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Soil moisture content is a key factor influencing plant growth and agricultural productivity, directly impacting water uptake, nutrient absorption, and stress resistance. This study proposes a rapid, low-cost, non-destructive method for dynamically monitoring soil moisture at depths of 0–200 cm throughout the crop growth period under dryland conditions, with validation in soybean cultivation. During critical soybean growth stages, UAV multispectral data of the canopy were collected, and ground measurements were conducted for three GPS-referenced 50 cm × 50 cm plots to obtain canopy leaf water content, coverage, and soil volumetric moisture at 20 cm intervals. Ten vegetation indices were constructed from multispectral data to explore statistical relationships between vegetation indices, surface soil moisture, canopy leaf water content, and deeper soil moisture. Predictive models were developed and evaluated. Results showed that the NDVI-based nonlinear regression model achieved the best performance for leaf water content (R2 = 0.725), and a significant correlation was found between canopy leaf water content and 0–20 cm soil moisture (R2 = 0.705), enabling predictions of deeper soil moisture. Surface soil models accurately estimated 0–200 cm soil moisture distribution (R2 = 0.9995). Daily water dynamics simulations provided robust support for precision irrigation management. This study demonstrates that UAV multispectral remote sensing combined with ground sampling is a valuable tool for soybean water management, supporting precision agriculture and sustainable water resource utilization.

To reduce the amount of pesticides in the environment, it is necessary to consider the wettability properties of pesticide droplets on the leaf surface to improve the spraying effect. The wettability properties of the droplet on the leaf surface are related not only to the properties of the liquid itself but also to the properties of the leaf surface. It is typically believed that leaf surface properties are difficult to control, and thus research has generally ignored this aspect of pesticide use. However, in the field environment, the structure and properties of the leaf surface can be altered by changing the moisture content of the soil where plants are grown. In this study, the roughness, contact angle, and surface free energy of the leaf surface were measured and calculated under different soil moisture contents to study the changes in the leaf surface wettability properties, with the aim of achieving efficient pesticide spraying by adjusting the soil water content. The results showed that the surface composition and microstructure of leaves were altered by the change in the soil moisture content, and the wettability properties of leaves decreased initially and then increased with a decrease in the soil moisture content. When the amount of soil water was sufficient or seriously insufficient, the wettability properties of the leaves were increased, but a lack of soil water may lead to irreversible damage to the plants. Therefore, before spraying pesticides on the leaf surfaces, the plants should be fully watered to improve the wettability properties of the leaf surface, which is conducive to the deposition and adhesion of pesticide droplets on the leaf surface and improved application effectiveness. The results of this study can provide a useful reference for the theoretical research and practices of precision spraying.

Field studies were conducted in the North China Plain (NCP) during the 2023–2024 season to investigate the vertical microclimate, yield, and yield-related characteristics of winter wheat during the grain-filling stage under no-till direct seeding and conventional tillage. The aim was to compare the differences in microclimate between the two tillage methods in wheat fields and the impact of microclimate on yield. The results indicated that, compared to conventional tillage, no-till direct seeding reduced the air temperature and increased the relative humidity of the air at 20 cm and 100 cm above the ground during the wheat grain-filling period. The soil moisture content at 20 cm below the ground under no-till direct seeding was higher than under conventional tillage during the early grain-filling stage. Seven days before the wheat harvest, the dry weight per plant and the dry weight per spike were significantly greater under no-till direct seeding than under conventional tillage. Consequently, the thousand-grain weight of no-till direct seeding was significantly higher than that of conventional tillage, with an increase of 7.9%. The number of wheat sterile spikelets under no-till direct seeding was significantly lower than that under conventional tillage. Furthermore, the number of grains per spike was higher than that of conventional tillage. Although the number of harvested spikes under no-till direct seeding was 10.8% lower than under conventional tillage, the increase in thousand-grain weight and the number of grains per spike compensated for the reduced number of harvested spikes. As a result, the grain yield of winter wheat under no-till direct seeding was higher than that of conventional tillage, increasing by 2.7%. Therefore, adopting no-till direct seeding in the NCP is conducive to increasing winter wheat production and efficiency, as well as supporting sustainable agricultural development.

Accurate estimation of soil moisture content (SMC) is vital for effective farmland water management and informed irrigation decision-making. The utilization of unmanned aerial vehicle (UAV)-based remote sensing technology to monitor SMC offers advantages such as mobility, high timeliness, and high spatial resolution, thereby compensating for the limitations of in-situ observations and satellite remote sensing. However, previous research has primarily focused on SMC diagnostics for the entire crop growth period, often neglecting the development of targeted soil moisture modeling paradigms that account for the specific characteristics of the canopy and root zone at different growth stages. Furthermore, the variations in soil moisture status between fields, resulting from the hysteresis of water flow in irrigation channels at different levels, may influence the development of soil moisture modeling schemes, an area that has been seldom explored. In this study, SMC models based on UAV spectral information were constructed using Random Forest (RF) and Particle Swarm Optimization-Support Vector Machine (PSO-SVM) algorithms. The soil moisture modeling paradigms (i.e., input–output mapping) under different growth stages and soil moisture conditions of summer maize were systematically compared and discussed, along with the corresponding physical interpretability. Our results showed that (1) the SMC modeling schemes differ significantly across the various growth stages, with distinct input–output mappings recommended for the early (i.e., jointing, tasselling, and silking stages), middle (i.e., blister and milk stages), and late (i.e., maturing stage) periods. (2) these machine learning-based models performed best at the jointing stage, while subsequently, their accuracy generally exhibited a downward trend as the maize grew. (3) the RF model demonstrates superior robustness in estimating soil moisture status across different fields (moisture conditions), achieving optimal estimation accuracy in fields with overall higher SMC in line with the PSO-SVM model. (4) unlike the RF model’s robustness in spatial SMC diagnostics, the PSO-SVM model more reliably captured the temporal dynamics of SMC across different growth stages of summer maize. This study offers technical references for future modelers in UAV-based SMC modeling across various spatial and temporal conditions, addressing both the types of models as well as their input features.

The accurate prediction of soil moisture content helps to evaluate the quality of farmland. Taking the black soil in the Nanguan District of Changchun City as the research object, this paper proposes a stacking ensemble learning model integrating hybrid neural networks to address the issue that it is difficult to improve the accuracy of inversion soil moisture content by a single model. First, raw hyperspectral data are processed by removing edge noise and standardization. Then, the gray wolf optimization (GWO) algorithm is adopted to optimize a convolutional neural network (CNN), and a gated recurrent unit (GRU) and an attention mechanism are added to construct a hybrid neural network model (GWO–CNN–GRU–Attention). To estimate soil water content, the hybrid neural network model is integrated into the stacking model along with Bagging and Boosting algorithms and the feedforward neural network. Experimental results demonstrate that the GWO–CNN–GRU–Attention model proposed in this paper can better predict soil water content; the stacking method of integrating hybrid neural networks overcomes the limitations of a single model’s instability and inferior accuracy. The relative prediction deviation (RPD), root mean square error (RMSE), and coefficient of determination (R2) on the test set are 4.577, 0.227, and 0.952, respectively. The average R2 and RPD increased by 0.056 and 1.418 in comparison to the base learner algorithm. The study results lay a foundation for the fast detection of soil moisture content in black soil areas and provide a data source for intelligent irrigation in agriculture.

Long-term intensive tillage has led to soil environment degradation, reduced fertility, and difficulty in increasing crop yield in the Mollisol region of northeast China. In order to improve the soil’shydrothermal environment and nutrient content, we conducted field experiments to investigate the effects of different tillage practices and the amount of straw mulching on soil hydrothermal environment and nutrient content in agricultural soils in seasonal permafrost areas. Four treatments were established: no-tillage without straw (NT0), no-tillage with half straw mulching (NT1), no-tillage with full straw mulching (NT2), and rotary tillage without straw (CK) as the control treatment. The results indicate that the no-tillage with straw mulching treatments increased the soil ammonium nitrogen (NH4+-N) and nitrate nitrogen (NO3−-N) content, accompanied by improvements in the soil’s water content and regulation of soil temperature changes, as compared to the CK treatment. Specifically, the soil’s NH4+-N and NO3−-N content in the NT2 treatment were significantly increased by 25.65% and 38.81%, respectively. Our study indicates that NT2 treatment is the most suitable tillage practice and straw-returning method in the Mollisol region of northeast China. This study can provide a theoretical basis and reference for the efficient utilization of farmland soil in seasonal permafrost areas.

