Soil Moisture Research Articles

Observational study on the surface radiation budget and energy balance closure of grassland in the eastern arid region of Northwest China

Based on the observation data of the meteorological environment monitoring system (QT-1010) and the open vorticity correlation system (LI-7500DS), we used the block aerodynamics and energy balance ratio method to analyze the surface radiation budget and energy balance closure characteristics of the grassland in the eastern arid region of Northwest China at different time scales.We found that (1) The daily variation curve of the surface radiation flux is unimodal, and the variation amplitude of each component is as follows: Rs↓> RL↑> RL↓> Rs↑. (2) The monthly peak of each radiation component occurs in summer, and the time of reaching the peak of different components has a lag. The changes of radiation components in sunny and cloudy days are multi-modal. In the precipitation weather, the fluctuation of each radiation flux is unimodal.The average annual surface albedo () is 0.24, which is higher in winter than in summer. There is a linear relationship between surface albedo and soil moisture. (3) The Net radiation (Rnet) dominates the sensible heat in spring and the latent heat in summer. H/Rnet and Gn/Rnet are both positive in a day, H/Rnet and LE/Rnet fluctuate wildly before and after sunrise, and Gn is dominant at night. (4) The energy closure is manifested as summer > winter > Autumn > spring, and the energy closure is good throughout the year.

Protection of understory vegetation by deer exclosure fences prevent the reduction of beech growth due to soil erosion

Overbrowsing by ungulates has degraded understory vegetation in worldwide forests. Understory degradation causes soil erosion and environmental degradation, causing mature tree decline. Although ungulate exclosure fences are installed to protect vegetation, their effectiveness in preventing tree decline associated with soil degradation remains unclear. We aimed to fill this gap on slopes of Mt. Shiraga, Japan, where sika deer have overbrowsed for >20 years. Tree-ring records in 1960–2022 for eight beeches per each of inside and outside fences were analyzed to compare stem basal area increment (BAI, cm−2 yr−1) and trend change timings for BAI (i.e., break-point). Soil properties, exposed root height (ERH) as cumulative soil erosion index, and intrinsic water use efficiency (iWUE) as water limitation proxy were also compared. Compared with areas inside fences, areas outside fences had no understory vegetation, higher ERH, and higher soil hardness and moisture. BAI outside fences turned their growth trend from positive to negative since 2004, but not inside fences. Break-point of BAI outside fences matched the understory degradation period in the field. The long-term BAI trend (i.e., regression slope of BAI) after the break-point was negatively related to ERH but not to soil properties (e.g., soil hardness and moisture). Furthermore, BAI outside fences was negatively related to iWUE after the break-point, whereas BAI inside fences was not. These results indicate that the maintenance of understory by fencing prevents overbrowsing-associated mature tree decline through mitigating root exposure and related water limitation, despite the potential of water competition with understory.

Harnessing Information From Shortwave Infrared Reflectance Bands to Enhance Satellite‐Based Estimates of Gross Primary Productivity

AbstractMonitoring gross primary productivity (GPP), the rate at which terrestrial ecosystems fix atmospheric carbon dioxide, is crucial for understanding global carbon cycling. Remote sensing offers a powerful tool for monitoring GPP using vegetation indices (VIs) derived from visible and near‐infrared reflectance (NIRv). While promising, these VIs often suffer from sensitivity to soil background, moisture, and variations in solar and view zenith angle (SZA and VZA). This study investigates the potential of incorporating shortwave infrared (SWIR) reflectance from MODIS and GOES‐R advanced baseline imager (ABI) sensors to improve GPP estimation. We evaluated various formulations for creating SWIR‐enhanced Near‐InfraRed reflectance of Vegetation (sNIRv) by integrating SWIR information into established VIs across 96 Ameriflux and NEON research sites. Our findings reveal that sNIRv improves correlation with GPP for ABI data by up to 0.19 on a half‐hourly basis for normalized difference vegetation index (NDVI) values below 0.25, with diminishing gains as NDVI values rise. Using MODIS data, sNIRv matches r values of NIRv for NDVI above 0.25, with a slight 0.05 increase for NDVI below 0.25. Analyses using SCOPE model simulations further support the ability of sNIRv to capture fractional photosynthetically active radiation, a proxy for GPP, especially for ecosystems with low leaf area index. Results highlight that sNIRv‐based VIs are less sensitive to soil background, SZA, and VZA compared to NIRv. SHapley Additive exPlanations (SHAP) value analysis also identifies sNIRv as the best feature for GPP estimation using machine learning modeling across different land covers, NDVI ranges, and soil water content levels.

