Investigating Correlations and the Validation of SMAP-Sentinel L2 and In Situ Soil Moisture in Thailand

Soil moisture plays a crucial role in various hydrological processes and energy partitioning of the global surface. The Soil Moisture Active Passive-Sentinel (SMAP-Sentinel) remote-sensing technology has demonstrated great potential for monitoring soil moisture with a maximum spatial resolution of 1 km. This capability can be applied to improve the weather forecast accuracy, enhance water management for agriculture, and managing climate-related disasters. Despite the techniques being increasingly used worldwide, their accuracy still requires field validation in specific regions like Thailand. In this paper, we report on the extensive in situ monitoring of soil moisture (from surface up to 1 m depth) at 10 stations across Thailand, spanning the years 2021 to 2023. The aim was to validate the SMAP surface-soil moisture (SSM) Level 2 product over a period of two years. Using a one-month averaging approach, the study revealed linear relationships between the two measurement types, with the coefficient of determination (R-squared) varying from 0.13 to 0.58. Notably, areas with more uniform land use and topography such as croplands tended to have a better coefficient of determination. We also conducted detailed soil core characterization, including soil-water retention curves, permeability, porosity, and other physical properties. The basic soil properties were used for estimating the correlation constants between SMAP and in situ soil moistures using multiple linear regression. The results produced R-squared values between 0.933 and 0.847. An upscaling approach to SMAP was proposed that showed promising results when a 3-month average of all measurements in cropland was used together. The finding also suggests that the SMAP-Sentinel remote-sensing technology exhibits significant potential for soil-moisture monitoring in certain applications. Further validation efforts and research, particularly in terms of root-zone depths and area-based assessments, especially in the agricultural sector, can greatly improve the technology's effectiveness and usefulness in the region.

Soil Water Deficit Reduced Root Hydraulic Conductivity of Common Reed (Phragmites australis)

Alterations in root hydraulics in response to varying moisture conditions remain a subject of debate. In our investigation, we subjected common reeds (Phragmites australis) to a 45-day treatment with four distinct soil moisture levels. The findings unveiled that, in response to drought stress, the total root length, surface area, volume, and average diameter exhibited varying degrees of reduction. Anatomically, drought caused a reduction in root diameter (RD), cortex thickness (CT), vessel diameter (VD), and root cross-sectional area (RCA). A decrease in soil moisture significantly reduced both whole- and single-root hydraulic conductivity (Lpwr, Lpsr). The total length, surface area, volume, and average diameter of the reed root system were significantly correlated with Lpwr, while RD, CT, and RCA were significantly correlated with Lpsr. A decrease in soil moisture content significantly influenced root morphological and anatomical characteristics, which, in turn, altered Lpr, and the transcriptome results suggest that this may be associated with the variation in the expression of abscisic acid (ABA) and aquaporins (AQPs) genes. Our initial findings address a gap in our understanding of reed hydraulics, offering fresh theoretical insights into how herbaceous plants respond to external stressors.

Multi-Source Soil Moisture Data Fusion Based on Spherical Cap Harmonic Analysis and Helmert Variance Component Estimation in the Western U.S

Soil moisture (SM) is a vital climate variable in the interaction process between the Earth's atmosphere and land. However, global soil moisture products from various satellite missions and land surface models are affected by inherently discontinuous observations and coarse spatial resolution, which limits their application at fine spatial scales. To address this problem, this paper integrates three diverse types of datasets from in situ, satellites, and models through Spherical cap harmonic analysis (SCHA) and Helmert variance component estimation (HVCE) to produce 1 km of spatio-temporally continuous SM products with high accuracy. First, this paper eliminates the bias between different datasets and in situ sites and resamples the datasets before data fusion. Then, multi-source SM data fusion is performed based on the SCHA and HVCE methods. Finally, this paper evaluates the fused products from three aspects, including the performance of representative sites under different climate types, the overall performance of validation sites, and the comparison with other products. The results show that the fused products have better performance than other SM products. In the representative sites, the minimal correlation coefficient (R) of the fused products is above 0.85, and the largest root mean square error (RMSE) is below 0.040 m3 m-3. For all validation sites, the R and RMSE of the fused products are 0.889 and 0.036 m3 m-3, respectively, while the R for other products is below 0.75 and the RMSE is above 0.06 m3 m-3. In comparison to other SM products, the fused products exhibit superior performance, generally align more closely with in situ measurements, and possess the ability to accurately and finely capture the spatial and temporal variability of surface SM.

