Soil Moisture Research Articles

Rare and Abundant phoD‐Harboring Bacteria Mediate the Mineralization of Organic Phosphorus Fractions in Soil Aggregates During Cut Slope Restoration

ABSTRACTPhosphorus (P) is the key nutritional element in the soil of cut road slopes undergoing ecological restoration, and the transformation of organic P (Po) is a crucial part of the P cycle. However, the role of phoD‐harboring bacteria in driving the mineralization of Po fractions in road slope soil aggregates is unclear. This study analyzed road slope soils that had undergone 7, 11, and 14 years of restoration in the western Sichuan Plateau of China. We examined the differences and associations between the Po fraction content, alkaline phosphatase (ALP) activity, and community composition of rare and abundant phoD‐harboring bacteria in soil aggregates of four particle sizes (0.053–0.25, 0.25–2, 0.053–0.25, and, > 2 mm). The results showed that NaHCO3‐extracted Po (NaHCO3‐Po) content in soil aggregates increased with restoration years, while NaOH‐extracted Po (NaOH‐Po) content decreased. ALP activity in soil aggregates increased with restoration years, but there was no significant relationship between ALP activity with the phoD‐harboring bacterial community. There were significant differences in the composition of rare and abundant phoD‐harboring bacterial communities during slope restoration. Soil moisture, pH, organic carbon, and the C:P ratio in soil aggregates were the primary factors affecting the distribution of the Po fractions and the phoD‐harboring bacterial community. NaHCO3‐Po and NaOH‐Po in soil aggregates were likely the main substrates for ALP‐mediated Po mineralization. More genera were involved in Po mineralization in slope soils restored for 14 years than in soils restored for 7 and 11 years, and rare phoD‐harboring genera were more actively involved in Po mineralization than abundant phoD‐harboring genera. This study provides some theoretical basis for P effectiveness enhancement and P management during slope soil restoration.

Understanding soil and ecosystem respiration in a dune-meadow cascade ecosystem

Arid and semi-arid regions, which account for more than 30% of the Earth's land area, increasingly dominate the spatiotemporal trends in global carbon fluxes. The Horqin Sandy Land is a typical semi-arid fragile ecosystem in northern China. Understanding the components of the carbon budget in ecosystems under conditions of extreme soil moisture limitations provides a foundation for comprehending the carbon balance in semi-arid ecosystems. The seasonal and diurnal variations in soil respiration (Rs) in semi-mobile dune (SD) and meadow wetland (MW) ecosystems of the Horqin Sandy Land were examined, and the sources of CO2 emissions from Rs were identified using stable carbon isotopes. The responses of Rs and ecosystem respiration (Reco) to environmental temperature, moisture and leaf area index (LAI) were revealed. The results showed that on a seasonal scale, in SD with soil moisture content (Ms) below field capacity (FC), Ms had a greater influence on Rs than soil temperature (Ts) during the growing season. Changes in the LAI during the middle and late growth period affected Rs by altering root carbon supply. In MW, the most favorable Ms for Rs was near FC. The increase in LAI before mowing could effectively promote root and soil microbial respiration, and the decomposition of litter driven by Ts was the main form of Rs at this time. After mowing, root respiration and soil microbial respiration were the main processes contributing to CO2 emissions. On a daily scale, relative humidity (RH) dominated the Rs variation under dry conditions, whereas in other conditions, the Rs was adequately explained by temperature in SD and MW. The overall Reco was larger than Rs, but occasionally Rs was greater than Reco. The effects of temperature, moisture and LAI on Reco and Rs varied with growing season. Adding factors, such as ecosystem type, vegetation growth, water, and heat, to the carbon cycle model can improve predictions of carbon emissions, and aid in further management decisions in arid and semi-arid areas.