Texas Water Observatory: A Distributed Network for Monitoring Water, Energy, and Carbon Cycles under Variable Climate and Land Use on Gulf Coast Plains

Abstract In the Gulf Coastal Plains of Texas, a state-of-the-art distributed network of field observatories, known as the Texas Water Observatory (TWO), is developed to better understand the water, energy, and carbon cycles across the critical zone (encompassing aquifers, soils, plants, and atmosphere) at different spatiotemporal scales. Using more than 300 advanced real-time/near-real-time sensors, this observatory monitors high-frequency water, energy, and carbon storage and fluxes in the Brazos River corridor, which are critical for coupled hydrologic, biogeochemical, and land–atmosphere process understanding in the region. TWO provides a regional resource for better understanding and/or managing agriculture, water resources, ecosystems, biodiversity, disasters, health, energy, and weather/climate. TWO infrastructure spans common land uses in this region, including traditional/aspirational cultivated agriculture, rangelands, native prairie, bottomland hardwood forest, and coastal wetlands. Sites represent landforms from low-relief erosional uplands to depositional lowlands across climatic and geologic gradients of central Texas. We present the overarching vision of TWO and describe site design, instrumentation specifications, data collection, and quality control protocols. We also provide a comparison of water, energy, and carbon budget across sites, including evapotranspiration, carbon fluxes, radiation budget, weather, profile soil moisture and soil temperature, soil hydraulic properties, hydrogeophysical surveys, groundwater levels, and groundwater quality reported at TWO primary sites for 2018–20 (with certain data gaps). In conjunction with various Earth-observing remote sensing and legacy databases, TWO provides a master testbed to evaluate process-driven or data-driven critical zone science, leading to improved natural resource management and decision support at different spatiotemporal scales. Significance Statement We provide the vision, design setup, and data acquisition of a state-of-the-art network of field observatories across the Gulf Coastal Plains of Texas. This observatory provides a wealth of measurements of the water, energy, and carbon fluxes, thereby providing a critical testbed for improving the understanding of terrestrial hydrological, biogeochemical, and atmospheric processes across diverse land-use and climate conditions.

Effects of freeze–thaw on the detachment capacity of soils with different textures on the Loess Plateau, China

Soil detachment capacity (Dc) is a crucial indicator for quantifying erosion intensity. However, the combined actions of freeze–thaw and water flow complicate the erosion process, leaving the variation mechanism of Dc under this condition systematically unexplored. This study examined five loess soils from a seasonal freeze–thaw area. The mechanism driving changes in Dc was quantified through freeze–thaw simulation combined with flow scouring tests, and a Dc prediction model was established. The results revealed that the shear strength (τm), cohesion (Coh), and internal friction angle (φ) in silt loam were higher than in sandy loam. After freeze–thaw cycles (FTC), τm, Coh, and φ of the five loess soils decreased by 1.02–1.37, 1.07–9.15, and 0.92–1.05 times, respectively. As FTC increased, τm and Coh gradually stabilized, while φ showed minimal fluctuation, indicating that FTC had a cumulative effect on the deterioration of soil mechanical properties. During FTC, Dc in Wuzhong sandy loam was the largest, being 1.14–3.24 times greater than in other soils, suggesting a significant main effect of soil type on Dc variation, with a contribution rate of 19.27 %. Dc eventually stabilized with increasing, indicating a critical FTC of around 10 for its impact on Dc. Compared to unfrozen soils, Dc increased by 33.69 %–102.40 % under the combined effects of freeze–thaw and water flow, clarifying that FTC aggravated soil instability. Effective stream power was the optimum hydraulic parameter, contributing the most to Dc (45.94 %). FTC (6.41 %) and initial soil moisture content (8.59 %) were less influential, as FTC initially degraded soil properties, and then the combined action with water flow intensified soil damage, causing the role of freeze–thaw factors to be obscured by other variables. A Dc prediction model using a general flow intensity index estimated well Dc, with both R2 and NSE at 0.94. Model performance comparison emphasized the need for validation when extending the application range beyond development conditions. These findings provide new insights into the detachment mechanisms of different textured soils under compound freeze–thaw and hydrodynamic influence in freeze–thaw region.