With the increasing focus on irrigation management, it is crucial to consider cost-effective alternatives for soil water monitoring, such as multi-point monitoring with low-cost soil moisture sensors. This study assesses the accuracy and functionality of low-cost sensors in a sandy loam (SL) soil amended with biochar at rates of 15.6 and 31.2 tons/ha by calibrating the sensors in the presence of two nitrogen (N) and potassium (K) commercial fertilizers at three salinity levels (non/slightly/moderately) and six soil water contents. Sensors were calibrated across nine SL-soil combinations with biochar and N and K fertilizers, counting for 21 treatments. The best fit for soil water content calibration was obtained using polynomial equations, demonstrating reliability with R2 values greater than 0.98 for each case. After a second calibration, low-cost soil moisture sensors provide acceptable results concerning previous calibration, especially for non- and slightly saline treatments and at soil moisture levels lower than 0.17 cm3cm−3. The results showed that at low frequencies, biochar and salinity increase the capacitance detected by the sensors, with calibration curves deviating up to 30% from the control sandy loam soil. Due to changes in the physical and chemical properties of soil resulting from biochar amendments and the conductive properties influenced by fertilization practices, it is required to conduct specific and continuous calibrations of soil water content sensor, leading to better agricultural management decisions.

To explore the yield-increasing mechanism of mechanized potato planting with corn straw mulching, a two-year (2021 and 2022) field experiment was conducted to study the effects of mechanized no-tillage with straw mulching on potato yield and water use efficiency. This experiment included mechanized no-tillage potato planting with corn straw mulch covering (JG), plastic film mulching (HM), and open flat planting (CK). The results showed that mechanical no-tillage with straw mulching significantly affected soil water content in the 0–100 cm soil layer, yield, and water use efficiency (p < 0.05). There was no significant difference in yield between JG and HM, but it was significantly higher than that of CK. The yield of JG was 3.09~12.27% higher than that of CK. The yield increase was mainly achieved by increasing the potato weight per plant (0.697~0.862 **) and the average single potato weight (0.048~0.631). The tuber weight per plant was positively correlated with the plant height at the seedling stage (0.03~0.92 **) and positively correlated with the dry weight of stems and leaves at the tuber expansion stage and starch accumulation stage (0.74 **~0.95 **). It was negatively correlated with the number of branches at the tuber formation stage (−0.33~−0.88 **). Compared with CK, JG could significantly improve the water use efficiency of potatoes and reduce water consumption during the whole growth period of crops. In 2021, JG was 6.5% higher than CK, and HM was 6.88% lower than CK. In 2022, JG and HM increased water use efficiency by 26.17% and 14.50% compared with CK. When HM is applied in heavy soil areas, soil compaction can easily occur, which affects seedling emergence and reduces yield. At the same time, JG has strong adaptability to soil types and good yield stability. It can be seen that JG is a green and efficient mechanized potato cultivation technology suitable for dry farming areas.

The distribution of vegetation in coastal wetlands is significantly influenced by soil properties. However, the mechanisms of how soil characteristics impact the physiological processes of Tamarix chinensis forests remain underexplored. This study examined changes in the soil physicochemical properties and structural attributes of natural T. chinensis forests in the Yellow River Delta with increasing distance from the shoreline. T. chinensis trees were classified into healthy, intermediate, and dying categories based on growth potential, and dynamic changes in salt ions and non-structural carbohydrates (NSCs) were investigated. Results indicated that increasing distance from the shoreline corresponded to decreased soil salinity and pH, and increased soil moisture. T. chinensis mortality rate decreased, while tree height and ground diameter increased with distance. Soil salt content was positively correlated with T. chinensis mortality, but negatively correlated with tree height and ground diameter. Trees with lower growth potential had higher Na+ but lower K+ and K+/Na+ ratio. Soil salt content was positively correlated with root and stem Na+, while soil moisture was positively correlated with leaf NSCs. These findings suggest that soil salt content and moisture significantly influence T. chinensis ion absorption and NSC accumulation, with sodium toxicity being a key factor in the spatial distribution of T. chinensis forests.

By integrating the thermal characteristics from thermal-infrared remote sensing with the physiological and structural information of vegetation revealed by multispectral remote sensing, a more comprehensive assessment of the crop soil-moisture-status response can be achieved. In this study, multispectral and thermal-infrared remote-sensing data, along with soil-moisture-content (SMC) samples (0~20 cm, 20~40 cm, and 40~60 cm soil layers), were collected during the flowering stage of soybean. Data sources included vegetation indices, texture features, texture indices, and thermal-infrared vegetation indices. Spectral parameters with a significant correlation level (p < 0.01) were selected and input into the model as single- and fuse-input variables. Three machine learning methods, eXtreme Gradient Boosting (XGBoost), Random Forest (RF), and Genetic Algorithm-optimized Backpropagation Neural Network (GA-BP), were utilized to construct prediction models for soybean SMC based on the fusion of UAV multispectral and thermal-infrared remote-sensing information. The results indicated that among the single-input variables, the vegetation indices (VIs) derived from multispectral sensors had the optimal accuracy for monitoring SMC in different soil layers under soybean cultivation. The prediction accuracy was the lowest when using single-texture information, while the combination of texture feature values into new texture indices significantly improved the performance of estimating SMC. The fusion of vegetation indices (VIs), texture indices (TIs), and thermal-infrared vegetation indices (TVIs) provided a better prediction of soybean SMC. The optimal prediction model for SMC in different soil layers under soybean cultivation was constructed based on the input combination of VIs + TIs + TVIs, and XGBoost was identified as the preferred method for soybean SMC monitoring and modeling, with its R2 = 0.780, RMSE = 0.437%, and MRE = 1.667% in predicting 0~20 cm SMC. In summary, the fusion of UAV multispectral and thermal-infrared remote-sensing information has good application value in predicting SMC in different soil layers under soybean cultivation. This study can provide technical support for precise management of soybean soil moisture status using the UAV platform.

Accurate estimation of soil moisture content (SMC) in the field is a critical aspect of precise irrigation management. The development of unmanned aerial vehicle (UAV) platforms has provided an economically efficient means for field-scale SMC measurements. However, previous studies have mostly focused on single-sensor estimates of SMC. Additionally, the lack of differentiation between various crops and their growth stages has resulted in an unclear understanding of how crop types and growth stages affect the accuracy of SMC estimation at different soil depths. Therefore, the purpose of this paper was to use UAV multimodal remote sensing and a machine learning algorithm to estimate the SMC in agricultural fields and investigate estimation’s effectiveness under different scenarios. The results indicated the following: (1) The multispectral remote sensing method provided higher accuracy in SMC estimation compared to thermal infrared remote sensing. Moreover, the integration of multimodal data improved the accuracy of SMC estimation, enhancing the coefficient of determination (R2) by approximately 14% over that achieved through the use of multispectral data alone and 39% over that of thermal infrared data alone. (2) Across the entire growth period, the optimal soil depths of SMC estimation for soybean were 10 cm and 20 cm (average R2 were 0.81 and 0.82, respectively), while for corn, they were 10 cm, 20 cm, and 40 cm (average R2 were 0.59, 0.60, and 0.55, respectively). (3) The SMC estimation model performed better for both crops during the first three growth stages, with accuracy declining in the maturity stage. These results demonstrate that this approach can provide relatively accurate root zone SMC estimates for different crops throughout their main growth periods. Thus, it can be employed for SMC monitoring and precision irrigation system design.

Many scholars have used samples of drying soil to observe the amount of soil condensation water, which may have exaggerated the amount of regional condensation water, as the soil water content in the natural state was not considered. To analyze the effect of soil moisture content on soil condensation in the natural state, the soil condensation volume in loess and sandy soil under different gravimetric moisture content conditions was measured in the autumn of 2022 and spring of 2023 using micro-lysimeters combined with the weighing method at the groundwater and environment site of Chang’an University, Guanzhong Basin, China. The results showed that the average daily condensation of loess under 0%, 3%, 7%, 11%, and 15% moisture content conditions was 0.34, 0.09, 0.01, −0.05, and −0.07 mm with the condensation days of 54, 40, 24, 21, and 16, respectively, and that of sandy soil was 0.21, −0.02, −0.28, −0.82, and −1.35 mm with condensation days of 54, 24, 2, 3, and 1, respectively. Under the same moisture content conditions, loess had more condensation and condensation days than sandy soil. Soil condensation water occurred mainly when the water content was less than 7%, and it was negatively correlated with soil water content. The quantity of soil condensation water was positively correlated with relative humidity and negatively correlated with air temperature–dew point differences. Relative humidity exceeding 60% and air temperature–dew point differences below 8 °C were more favorable for the formation of soil condensation water. This study provides a reference for the assessment of soil condensation in the natural state.