Predicting alfalfa leaf area index by non-linear models and deep learning models

Leaf area index (LAI) of alfalfa is a crucial indicator of its growth status and a predictor of yield. The LAI of alfalfa is influenced by environmental factors, and the limitations of non-linear models in integrating these factors affect the accuracy of LAI predictions. This study explores the potential of classical non-linear models and deep learning for predicting alfalfa LAI. Initially, Logistic, Gompertz, and Richards models were developed based on growth days to assess the applicability of nonlinear models for LAI prediction of alfalfa. In contrast, this study combines environmental factors such as temperature and soil moisture, and proposes a time series prediction model based on mutation point detection method and encoder-attention-decoder BiLSTM network (TMEAD-BiLSTM). The model’s performance was analyzed and evaluated against LAI data from different years and cuts. The results indicate that the TMEAD-BiLSTM model achieved the highest prediction accuracy (R² > 0.99), while the non-linear models exhibited lower accuracy (R² > 0.78). The TMEAD-BiLSTM model overcomes the limitations of nonlinear models in integrating environmental factors, enabling rapid and accurate predictions of alfalfa LAI, which can provide valuable references for alfalfa growth monitoring and the establishment of field management practices.

Mapping Rice Paddy Areas in Nueva Ecija Using Temporal Sentinel -1 SAR Imagery

Abstract. Remote Sensing is proven to be helpful in various ways, and for an agricultural country like the Philippines, mapping in farmlands is not that common. Using the Sentinel 1 Synthetic Aperture Radar (SAR) data, the study reveals rice paddy classification brought by supervised machine learning on the municipality of Guimba, in Nueva Ecija - the Rice Bowl Capital of the country. The study in Guimba, Nueva Ecija, addresses the absence of comprehensive rice classification studies, focusing on creating a classified rice paddy map and assessing spectral indices (NDMI, NDVI, NDWI) using synthetic aperture radar and optical imager. Results show successful differentiation between rice and non-rice paddies. Temporal analysis emphasizes monitoring water availability, soil moisture, and vegetation health. Despite signs of overprediction in the CART model, its effectiveness in mapping rice paddies is notable. The spatial distribution maps contribute to targeted monitoring, enabling efficient interventions and improved agricultural practices. This research highlights the inherent use of remote sensing in rice crop management, offering valuable insights for farmers and country's food security.

Variation in Soil Moisture Content and Soil Organic Carbon in Different Agroforestry Practices in Moshi Rural District, Northern Tanzania

Agroforestry has been widely practiced in the northern highlands of Tanzania because of its prominent effects in reducing soil losses, maintaining soil moisture contents (SMCs), and improving land-use efficiency and farmer’s livelihood. We tested the hypothesis that variations of soil moisture contents in 0-20 cm and 20-40 cm depths from various agroforestry Practices differ significantly in intensified small-scale agroforestry systems. The relationship between SMCs and soil organic carbon was also evaluated. Soil samples were collected from 10 x 10 m2 plot in three points along the diagonal of the quadrat using soil auger. SMCs were determined by gravimetric method and expressed as SMC%. SOC was determined by Walkley and Black method and expressed as SOC %. The variation in SMCs% among the different agroforestry Practices were statistically determined by ANOVA in R software while the correlation between SMCs and SOC were statistically determined by Pearson product-moment analysis. Variation of SMCs was statistically insignificant (p>0.05) among surveyed sites. SMCs increased significantly (p<0.05) with increasing soil depth from 0-40 cm depth in all agroforestry Practices except in the mixed intercropping agroforestry practice (MAP). At 0-20 cm and 20-40 cm, SMCs differed significantly among agroforestry Practices (P = 0.071) and (P = 0.003), respectively. Coffee Intercropping Agroforestry Practice (CIAP) had higher (P<0.05) SMCs at 0-20 cm depth compared to BAP and MWPAP. But, at 20-40 cm depth, soil moisture contents in CIAP differed significantly (P<0.05) with SMCs all AFPs. SMCs showed a positively significant (P<0.05) relationship with SOC within 0-20 cm and 20-40 cm depths. The current study confirms that different agroforestry practices have different influence on the amount and vertical distribution of soil moisture. Therefore, management practices in agroforestry systems should aim to encourage the use of practices which ensure a stable amount of moisture contents in the soil

Integrating Nitrogen, Water, and Other Management Practices to Improve Grain and Ratoon Forage Yields in Perennial Rice