Research on Soil Moisture Estimation of Multiple-Track-GNSS Dual-Frequency Combination Observations Considering the Detection and Correction of Phase Outliers

Soil moisture (SM), as one of the crucial environmental factors, has traditionally been estimated using global navigation satellite system interferometric reflectometry (GNSS-IR) microwave remote sensing technology. This approach relies on the signal-to-noise ratio (SNR) reflection component, and its accuracy hinges on the successful separation of the reflection component from the direct component. In contrast, the presence of carrier phase and pseudorange multipath errors enables soil moisture retrieval without the requirement for separating the direct component of the signal. To acquire high-quality combined multipath errors and diversify GNSS-IR data sources, this study establishes the dual-frequency pseudorange combination (DFPC) and dual-frequency carrier phase combination (L4) that exclude geometrical factors, ionospheric delay, and tropospheric delay. Simultaneously, we propose two methods for estimating soil moisture: the DFPC method and the L4 method. Initially, the equal-weight least squares method is employed to calculate the initial delay phase. Subsequently, anomalous delay phases are detected and corrected through a combination of the minimum covariance determinant robust estimation (MCD) and the moving average filter (MAF). Finally, we utilize the multivariate linear regression (MLR) and extreme learning machine (ELM) to construct multi-satellite linear regression models (MSLRs) and multi-satellite nonlinear regression models (MSNRs) for soil moisture prediction, and compare the accuracy of each model. To validate the feasibility of these methods, data from site P031 of the Plate Boundary Observatory (PBO) H2O project are utilized. Experimental results demonstrate that combining MCD and MAF can effectively detect and correct outliers, yielding single-satellite delay phase sequences with a high quality. This improvement contributes to varying degrees of enhanced correlation between the single-satellite delay phase and soil moisture. When fusing the corrected delay phases from multiple satellite orbits using the DFPC method for soil moisture estimation, the correlations between the true soil moisture values and the predicted values obtained through MLR and ELM reach 0.81 and 0.88, respectively, while the correlations of the L4 method can reach 0.84 and 0.90, respectively. These findings indicate a substantial achievement in high-precision soil moisture estimation within a small satellite-elevation angle range.

Retrieving soil moisture using cosmic-ray neutron technology based on COSMIC model in the desert-oasis region

Cosmic-ray neutron technology could estimate average soil moisture on scale of hectometers by monitoring the neutron intensity near the ground, which has been successfully applied in forest, grassland, farmland, and other ecosystems. To verify the reliability of Cosmic-ray Soil Moisture Interaction Code (COSMIC) model for retrieving mesoscale soil moisture in arid regions, we carried out soil moisture observation experiment by using the cosmic-ray neutron rover in the desert-oasis region of the middle reaches of Heihe River. The results showed that the fast neutron intensity in the desert-oasis region were 350-715 counts·(30 s)-1, and the calibrated high energy neutron intensity (Ncosmic) were (38.5±2.2) counts·(30 s)-1, which was affected by land surface characteristics. Both COSMIC model (root mean square error=0.019 g·g-1) and N0 equation (root mean square error=0.018 g·g-1) could well assess the mesoscale soil moisture, with the accuracy of soil moisture being higher considering soil lattice water. The average penetration depth was 19 cm in the oasis region and 36 cm in the desert region during the experiment. COSMIC model could be used to retrieve soil moisture by cosmic ray neutron in the desert-oasis regions, which had great potential to realize data assimilation of surface meteorological-hydrological-ecological variables by combining with land surface models.

IoT-Based Agro-Toolbox for Soil Analysis and Environmental Monitoring

The agricultural sector faces numerous challenges in ensuring optimal soil health and environmental conditions for sustainable crop production. Traditional soil analysis methods are often time-consuming and labor-intensive, and provide limited real-time data, making it challenging for farmers to make informed decisions. In recent years, Internet of Things (IoT) technology has emerged as a promising solution to address these challenges by enabling efficient and automated soil analysis and environmental monitoring. This paper presents a 3D-printed IoT-based Agro-toolbox, designed for comprehensive soil analysis and environmental monitoring in the agricultural domain. The toolbox integrates various sensors for both soil and environmental measurements. By deploying this tool across fields, farmers can continuously monitor key soil parameters, including pH levels, moisture content, and temperature. Additionally, environmental factors such as ambient temperature, humidity, intensity of visible light, and barometric pressure can be monitored to assess the overall health of agricultural ecosystems. To evaluate the effectiveness of the Agro-toolbox, a case study was conducted in an aquaponics floating system with rocket, and benchmarking was performed using commercial tools that integrate sensors for soil temperature, moisture, and pH levels, as well as for air temperature, humidity, and intensity of visible light. The results showed that the Agro-toolbox had an acceptable error percentage, and it can be useful for agricultural applications.