Attribution of summer 2022 extreme wildfire season in Southwest France to anthropogenic climate change

Summer 2022 was exceptionally hot and dry in Europe and especially in Southwest France, where the most important wildfires since 1949 had serious environmental and socio-economic impacts. Here we conduct an impact-oriented climate change attribution study by first investigating which climate indices are the most correlated with the burnt area between 2003 and 2022. We find that an index combining soil moisture integrated over 6 months and temperature and vapour pressure deficit integrated over 3 months is correlated with large burnt areas. Using the index developed, we estimate that anthropogenic climate change made climate conditions propitious for wildfire development, such as the ones of July 2022, two times more likely, with a return period of 13 years in the current climate. Our study raises the question of the sustainability of the Landes Forest and stresses the urgent need to mitigate greenhouse gas emissions and adapt to climate change.

New Rankine Vortex Models Developed Based on SMAP Measurements

Abstract The L-band passive microwave radiometer on board the NASA Soil Moisture Active Passive (SMAP) satellite can measure brightness temperature to retrieve sea surface wind speed under tropical cyclone (TC) conditions without being affected by rainfall or signal saturation caused by high wind speeds. Based on this advantage, this paper used the SMAP wind products for parameterizing the key decay exponent α of the Rankine vortex model (a traditional parametric model of the TC wind field) and finally developed new Rankine models. The SMAP dataset included 67 TC cases. Through data statistics, we examined the relationship between α and the maximum wind speed Um, the relationship between α and the radius of maximum wind speed (Rm), and the relationship between Rm and the averaged radius of 17 m s−1 (R17). Results showed that the three relationships were both positive correlations, indicating that α can be parameterized in three empirical ways. The first way is to calculate solely with Um. The second way is to calculate solely with Rm. The third way is to calculate Um and Rm together. The three ways correspond to three new models: the SMAP Rankine Model-1 (SRM-1), the SMAP Rankine Model-2 (SRM-2), and the SMAP Rankine Model-3 (SRM-3). Finally, comparisons were made between the new models and three existing Rankine models, according to the model simulations and the Advanced Microwave Scanning Radiometer 2 measurements of 49 TC cases. Results showed that the SRM-3 performed best overall. Significance Statement Ocean surface wind fields are important driving factors for numerical models to guide evolution forecasting and risk assessment of tropical cyclones. The purpose of this study is to utilize the SMAP products to modify the traditional Rankine vortex model for tropical cyclone surface wind field estimation. Rankine decay exponent was found between 0.3 and 0.9 and positively dependent upon maximum wind speed and its radius. Based on these characteristics, the proposed new Rankine models could calculate the decay exponent and further the TC surface wind field symmetrically with high accuracy, simply using maximum wind and its radius. This paper shows a practical use of SMAP measurements on TC wind field monitoring, analyzing, and modeling.