Perennial rice has recently garnered global attention due to its potential to save on seeds and labor costs and its high production efficiency. The “mid-season rice–ratoon forage” mode is a new planting system that has emerged in recent years. However, detailed information is still lacking on the regenerative characteristics, grain and ratoon forage yields, and forage nutrient content of perennial rice under different planting densities, nitrogen (N) rates, stubble heights, and water management practices. Four experiments with perennial rice were conducted in Sichuan Province, Southwest China, from 2017 to 2022. The results show that the rice grain and ratoon forage yields were significantly affected by year, planting density, and N. The grain yield was 28.18% and 60.81% lower in 2018F and 2019F, respectively, than in 2017F; similarly, the ratoon forage yield was 29.01% and 52.74% lower in 2018S and 2019S, respectively, than in 2017S. The low grain yield was mainly associated with lower numbers of spikelets per panicle and panicles per m2, which resulted from a lower regrowth rate, and the low ratoon forage yield was mainly attributed to the lower regrowth rate. The rice grain and ratoon forage yields increased with an increase in the N rate and planting density. The ratoon forage was found to be rich in crude protein, crude fat, crude fiber, calcium, nitrogen, phosphorus, potassium, and other nutrients. Moreover, the content of these nutrients increased significantly with an increase in the N rate. The regrowth rate and maximum tillers showed trends of first increasing and then decreasing with an increase in the stubble height under dry and wet alternation irrigation during the winter season. When the relative soil moisture decreased to below 80% during the winter season, the regrowth rate and seedling development index could reach more than 99% and 84%, respectively. Our results suggest that integrating N, water, and other management practices (including the combination of a 150 kg ha−1 N rate, 18.0 hills per m2, 10–20 cm rice stubble height, and alternating dry and wet irrigation during the winter season) is a feasible approach for achieving high grain and ratoon forage yields in perennial rice systems.

Satellite-Observed Hydrothermal Conditions Control the Effects of Soil and Atmospheric Drought on Peak Vegetation Growth on the Tibetan Plateau

Recent research has demonstrated that global warming significantly enhances peak vegetation growth on the Tibetan Plateau (TP), underscoring the influence of climatic factors on vegetation dynamics. Nevertheless, the effects of different drought types on peak vegetation growth remain underexplored. This study utilized satellite-derived gross primary productivity (GPP) and the normalized difference vegetation index (NDVI) to assess the impacts of soil moisture (SM) and vapor pressure deficit (VPD) on peak vegetation growth (GPPmax and NDVImax) across the TP from 2001 to 2022. Our findings indicate that NDVImax and GPPmax exhibited increasing trends in most regions, displaying similar spatial patterns, with 65.28% of pixels showing an increase in NDVImax and 72.98% in GPPmax. In contrast, the trend for SM primarily showed a decrease (80.86%), while VPD showed an increasing trend (74.75%). Through partial correlation analysis and ridge regression, we found that peak vegetation growth was significantly affected by SM or VPD in nearly 20% of the study areas, although the magnitude of these effects varied considerably. Furthermore, we revealed that hydrothermal conditions modulated the responses of peak vegetation growth to SM and VPD. In regions with annual precipitation less than 650 mm and an annual mean temperature below 10 °C, decreased SM and increased VPD generally inhibited peak vegetation growth. Conversely, in warm and humid areas, lower SM and higher VPD promoted peak vegetation growth. These findings are crucial for deepening our understanding of vegetation phenology and its future responses to climate change.

2022 Flood Impact in Pakistan: Remote Sensing Assessment of Agricultural and Urban Damage

Pakistan was hit by the world's deadliest flood in June 2022, causing agriculture and infrastructure damage across the country. Remote sensing technology offers a cost-effective and efficient method for flood impact assessment. This study is aimed to assess the impact of flooding on crops and built-up areas. Landsat 9 imagery, European Space Agency-Land Use/Land Cover (ESA-LULC) and Soil Moisture Active Passive (SMAP) data are used to identify and quantify the extent of flood-affected areas, crop damage, and built-up area destruction. The findings indicate that Sindh, a province in Pakistan, suffered the most. This impact destroyed most Kharif season crops, typically cultivated from March to November. Using the SMAP satellite data, it is assessed that the high amount of soil moisture after flood also caused a significant delay in the cultivation of Rabi crops. The findings of this study provide valuable information for decision-makers and stakeholders involved in flood risk management and disaster response.