Evaluating the Hydrological Components Contributions to Terrestrial Water Storage Changes in Inner Mongolia with Multiple Datasets

In this study, multiple remote sensing data were used to quantitatively evaluate the contributions of surface water, soil moisture and groundwater to terrestrial water storage (TWS) changes in five groundwater resources zones of Inner Mongolia (GW_I, GW_II, GW_III, GW_IV and GW_V), China. The results showed that TWS increased at the rate of 2.14 mm/a for GW_I, while it decreased at the rate of 4.62 mm/a, 5.89 mm/a, 2.79 mm/a and 2.62 mm/a for GW_II, GW_III, GW_IV and GW_V during 2003-2021. Inner Mongolia experienced a widespread soil moisture increase with the rate of 4.17 mm/a, 2.13 mm/a, 1.20 mm/a, 0.25 mm/a and 1.36 mm/a for the five regions, respectively. Significant decreases were detected for regional groundwater storage (GWS) with the rate of 2.21 mm/a, 6.76 mm/a, 6.87 mm/a, 3.01 mm/a, and 4.14 mm/a, respectively. Soil moisture was the major contributor to TWS changes in GW_I, which accounted 58% of the total TWS changes. Groundwater was the greatest contributor to TWS changes in other four regions, especially GWS changes, which accounted for 76% TWS changes in GW_IV. In addition, this study found that the role of surface water was notable for calculating regional GWS changes.

Soil moisture determines the recovery time of ecosystems from drought

Recovery time, the time it takes for ecosystems to return to normal states after experiencing droughts, is critical for assessing the response of ecosystems to droughts; however, the spatial dominant factors determining recovery time are poorly understood. We identify the global patterns of terrestrial ecosystem recovery time based on remote sensed vegetation indices, analyse the affecting factors of recovery time using random forest regression model, and determine the spatial distribution of the dominant factors of recovery time based on partial correlation. The results show that the global average recovery time is approximately 3.3 months, and that the longest recovery time occurs in mid-latitude drylands. Analysis of affecting factors of recovery time suggests that the most important environmental factor affecting recovery time is soil moisture during the recovery period, followed by temperature and vapour pressure deficit (VPD). Recovery time shortens with increasing soil moisture and prolongs with increasing VPD; however, the response of recovery time to temperature is nonmonotonic, with colder or hotter temperatures leading to longer recovery time. Soil moisture dominates the drought recovery time over 58.4% of the assessed land area, mostly in the mid-latitudes. The concern is that soil moisture is projected to decline in more than 65% regions in the future, which will lengthen the drought recovery time and exacerbate drought impacts on terrestrial ecosystems, especially in southwestern United States, the Mediterranean region and southern Africa. Our research provides methodological insights for quantifying recovery time and spatially identifies dominant factors of recovery time, improving our understanding of ecosystem response to drought.

Biocrusts intensify water redistribution and improve water availability to dryland vegetation: insights from a spatially-explicit ecohydrological model

Biocrusts are ecosystem engineers in drylands and structure the landscape through their ecohydrological effects. They regulate soil infiltration and evaporation but also surface water redistribution, providing important resources for vascular vegetation. Spatially-explicit ecohydrological models are useful tools to explore such ecohydrological mechanisms, but biocrusts have rarely been included in them. We contribute to closing this gap and assess how biocrusts shape spatio-temporal water fluxes and availability in a dryland landscape and how landscape hydrology is affected by climate-change induced shifts in the biocrust community. We extended the spatially-explicit, process-based ecohydrological dryland model EcoHyD by a biocrust layer which modifies water in- and outputs from the soil and affects surface runoff. The model was parameterized for a dryland hillslope in South-East Spain using field and literature data. We assessed the effect of biocrusts on landscape-scale soil moisture distribution, plant-available water and the hydrological processes behind it. To quantify the biocrust effects, we ran the model with and without biocrusts for a wet and dry year. Finally, we compared the effect of incipient and well-developed cyanobacteria- and lichen biocrusts on surface hydrology to evaluate possible paths forward if biocrust communities change due to climate change. Our model reproduced the runoff source-sink patterns typical of the landscape. The spatial differentiation of soil moisture in deeper layers matched the observed distribution of vascular vegetation. Biocrusts in the model led to higher water availability overall and in vegetated areas of the landscape and that this positive effect in part also held for a dry year. Compared to bare soil and incipient biocrusts, well-developed biocrusts protected the soil from evaporation thus preserving soil moisture despite lower infiltration while at the same time redistributing water toward downhill vegetation. Biocrust cover is vital for water redistribution and plant-available water but potential changes of biocrust composition and cover can reduce their ability of being a water source and sustaining dryland vegetation. The process-based model used in this study is a promising tool to further quantify and assess long-term scenarios of climate change and how it affects ecohydrological feedbacks that shape and stabilize dryland landscapes.