Efficient acquisition of crop leaf moisture information holds significant importance for agricultural production. This information provides farmers with accurate data foundations, enabling them to implement timely and effective irrigation management strategies, thereby maximizing crop growth efficiency and yield. In this study, unmanned aerial vehicle (UAV) multispectral technology was employed. Through two consecutive years of field experiments (2021–2022), soybean (Glycine max L.) leaf moisture data and corresponding UAV multispectral images were collected. Vegetation indices, canopy texture features, and randomly extracted texture indices in combination, which exhibited strong correlations with previous studies and crop parameters, were established. By analyzing the correlation between these parameters and soybean leaf moisture, parameters with significantly correlated coefficients (p < 0.05) were selected as input variables for the model (combination 1: vegetation indices; combination 2: texture features; combination 3: randomly extracted texture indices in combination; combination 4: combination of vegetation indices, texture features, and randomly extracted texture indices). Subsequently, extreme learning machine (ELM), extreme gradient boosting (XGBoost), and back propagation neural network (BPNN) were utilized to model the leaf moisture content. The results indicated that most vegetation indices exhibited higher correlation coefficients with soybean leaf moisture compared with texture features, while randomly extracted texture indices could enhance the correlation with soybean leaf moisture to some extent. RDTI, the random combination texture index, showed the highest correlation coefficient with leaf moisture at 0.683, with the texture combination being Variance1 and Correlation5. When combination 4 (combination of vegetation indices, texture features, and randomly extracted texture indices) was utilized as the input and the XGBoost model was employed for soybean leaf moisture monitoring, the highest level was achieved in this study. The coefficient of determination (R2) of the estimation model validation set reached 0.816, with a root-mean-square error (RMSE) of 1.404 and a mean relative error (MRE) of 1.934%. This study provides a foundation for UAV multispectral monitoring of soybean leaf moisture, offering valuable insights for rapid assessment of crop growth.

Soil moisture content plays a vital role in agricultural production, significantly influencing crop growth, development, and yield. Thoroughly understanding the specific soil moisture content in cotton fields is crucial for enhancing agricultural efficiency and driving sustainable agricultural development. This study utilized the gradient-boosting regression–random forest (GBR-RF) algorithm and the GBR and RF algorithms separately, in conjunction with Sentinel-2 satellite images, to estimate cotton soil moisture content, focusing on the B1–B8 bands and in particular the sensitive B6, B7, and B8 bands. The soil data in the jujube orchard of the study area were collected using soil augers at a depth of 30 cm, with soil data collected from a depth of 20 to 30 cm. The findings revealed that the integrated learning algorithm GBR-RF demonstrated high accuracy, with R2, MAE, and MSE results of 0.8838, 1.0121, and 1.6168, respectively. In comparison, the results using just the GBR algorithm yielded R2, MAE, and MSE values of 0.8158, 1.1327, and 1.9645, respectively, while those obtained from the RF algorithm were 0.8415, 1.0680, and 1.8331, respectively. These results indicate that the algorithms exhibited strong generalization, robustness, and accuracy, with GBR-RF outperforming GBR and RF by 8.34% and 5.03%, respectively, in combination with using the B1–B8 bands for inversion. Furthermore, utilizing the full-band data resulted in R2 values that were up to 24.27% higher than those of the individual bands, affirming the efficacy of band combinations for improved accuracy. This study’s demonstration of the positive impact of integrated learning algorithms on estimating cotton soil moisture content underscores the advantages of multi-band data combinations over single-band data, highlighting their ability to enhance accuracy without significantly impacting errors. Importantly, this study’s findings, while not limited to a single experimental field, have broad applicability in cotton precision agriculture, offering valuable insights for research on yield enhancement and agricultural efficiency.

Cyperus esculentus is a hard-to-control, destructive perennial weed propagating and spreading through rhizomes and tubers. Currently, a combination of mechanical, cultural, and chemical measures sustained over time is required for satisfactory control of arable crops. Hood steaming is a promising thermal technique for rapid depletion of the soil tuber bank. The effect of hood steaming on C. esculentus tuber vitality was investigated using a dose–response experiment on two localities in Belgium. Steaming was performed for five durations (2, 4, 8, 16, and 32 min). Moreover, the effects of tuber burial depth (5, 15, and 25 cm), genetic C. esculentus clone (three different clones), and pedohydrological parameters (sandy and sandy, loamy soil) on hood steaming efficacy were determined. Additionally, the heat sensitivity of genetically diverse C. esculentus tubers was investigated in a laboratory incubator experiment in which incubation temperature and duration varied. To control tubers at depths of 5, 15, and 25 cm, treatment durations of 8, 16, and 32 min were required, respectively. In general, clones producing large tubers showed lower heat sensitivity. As pedohydrological parameters may affect heat transfer into the soil, they may affect steaming efficacy as well. To obtain complete control of C. esculentus tubers, soil temperature should be 50 °C or more for at least 42 min. Hood steaming is a highly suitable alternative technique for a rapid and strong depletion of the soil tuber bank in small well-delineated C. esculentus patches.

Drone multispectral technology enables the real-time monitoring and analysis of soil moisture across vast agricultural lands. overcoming the time-consuming, labor-intensive, and spatial discontinuity constraints of traditional methods. This study establishes a rapid inversion model for deep soil moisture (0–200 cm) in dryland agriculture using data from drone-based multispectral remote sensing. Maize, millet, sorghum, and potatoes were selected for this study, with multispectral data, canopy leaf, and soil moisture content at various depths collected every 3 to 6 days. Vegetation indices highly correlated with crop canopy leaf moisture content (p < 0.01) and were identified using Pearson correlation analysis, leading to the development of linear and nonlinear regression models for predicting moisture content in canopy leaves and soil. The results show a significant linear correlation between the predicted and actual canopy leaf moisture levels for the four crops, according to the chosen vegetation indices. The use of canopy leaf moisture content to predict surface soil moisture (0–20 cm) demonstrated enhanced accuracy. The models designed for the top 20 cm of soil moisture successfully estimated deep soil moisture levels (up to 200 cm) for all four crops. The 20 cm range soil moisture model showed improvements over the 10 cm range model, with increases in Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Coefficient of Determination (R2), and Nash–Sutcliffe Efficiency Coefficient (NSE) by 0.4, 0.8, 0.73, and 0.34, respectively, in the corn area; 0.28, 0.69, 0.48, and 0.25 in the millet area; 0.4, 0.48, 0.22, and 0.52 in the sorghum area; and 1.14, 0.81, 0.73, and 0.56 in the potato area, all with an average Relative Error (RE) of less than 10% across the crops. Using drone-based multispectral technology, this study forecasts leaf water content via vegetation index analysis, facilitating swift and effective soil moisture inversion. This research introduces a novel method for monitoring and managing agricultural water resources, providing a scientific basis for precision farming and moisture variation monitoring in dryland areas.

A tremendous number of landmines has been buried during the last decade. In recent years, various autonomous platforms equipped with ground-penetrating radars (GPRs) have been proposed for the detection of landmines. These systems have already demonstrated their performance in controlled environments with known ground truth. However, it has been observed that the influence of surface conditions in the form of vegetation and roughness as well as soil moisture content significantly reduce the detection probability. The influence of these individual factors on a ground-offset GPR is presented and discussed in this work. Each of these factors significantly degrades the backscattered signal. With increasing soil moisture, the signal gets attenuated more strongly; however, the signature is maintained in the phase of the C-Scans. An increase in surface roughness deteriorates the target pattern making it difficult to detect buried objects unambiguously. Vegetation, especially with irregular leaf structures, can appear as a ghost target and scatter the electromagnetic waves. In most cases, the target is easier to detect in the phase of the B- or C-Scan.

This study adopted the method of “exchanging space for time” and set up three experimental groups based on the shape, degree of damage, and degree of humification of the litter, namely the undecomposed layer, the semi-decomposed layer, and the decomposed layer. Using typical slopes of arbor and bamboo forests in the Pi River Basin as the research object, from October 2021 to December 2022, the soil carbon release flux was measured by using a closed static chamber gas chromatography method to reveal the carbon release law at the soil–air interface during the decomposition process of litter and quantitatively characterize the dynamic impact of the litter decomposition process on soil carbon release flux. Results showed that soil methane flux remained negative (sink) while soil carbon dioxide flux was positive (source) in both litter-covered and bare soil conditions. The methane and carbon dioxide release from soil was positively correlated with and significantly influenced by environmental factors such as soil moisture content and temperature. The methane release flux from soil showed a linear fitting relationship with soil moisture content and temperature, while the carbon dioxide release flux from soil was more in line with the exponential fitting relationship with soil moisture content and temperature. However, there were significant differences in the roles of various factors under different types of litter.