Impact of Flooding on Lands with Emerging Contaminants on the Quality of Receiving Water Bodies

Among crucial factors that control flooding events are extreme climate, urban growth, and mismanagement. Islands in the Nile River have experienced successive cycles of flooding and drying due to recent rainy years in the Nile Basin countries. This paper focuses on the impact of floods on the amounts of ammonium-N released, total concentrations of heavy metals, enzyme activities, and microbial biomass (C and N) in flood soils of 11 Nile River islands. Field moist soils and their airdried counterparts were collected from the outskirts of the island and incubated for 15 days at 30 °C to be analyzed. Results reflected that the amounts of released NH4-N were higher in airdried than in wet soils. The average hydrolysis rates of the studied six enzyme substrates were correlated significantly with organic C, organic N, microbial bio-mass C and N in the field moist soils. The highest correlation coefficients (r) were with rates of hydrolysis of substrates of asparaginase, amidase, and β-glycosaminidase. A significant intercorrelation between rates of hydrolysis of the six studied enzyme substrates and r values which range from 0.81 to 0.98. At 10 mmol kg−1 soil, Co+2, Cd+2, Pb+2, Cu+2, Cr+3, and Ni+2 inhibited the release of NH4-N. Arginine hydrolysis was inhibited by the six heavy metals at 5 mmol kg−1 soil; the most effective inhibitors were Pb+2, Ni+2, and Cr+3. Due to the complex soil enzymatic and organic nitrogen hydrolase activities that occur during floods, fertile fields on the Nile River islands should not be fertilized before the summer flood seasons, which will prevent contamination of the Nile River water and loss of its island soil fertility.

Empirical analysis of the influence mechanism of vegetation and environment on negative air ion in warm temperate forest ecosystems

Air pollution presents a significant threat to public health in megacities globally. Negative air ions (NAI), often referred to as “air vitamins,” are recognized for their effectiveness in alleviating the harmful effects of air pollution. Forest ecosystems serve as natural generators of NAI, with both vegetation and environmental conditions playing critical roles in the formation and persistence of NAI. Gaining a comprehensive understanding of how forest ecosystems regulate NAI production is essential for leveraging their potential to enhance air quality. However, the intricate dynamics of forest ecosystems, along with seasonal fluctuations in vegetation and environmental factors, introduce uncertainties in NAI generation. This study utilized long-term observational data to explore the relationships between environmental variables, vegetation photosynthetic capacity (using solar-induced chlorophyll fluorescence, SIF), and NAI concentrations. By employing machine learning algorithms, we analyzed the spatiotemporal distribution of NAI, identifying the key contributing factors and their relative influence within forest ecosystems. The results revealed distinct seasonal variations in NAI levels, with higher values in summer and lower in winter. SIF and PM2.5 primarily influenced NAI through direct effects across seasons, whereas ambient temperature (TA), relative humidity, photosynthetically active radiation (PAR), and soil moisture predominantly impacted SIF on NAI through indirect effects in summer. TA was the primary influencing factor in spring and winter, contributing 28% and 25%, respectively, while PAR played a more significant role in summer and autumn, accounting for 37% and 27%. Vegetation had a greater impact on NAI levels during spring and summer, contributing 66% and 62%, whereas environmental factors dominated in autumn and winter, with contributions of 83% and 89%. This study offers both a theoretical foundation and technical guidance for enhancing the role of forest ecosystems in improving air quality and human living environments.