An Illustration of FY-3E GNOS-R for Global Soil Moisture Monitoring

An effective soil moisture retrieval method for FY-3E (Fengyun-3E) GNOS-R (GNSS occultation sounder II-reflectometry) is developed in this paper. Here, the LAGRS model, which is totally oriented for GNOS-R, is employed to estimate vegetation and surface roughness effects on surface reflectivity. Since the LAGRS (land surface GNSS reflection simulator) model is a space-borne GNSS-R (GNSS reflectometry) simulator based on the microwave radiative transfer equation model, the method presented in this paper takes more consideration on the physical scattering properties for retrieval. Ancillary information from SMAP (soil moisture active passive) such as the vegetation water content and the roughness coefficient are investigated for the final algorithm's development. At first, the SR (surface reflectivity) data calculated from GNOS-R is calculated and then calibrated, and then the vegetation roughness factor is achieved and used to eliminate the effects on both factors. After receiving the Fresnel reflectivity, the corresponding soil moisture estimated from this method is retrieved. The results demonstrate good consistency between soil moisture derived from GNOS-R data and SMAP soil moisture, with a correlation coefficient of 0.9599 and a root mean square error of 0.0483 cm³/cm³. This method succeeds in providing soil moisture on a global scale and is based on the previously developed physical LAGRS model. In this way, the great potential of GNOS-R for soil moisture estimation is presented.

A Data-Driven Approach for Building the Profile of Water Storage Capacity of Soils

The soil water storage capacity is critical for soil management as it drives crop production, soil carbon sequestration, and soil quality and health. It depends on soil textural class, depth, land-use and soil management practices; therefore, the complexity strongly limits its estimation on a large scale with conventional-process-based approaches. In this paper, a machine learning approach is proposed to build the profile of the soil water storage capacity. A neural network is designed to estimate the soil moisture from the meteorology data input. By taking the soil moisture as a proxy in the modelling, the training captures those impact factors of soil water storage capacity and their nonlinear interaction implicitly without knowing the underlying soil hydrologic processes. An internal vector of the proposed neural network assimilates the soil moisture response to meteorological conditions and is regulated as the profile of the soil water storage capacity. The proposed approach is data-driven. Since the low-cost soil moisture sensors have made soil moisture monitoring simple and the meteorology data are easy to obtain, the proposed approach enables a convenient way of estimating soil water storage capacity in a high sampling resolution and at a large scale. Moreover, an average root mean squared deviation at 0.0307m3/m3 can be achieved in the soil moisture estimation; hence, the trained model can be deployed as an alternative to the expensive sensor networks for continuous soil moisture monitoring. The proposed approach innovatively represents the soil water storage capacity as a vector profile rather than a single value indicator. Compared with the single value indicator, which is common in hydrology, a multidimensional vector can encode more information and thus has a more powerful representation. This can be seen in the anomaly detection demonstrated in the paper, where subtle differences in soil water storage capacity among the sensor sites can be captured even though these sensors are installed on the same grassland. Another merit of vector representation is that advanced numeric methods can be applied to soil analysis. This paper demonstrates such an advantage by clustering sensor sites into groups with the unsupervised K-means clustering on the profile vectors which encapsulate soil characteristics and land properties of each sensor site implicitly.

Combining thermal imaging and soil water content sensors to assess tree water status in pear trees

Volumetric soil water content is commonly used for irrigation management in fruit trees. By integrating direct information on tree water status into measurements of soil water content, we can improve detection of water stress and irrigation scheduling. Thermal-based indicators can be an alternative to traditional measurements of midday stem water potential and stomatal conductance for irrigation management of pear trees (Pyrus communis L.). These indicators are easy, quick, and cost-effective. The soil and tree water status of two cultivars of pear trees 'D'Anjou' and 'Bartlett' submitted to regulated deficit irrigation was measured regularly in a pear orchard in Rock Island, WA (USA) for two seasons, 2021 and 2022. These assessments were compared to the canopy temperature (Tc), the difference between the canopy and air temperature (Tc-Ta) and the crop water stress index (CWSI). Trees under deficit irrigation had lower midday stem water potential and stomatal conductance but higher Tc, Tc-Ta, and CWSI. Tc was not a robust method to assess tree water status since it was strongly related to air temperature (R = 0.99). However, Tc-Ta and CWSI were greater than 0°C or 0.5, respectively, and were less dependent on the environmental conditions when trees were under water deficits (midday stem water potential values< -1.2 MPa). Moreover, values of Tc-Ta = 2°C and CWSI = 0.8 occurred when midday stem water potential was close to -1.5 MPa and stomatal conductance was lower than 200 mmol m^-2s^-1. Soil water content (SWC) was the first indicator in detecting the deficit irrigation applied, however, it was not as strongly related to the tree water status as the thermal-based indicators. Thus, the relation between the indicators studied with the stem water potential followed the order: CWSI > Tc-Ta > SWC = Tc. A multiple regression analysis is proposed that combines both soil water content and thermal-based indices to overcome limitations of individual use of each indicator.