The accurate measurement of soil moisture content emerges as a critical parameter within the ambit of agricultural irrigation management, wherein the precise prediction of this variable plays an instrumental role in enhancing the efficiency and conservation of agricultural water resources. This study introduces an innovative, cutting-edge hybrid model that ingeniously integrates Gated Recirculation Unit (GRU) and Transformer technologies, meticulously crafted to amplify the precision and reliability of soil moisture content forecasts. Leveraging meteorological and soil moisture datasets amassed from eight monitoring stations in Hebei Province, China, over the period from 2011 to 2018, this investigation thoroughly assesses the model’s efficacy against a diverse array of input variables and forecast durations. This assessment is concurrently contrasted with a range of conventional machine learning and deep learning frameworks. The results demonstrate that (1) the GRU–Transformer model exhibits remarkable superiority across various aspects, particularly in short-term projections (1- to 2-day latency). The model’s mean square error (MSE) for a 1-day forecast is notably low at 5.22%, reducing further to a significant 2.71%, while the mean coefficient of determination (R2) reaches a high of 89.92%. Despite a gradual increase in predictive error over extended forecast periods, the model consistently maintains robust performance. Moreover, the model shows exceptional versatility in managing different soil depths, notably excelling in predicting moisture levels at greater depths, thereby surpassing its performance in shallower soils. (2) The model’s predictive error inversely correlates with the reduction in parameters. Remarkably, with a streamlined set of just six soil moisture content parameters, the model predicts an average MSE of 0.59% and an R2 of 98.86% for a three-day forecast, highlighting its resilience to varied parameter configurations. (3) In juxtaposition with prevalent models such as Support Vector Regression (SVR), K-Nearest Neighbors (KNN), Gradient Boosting Decision Tree (GBDT), XGBoost, Random Forest, and deep learning models like Deep Neural Network (DNN), Convolutional Neural Network (CNN), and standalone GRU-branch and Transformer-branch models, the GRU–Transformer framework demonstrates a significant advantage in predicting soil moisture content with enhanced precision for a five-day forecast. This underscores its exceptional capacity to navigate the intricacies of soil moisture data. This research not only provides a potent decision-support tool for agricultural irrigation planning but also makes a substantial contribution to the field of water resource conservation and optimization in agriculture, while concurrently imparting novel insights into the application of deep learning techniques in the spheres of agricultural and environmental sciences.

The Hapke model has been widely used in the field of soil remote sensing. However, the latest development of the Hapke model (i.e., Hapke-HSR model) adopted a simple hypothesis to consider the influence of the soil moisture content (SMC), which brought great difficulties to SMC parameter inversion. This paper presents a method to improve the Hapke model using the improved multilayer radiative transfer model of soil reflectance (MARMIT-2), which can effectively improve the ability of the Hapke-HSR model to characterize the variation in the SMC. Finally, we used the soil database to comprehensively verify the ability of the improved Hapke model. The results show that the improved Hapke can effectively characterize the spectral characteristics of soil and show a higher fitting accuracy (RMSE = 0.009) compared with the Hapke-HSR model (RMSE = 0.031), especially at a high SMC (≥30%). Therefore, the improved Hapke model can better understand soil physical properties and improve the inversion accuracy of soil–vegetation physical parameters, which can be used to enhance agricultural water use efficiency.

Applying hyperspectral remote sensing technology to the prediction of soil moisture content (SMC) during the growth stage of soybean emerges as an effective approach, imperative for advancing the development of modern precision agriculture. This investigation focuses on SMC during the flowering stage under varying nitrogen application levels and film mulching treatments. The soybean canopy’s original hyperspectral data, acquired at the flowering stage, underwent 0–2-order differential transformation (with a step size of 0.5). Five spectral indices exhibiting the highest correlation with SMC were identified as optimal inputs. Three machine learning methods, namely support vector machine (SVM), random forest (RF), and back propagation neural network (BPNN), were employed to formulate the SMC prediction model. The results indicate the following: (1) The correlation between the optimal spectral index of each order, obtained after fractional differential transformation, and SMC significantly improved compared to the original hyperspectral reflectance data. The average correlation coefficient between each spectral index and SMC under the 1.5-order treatment was 0.380% higher than that of the original spectral index, with mNDI showing the highest correlation coefficient at 0.766. (2) In instances of utilizing the same modeling method with different input variables, the SMC prediction model’s accuracy follows the order: 1.5 order > 2.0 order > 1.0 order > 0.5 order > original order. Conversely, with consistent input variables and a change in the modeling method, the accuracy order becomes RF > SVM > BPNN. When comprehensively assessing model evaluation indicators, the 1.5-order differential method and RF method emerge as the preferred order differential method and model construction method, respectively. The R2 for the optimal SMC estimation model in the modeling set and validation set were 0.912 and 0.792, RMSEs were 0.005 and 0.004, and MREs were 2.390% and 2.380%, respectively. This study lays the groundwork for future applications of hyperspectral remote sensing technology in developing soil moisture content estimation models for various crop growth stages and sparks discussions on enhancing the accuracy of these different soil moisture content estimation models.

Cyperus esculentus is considered the sixteenth worst weed in the world. The weed causes huge losses in arable crops. Current control strategies are based on combinations of chemical and mechanical methods, repeated over years, and aim to deplete the belowground bud bank. However, this is a slow process. Anaerobic soil disinfestation (ASD) using readily decomposable carbon sources may be a promising innovative method to quickly deplete the bud bank. This study investigated the effect of ASD with fresh grass clippings (dosage of 80 tonnes ha−1) differing in C:N ratio and Herbie® (consists of organic by-products from the food processing industry, dosage of 25 tonnes ha−1) on the vitality of small and large C. esculentus tubers buried at three depths (5, 15, and 30 cm) into two soils differing in soil type and soil moisture content. Their effects were compared with the effect of chemical soil disinfestation (CSD) with metam-sodium (153 kg ha−1). ASD with Herbie® showed at least equal performance compared with CSD with metam-sodium, with reductions in tuber vitality up to 97.5%. The performance of ASD with grass clippings was less consistent across soils and was affected by the C:N ratio of the grass. Both ASD and CSD showed the highest performance in moist, sandy soil and on small tubers. ASD is an effective and promising method to quickly deplete the C. esculentus bud bank, provided that the soil is sandy and moist, the carbon source has a C:N ratio of about 10, and the incorporation depth is at least 25 cm. To foster the implementation of ASD, future research should evaluate its performance consistency across environments and years.

To solve the problem of low straw-cutting efficiency of single-disc openers of no-till planters under conditions of high soil moisture content, a single-disc furrowing and straw-cutting device was designed based on the support-cutting principle. To improve the straw-cutting ability of the disc opener when it operates under high-moisture-content soil conditions and to make sure that the straw that is not cut by the disc coulter can be cut smoothly by the disc opener, the support shovel was designed, and the operation mechanism of the support shovel device was analyzed. The soil moisture content, the support shovel’s entry angle, the support shovel’s entry gap angle, and the support shovel’s tip margin were identified as the factors influencing the device design through the theoretical analysis of the furrowing and straw-cutting device. Through the discrete element method (DEM), a single-factor simulation test was first conducted to analyze how different soil moisture contents affected the device’s ability to cut straw, and the results showed that the number of broken bonds was lowest when the soil moisture content was 20 ± 1%, and the time taken for the straw to be wholly cut off was also the longest. Then, a quadratic orthogonal simulation test was conducted to construct a regression model and optimize the parameters at the soil moisture content of 20 ± 1%, and the results revealed that the significant order of each factor’s influence on the number of broken bonds is as follows: entry gap angle, entry angle, and shovel tip margin. In addition, the device’s overall operation quality was better when the entry angle was 49°, the entry gap angle was 0°, and the shovel tip margin was 10 mm. At this time, the number of broken bonds was predicted to be 506. Finally, the simulation validation test was run, and the number of broken bonds was obtained to be 478, with a relative error of 5.6% from the predicted value. According to the optimal parameters to complete the device trial production and field test, the results show that the average cut-off rate of the device is 71.7%, the stability coefficient of the furrowing depth is 90.87%, and the performance of the furrow opening is excellent, which meets the requirements of a no-tillage seeding operation. This study can provide a reference for the design and improvement of no-tillage seeding machines under conditions of high soil moisture content.

Soil pore structure and soil water content are critical regulators of microbial activity and associated carbon dioxide (CO2) emissions. This study evaluated the impacts of soil bulk density and matric potential on carbon dioxide (CO2) emissions through modifications of total porosity, air-filled porosity, water retention, and gas diffusivity. Soil samples were manipulated into four bulk densities (1.0, 1.1, 1.2, and 1.3 Mg m−3) and ten matric potential levels (−1, −2, −3, −4, −5, −6, −7, −8, −9, and −10 kPa) in controlled soil cores. The results showed that lower bulk densities enhanced while higher densities suppressed CO2 emissions. Similarly, wetter matric potentials decreased fluxes, but emission increased with drying. Correlation and regression analyses revealed that total porosity (r = 0.28), and gravimetric water content (r = 0.29) were strongly positively related to CO2 emissions. In contrast, soil bulk density (r= −0.22) and matric potential (r= −0.30) were negatively correlated with emissions. The results highlight that compaction and excessive water content restrict microbial respiration and gas diffusion, reducing CO2 emissions. Proper management of soil structure and water content is therefore essential to support soil ecological functions and associated ecosystem services.