Response of Soil Moisture to Precipitation at Different Smash-Ridging Tillage Depths in Typical Sugarcane Fields in Guangxi, China

Response of Soil Moisture to Precipitation at Different Smash-Ridging Tillage Depths in Typical Sugarcane Fields in Guangxi, China

Enhancing field soil moisture content monitoring using laboratory-based soil spectral measurements and radiative transfer models

Enhancing field soil moisture content monitoring using laboratory-based soil spectral measurements and radiative transfer models

Spatio-temporal dynamics of root water uptake and identification of soil moisture thresholds for precision irrigation in a Mediterranean yellow-fleshed kiwifruit orchard

Spatio-temporal dynamics of root water uptake and identification of soil moisture thresholds for precision irrigation in a Mediterranean yellow-fleshed kiwifruit orchard

Strip clear-cutting transformations increase soil N2O emissions in abandoned Moso bamboo forests

Forest transformation can markedly impact soil greenhouse gas emissions and soil environmental factors. Due to increasing labor costs and declining bamboo prices, the abandonment of Moso bamboo forests is sharply escalating in recent years, which weakens the carbon sequestration capacity and decreases the ecological function of forests. To improve the ecological quality of abandoned Moso bamboo forests, transformations of abandoned bamboo forests have occurred. However, the impact of such transformations on N2O emissions remains elusive. To bridge the knowledge gap, we conducted a 23-month field experiment to compare the effects of various forest management practices on soil N2O emissions and soil environmental factors in abandoned Moso bamboo forests in subtropical China. These practices included uncut abandonment as a control, intensive management, three intensities (light, moderate, and heavy) of strip clear-cutting with replanting local tree species, and clear-cutting with replanting transformation. During the experimental period, the mean soil N2O flux in abandoned Moso bamboo forests was 13.2 ± 0.1 μg m-2 h-1, representing a 44% reduction compared to intensive management forests. In comparison to the uncut control, light, moderate, and heavy strip clear-cutting and clear-cutting transformations increased soil N2O emission rates by 20%, 43%, 64%, and 94%, respectively. Soil temperature (69-71%), labile C (2-6%) and N (3-8%) were the main factors that explain N2O emissions following the transformation of abandoned Moso bamboo forests. Additionally, replanting could decrease soil N2O emissions by increasing the contribution of soil moisture. Overall, the light strip clear-cutting transformation is suggested to convert abandoned Moso bamboo forests to mitigate N2O emissions.

Impacts of changing weather patterns on the dynamics of water pollutants in agricultural catchments: Insights from 11-year high temporal resolution data analysis

Widespread and long-term shifts in weather patterns are contributing to further degradation of surface water quality. This challenge caused by the increasing frequency of extreme weather events requires appropriate adaptation of current mitigation strategies. But to confirm the need to redesign such strategies, an understanding of the impacts of increasing weather extremes on pollutant losses in different catchment types is required. With this in view, this study investigated the impact of changing weather patterns on the inter-seasonal and inter-annual dynamics of nutrient losses in six agricultural catchments in Ireland over 11 years. The high temporal resolution data (10-min) from these intensively managed catchments represented different characteristics and management practices. Mann-Kendall Trend Analysis and Generalised Additive Models were used to study nutrient concentration trends, and to investigate the significance of water discharge, precipitation, potential evapotranspiration, soil moisture deficit, air temperature, and soil temperature on the losses of nutrients, respectively. The analysis of historical data revealed changes in the trends of daily average nitrate (NO3-N), phosphorus (P), and suspended sediment (SS) concentrations in association with significant increasing trends in air temperature, soil temperature, and precipitation across the same month over 11 years of monitoring. While discharge was significantly contributing to the concentrations of NO3-N, P, and SS across different catchments, air and soil temperature were significantly correlated to NO3-N losses, and precipitation was the major contributor to regulating P (total P and total reactive P) concentrations. In short, air temperature, soil temperature, soil moisture deficit, and precipitation were the main climatic drivers regulating the nutrient concentrations while the soil chemistry and drainage status were the non-climatically related drivers. The results revealed that the extent of the impact of climatic drivers depends on catchment characteristics. Therefore, expanding the application of this type of study would facilitate better understanding of current and future challenges to water management and provision of climate-resilient mitigation strategies for different catchment typologies.

