in Moghan irrigation district, Ardabil province of Iran, which has in-situ soil moisture measurements. Even in the least accurate scenario of ours by which MODIS-LST was assimilated, RMSE varies in the range of 0.012–0.013 cm 3 ·cm −3 demonstrated to be superior compared to preceding recent works in the literature of satellite soil moisture retrieval. Moreover, the scenario of assimilating LANDSAT-LST data leads to higher parameter uncertainty compared to the assimilation of solely in-situ soil moisture or MODIS-LST which is related to higher temporal resolution of both in-situ and MODIS data compared to LANDSAT data and the error stems from the algorithm of deriving LANDSAT-LST. Accordingly, our study recommend that assimilation of the satellite-based land surface temperature of both LANDSAT-8 and MODIS are appropriate alternatives for expensive in-situ measurement.

Shrub encroachment in alpine meadows increases the potential risk of surface soil salinization by redistributing soil water

Alpine meadows are undergoing shrub encroachment due to climate change and human activities, which affects the health of alpine meadow ecosystems. Previous studies mostly focused on the impact of shrub encroachment on vegetation communities, but few studies were focused on the impact of shrub encroachment on soil pH. This study shed light on this topic by measuring soil moisture and water holding capacity under different levels of shrub encroachment (shrub coverage ranging from 8% to 90%) in alpine meadows located on the Qinghai-Tibet Plateau. Results showed that the intensification of shrub encroachment increased soil saturated water content, soil capillary moisture capacity and soil field capacity, but decreased soil moisture at the uppermost soil layer (0–10 cm). The increase of soil saturated water content and soil capillary moisture capacity increased soil pH. The intensification of shrub encroachment increased soil pH at soil depth of 0–10 cm, but decreased soil pH at soil depth of 20–30 cm. Overall, shrub encroachment could increase soil water and salt upward transport by reducing soil moisture and enhancing soil capillary moisture capacity, resulting in an increase of soil pH. These findings are conducive to better understand the impact of shrub encroachment on the redistribution of soil salinity in alpine meadows, and can provide a theoretical basis for predicting the changes of alpine meadow community facing future climatic change and human activities.

Smart Greenhouse Monitoring With Soil Temperature and Humidity Control on Internet of Things (IoT) Based Orchid Plants

Research on monitoring systems with control has been developed with several different inputs and outputs. This research has realized a smart greenhouse monitoring tool with temperature and soil moisture control on orchid plants based on the Internet of Things (IoT). This study aims to create a monitoring tool for temperature, air humidity, soil moisture, and water level. In the system, the microcontroller used is Wemos D1 R1, with inputs in the form of a DHT-11 sensor to measure air temperature and humidity, a soil moisture sensor to measure soil moisture, and an ultrasonic sensor to measure the water level in the containers. The resulting system output is in the form of pump and fan control. Based on the results of sensor testing, the accuracy of the DHT-11 sensor is 99.97%, the error is 0.03%, the soil moisture sensor is 98.63% accurate, the error is 1.37%, and the ultrasonic sensor is 97, 61% with an error of 2.89%. Based on the research results, the system can run well, as shown by Thingspeak, and the website smartgreenhouseanggrek.weebly.com can receive the results of monitoring sensor data using an internet connection. The tool will carry out the process of wateringplants when the soil moisture value read by the sensor is 20% and will stop watering when the sensor reads the soil moisture value reaches >= 50%. In contrast, the air temperature control is done by turning on the fan if the temperature reaches 30° C.

Forward and inverse modeling of water flow in unsaturated soils with discontinuous hydraulic conductivities using physics-informed neural networks with domain decomposition

Abstract. Modeling water flow in unsaturated soils is vital for describing various hydrological and ecological phenomena. Soil water dynamics is described by well-established physical laws (Richardson–Richards equation – RRE). Solving the RRE is difficult due to the inherent nonlinearity of the processes, and various numerical methods have been proposed to solve the issue. However, applying the methods to practical situations is very challenging because they require well-defined initial and boundary conditions. Recent advances in machine learning and the growing availability of soil moisture data provide new opportunities for addressing the lingering challenges. Specifically, physics-informed machine learning allows both the known physics and data-driven modeling to be taken advantage of. Here, we present a physics-informed neural network (PINN) method that approximates the solution to the RRE using neural networks while concurrently matching available soil moisture data. Although the ability of PINNs to solve partial differential equations, including the RRE, has been demonstrated previously, its potential applications and limitations are not fully known. This study conducted a comprehensive analysis of PINNs and carefully tested the accuracy of the solutions by comparing them with analytical solutions and accepted traditional numerical solutions. We demonstrated that the solutions by PINNs with adaptive activation functions are comparable with those by traditional methods. Furthermore, while a single neural network (NN) is adequate to represent a homogeneous soil, we showed that soil moisture dynamics in layered soils with discontinuous hydraulic conductivities are correctly simulated by PINNs with domain decomposition (using separate NNs for each unique layer). A key advantage of PINNs is the absence of the strict requirement for precisely prescribed initial and boundary conditions. In addition, unlike traditional numerical methods, PINNs provide an inverse solution without repeatedly solving the forward problem. We demonstrated the application of these advantages by successfully simulating infiltration and redistribution constrained by sparse soil moisture measurements. As a free by-product, we gain knowledge of the water flux over the entire flow domain, including the unspecified upper and bottom boundary conditions. Nevertheless, there remain challenges that require further development. Chiefly, PINNs are sensitive to the initialization of NNs and are significantly slower than traditional numerical methods.