Cultivar-dependent differences in tuber growth cause increased soil resistance in potato fields

Since soil compaction of potato fields delays shoot emergence and decreases total yield, the causes and effects of this compaction need to be better understood. In a controlled environment trial with young (before tuber initiation) plants, roots of cv. Inca Bella (a phureja group cultivar) were more sensitive to increased soil resistance (3.0 MPa) than cv. Maris Piper (a tuberosum group cultivar). Such variation was hypothesized to cause yield differences in two field trials, in which compaction treatments were applied after tuber planting. Trial 1 increased initial soil resistance from 0.15 MPa to 0.3 MPa. By the end of the growing season, soil resistance increased three-fold in the upper 20 cm of the soil, but resistance in Maris Piper plots was up to twice that of Inca Bella plots. Maris Piper yield was 60% higher than Inca Bella and independent of soil compaction treatment, whilst compacted soil reduced Inca Bella yield by 30%. Trial 2 increased initial soil resistance from 0.2 MPa to 1.0 MPa. Soil resistance in the compacted treatments increased to similar, cultivar-dependent resistances as trial 1. Maris Piper yield was 12% higher than Inca Bella, but cultivar variation in yield response to compacted soil did not occur. Soil water content, root growth and tuber growth were measured to determine whether these factors could explain cultivar differences in soil resistance. Soil water content was similar between cultivars, thus did not cause soil resistance to vary between cultivars. Root density was insufficient to cause observed increases soil resistance. Finally, differences in soil resistance between cultivars became significant during tuber initiation, and became more pronounced until harvest. Increased tuber biomass volume (yield) of Maris Piper increased estimated mean soil density (and thus soil resistance) more than Inca Bella. This increase seems to depend on initial compaction, as soil resistance did not significantly increase in uncompacted soil. While increased soil resistance caused cultivar-dependent restriction of root density of young plants that was consistent with cultivar variation in yield, tuber growth likely caused cultivar-dependent increases in soil resistance in field trials, which may have further limited Inca Bella yield.

Effects of grazing on soil water recharge of rehabilitated grassland under natural rainfall on the Loess Pla-teau, Northwest China

Rainfall is critical to the regulation of slope runoff and soil water recharge. Grazing affects land cover and soil structure, with consequence on slope runoff generation and soil water recharge. Little attention has been paid to the effects of rainfall on soil water recharge caused by grazing. In this study, we examined land covers and soil water contents under different grazing intensities (G1-G5: 2.2, 3.0, 4.2, 6.7, 16.7 sheep·hm-2) and no grazing sites (NG), aiming to analyze soil water recharge under natural rainfall conditions after grazing. The results showed that grazing exerted significant effects on vegetation and biocrust coverage. The vegetation coverage was decreased by 8.3%-16.4% under G1-G5 grazing, while the biocrust coverage was increased by 106.9% under G2 grazing compared to NG. The soil surface roughness under G1-G5 grazing was increased by 53.1%-152.5%, and the thickness of biocrust was decreased by 24.1% under G5. Soil wetting front velocity decreased with increasing rainfall intensity, and that of 0-5 cm layer under the G2 grazing intensity decreased by 60.0% to 83.3% under rainfall between 18.0 mm and 70.3 mm compared to NG. The effect of grazing on soil wetting front velocity was significantly related to biocrust coverage and soil bulk density of 0-5 cm soil layer. Generally, grazing did not affect soil water recharge rates of the slope grassland on the Loess Plateau. G2 grazing may prolong the migration time of soil water in the surface layer by increasing the coverage of cyanobacteria biocrusts, which may be beneficial to the restoration of soil microenvironment. Our results provided scientific basis for water management in the enclosure grassland of the Loess Plateau in the "post-conversion era".

Compound droughts slow down the greening of the Earth

Vegetation response to soil and atmospheric drought has raised extensively controversy, however, the relative contributions of soil drought, atmospheric drought, and their compound droughts on global vegetation growth remain unclear. Combining the changes in soil moisture (SM), vapor pressure deficit (VPD), and vegetation growth (normalized difference vegetation index [NDVI]) during 1982-2015, here we evaluated the trends of these three drought types and quantified their impacts on global NDVI. We found that global VPD has increased 0.22 ± 0.05 kPa·decade^-1 during 1982-2015, and this trend was doubled after 1996 (0.32 ± 0.16 kPa·decade^-1 ) than before 1996 (0.16 ± 0.15 kPa·decade^-1 ). Regions with large increase in VPD trend generally accompanied with decreasing trend in SM, leading to a widespread increasing trend in compound droughts across 37.62% land areas. We further found compound droughts dominated the vegetation browning since late 1990s, contributing to a declined NDVI of 64.56%. Earth system models agree with the dominant role of compound droughts on vegetation growth, but their negative magnitudes are considerably underestimated, with half of the observed results (34.48%). Our results provided the evidence of compound droughts-induced global vegetation browning, highlighting the importance of correctly simulating the ecosystem-scale response to the under-appreciated exposure to compound droughts as it will increase with climate change.