Soil dust generated by explosions can lead to the absorption and scattering of lasers, resulting in low detection and recognition accuracy for laser-based devices. Field tests to assess laser transmission characteristics in soil explosion dust are dangerous and involve uncontrollable environmental conditions. Instead, we propose using high-speed cameras and an indoor explosion chamber to assess the backscattering echo intensity characteristics of lasers in dust generated by small-scale explosive blasts in soil. We analyzed the influence of the mass of the explosive, depth of burial, and soil moisture content on crater features and temporal and spatial distributions of soil explosion dust. We also measured the backscattering echo intensity of a 905 nm laser at different heights. The results showed that the concentration of soil explosion dust was highest in the first 500 ms. The minimum normalized peak echo voltage ranged from 0.318 to 0.658. The backscattering echo intensity of the laser was found to be strongly correlated with the mean gray value of the monochrome image of soil explosion dust. This study provides experimental data and a theoretical basis for the accurate detection and recognition of lasers in soil explosion dust environments.

Drought is a severe threat to agriculture production that affects all growth stages of plants, including corn (Zea mays L.). Any factor affecting early seedling growth and development will significantly impact yield. Despite the recurrence of low rainfall during the growing seasons, corn responses to different early-season soil moisture content levels have not been investigated. In this study, we investigated how corn morpho-physiological and biomass traits responded to varied soil moisture content during the early vegetative stage. Two corn hybrids were grown in a pot-culture facility under five different soil moisture treatments (0.15, 0.12, 0.09, 0.06, and 0.03 m3 m−3 volumetric water content, VWC) to assess the growth and developmental responses to varied soil moisture content during early-season growth (V2 to V7) stage. Sub-optimal soil moisture content limited plant growth and development by reducing physiological and phenotypic expression. Stomatal conductance and transpiration were decreased by an average of 65% and 59% across stress treatments relative to optimum conditions. On average, soil moisture deficit reduced the total leaf area by 71% and 72% compared to the control in ‘A6659VT2RIB’ and ‘P1316YHR’, respectively. Shoot and root dry weights were reduced by 74% and 43% under 0.03 m3 m−3 VWC. An increase in the root-to-shoot ratio was noticed under low VWC conditions compared to the control. Based on the stress tolerance index, the physiology and leaf growth parameters were more sensitive to soil moisture deficit. Our results highlight the impact of sub-optimal soil moisture on physiology and morphological traits during early-season growth. ‘P1316YHR’ demonstrated better physiological performance under stress conditions, while ‘A6659VT2RIB’ produced relatively better root growth. The findings suggest that biomass partitioning between shoot and root components is dynamic and depends on stress intensity. The current findings can help to prioritize traits associated with the early-season drought tolerance in corn. The functional relationships developed between soil moisture content and growth and developmental responses can be integrated into corn crop modeling to allow better irrigation management decisions.

Optimal winter irrigation may be an alternative method for controlling soil salinization under seasonal freezing and thawing conditions in the Yellow River Delta. However, few studies have focused on optimal winter irrigation based on the dynamics of soil water, heat, and salt during the freezing–thawing period in this region. Taking the seedling stage of winter wheat in the Yellow River Delta as the research object and using observation data of hydrothermal salt from the Shandong agricultural high-tech demonstration base from 17 October 2019 to 15 June 2021, a numerical simulation of the hydrothermal coupling process of freeze–thaw soil in the experimental area was carried out through the Simultaneous Heat and Water Model (SHAW). The simulation results of the model were evaluated according to the mean error (ME), root mean square error (RMSE), and Nash efficiency coefficient (NSE). The results showed that the SHAW can well simulate the soil moisture (SM), soil temperature, and soil salt during the growth of winter wheat in this region: the SM at the depth of 0–80 cm with an ME < 0.038 cm3 cm−3, RMSE < 0.064 cm3 cm−3, and NSE > 0.669; the soil temperature with an ME < 1.311 °C, RMSE < 1.493 °C, and NSE > 0.738; and the soil salinity with an ME < 0.005 g kg−1, RMSE < 0.014 g kg−1, and NSE > 0.607. Moreover, the model was used to simulate the distribution of soil water and salt in the winter wheat seedling stage under different winter irrigation methods during wet, normal, and dry years. It was suggested that the appropriate winter irrigation amount was 80 mm in wet years and normal years and 100 mm in dry years, which could be beneficial to winter wheat growth during the seedling stage. These results provide a reference for irrigation optimization in the Yellow River Delta and other similar areas.

Machine learning (ML) is a branch of artificial intelligence (AI) that has been successfully applied in a variety of remote sensing applications, including geophysical information retrieval such as soil moisture content (SMC). Deep learning (DL) is a subfield of ML that uses models with complex structures to solve prediction problems with higher performance than traditional ML. In this study, a framework based on DL was developed for SMC retrieval. For this purpose, a sample dataset was built, which included synthetic aperture radar (SAR) backscattering, radar incidence angle, and ground truth data. Herein, the performance of five optimized ML prediction models was evaluated in terms of soil moisture prediction. However, to boost the prediction performance of these models, a DL-based data augmentation technique was implemented to create a reconstructed version of the available dataset. This includes building a sparse autoencoder DL network for data reconstruction. The Bayesian optimization strategy was employed for fine-tuning the hyperparameters of the ML models in order to improve their prediction performance. The results of our study highlighted the improved performance of the five ML prediction models with augmented data. The Gaussian process regression (GPR) showed the best prediction performance with 4.05% RMSE and 0.81 R2 on a 10% independent test subset.

Inoculating microbial inoculants to speed up the decomposition of returning straw is currently a hot topic. Meanwhile, the soil moisture content (SMC) could change the diversity, abundance, and metabolism of the soil microbial community structure, which affects the straw degradation rate under the straw returning condition. In this research, rumen microorganisms with strong decomposing abilities in natural systems were used as inoculants to promote straw decomposing and returning to the field. The effects of the SMC on straw decomposition under rumen fluid (RF)-induced returning were investigated. Experiments were conducted for 30 days with typical paddy soil in the south of China under conditions of 30%, 70%, and 100% SMC. With an increase in the SMC within a certain range (30~100%), the decomposition rate of straw showed a trend of first rising and then falling. Treatments of 70% SMC with RF addition generally achieved the maximum rate of straw degradation. The peak value was 49.96%, which was 2.67-fold higher than the treatments of 30% SMC with RF addition (18.74%) and 24.1% higher than those of the control with 70% SMC (40.3%) (p < 0.05). Moreover, a straw structural analysis proved that at 70% SMC, microorganisms from RF favored the destruction of functional groups on the straw surface and the degradation of cellulose. Meanwhile, it was shown that RF could promote the decay of straw, leading to increments in enzyme activities and soil nutrients. The higher the soil moisture content, the higher the key soil enzyme activities. This indicates that the diversity and abundance of cellulose-degrading bacteria and fungi in soil microorganisms and rumen microorganisms were changed with different soil moisture contents. The experimental findings suggest an innovative way to further utilize rumen microorganisms.

Near-surface soil moisture content variation is a major factor in the frequent shallow slope failures observed on Mississippi’s highway slopes built on expansive clay. Soil moisture content variation is monitored generally through borehole sensors in highway embankments and slopes. This point monitoring method lacks spatial resolution, and the sensors are susceptible to premature failure due to wear and tear. In contrast, Unmanned/Uncrewed Aerial Vehicles (UAVs) have higher spatial and temporal resolutions that enable more efficient monitoring of site conditions, including soil moisture variation. The current study focused on developing two methods to predict soil moisture content (θ) using UAV-captured optical and thermal combined with machine learning and statistical modeling. The first method used Red, Green, and Blue (RGB) color values from UAV-captured optical images to predict θ. Support Vector Machine for Regression (SVR), Extreme Gradient Boosting (XGB), and Multiple Linear Regression (MLR) models were trained and evaluated for predicting θ from RGB values. The XGB model and MLR model outperformed the SVR model in predicting soil moisture content from RGB values. The R2 values for the XGB and MLR models were >0.9 for predicting soil moisture when compared to SVR (R2 = 0.25). The Root Mean Square Error (RMSE) for XGB, SVR, and MLR were 0.009, 0.025, and 0.01, respectively, for the test dataset, affirming that XGB was the best-performing model among the three models evaluated, followed by MLR and SVR. The better-performing XGB and MLR models were further validated by predicting soil moisture using unseen input data, and they provided good prediction results. The second method used Diurnal Land Surface Temperature variation (ΔLST) from UAV-captured Thermal Infrared (TIR) images to predict θ. TIR images of vegetation-covered areas and bare ground areas of the highway embankment side slopes were processed to extract ΔLST amplitudes. The underlying relationship between soil surface thermal inertia and moisture content variation was utilized to develop a predictive model. The resulting single-parameter power curve fit model accurately predicted soil moisture from ΔLST, especially in vegetation-covered areas. The power curve fit model was further validated on previously unseen TIR, and it predicted θ with an accuracy of RMSE = 0.0273, indicating good prediction performance. The study was conducted on a field scale and not in a controlled environment, which aids in the generalizability of the developed predictive models.