Sustainable Soil Management in Alkaline Soils: The Role of Biochar and Organic Nitrogen in Enhancing Soil Fertility

Biochar (BC) serves a vital function in sequestering carbon, improving nutrient cycles, and boosting overall soil quality. This research explored the enhancement of the chemical and physical properties of soil (alkaline) using nitrogen and biochar (from organic and inorganic sources) in a semi-arid climate during the autumn seasons of 2015–2016 and 2016–2017. The study involved applying biochar at various rates (0, 10, 20, and 30 t ha⁻1) and nitrogen at different levels (0, 90, 120, and 150 kg ha⁻1) using urea, poultry manure (PM), and farmyard manure (FYM) as nitrogen sources, which were applied to the field in a randomized complete block design with split-plot arrangement. The application of biochar at the highest rate (30 t ha⁻1) resulted in a significant increase of over 120% in soil organic matter (SOM), soil organic carbon (SOC), and soil moisture content (SMC). Additionally, it increased total soil nitrogen (STN) by 14.16% and mineral nitrogen (SMN) by 9.09%. In contrast, applying biochar at this rate reduced soil bulk density (SBD), pH, and electrical conductivity (EC) by 28.52%, 3.38%, and 2.27%, respectively, compared to the control. Similarly, applying nitrogen at 150 kg ha⁻1 using FYM significantly improved SOC, SOM, SMC, and SBD. At the same rate, using PM as a nitrogen source enhanced STN and SMN while reducing soil pH and EC. In conclusion, this study shows that applying biochar at 30 t ha⁻1 combined with nitrogen at 150 kg ha⁻1, sourced from either PM or FYM, offers great potential for improving soil fertility and promoting carbon sequestration in alkaline soils of semi-arid regions. These findings highlight the value of integrating BC and organic N sources for enhancing agroecosystem sustainability. Thus, this study provides a promising pathway to enhance soil quality, improve crop productivity, and support sustainable agricultural practices in challenging environments.

Climate-Smart Drip Irrigation with Fertilizer Coupling Strategies to Improve Tomato Yield, Quality, Resources Use Efficiency and Mitigate Greenhouse Gases Emissions

Background: Integrated water and fertilizer management is important for promoting the sustainable development of agriculture. Climate-smart drip irrigation with fertilizer coupling strategies plays an important role to mitigate greenhouse gas emissions, ensuring food production, and alleviating water scarcity and excessive use of fertilizers. Methods: The greenhouse experiment consists of three drip irrigation treatments which include D1: drip irrigation (100 mm); D2: drip irrigation (200 mm); D3: drip irrigation (300 mm) under three different fertilizer management practices N1: nitrogen level (150 kg N ha−1); N2: nitrogen level (300 kg N ha−1); N3: nitrogen level (450 kg N ha−1). Results: The results showed that significantly improved soil moisture contents, quality and tomato yield, while reduced (38.6%) greenhouse gas intensity (GHGI) under the D3N3 treatment. The D2 and D3 drip irrigation treatments with 450 kg nitrogen ha−1 considerably improved NH4+-N contents, and NO3−-N contents at the fruit formation stage. The improve in net primary productivity (NPP), net ecosystem productivity (NEP), evapotranspiration (ET), and ecosystem crop water productivity (CWPeco) through D3N3 treatment is higher. The D3N3 treatment improved (28.2%) the net global warming potential (GWP), but reduced GHGI, due to improved (18.4%) tomato yield. The D3N3 treatment had significantly greater irrigation water productivity (IWP) (42.8%), total soluble sugar (TSS) (32.9%), vitamin C content (VC) (39.2%), soluble sugar content (SSC) (44.2%), lycopene content (41.3%) and nitrogen use efficiency (NUE) (52.4%), as compared to D1N1 treatment. Conclusions: Therefore, in greenhouse experiments, the D3N3 may be an effective water-saving and fertilizer management approach, which can improve WUE, tomato yield, and quality while reducing the effect of global warming.

Weakening of global terrestrial carbon sequestration capacity under increasing intensity of warm extremes.