Effects of intra-annual precipitation patterns on grassland productivity moderated by the dominant species phenology

Phenology and productivity are important functional indicators of grassland ecosystems. However, our understanding of how intra-annual precipitation patterns affect plant phenology and productivity in grasslands is still limited. Here, we conducted a two-year precipitation manipulation experiment to explore the responses of plant phenology and productivity to intra-annual precipitation patterns at the community and dominant species levels in a temperate grassland. We found that increased early growing season precipitation enhanced the above-ground biomass of the dominant rhizome grass, Leymus chinensis, by advancing its flowering date, while increased late growing season precipitation increased the above-ground biomass of the dominant bunchgrass, Stipa grandis, by delaying senescence. The complementary effects in phenology and biomass of the dominant species, L. chinensis and S. grandis, maintained stable dynamics of the community above-ground biomass under intra-annual precipitation pattern variations. Our results highlight the critical role that intra-annual precipitation and soil moisture patterns play in the phenology of temperate grasslands. By understanding the response of phenology to intra-annual precipitation patterns, we can more accurately predict the productivity of temperate grasslands under future climate change.

Artificial Grassland Had Higher Water Use Efficiency in Year with Less Precipitation in the Agro-Pastoral Ecotone

Improving plant water use efficiency is a key strategy for the utilization of regional limited water resources as well as the sustainable development of agriculture industry. To investigate the effects of different land use types on plant water use efficiency and their mechanisms, a randomized block experiment was designed in the agro-pastoral ecotone of northern China during 2020-2021. The differences in dry matter accumulation, evapotranspiration, soil physical and chemical properties, soil water storage and water use efficiency and their relationships among cropland, natural grassland and artificial grassland were studied. The results show that: In 2020, the dry matter accumulation and water use efficiency of cropland were significantly higher than those of artificial and natural grassland. In 2021, dry matter accumulation and water use efficiency of artificial grassland increased significantly from 364.79 g·m^-2 and 24.92 kg·ha^-1·mm^-1 to 1037.14 g·m^-2 and 50.82 kg·ha^-1·mm^-1, respectively, which were significantly higher than cropland and natural grassland. The evapotranspiration of three land use types showed an increasing trend in two years. The main reason affecting the difference of water use efficiency was that land use type affected soil moisture and soil nutrients, and then changed the dry matter accumulation and evapotranspiration of plants. During the study period, the water use efficiency of artificial grassland was higher in years with less precipitation. Therefore, expanding the planted area of artificial grassland may be one of the effective ways to promote the full utilization of regional water resources.

Earlier leaf senescence dates are constrained by soil moisture

The unprecedented warming that has occurred in recent decades has led to later autumn leaf senescence dates (LSD) throughout the Northern Hemisphere. Yet, great uncertainties still exist regarding the strength of these delaying trends, especially in terms of how soil moisture affects them. Here we show that changes in soil moisture in 1982-2015 had a substantial impact on autumn LSD in one-fifth of the vegetated areas in the Northern Hemisphere (>30° N), and how it contributed more to LSD variability than either temperature, precipitation or radiation. We developed a new model based on soil-moisture-constrained cooling degree days (CDD_SM ) to characterize the effects of soil moisture on LSD and compared its performance with the CDD, Delpierre and spring-influenced autumn models. We show that the CDD_SM model with inputs of temperature and soil moisture outperformed the three other models for LSD modelling and had an overall higher correlation coefficient (R), a lower root mean square error and lower Akaike information criterion (AIC) between observations and model predictions. These improvements were particularly evident in arid and semi-arid regions. We studied future LSD using the CDD_SM model under two scenarios (SSP126 and SSP585) and found that predicted LSD was 4.1 ± 1.4 days and 5.8 ± 2.8 days earlier under SSP126 and SSP585, respectively, than other models for the end of this century. Our study therefore reveals the importance of soil moisture in regulating autumn LSD and, in particular, highlights how coupling this effect with LSD models can improve simulations of the response of vegetation phenology to future climate change.