The strip mulch of corn straw planting technique is widely used in the Northwest rain-fed agricultural region of China due to the region’s good soil moisture-temperature properties. However, the hydrothermal properties and yield effects of this planting technique on different potato varieties are not clear. Therefore, a field experiment was conducted for two years (2015 and 2016) in the rain-fed area of Northwest China, to study the effects of different mulching treatments on the yield and water use efficiency (WUE) of potato with different maturation characteristics (potato varieties Longshu 7 and LK99). A split-plot experimental design was used. The experiment included corn straw strip flat cover planting (SMF), plastic film mulching (PMF), corn straw strip ditch mulching and ridge planting (SMFR), straw flat fully covered (SMWF), and uncovered and flat planting (CK). The results showed that straw mulch and plastic film mulch had significant effects on yield and WUE (p < 0.05). Compared with CK, SMF significantly increased the yield by 3.75–63.17% in the late-maturing varieties, and increased the yield by 24.96–79.02% in the early-maturing varieties. Among the mulching treatments, compared with SMF, PMF made no significant difference in the yield and WUE between the late-maturing varieties in the normal year and the early-maturing varieties in the dry year, but the yield and WUE of the early-maturing varieties in the normal year significantly decreased by 17.25% and 18.44%, respectively, and the yield and WUE of the late-maturing varieties in the dry year significantly increased by 27.57% and 29.26% respectively. Compared with SMF, SMFR reduced yield by 1.44–13.62%; SMWF decreased the yield of late-maturing varieties by 25.94% (p < 0.05), and WUE decreased by 7.65–23.44% (p < 0.05). It can be seen that under this experimental condition, SMF is more suitable for early-maturing varieties and PMF is more suitable for late-maturing varieties.

Drought is among the major abiotic stresses on rice production that can cause yield losses of up to 100% under severe drought conditions. Neither of the rice varieties currently grown in Burundi can withstand very low and irregular precipitation. This study identified genotypes that have putative quantitative trait loci (QTLs) associated with drought tolerance and determined their performance in the field. Two hundred and fifteen genotypes were grown in the field under both drought and irrigated conditions. Genomic deoxyribonucleic acid (DNA) was extracted from rice leaves for further genotypic screening. The results revealed the presence of the QTLs qDTY12.1, qDTY3.1, qDTY2-2_1, and qDTY1.1 in 90%, 85%, 53%, and 22% of the evaluated genotypes, respectively. The results of the phenotypic evaluation showed a significant yield reduction due to drought stress. Yield components and other agronomic traits were also negatively affected by drought. Genotypes having high yield best linear unbiased predictions (BLUPs) with two or more major QTLs for drought tolerance, including IR 108044-B-B-B-3-B-B, IR 92522-45-3-1-4, and BRRI DHAN 55 are of great interest for breeding programs to improve the drought tolerance of lines or varieties with other preferred traits.

The global navigation satellite system–interferometric reflectometry (GNSS-IR) was developed more than a decade ago to monitor soil moisture content (SMC); a system that is essentially finished has emerged. The standard GNSS-IR model typically considers soil to be a single layer of medium and measures the average SMC between 1 and 10 cm below the soil surface. The majority of the SMC is not distributed uniformly along the longitudinal axis. This study is based on a simulation platform and suggests a SMC-stratified measurement model that can be used to recover the SMC at different depths in the sink and reverse osmosis to address the issue that conventional techniques cannot accurately measure soil moisture at different depths. The soil moisture of each layer was assessed by utilizing the GNSS signals reflected by various soil layers, and this study employed total transmission when the vertical linearly polarized component of the electromagnetic wave was conveyed by the GNSS signal reflected by the soil. This work employed the Hilbert transform to obtain the interference signal envelope, which increases the visibility of the interference signal’s “notch” and reduces the burr impact of the interference signal brought on by ambient noise. The accuracy of the SMC measurement at the bottom declines due to the soil’s attenuation of the GNSS signal power, but the correlation between the predetermined value and SMC retrieved by the GNSS-IR multilayer SMC measurement model similarly approached 0.92.

Agriculture is closely associated with food and water. Agriculture is the first source of food but the biggest consumer of freshwater. The population is constantly increasing. Smart agriculture is one of the means of achieving food and water security. Smart agriculture can help improve water management and increase agricultural production, thus counteracting rapid population growth requirements. Soil moisture estimation is a critical step in agricultural water management. Soil moisture measurement techniques in situ are point measurements, labor-intensive, time-consuming, tedious, and expensive. We propose, in this research, a new approach to predict soil moisture over vegetation-covered areas from Sentinel-2 images based on a convolutional neural network (CNN). CNN architecture (3) consisting of six convolutional layers, one pooling layer, and two fully connected layers has achieved the highest prediction accuracy. Three well-known criteria including coefficient of determination (R2), mean absolute error (MAE), and root mean square error (RMSE) are utilized to measure the accuracy of the proposed algorithm. The Red Edge 3, NIR, and SWIR 1 are the most appropriate Sentinel-2 bands for retrieving soil moisture in vegetation-covered areas. Normalized Difference Water Index (NDWI) and Normalized Difference Vegetation Index (NDVI) are the best indicators. The use of the indicator is more proper than the use of the single Sentinel-2 band as input data for the proposed CNN architecture for predicting soil moisture. However, using combinations “that consist of some number of Sentinel-2 bands” as input data for CNN architecture is better than using each indicator separately or all of them as a group. The best values of the performance metrics were achieved using the sixth combination (R2=0.7094, MAE=0.0277, RMSE=0.0418) composed of the Red, Red Edge 1, Red Edge 2, Red Edge 3, NIR, and Red Edge 4 bands as input data to the CNN architecture (3), as well as by using the fifth combination (R2=0.7015, MAE=0.0287, RMSE=0.0424) composed of the Red Edge 3, NIR, Red Edge 4, and SWIR 1 bands.

Shrubland is a pivotal terrestrial ecosystem in China. Soil nitrogen transformations play a crucial role in maintaining the productivity of this ecosystem, yet the driving forces underlying it have not been sufficiently addressed, particularly under ongoing climate changes. Herein, by incorporating 15N isotope pool dilution method in laboratory incubation, the rates of gross N ammonification, nitrification, and inorganic N consumption in soils in response to varying temperature and humidity conditions were determined at different depths (SL10: 0–10 cm, and SL20: 10–20 cm) in a typical shrub ecosystem in the Yanshan mountain and hilly region, North China. The gross rates of ammonification and nitrification of soils in SL10 were higher than those in SL20, which was likely affected by the higher soil organic matter and total N contents at a shallower depth. Both temperature and humidity significantly affected the N transformations. The gross ammonification and nitrification were significantly stimulated as the incubation temperature increased from 5 to 35 °C. The gross ammonification increased exponentially, while the gross nitrification increased differently in different temperature ranges. The increment of soil water contents (from 30% WHC to 60% and 100% WHC) promoted the gross nitrification rate more significantly than the gross ammonification rate. The gross nitrification ceased until soil water content reached 60%WHC, indicating that soil water availability between 60% and 100% WHC was not a limiting factor in the nitrification process for the shrubland soils in this study. The ammonium (NH4+) immobilization was significantly lower than nitrification irrespective of varying environmental conditions, even though the NH4+ consumption rate might be overestimated, uncovering two putative processes: (1) heterotrophic nitrification process; (2) and more competitive nitrifying bacteria than NH4+-immobilizing microorganisms. Our study is indispensable for assessing the stability and sustainability of soil N cycling in the shrub ecosystem under climate changes.

A trustworthy assessment of soil moisture content plays a significant role in irrigation planning and in controlling various natural disasters such as floods, landslides, and droughts. Various machine learning models (MLMs) have been used to increase the accuracy of soil moisture content prediction. The present investigation aims to apply MLMs with novel structures for the estimation of daily volumetric soil water content, based on the stacking of the multilayer perceptron (MLP), random forest (RF), and support vector regression (SVR). Two groups of input variables were considered: the first (Model A) consisted of various meteorological variables (i.e., daily precipitation, air temperature, humidity, and wind speed), and the second (Model B) included only daily precipitation. The stacked model (SM) had the best performance (R2 = 0.962) in the prediction of daily volumetric soil water content for both categories of input variables when compared with the MLP (R2 = 0.957), RF (R2 = 0.956) and SVR (R2 = 0.951) models. Overall, the SM, which, in general, allows the weaknesses of the individual basic algorithms to be overcome while still maintaining a limited number of parameters and short calculation times, can lead to more accurate predictions of soil water content than those provided by more commonly employed MLMs.