The net ecosystem exchange (NEE), determining terrestrial carbon sequestration capacity, is strongly controlled by climate change and has exhibited substantial year-to-year fluctuations. How the increased frequency and intensity of warm extremes affect NEE variations remains unclear. Here, we combined multiple NEE datasets from atmospheric CO2 inversions, Earth system models, eddy-covariance data-driven methods and climate datasets to show that the terrestrial carbon sequestration capacity is weakened during warm extreme occurrences over the past 40 years, primarily contributed by tropical regions (81% ± 48%). The underlying mechanism can be rooted in the overwhelmingly decreased trend of gross primary productivity compared with terrestrial ecosystem respiration. Additionally, the weakened terrestrial carbon sequestration capacity is mainly driven by the transition from temperature or soil moisture control to vapour pressure deficit control, which is associated with the increasing intensity of warm extremes. Our findings suggest that warm extremes threaten the global carbon sequestration function of terrestrial ecosystems. Therefore, more attention should be given to the evolution of the increasing intensity of warm extremes in future climate projections.

Physical Properties of Soils in Selected Subdivisions in Tacloban City and Palo, Leyte

The study was conducted to analyzed and to determine the physical properties of soil such as texture, bulk density, water holding capacity, soil moisture content, pH, and organic matter content in selected subdivisions in Tacloban City and Palo, Leyte. Analysis of pH, texture, water holding capacity, and organic matter was at the Soil Research Testing and Plant Analysis Laboratory (SRTPAL) Department of Agronomy and Soil Science (DASS) VSU Baybay, Leyte. While the bulk density, soil moisture content was computed using the standard formula. To analyzed the result Analysis of Variance (ANOVA) using the SPSS Program to determine whether soils from subdivisions categorized as waterlogged and mountainous, developed and developing subdivisions derived from their filling and parent materials were significant different. The results showed that soils from various categories from subdivisions had no significant effect on bulk density, water holding capacity, & soil moisture content. However, some properties such as soil pH were significant in the waterlogged and mountainous area. It is recommended that a further study be conducted to include all appropriate soil parameters and procedures such as liming for acid soils, cause of erosion, flooding, water drainage capacity, depth to water table and other significant soil quality parameters. Also recommend for in-depth study by the developers and engineers to ensure that the soil quality be suitable for housing and for the safety of the homeowners.

Climate Regulates the Effects of Abrupt Vegetation Shifts on Soil Moisture in the Loess Plateau, China

Climate Regulates the Effects of Abrupt Vegetation Shifts on Soil Moisture in the Loess Plateau, China

Characterization and prediction of hydraulic properties of traffic-compacted forest soils based on soil information and traffic treatments

Key messageThe hydraulic properties of compacted and rutted soils were evaluated through in-situ infiltration experiments and predicted based on soil texture class and traffic treatments. A significant decrease in saturated soil water content and soil hydraulic conductivity at saturation was observed. The resulting soil hydraulic parameters, when integrated into a soil water transfer model, effectively simulated water dynamics in these impacted forest soils, providing a crucial first step toward developing decision support tools for real-time trafficability. This approach can assist forest managers in minimizing the extent of soil compaction.ContextTo overcome trafficability issues of forest soils induced by heavy logging machinery, planning support tools are needed to determine suitable soil moisture conditions for traffic.AimsThis study aimed to identify the soil properties that differ significantly between undisturbed and compacted soils and to provide several estimation tools to predict the hydraulic properties of compacted soils beneath the skid trails.MethodsFour hundred seventeen water infiltration tests were conducted on 19 forest sites, mostly in North-eastern France, and analysed with the BEST method to estimate the hydraulic properties of the skid trails and undisturbed soils. The hydraulic properties of the skid trails were predicted thanks to linear mixed effect models using a bulk treatment effect, a site effect, or a skid trail degradation score. The predicted hydraulic properties were tested using a water flow model to assess their relevance regarding the prediction of water dynamics in skid trails.ResultsThe compaction effect was only significant for the logarithm of the hydraulic conductivity at saturation (log10(Ksat)) and the soil water content at saturation (θsat). For the skid trails, θsat was reduced by - 0.02 and − 0.11 m3m−3 in the 0 − 10 cm and 15 − 25 cm layers respectively, compared to undisturbed topsoil (0 − 10 cm). log10(Ksat) was reduced by − 0.38 and − 0.85 for skid trails in the 0 − 10 and 15 − 25 cm soil layers respectively, compared to undisturbed topsoil. The use of a pedotransfer function, in replacement of water infiltration tests, and their combination with the same correction coefficients proved to efficiently simulate the difference in water dynamics between skid trails and undisturbed forest soils.ConclusionEstimation of soil hydraulic properties based on in situ water infiltration experiments proved efficient to simulate water dynamics in compacted and rutted forest soils. Yet, further studies are needed to identify the most adapted pedotransfer function to forest soils and to test the generalisation of our findings in different conditions, especially deeply rutted soils (rut depths > 12 cm).