Extreme drought disrupts plant phenology: Insights from 35 years of cloud forest data in Venezuela

The potential effects of climate change on plant reproductive phenology include asynchronies with pollinators and reductions in plant fitness, leading to extinction and loss of ecosystem function. In particular, plant phenology is sensitive to extreme weather events, which are occurring with increasing severity and frequency in recent decades and are linked to anthropogenic climate change and shifts in atmospheric circulation. For 15 plant species in a Venezuelan cloud forest, we documented dramatic changes in monthly flower and fruit community composition over a 35-year time series, from 1983 to 2017, and these changes were linked directly to higher temperatures, lower precipitation, and decreased soil water availability. The patterns documented here do not mirror trends in temperate zones but corroborate results from the Asian tropics. More intense droughts are predicted to occur in the region, which will cause dramatic changes in flower and fruit availability.

Automated Low-Cost Soil Moisture Sensors: Trade-Off between Cost and Accuracy

Automated soil moisture systems are commonly used in precision agriculture. Using low-cost sensors, the spatial extension can be maximized, but the accuracy might be reduced. In this paper, we address the trade-off between cost and accuracy comparing low-cost and commercial soil moisture sensors. The analysis is based on the capacitive sensor SKU:SEN0193 tested under lab and field conditions. In addition to individual calibration, two simplified calibration techniques are proposed: universal calibration, based on all 63 sensors, and a single-point calibration using the sensor response in dry soil. During the second stage of testing, the sensors were coupled to a low-cost monitoring station and installed in the field. The sensors were capable of measuring daily and seasonal oscillations in soil moisture resulting from solar radiation and precipitation. The low-cost sensor performance was compared to commercial sensors based on five variables: (1) cost, (2) accuracy, (3) qualified labor demand, (4) sample volume, and (5) life expectancy. Commercial sensors provide single-point information with high reliability but at a high acquisition cost, while low-cost sensors can be acquired in larger numbers at a lower cost, allowing for more detailed spatial and temporal observations, but with medium accuracy. The use of SKU sensors is then indicated for short-term and limited-budget projects in which high accuracy of the collected data is not required.

Biochar with Inorganic Nitrogen Fertilizer Reduces Direct Greenhouse Gas Emission Flux from Soil

Agricultural waste can have a catastrophic impact on climate change, as it contributes significantly to greenhouse gas (GHG) emissions if not managed sustainably. Swine-digestate-manure-derived biochar may be one sustainable way to manage waste and tackle GHG emissions in temperate climatic conditions. The purpose of this study was to ascertain how such biochar could be used to reduce soil GHG emissions. Spring barley (Hordeum vulgare L.) and pea crops in 2020 and 2021, respectively, were treated with 25 t ha^-1 of swine-digestate-manure-derived biochar (B₁) and 120 kg ha^-1 (N₁) and 160 kg ha^-1 (N₂) of synthetic nitrogen fertilizer (ammonium nitrate). Biochar with or without nitrogen fertilizer substantially lowered GHG emissions compared to the control treatment (without any treatment) or treatments without biochar application. Carbon dioxide (CO₂), nitrous oxide (N₂O), and methane (CH₄) emissions were directly measured using static chamber technology. Cumulative emissions and global warming potential (GWP) followed the same trend and were significantly lowered in biochar-treated soils. The influences of soil and environmental parameters on GHG emissions were, therefore, investigated. A positive correlation was found between both moisture and temperature and GHG emissions. Thus, biochar made from swine digestate manure may be an effective organic amendment to reduce GHG emissions and address climate change challenges.

Ecophysiology of Endangered Plant Species Saussurea esthonica: Effect of Mineral Nutrient Availability and Soil Moisture

Saussurea esthonica is an endangered plant species typical for wet inland habitats such as calcareous fens. Due to its limited population size and distribution, non-invasive sampling of is important in the research of S. esthonica. The aim of the present study was to assess the effect of mineral nutrient availability and substrate moisture on the growth, physiological status, and mineral nutrition of S. esthonica. The non-destructive measurement of physiological parameters was performed in native habitats during three vegetative seasons, followed by two experiments in controlled conditions. Soil at the two Estonian sites had a relatively larger similarity in the composition of plant-available mineral nutrients in comparison to the two Latvian sites. The chlorophyll a fluorescence parameter Performance Index correlated with the total precipitation in the respective month before measurement, but no significant relationship with other environmental variables was found. For mineral nutrient experiments, plants were grown in four substrates with different mineral nutrient composition, resembling that of soil at different S. esthonica sites. Plant growth and physiological indices were significantly affected by the mineral composition of the substrate. Differences in leaf and root mineral nutrient concentrations of S. esthonica plants in part reflected differences in substrate mineral concentration. To evaluate the effect of soil moisture on growth and photosynthesis-associated parameters of S. esthonica, plants were cultivated in "Pope+" substrate at four different moisture treatments (dry, normal, wet, and waterlodged). The most intense growth of S. esthonica plants was evident in waterlodged conditions, which decreased with a decrease in soil moisture. The biomass of leaves increased by 106% and that of the roots increased by 72% as soil moisture increased from dry to normal. For waterlodged plants, leaf biomass increased by 263% and root biomass increased by 566%, in comparison to that for plants cultivated in dry substrate. Substrate drying had a more negative effect on the growth of S. esthonica plants in comparison to that of waterlodging, and this can be directly linked to prevalent hydrological conditions of an alkaline fen habitat native to the species. Therefore, the preservation of the natural water regime in natural habitats is critical to the conservation of the species.