Agroforestry is considered a means to provide sustainable and productive agriculture. This work aims to study the effect of fruit-grass agroforestry patterns on the soil moisture, salinity, growth, and yield of fruit trees, as well as to provide a reference for the development of agroforestry complex systems in Northwest China. The study has been designed with two cropping patterns: monocropped apple and apple-ryegrass intercropping. The results showed that compared to monocropped apples, intercropped apples have increased soil moisture content by 33.38–39.02%, net photosynthetic rate by 35.33–42.26%, transpiration rate by 29.62–29.76%, and stomatal conductance by 15.65–16.55% in the 0–60 cm soil layer. Intercrop reduced the total soil salt content by 36.41–38.58%, and the intercellular CO2 concentration decreased by 5.96–6.61%. In addition, intercropping improves fruit yield and quality by improving the orchard environment and increasing tree height, breast height, north-south crown spread, and east-west crown spread. Therefore, increased yield and quality can be achieved by changing the fruit tree and ryegrass planting method, which is beneficial to the sustainable development of agriculture in Northwest China.

The Loess Plateau is one of the three most severely affected geological disaster areas in China. Water sensitivity is the most significant feature of the loess. Under the action of continuous heavy rainfall, rainwater infiltrates the loess, resulting in a rapid increase in soil saturation and changes in soil moisture. This affects the shear strength of the soil and induces shallow loess landslides. Therefore, it is significant to our country’s disaster prevention and mitigation efforts to effectively monitor the deformation and inducement of such landslides. At present, the global navigation satellite system (GNSS) is widely used in the field of landslide disaster monitoring as a technical means to directly obtain real-time three-dimensional vector deformation of the surface. At the same time, GNSS can also provide a steady stream of L-band microwave signals to obtain surface environmental information, such as soil moisture around the station. In past landslide disaster monitoring research, GNSS was only used to provide three-dimensional deformation information, and its ability to provide environmental information around the station was almost completely ignored. This study proposes a ground-based GNSS remote sensing comprehensive monitoring system integrating “three-dimensional deformation and soil moisture content” combined with a rainfall-type shallow loess landslide event in Linxia City. The ability of ground-based GNSS to comprehensively monitor shallow loess landslide disasters was analysed. Experiments show that GNSS can provide high-precision deformation time series characteristics and monitor the changes in soil moisture content around the station at the same time; the two have a certain response relationship, which can comprehensively evaluate the stability of shallow loess landslides. As heavy rainfall is a key factor affecting the change in soil water content, this study adds the atmospheric water vapour content calculated by ground-based GNSS refraction remote sensing in the discussion chapter and analyses the relationship between precipitable water vapour and rainfall in this area to give full play to ground-based GNSS remote sensing. In the role of landslide disaster monitoring, we hope to build a more comprehensive ground-based GNSS remote sensing monitoring system to better serve the monitoring of landslide disasters.

Cardiocrinum cordatum, known as ubayuri in Japan, has antihypertensive properties and has been shown to inhibit angiotensin-converting enzyme (ACE), which contributes to the production of angiotensin II, a hypotensive substance in the renin–angiotensin system. C. cordatum has been the subject of various studies as a useful plant and is applied as a functional food. Due to the limited distribution, loss of natural habitat by frequent natural disasters, and environmental conditions, the chemical content and biological activity of C. cordatum have been drastically affected. Obtaining a stable supply of Cardiocrinu cordatum material with high biological activity is still a challenge. Understanding the native habitat environment and suitable cultivation sites could help in solving this issue. Therefore, in the current study we investigated the effect of environmental parameters on the hypertensive and antioxidant activities of C. cordatum collected at Chiburijima, Oki Islands, Shimane Prefecture, Japan. We also predicted the habitat suitability of C. cordatum using a geographic information system (GIS) and MaxEnt model with various conditioning factors, including the topographic, soil, environmental, and climatic factors of the study area. A total of 37 individual plant samples along with soil data were collected for this study. In vitro assays of ACE inhibitory and antioxidant activity were conducted on the collected samples. The results show that plants at 14 out of 37 sites had very strong ACE inhibitory activity (IC50 < 1 mg mL−1). However, the collected plants showed no signs of strong antioxidant activity. Statistical analysis using analysis of variance (ANOVA) showed that BIO05 (F value = 2.93, p < 0.05), nitrate–nitrogen (F value = 2.46, p < 0.05), and silt (F value = 3.443, p < 0.05) significantly affected ACE inhibitory activity. On the other hand, organic carbon content (F value = 10.986, p < 0.01) was found to significantly affect antioxidant activity. The final habitat suitability map shows 3.3% very high and 6.8% high suitability regions, and samples with ACE inhibition activity were located within these regions. It is recommended further investigations and studies are conducted on C. cordatum in these locations. The prediction suitability model showed accuracy with AUC-ROC of 96.7% for the study area.

Adjusting tillage parameters according to soil conditions can reduce energy consumption. In this study, the working parameters and soil physical parameters of plowing were determined using a designed electric suspension platform and soil instrument. The soil conditions were classified into three physical states, namely ‘hard’, ‘zero’, and ‘soft’ using a fuzzy C-means clustering algorithm, taking the soil moisture content and cone penetration resistance as the grading indexes. The Takagi–Sugeno (T–S) fuzzy neural network classifier was constructed using traction resistance, operating velocity, and plowing depth as inputs to indirectly identify the soil’s physical state. The results show that when 280 groups of test data were used to verify the model, 264 groups were correctly identified, indicating a soil physical state identification accuracy of 94.29%. The T–S fuzzy neural network prediction model can achieve the real-time and accurate physical state identification of paddy soil during plowing.

Based on the meteorological observation data of air temperature, surface temperature and albedo data retrieved from remote sensing images inside and outside the photovoltaic station, as well as the measured soil moisture content and bulk density at different locations of the photovoltaic power station in 2019, the impact of large-scale desert photovoltaic power plants on climate and environment was studied. The results show that air temperature, surface temperature and albedo inside the photovoltaic power station are lower than those outside the station, which are obvious in winter and not obvious in summer. Therefore, the photovoltaic power station is a cold source and an energy sink. The soil moisture content under and between the photovoltaic arrays is larger than other sampling points, and the soil bulk density gradually decreases with the distance from the center of the photovoltaic power station. Therefore, future plans for desert photovoltaic power station construction should take into account the impacts on local climate and environment.

Soil is one of the most significant natural resources in the world, and its health is closely related to food security, ecological security, and water security. It is the basic task of soil environmental quality assessment to monitor the temporal and spatial variation of soil properties scientifically and reasonably. Soil moisture content (SMC) is an important soil property, which plays an important role in agricultural practice, hydrological process, and ecological balance. In this paper, a hyperspectral SMC estimation method for mixed soil types was proposed combining some spectral processing technologies and principal component analysis (PCA). The original spectra were processed by wavelet packet transform (WPT), first-order differential (FOD), and harmonic decomposition (HD) successively, and then PCA dimensionality reduction was used to obtain two groups of characteristic variables: WPT-FOD-PCA (WFP) and WPT-FOD-HD-PCA (WFHP). On this basis, three regression models of principal component regression (PCR), partial least squares regression (PLSR), and back propagation (BP) neural network were applied to compare the SMC predictive ability of different parameters. Meanwhile, we also compared the results with the estimates of conventional spectral indices. The results indicate that the estimation results based on spectral indices have significant errors. Moreover, the BP models (WFP-BP and WFHP-BP) show more accurate results when the same variables are selected. For the same regression model, the choice of variables is more important. The three models based on WFHP (WFHP-PCR, WFHP-PLSR, and WFHP-BP) all show high accuracy and maintain good consistency in the prediction of high and low SMC values. The optimal model was determined to be WFHP-BP with an R2 of 0.932 and a prediction error below 2%. This study can provide information on farm entropy before planting crops on arable land as well as a technical reference for estimating SMC from hyperspectral images (satellite and UAV, etc.).

Global navigation satellite system interferometric reflectometry (GNSS-IR) is a new type of microwave remote sensing technology that can measure soil moisture content (SMC). GNSS-IR soil moisture retrieval methods based on the satellite signal-to-noise ratio (SNR) and triple-frequency signal combination have the following shortcomings: SNR does not always exist in the original GNSS file, and the number of triple-frequency signal observation satellites is small, resulting in GNSS-IR soil moisture observation time resolution being low. Based on the above problems, in this study, we constructed a soil moisture inversion method based on multisatellite dual-frequency combined multipath error is proposed: the multipath error calculation model of dual-frequency carrier phase (L4 Ionosphere Free, L4_IF) and dual-frequency pseudorange (DFP) without ionospheric effect is constructed. We selected the data of the five epochs before and after the time point of the effective satellite period to construct the multipath error model and error equation, and we solved the delay phase for soil moisture retrieval. We verified the method using Plate Boundary Observatory (PBO) P041 site data. The results showed that the Pearson correlation coefficients (R) of L4_IF and DFP methods at P041 station are 0.97 and 0.91, respectively. To better verify the results’ reliability and the proposed method’s effectiveness, the soil moisture data of the MFLE station about 210 m away from P041 station are used as the verification data in this paper. The results showed that the delay phase solved by multipath error and soil moisture strongly correlate. Pearson correlation coefficients (R) of L4_IF and DFP methods at MFLE station are 0.93 and 0.86, respectively. In order to improve the inversion accuracy of GNSS-IR soil moisture, this paper constructs the prediction model of soil moisture by using the linear regression (ULR), back propagation neural network (BPNN) and radial basis function neural network (RBFNN), and evaluates the accuracy of each model. The results showed that the soil moisture retrieval method based on multisatellite dual-frequency combined multipath error can replace the traditional retrieval method and effectively improve the time resolution of GNSS-IR soil moisture estimation. To perform highly dynamic monitoring of soil moisture, higher retrieval accuracy can only be obtained with a small epoch multipath error.