Advanced Bayesian Method for Timely Small-Scale Forest Loss Detection in the Brazilian Amazon and Cerrado with Sentinel-1 Time-Series

Abstract. The world’s forests are undergoing significant changes due to loss and degradation, emphasizing the need for Near Real-Time (NRT) monitoring to prevent further damage. Traditional monitoring methods using optical imagery are hindered by cloud coverage, while newer Synthetic Aperture Radar (SAR) systems, although operational in all weather conditions, face challenges such as sensitivity to soil moisture and the need for spatial filtering to reduce speckle effects. These limitations affect the detection of small-scale forest loss, especially in seasonally variable regions like dry forests and savannas. This paper presents a SAR-based forest disturbance detection method using Bayesian inference. Unlike traditional methods, this approach maintains the native resolution of the data by avoiding spatial filtering. Forest disturbance is modelled as a change-point detection problem within a non-filtered Sentinel-1 time series, where each new observation updates the probability of forest loss by leveraging prior information and a data model. This sequential adaptation ensures robustness against variations and trends, making it effective in monitoring disturbances across diverse forest types, including areas affected by seasonality. The proposed method was tested against other NRT monitoring systems for the year 2020, using small validation polygons (under 1 hectare) in the Brazilian Amazon and Cerrado savanna. Results demonstrate significant improvements in detecting small-scale disturbances and drastically reduced false alarm rates in both biomes. Notably, in the seasonality-sensitive Cerrado, our solution completely outperforms the leading and only existing optical technology.

Optimizing residue return with soil moisture and nutrient stoichiometry reduced greenhouse gas fluxes in Alfisols

Optimum soil moisture and high crop residue return (RR) can increase the active pool of soil organic carbon and nitrogen, thus modulating the magnitude of greenhouse gas (GHG) fluxes. To determine the effect of soil moisture on the threshold level of RR for the wheat production system, we analyzed the relationship between GHG fluxes and RR at four levels, namely 0, 5, 10, and 15 Mg ha−1 (R0, R5, R10, and R15) under two soil moisture content (80% FC and 100% FC) and three levels of nutrient management (NS0: no nutrient; NS1, NS2= 3x NS1). Nutrient input (N and P) in NS1 balanced the residue C/nutrient stoichiometry to achieve 30% stabilization of the residue C input in RR (R5). All RR treatments (cf. R0) were found to significantly reduce N2O emission in moderate soil moisture content (80% FC) by 22–56% across nutrient management due to enhanced soil C mineralization, microbial biomass carbon, and N immobilization. However, averaged across nutrient management, a linear increase in N2O emission was observed with increasing RR under 100% FC soil moisture. A significant decrease in CH4 emission by ca. 46% in most RR treatments was observed in 100% FC compared with the R0. The N2O emission was negatively correlated (p = <0.001) with nutrient stoichiometry. Partial least square (PLS) regression indicated that GHG emissions were more responsive (values > 0.8) to management variables (RR rate, nitrogen (N) input rate, soil moisture, and nutrient stoichiometry of C: N) and post-incubation soil properties (SMBC and NO3-N) in Alfisols. This study demonstrated that the mechanisms responsible for RR effects on soil N2O, CH4 fluxes, and carbon mineralization depend on soil moisture and nutrient management, shifting the nutrient stoichiometry of residue C: N: P.