Development of a Soil Moisture Prediction Model Based on Recurrent Neural Network Long Short-Term Memory (RNN-LSTM) in Soybean Cultivation

Due to climate change, soil moisture may increase, and outflows could become more frequent, which will have a considerable impact on crop growth. Crops are affected by soil moisture; thus, soil moisture prediction is necessary for irrigating at an appropriate time according to weather changes. Therefore, the aim of this study is to develop a future soil moisture (SM) prediction model to determine whether to conduct irrigation according to changes in soil moisture due to weather conditions. Sensors were used to measure soil moisture and soil temperature at a depth of 10 cm, 20 cm, and 30 cm from the topsoil. The combination of optimal variables was investigated using soil moisture and soil temperature at depths between 10 cm and 30 cm and weather data as input variables. The recurrent neural network long short-term memory (RNN-LSTM) models for predicting SM was developed using time series data. The loss and the coefficient of determination (R²) values were used as indicators for evaluating the model performance and two verification datasets were used to test various conditions. The best model performance for 10 cm depth was an R² of 0.999, a loss of 0.022, and a validation loss of 0.105, and the best results for 20 cm and 30 cm depths were an R² of 0.999, a loss of 0.016, and a validation loss of 0.098 and an R² of 0.956, a loss of 0.057, and a validation loss of 2.883, respectively. The RNN-LSTM model was used to confirm the SM predictability in soybean arable land and could be applied to supply the appropriate moisture needed for crop growth. The results of this study show that a soil moisture prediction model based on time-series weather data can help determine the appropriate amount of irrigation required for crop cultivation.

Land Surface Model Calibration Using Satellite Remote Sensing Data

Satellite remote sensing provides a unique opportunity for calibrating land surface models due to their direct measurements of various hydrological variables as well as extensive spatial and temporal coverage. This study aims to apply terrestrial water storage (TWS) estimated from the gravity recovery and climate experiment (GRACE) mission as well as soil moisture products from advanced microwave scanning radiometer-earth observing system (AMSR-E) to calibrate a land surface model using multi-objective evolutionary algorithms. For this purpose, the non-dominated sorting genetic algorithm (NSGA) is used to improve the model's parameters. The calibration is carried out for the period of two years 2003 and 2010 (calibration period) in Australia, and the impact is further monitored over 2011 (forecasting period). A new combined objective function based on the observations' uncertainty is developed to efficiently improve the model parameters for a consistent and reliable forecasting skill. According to the evaluation of the results against independent measurements, it is found that the calibrated model parameters lead to better model simulations both in the calibration and forecasting period.

Conifer water-use patterns across temporal and topographic gradients in the southern Sierra Nevada

Climate change is increasing the severity and duration of drought events experienced by forest ecosystems. Because water is essential for tree physiological processes, the ability of trees to survive prolonged droughts will largely depend on whether they have access to reliable water sources. While many woody plant species exhibit the ability to shift water sources between different depths of soil and rock water in response to changes in climate and water availability, it is unclear if Sierra Nevada conifers exhibit this plasticity. Here we analysed the δ18O and δ13C values of annual tree rings to determine the water-use patterns of large Sierra Nevada conifers during the 2012-16 California drought and 4 years before this drought event (2004-07). We analysed four species (Pinus jeffreyi Grev. & Balf. (Jeffrey pine), Pinus lambertiana Dougl. (sugar pine), Abies concolor (Gord. & Glend.) Lindl. Ex Hilderbr (white fir) and Calocedrus decurrens (Torr.) Florin (incense-cedar)) across a range of topographic positions to investigate differences in water-use patterns by species and position on the landscape. We found no significant differences in δ18O and δ13C values for the pre-drought and drought periods. This stability in δ18O values suggests that trees did not shift their water-use patterns in response to the 2012-16 drought. We did find species-specific differences in water-use patterns, with incense-cedar exhibiting more depleted δ18O values than all other species. We also found trends that suggest the water source used by a tree may depend on topographic and growing environment attributes such as topographic wetness and the surrounding basal area. Overall, our results suggest that the water source used by trees varies by the species and topographic position, but that Sierra Nevada conifers do not switch their water-use patterns in response to the drought. This lack of plasticity could make Sierra Nevada conifers particularly vulnerable to drought mortality as their historically reliable water sources begin to dry out with climate change.