Changes in vegetation phenology and its response to different layers of soil moisture in the dry zone of Central Asia, 1982–2022

Changes in vegetation phenology and its response to different layers of soil moisture in the dry zone of Central Asia, 1982–2022

Effects of water table depth variations on canopy transpiration from Mongolian pine plantations in arid ecosystems

Groundwater is a critical moisture source for the growth and survival of dryland forests. Spatiotemporal variations in water table depth (WTD) affect soil water availability in the root zone, which influences canopy transpiration (Ec). However, to date, relatively few studies have been conducted on how changes in WTD regulate Ec. We measured the meteorological variables, volumetric water content (VWC), sap flow, and WTD in 2022 and 2023 from April to October in three Mongolian pine plantations located in Mu Us Sandy Land, China. The initial WTDs were 4 m (WTD4), 9 m (WTD9), and 13 m (WTD13), respectively. The mean VWC did not differ significantly between WTD4, WTD9, and WTD13 in the 0–50 cm layer. However, there was a significant decrease in the mean VWC with increasing WTD in the 50–200 cm layer (p < 0.05). Ec decreased significantly with increasing WTD (p < 0.01), with mean daily Ec of 1.25, 0.75, and 0.28 mm day−1 for WTD4, WTD9, and WTD13, respectively. Under soil drought conditions, Ec decreased significantly at WTD9 and WTD4 (p < 0.05). Meanwhile, Ec at WTD13 did not vary significantly under the different soil moisture conditions. At WTD9 and WTD13, Ec was mainly influenced by VWC. Meanwhile, at WTD4, Ec was influenced by the vapor pressure deficit and solar radiation. The diameter at breast height and leaf area index at WTD13 were significantly lower than those at WTD9 and WTD4 (p < 0.05). Although Mongolian pine could alleviate soil drought by adjusting Ec through morphological and physiological adaptations, it still suffered from the adverse effects of reduced water availability at deep WTD sites. This study establishes a basis for predicting the response of forests in semi-arid areas to changes in WTD. It contributes to the optimization of silvicultural design in similar areas and reduces ecological risks from the over-exploitation of groundwater.

Drivers of Widespread Floods in Indian River Basins

Abstract Widespread floods affecting multiple subbasins in a river basin have implications for infrastructure, agriculture, environment, and groundwater recharge. However, the crucial linkage between widespread floods and their drivers remains unexplored for Indian subcontinental river basins. Here, we examine the occurrence and drivers of widespread flooding in seven Indian subcontinental river basins during the observed climate (1959–2020). The peninsular river basins have a high probability of widespread flooding, compared to the transboundary basins of the Ganga and Brahmaputra. Favorable antecedent baseflow and soil moisture conditions, uniform precipitation distribution, and precipitation seasonality determine the probability of widespread floods in Indian river basins. The widespread floods are associated with large atmospheric circulations that cause precipitation in a large part of a river basin. Our findings highlight the prominent drivers and mechanisms of widespread floods with implications for flood mitigation in India.

Sampling volume evaluation of seven electromagnetic soil moisture sensors

The sampling volume of electromagnetic (EM) soil moisture sensors is of significant interest due to the diverse applications for which they are utilized. However, this volume can vary considerably based on multiple factors, including the number, spacing, and length of sensor rods, as well as the operational frequency. The sensor's volume of influence is dictated by electric field lines or ‘fringing fields’ that develop between pairs of powered electrodes. This study involved the development of an accurate sensor positioning system employed to characterize the EM sensor's electrical fringing field in 3-directional - x, y, and z space. Sensor outputs were recorded at mm increments beginning with the sensor positioned 20 mm above the air-water interface followed by incremental immersion into a water bath until the maximum sensor output was achieved. For each measurement cycle, the sensor was repositioned to test signal output approaching the air-water interface in −x, +x, −y, +y, −z, and +z orientations. The sensor sampling volume was determined based on the normalized 99% output value, which was assumed to represent the sensor position relative to an air-water interface where the sensor fringing field just begins to ‘sense’ or to become impacted by the air phase, thereby reducing the signal amplitude. Our results vary somewhat from other studies using a similar evaluation approach, leading us to conclude that EM-sensor sampling volume determination is both complex and imprecise as a result of the global sensor market's varied circuits and electrode configurations. EM soil moisture sensors evaluated in this study exhibited sampling volumes of 386 cm3 (TDR-315), 66 cm3 (TDR-310H), 398 cm3 (CS655), 45 cm3 (EC-5), 40 cm3 (HydraProbe II), 148 cm3 (WET150), and 49 cm3 (TEROS 12), which were smaller than manufacturer reported volumes.