Soil moisture content (SMC) is an indispensable basic element for crop growth and development in agricultural production. Obtaining accurate information on SMC in real time over large agricultural areas has important guiding significance for crop yield estimation and production management. In this study, the paper reports on the retrieval of SMC from RADARSAT-2 polarimetric SAR data. The proposed SMC retrieval algorithm includes vegetation correction based on a ratio method and roughness correction based on the optimal roughness method. Three vegetation description parameters (i.e., RVI, LAI, and NDVI) serve as vegetation descriptors in the parameterization of the algorithm. To testify the vegetation correction result of the algorithm, the water cloud model (WCM) was compared with the algorithm. The calibrated integrated equation model (CIEM) was employed to describe the backscattering from the underlying soil. To improve the accuracy of SMC retrieval, the CIEM model was optimized by using the optimal roughness parameter and the normalization method of reference incidence angle. Validation against ground measurements showed a high correlation between the measured and estimated SMC when the NDVI serves as vegetation descriptor (R2 = 0.68, RMSE = 4.15 vol.%, p < 0.01). The overall estimation performance of the proposed SMC retrieval algorithm is better than that of the WCM. It demonstrates that the proposed algorithm has an operational potential to estimate SMC over wheat fields during the growing season.

Crop production is highly impacted by soil phosphorus (P) availability which is poor and susceptibly affected by soil moisture. However, how water management and straw incorporation affect paddy soil P availability is still not well known. A 40-day incubation experiment was conducted to evaluate the effects of two water management regimes: continuous flooding irrigation (CF) and alternate wetting and drying irrigation (AWD) combined with different straw addition rates (equivalent to 0, 50%, 100%, 200%, and 300% straw incorporation rates in field) on P availability in paddy soil. Water management significantly affected soil available P, microbial biomass P, total reductant, and ferrous iron. However, straw addition showed no effect on soil P availability in the short term. Compared to CF, AWD consistently decreased the soil available P content under straw addition at different rates. The main reason was that AWD increased microbial biomass for immobilizing P and decreased ferrous iron content for increasing soil P absorption, reducing available P content. In conclusion, AWD reduces available P content in paddy soil compared to CF. Water management has a more significant regulatory effect on soil P availability than straw incorporation in the field management.

Soil moisture is one of the most important components of all the soil properties affecting the global hydrologic cycle. Optical remote sensing technology is one of the main parts of soil moisture estimation. In this study, we promote a soil moisture-estimating method with applications regarding various soil organic matters. The results indicate that the soil organic matter had a significant spectral feature at wavelengths larger than 900 nm. The existence of soil organic matter would lead to darker soil, and this feature was similar to the soil moisture. Meanwhile, the effect of the soil organic matter on its reflectance overlaps with the effect of soil moisture on its reflected spectrum. This can lead to the underestimation of the soil moisture content, with an MRE of 21.87%. To reduce this effect, the absorption of the soil organic matter was considered based on the Lambert–Beer law. Then, we established an SMCg-estimating model based on the radiative transform theory while considering the effect of the soil organic matter. The results showed that the effect of the soil organic matter can be effectively reduced and the accuracy of the soil moisture estimation was increased, while MRE decreased from 21.87% to 6.53%.

Although many unsaturated soil experiments have successfully delivered positive outcomes, most studies just concisely illustrated sensor techniques, because their main objectives focused on bridging research gaps. Inexperienced research fellows might rarely follow up those techniques, so they could encounter very trivial and skill-demanding difficulties, undermining the quality of experimental outcomes. With a motivation to avoid those, this work introduces technical challenges in applying three sensor techniques: high precision tensiometer, spatial time-domain reflectometry (spatial TDR) and digital bench scales, which were utilized to measure three fundamental variables: soil suction, moisture content and accumulative outflow. The technical challenges are comprehensively elaborated from five aspects: the functional mechanism, assembling/manufacturing approaches, installation procedure, simultaneous data-logging configurations and post data/signal processing. The conclusions drawn in this work provide sufficient technical details of three sensors in terms of the aforementioned five aspects. This work aims to facilitate any new research fellows who carry out laboratory-scale soil column tests using the three sensors mentioned above. It is also expected that this work will salvage any experimenters having troubleshooting issues with those sensors and help researchers bypass those issues to focus more on their primary research interests.

To reveal the variability of different reconstructed soil ratios and different vegetation growth grades on the water holding capacity of recon-structed soil, the most suitable ratio of reconstructed soil materials was explored. Taking the inner dump of Shengli mining area in Inner Mongolia of China as the research area, the reconstructed soil of reclaimed land was investigated and sampled. One-way analysis of variance was used to analyze the difference of the moisture content of the reconstructed soil, and the reasons for the difference were discussed. The results showed that: (1) Among the different soil reconstructions, soil moisture content was higher when soil reconstructions were rock and soil stripping material: coal gangue: fly ash = 3:4:3. The soil moisture content of un-reclaimed land was mostly at a high level when the soil reconstruction method was rock and soil stripping material; and the soil moisture content was not at the highest level when the vegetation growth grade was higher. This indicates that it was not the case that the better the vegetation growth condition was, the higher the soil moisture content was. (2) In the case where the soil reconstruction method was rock and soil stripping material: coal gangue = 2:3, the soil moisture content of the reclaimed land decreased with the reduction of tillage frequency when the vegetation growth condition was optimal (vegetation growth grade was 4). (3) In the case where the soil reconstruction method was rock and soil stripping material: coal gangue: fly ash = 3:4:3, when the vegetation growth condition was better (vegetation growth grade was 3 and 4), the soil moisture content of the re-claimed land was highest when it was tilled once every 15 days. It was also found in combination with other soil reconstruction methods that it was not always the case that the higher the frequency of tillage, the higher the soil moisture content. The study of coal gangue, fly ash and rocky soil stripping as topsoil substitute materials with their different ratios on reconstructed soil provides support for the reclamation work in topsoil scarce mines and provides technical reference for the ecological restoration project of grassland open-pit mines in the same climate zone.

Winter irrigation is important for wheat in meeting crop water requirements in spring, but it alters soil moisture dynamics and affects soil N2O production and emission. To assess the effects of winter irrigation on soil N2O emissions in a winter wheat field, an in situ experiment was conducted from 1 October 2019 to 1 March 2020 with one control treatment (CK) and five levels of winter irrigation quantities (irrigated to 60%, 70%, 80%, 90%, 100% of the soil water holding capacity, namely WHC60–WHC100, respectively). The results showed that winter irrigation had an impact on soil N2O emission. The emission peaks were not investigated immediately after winter irrigation, but at two days after, which were increased by 4.3–17.0 μg·m−2·h−1 under WHC60–100 treatments compared to the CK. The cumulative N2O emissions after winter irrigation from WHC60–100 were 1.1–3.9 times higher than that of CK, indicating that the cumulative N2O emission has an increase trend with the increase of soil water content regulated by irrigations. Pearson correlation analysis showed that the correlation between soil N2O flux and soil temperature were moderate with correlation coefficients of about 0.65. While the correlation between soil N2O flux and soil water content was poor during the investigate winter season with correlation coefficients ranging between 0.08 and 0.25. Future studies should focus on the general N2O emission responses to winter irrigation and environmental factors with the support of experiment data from several winter seasons.

In the last two decades in Central Yakutia, there has been a significant change in cryogenic landscapes related to climate warming and anthropogenic disturbances. This period is characterized by the activity of forest fires, which significantly impact permafrost landscapes. We observed the dynamics of cryogenic landscapes after a forest fire in 2001 at the Neleger station in Central Yakutia, 35 km northwest of Yakutsk. The observations included ground temperature and active layer thickness monitoring and statements of changes in the soil moisture content of the active layer. Increases in ground temperature, the active layer thickness, and soil moisture content on the burnt site after a forest fire in Neleger station were noted in the first six to seven years after the disturbance. We found that, following forest fires, permafrost progressively restabilizes as forest cover redevelops over time. The results of the studies will become the basis for planning restoration work after forest fires in permafrost landscapes of Central Yakutia.