Long-term changes in soil biological activity and other properties of raised beds in Longan orchards

Introduction The Longan fruit tree of the Vietnam Mekong Delta is grown in raised beds to improve water drainage during the rainy season and can live as long as 100 years. Objective This research explores the extent to which the soil microorganisms as well as soil physical and chemical properties of these raised beds degrade over a period of 60 years under traditional management practices. Materials and Methods Raised bed topsoil samples at depths of 0–20 cm were obtained from four different Longan orchards raised bed age groups: group 1) 15–25 years (L1–L5); group 2) 26–37 years (L6–L10); group 3) 38–45 years (L11–L15); and group 4) 46–60 years. Soil biological properties were tested for nitrogen-fixing bacteria, phosphorus solubilizing bacteria, potassium solubilizing bacteria, calcium solubilizing bacteria and silicate solubilizing bacteria, β-glucosidase, urease, phosphomonoesterase, and phytase. Soil samples were also tested for moisture content, soil texture, soil porosity, and bulk density as well as soil chemical properties including pH, electrical conductivity (EC), soil organic matter (SOM), total nitrogen (TN), total phosphorus (TP), total potassium (TK), available nitrogen (NH4+, NO3−), available phosphorus (AP), exchangeable potassium (K+), exchangeable calcium (Ca2 +), available silicate (SiO2), available copper (Cu), zinc (Zn), boron (B) and manganese (Mn). Key findings: The results showed that soil moisture, soil porosity, sand content, SOM, TP, TK, available P, exchangeable Ca2 +, available Si, nitrogen fixing bacteria number, β-glucosidase, urease, phosphomonoesterase, and phytase gradually and significantly decreased in the raised bed soil as the Longan orchard increased in age. Pearson correlation analysis between the ages of Longan orchards and soil properties revealed that raised bed ages were positively correlated with soil bulk density, but negatively correlated with soil moisture content, soil porosity, SOM, TN, β-glucosidase, urease, phosphomonoesterase, and phytase. Principal component analysis (PCA) showed Longan yields had a positive correlation with available NO3− but negative correlation with NFB, exchangeable Ca2 +, pH, and available B. These findings reveal that traditional long-term management of Longan trees in raised beds significantly reduce soil organic matter, moisture content, porosity, and soil fertility with impacts on soil microbial numbers and activity within raised bed soils. Future Directions This suggests that more sustainable management practices, such as mulch and cover crops that decrease soil compaction and increase soil organic matter, improve soil porosity, total N, and feed soil microorganisms that are critical to nutrient cycling are needed to improve raised bed soil quality.

Mapping soil moisture across the UK: assimilating cosmic-ray neutron sensors, remotely sensed indices, rainfall radar and catchment water balance data in a Bayesian hierarchical model

Abstract. Soil moisture is important in many hydrological and ecological processes. However, data sets which are currently available have issues with accuracy and resolution. To translate remotely sensed data to an absolute measure of soil moisture requires mapped estimates of soil hydrological properties and estimates of vegetation properties, and this introduces considerable uncertainty. We present an alternative methodology for producing daily maps of soil moisture over the UK at 2 km resolution (“SMUK”). The method is based on a simple linear statistical model, calibrated with 5 years of daily data from cosmic-ray neutron sensors at ∼ 40 sites across the country. The model is driven by precipitation, humidity, a remotely sensed “soil water index” satellite product and soil porosity. The spatial variation in the parameter describing the soil water retention (and thereby the response to precipitation) was estimated using daily water balance data from ∼ 1200 catchments with good coverage across the country. The model parameters were estimated by Bayesian calibration using a Markov chain–Monte Carlo method, so as to characterise the posterior uncertainty in the parameters and predictions. The approach reduces uncertainty by integrating multiple data sources, all of which have weaknesses but together act as a better constraint on the true soil moisture. The model explains around 70 % of the variance in the daily observations with a root-mean-square error of 0.05 m3 m−3, better than results from more complex process-based models. Given the high resolution of the inputs in time and space, the model can predict the very detailed variation in soil moisture which arises from the sporadic nature of precipitation events, including the small-scale and short-term variations associated with orographic and convective rainfall. Predictions over the period 2016 to 2023 demonstrated realistic patterns following the passage of weather fronts and prolonged droughts. The model has negligible computation time, and inputs and predictions are updated daily, lagging approximately 1 week behind real time.