Tropical cyclone-induced rainfall variability and its implications for drought in Taiwan: Insights from 1981 to 2022

This study analyzes the interplay between tropical cyclones (TCs) and drought in Taiwan over the period from 1981 to 2022, leveraging data from CHIRPS, FLDAS, IBTrACS, and Sentinel and Landsat satellite imagery. Our findings reveal a significant decline in TC-induced rainfall over the last decade, with a turning point in 2017. We establish a low to moderate positive correlation between TC-induced rainfall and subsequent spring soil moisture levels, with April exhibiting the strongest connection. This relationship strengthens in transitions from agricultural to forested areas. Additionally, our research identifies a robust correlation (r = 0.77) between TC-induced rainfall and reservoir water levels, underscoring the consistent link between TCs and hydrological drought. However, intriguing disparities emerge in water availability patterns. In 2007 and 2020, reservoirs maintained normal levels despite reduced typhoon rainfall in prior years. Conversely, in 2019, water shortages occurred despite typical TC-induced rainfall. These cases suggest that water availability is influenced by factors beyond TCs, including water demand, infrastructure, monsoon and spring rainfall, air temperature, and water management strategies. Furthermore, we uncover a symmetrical relationship between TCs and monsoon-induced rainfall, indicating their potential to balance reservoir water supply. Nevertheless, both TCs and the monsoon exhibit declining trends in the past decade, heightening the risk of spring season water scarcity in Taiwan. Effective water resource management demands consideration of natural and anthropogenic factors, encompassing land use changes, deforestation, production activities, water pollution, and policy strategies. These measures are essential to mitigate water scarcity and promote sustainability in Taiwan.

Factors determining the invasion pattern of Ageratina adenophora Spreng. in Kumaun Himalaya India

Factors such as topography, soil composition, and nutrient availability significantly influence the density patterns of Ageratina adenophora. Understanding these dynamics addresses a gap in our knowledge of the species' adaptive mechanisms in mountainous regions. Furthermore, the impact of habitat features along road corridors on the population dynamics of invasive plants remains underexplored, particularly regarding the effects of disturbance levels, light availability, and soil properties on their establishment. A species-specific rapid ecological assessment was conducted using stratified random sampling, with parallel transects of 50 × 2m established in triplicates at 20m intervals. This resulted in 43 main transects across the identified plots and 67 parallel transects in adjacent habitats. The number of individuals of A. adenophora and its clumps were recorded from each quadrat. Chemical and physical parameters of soil were measured for soil collected from 0-15cm depth. Linear Mixed Model analysis revealed a significant negative effect of elevation (p<0.05) on the density of clumped individuals (Estimate: -0.31, t-value: -3.05), total individuals (Estimate: -0.27, t-value: -2.61), and clump number (Estimate: -0.30, t-value: -4.78). Western aspect also showed a significant decrease in clumped individuals (Estimate: -1.83, t-value: -2.80), total individuals (Estimate: -2.24, t-value: -3.47), and clump number (Estimate: -0.81, t-value: -1.97). Total A. adenophora density was highest near settlements (133 ind. m², Estimate: 1.19) and grasslands (103 ind. m², Estimate: 1.16), but lowest in broadleaf forests (26 ind. m²). Density decreased significantly with increasing distance from road verges (Estimate: -0.24, t-value: -2.34). Soil moisture content positively influenced total individuals (Estimate: 0.19, t-value: 2.75), clumped individuals (Estimate: 0.23, t-value: 3.20), clump numbers (Estimate: 0.05, t-value: 1.09), and individuals per clump (Estimate: 0.37, t-value: 3.28). Available nitrogen positively influenced non-clumped individuals (Estimate: 0.17, t-value: 2.04) but negatively affected individuals per clump (Estimate: -0.25, t-value: -2.21), indicating that lower nitrogen levels correlate with higher individual density per clump. Hence, effective restoration efforts are needed including soil improvement, invasive species removal and control, and the planting of native species.

Satellite Imagery, Big Data, IoT and Deep Learning Techniques for Wheat Yield Prediction in Morocco

In the domain of efficient management of resources and ensuring nutritional consistency, accuracy in predicting crop yields becomes crucial. The advancement of artificial intelligence techniques, synchronized with satellite imagery, has emerged as a potent approach for forecasting crop yields in modern times. We used two types of data: spatial data and temporal data. Spatial data are gathered from satellite imagery and processed using ArcGIS to extract data about crops based on several indices like NDVI and NWDI. Temporal data are gathered from agricultural sensors such as temperature sensors, rainfall sensor, precipitation sensor and soil moisture sensor. In our case we used Sentinel 2 satellite to extract vegetation indices. We have used IoT systems, especially Raspberry Pi B+ to collect and process data coming from sensors. All data collected are then stored into a NoSQL server to be analysed and processed. Several machine learning and deep learning algorithms have been used for the processing of crop recommendation system, such as logistic regression, KNN, decision tree, support vector machine, LSTM, and Bi-LSTM through the collected dataset. We used GRU deep learning model for the best performance, the RMSE and R2 for this model was 0,00036 and 0,99 respectively.The main contribution of our paper is the development of a new system that can predict several crop yields, such as wheat, maize, etc, using IoT, satellite imagery for spatial data and the use of sensors for temporal data. We are the first paper that has combined spatial data and temporal data to predict crop yield based on deep learning algorithms, unlike other works that uses only remote sensing data or temporal data. We created an E-monitoring crop yield prediction system that helps farmers track all information about crops and show the result of prediction in a mobile application. This system helps farmers with more efficient decision making to enhance crop production. The main production regions of wheat in Morocco are in the rainfed areas of the plains and plateaus of Chaouia, Abda, Haouz, Tadla, Gharb and Saïs. We studied three main regions well known for wheat production which are Rabat-Salé, Fez-Meknes, Casablanca-Settat.

Dynamic characteristics of soil pore structure and water-heat variations during freeze-thaw process

Freeze-thaw processes in cold regions alter soil pore structure and properties, leading to engineering geological issues. Soil pores are crucial, but research on their changes and freeze-thaw impacts is limited. This study used MRI-Cryogenic Soil Moisture Analyzer (MRI-CSMA) to explore pore structure, water, and temperature changes in saturated loess during freeze-thaw, and Scanning Electron Microscopy (SEM) to compare changes before and after. The results indicate that during the freezing process, the temperature in the frozen zone of the soil sample exhibited a staged change characterized by rapid cooling, transitional cooling, and stabilization at low temperatures, while the temperature decrease in the unfrozen zone showed no significant stages. Freeze-thaw action significantly affected the macropores and mesopores in the frozen zone, with an average increase of 15 % in macropores, a decrease of 16 % in mesopores, and minimal change in micropores (about a 1 % increase). In the unfrozen zone, there was a slight increase in micropores and mesopores (2 % and 3 %, respectively), and a 4 % decrease in macropores. Furthermore, during the freezing process, macropores in the unfrozen zone gradually decreased, while mesopores and micropores increased, leading to soil structure densification and promoting water migration towards the freezing front. This resulted in an initial increase followed by a decrease in water content near the freezing front during the early stages of freezing, confirming the view that pore structure compression drives water migration in the early stages of soil freezing. This study provides important insights for addressing engineering geological issues in cold regions under freeze-thaw conditions.